System

Abstract

An object of the system according to the embodiment is to immediately score a learning activity, issue a redo instruction, and share a learning history.SOLUTION: A system according to an embodiment includes a scoring unit, a redo instruction unit, a history recording unit, and a sharing unit. The scoring unit immediately scores the learning activity using the generated AI. The redo instruction unit issues an instruction to redo a question that has been scored incorrectly by the scoring unit. The history recording unit records a learning history by the scoring unit and the redo instruction unit. The sharing unit shares the learning history recorded by the history recording unit with the child, the teacher, and the guardian.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies leave room for improvement, as they do not provide immediate grading of learning activities or instructions for rework, and do not adequately share learning histories.

[0005] The system according to the embodiment aims to instantly grade learning activities, provide rework instructions, and share learning histories. [Means for solving the problem]

[0006] The system according to the embodiment includes a scoring unit, a retry instruction unit, a history recording unit, and a sharing unit. The scoring unit instantly scores learning activities using a generating AI. The retry instruction unit issues instructions to retry questions that have been answered incorrectly by the scoring unit. The history recording unit records the learning history created by the scoring unit and the retry instruction unit. The sharing unit shares the learning history recorded by the history recording unit with the child, teacher, and parent. [Effects of the Invention]

[0007] Embodiments of the system can instantly grade learning activities, provide rework instructions, and share learning histories. [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 learning support system according to an embodiment of the present invention instantly grades and allows students to redo their learning activities, such as homework, drills, worksheets, and handouts. This system records the history of activities and allows the information to be shared among students, teachers, and parents. This allows the learning support system to understand each student's strengths and weaknesses, contributing to the reform of IoT education. Furthermore, it streamlines the process of checking and grading submitted work, allowing for more time for communication and play.

[0029] A learning support system according to an embodiment includes a scoring unit, a retry instruction unit, a history recording unit, and a sharing unit. The scoring unit instantly scores learning activities using a generating AI. For example, the generating AI determines whether a problem is correct or incorrect and displays the results in real time. After solving a math problem, the generating AI also provides immediate feedback, such as "That's correct" or "That's incorrect." The generating AI receives prompts containing the answer as input and scores the problem based on the prompts. The retry instruction unit instructs a child to retry a problem that the scoring unit has made incorrectly. For example, the retry instruction unit encourages a child to try again, such as "Try solving this problem again." This allows children to understand their mistakes and improve their learning by trying again. The history recording unit records the learning history recorded by the scoring unit and the retry instruction unit. For example, it records information such as how many times a child has attempted a problem, and which problems the child is good at and which problems the child is weak at. The sharing unit shares the learning history recorded by the history recording unit with the child, teacher, and parent. For example, teachers and parents can understand their children's learning status and provide appropriate support. This allows the learning support system to improve the efficiency of learning activities and enable information sharing.

[0030] The scoring unit can provide optimal learning advice to individual students in real time based on the scoring results. For example, the generating AI analyzes the scoring results and provides specific learning advice to each student. For example, it can identify mistakes made in math problems and provide advice encouraging students to review that area. The generating AI also provides advice based on the scoring results according to the student's learning progress. For example, it can suggest additional practice problems for areas where understanding is low. The generating AI also analyzes the scoring results and provides advice tailored to the student's learning style. For example, it can provide explanations using diagrams for students who are good at visual learning. This makes it possible to provide individual learning advice to each student.

[0031] The scoring unit can automatically suggest the next learning assignment to each student based on the scoring results, optimizing their learning progress. For example, the scoring unit uses a generating AI to analyze the scoring results and automatically suggest the next learning assignment based on the student's level of understanding. For example, if the student does not understand the basics, it will suggest basic questions, and if application is required, it will suggest application questions. The scoring unit also uses the generating AI to suggest assignments to optimize the student's learning progress based on the scoring results. For example, if the student's level of understanding in a particular field is low, it will provide assignments specialized for that field. The scoring unit also uses the generating AI to analyze the scoring results and suggest the next assignment that matches the student's learning goals. For example, it will provide assignments that focus on fields that are important for exam preparation. This makes it possible to optimize the student's learning progress.

[0032] The scoring unit supports both voice and handwritten input, allowing it to accommodate a variety of students' learning styles. For example, the scoring unit's generation AI supports voice input, allowing students to verbally state their answers and score them. For example, it can score English pronunciation practice or speeches. The scoring unit can also support handwritten input, allowing students to write their answers by hand, which the generation AI can analyze and score. For example, it can score handwritten mathematical calculations or dictation of kanji. The scoring unit can also support both voice and handwritten input, allowing it to score according to the student's learning style. For example, it can support a format where students state their answers by voice and then provide supplementary explanations by hand. This allows it to accommodate a variety of learning styles.

[0033] The scoring unit can link the scoring results with an educational platform to perform comprehensive learning assessment. For example, the generation AI in the scoring unit links the scoring results with other educational platforms to perform comprehensive learning assessment. For example, it links with an online learning platform to consolidate grades. The scoring unit also sends the scoring results to the educational platform and integrates them with other learning data to perform comprehensive assessment. For example, it links with a school's grade management system to centrally manage grades. The scoring unit also links the generation AI in the scoring unit with other educational platforms to comprehensively evaluate students' learning progress. For example, it integrates data from home learning and school learning for assessment. This makes comprehensive learning assessment possible.

[0034] The retry instruction unit can provide hints and explanations for questions that have been answered incorrectly, helping students to deepen their understanding. For example, the retry instruction unit can provide specific hints for questions that the generating AI has answered incorrectly. For example, if a math question is answered incorrectly, a hint showing the steps to solving the question can be provided. The retry instruction unit can also provide a detailed explanation for questions that the generating AI has answered incorrectly. For example, if a history question is answered incorrectly, the unit can explain the background and important points of the event. The retry instruction unit can also provide additional materials and reference links to deepen students' understanding of questions that the generating AI has answered incorrectly. For example, if a science question is answered incorrectly, the unit can introduce related videos and articles. This makes it possible to support students in deepening their understanding.

[0035] The retry instruction unit can adjust the difficulty of the problem when retrying, encouraging the student to try again according to their level of understanding. For example, the generation AI adjusts the difficulty of the problem to be retried according to the student's level of understanding. For example, it provides easy problems if the student does not understand the basics, and difficult problems if application is required. The retry instruction unit also allows the generation AI to gradually increase the difficulty of the problem when retrying. For example, starting with easy problems and gradually increasing the difficulty deepens the student's understanding. The retry instruction unit also allows the generation AI to analyze the student's level of understanding and individually adjust the difficulty of the problem to be retried. For example, if the student's level of understanding in a particular field is low, it provides problems specialized in that field. This allows the student to try again according to their level of understanding.

[0036] The retry instruction unit can expand the retry function so that it can also be used in group learning and pair work formats. For example, the generation AI makes the retry function compatible with group learning, providing a format in which multiple students solve problems collaboratively. For example, solving problems while discussing in a group. The retry instruction unit also makes the retry function available in a pair work format, in which students solve problems while teaching each other. For example, solving problems in pairs and providing feedback to each other. The retry instruction unit also provides real-time feedback during group learning and pair work, improving learning effectiveness. For example, the generation AI can monitor the progress of the entire group and provide appropriate advice. This allows it to be used in group learning and pair work formats.

[0037] The retry instruction unit can link the results of the retry with a learning application to provide comprehensive learning support. For example, the retry instruction unit links the results of the retry by the generation AI with other learning applications to provide comprehensive learning support. For example, it links with an online learning platform to integrate grades. The retry instruction unit also sends the results of the retry to an educational application and integrates them with other learning data to provide a comprehensive evaluation. For example, it links with a school's grade management system to centrally manage grades. The retry instruction unit also links the results of the retry by the generation AI with other learning applications to comprehensively evaluate the student's learning progress. For example, it integrates data from home learning and school learning for evaluation. This makes it possible to provide comprehensive learning support.

[0038] The history recording unit analyzes learning history, understands long-term learning trends, and can propose individualized learning plans. In the history recording unit, for example, the generation AI analyzes learning history and understands the student's long-term learning trends. For example, learning trends are analyzed based on past grades and frequency of effort. The history recording unit also uses the generation AI to understand the student's learning trends based on the learning history and propose individualized learning plans. For example, if a student's grades are low in a particular subject, a learning plan specialized for that subject is provided. The history recording unit also uses the generation AI to analyze learning history and propose long-term learning plans based on the student's learning trends. For example, a learning plan is provided that focuses on subjects important for exam preparation. This makes it possible to propose individualized learning plans.

[0039] The history recording unit visualizes a student's progress based on their learning history and can display the degree of goal achievement in real time. For example, the generation AI visualizes a student's progress in graphs and charts based on their learning history. For example, it displays the progress of grades in each subject and the frequency of effort. The history recording unit also displays a student's degree of goal achievement in real time based on their learning history. For example, it displays the degree of achievement of set learning goals as a percentage. The history recording unit also provides a dashboard for the generation AI to analyze the learning history and visualize the student's progress. For example, it allows the progress and goal achievement of each subject to be checked at a glance. This makes it possible to display a student's progress and goal achievement in real time.

[0040] The history recording unit can link the learning history with educational institutions and platforms to perform comprehensive learning assessments. In the history recording unit, for example, the generation AI links the learning history with other educational institutions to perform comprehensive learning assessments. For example, it links with a school's grade management system to centrally manage grades. The history recording unit also sends the learning history to an educational platform and integrates it with other learning data to perform comprehensive assessments. For example, it links with an online learning platform to integrate grades. In addition, the history recording unit can link the learning history with other educational institutions and platforms to comprehensively evaluate the student's learning progress. For example, it can integrate data on home learning and school learning to perform assessments. This makes comprehensive learning assessments possible.

[0041] The history recording unit can suggest future career paths and plans for students based on their learning history. For example, the generation AI in the history recording unit analyzes learning history to suggest future career paths and plans for students. For example, if students have excellent grades in a particular field, it will suggest career paths related to that field. The generation AI in the history recording unit also suggests career plans based on the student's interests and aptitudes based on their learning history. For example, it will introduce occupations and universities related to their field of interest. The generation AI in the history recording unit also analyzes learning history to suggest career paths and plans that match the student's future goals. For example, it will suggest the skills and qualifications required for the desired occupation. This makes it possible to suggest future career paths and career plans for students.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The learning support system can also be equipped with customization functions according to the student's learning style. For example, visually-inclined students can be provided with educational materials using diagrams and videos. Auditory-inclined students can be provided with educational materials in the form of audio commentary or podcasts. Furthermore, tactile-inclined students can be provided with interactive simulations and experiment kits. This allows the system to provide the optimal learning environment according to each student's learning style.

[0044] The learning support system can also be equipped with a reward system that corresponds to the student's learning progress. For example, badges or points can be awarded when a specific goal is achieved. Also, benefits or rewards can be provided according to accumulated points. Furthermore, the reward system can increase the difficulty level in stages according to the student's learning progress, thereby increasing the student's willingness to take on new challenges. This can maintain the student's desire to learn and increase their motivation.

[0045] The learning support system can also suggest future career paths and plans based on a student's learning history. For example, if a student has excellent grades in a particular field, it can suggest a career path related to that field. It can also suggest career plans that match the student's interests and aptitudes. Furthermore, by suggesting career paths and plans that match their future goals, it can increase students' motivation to learn. This can support students' future career paths and plans.

[0046] The learning support system can also propose individualized learning plans based on the student's learning history. For example, if a student's grades are low in a particular subject, a learning plan specialized for that subject can be provided. It can also propose long-term learning plans based on the student's learning tendencies. Furthermore, by providing learning plans that focus on subjects that are important for exam preparation, the student's learning effectiveness can be improved. This makes it possible to propose individualized learning plans.

[0047] The learning support system can also visualize learning progress based on the student's learning history and display the degree of goal achievement in real time. For example, it can display the progress of grades in each subject and the frequency of efforts in graphs and charts. It can also display the degree of achievement toward set learning goals as a percentage. Furthermore, it can analyze learning history and provide a dashboard to visualize the student's progress. This makes it possible to display the student's progress and degree of goal achievement in real time.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The scoring unit uses the generative AI to instantly score learning activities. For example, the generative AI determines whether a problem is correct or incorrect and displays the results in real time. After solving a math problem, the generative AI also provides immediate feedback such as "That's correct" or "That's incorrect." The generative AI receives a prompt containing the answer as input and scores based on that prompt. Step 2: The retry instruction section gives instructions to retry problems that were incorrectly marked by the marking section. For example, it encourages children to try again by saying, "Try solving this problem again." This allows children to understand their mistakes and improve their learning by trying again. Step 3: The history recording unit records the learning history created by the grading unit and the retry instruction unit. For example, it records information such as how many times each problem was tackled, and which problems the student is good at and which problems he or she is not good at. Step 4: The sharing unit shares the learning history recorded by the history recording unit with the child, teacher, and guardian. For example, teachers and guardians can understand the child's learning situation and provide appropriate support. This enables the learning support system to improve the efficiency of learning activities and share information.

[0050] (Example 2) The learning support system according to an embodiment of the present invention instantly grades and allows students to redo their learning activities, such as homework, drills, worksheets, and handouts. This system records the history of activities and allows the information to be shared among students, teachers, and parents. This allows the learning support system to understand each student's strengths and weaknesses, contributing to the reform of IoT education. Furthermore, it streamlines the process of checking and grading submitted work, allowing for more time for communication and play.

[0051] A learning support system according to an embodiment includes a scoring unit, a retry instruction unit, a history recording unit, and a sharing unit. The scoring unit instantly scores learning activities using a generating AI. For example, the generating AI determines whether a problem is correct or incorrect and displays the results in real time. After solving a math problem, the generating AI also provides immediate feedback, such as "That's correct" or "That's incorrect." The generating AI receives prompts containing the answer as input and scores the problem based on the prompts. The retry instruction unit instructs a child to retry a problem that the scoring unit has made incorrectly. For example, the retry instruction unit encourages a child to try again, such as "Try solving this problem again." This allows children to understand their mistakes and improve their learning by trying again. The history recording unit records the learning history recorded by the scoring unit and the retry instruction unit. For example, it records information such as how many times a child has attempted a problem, and which problems the child is good at and which problems the child is weak at. The sharing unit shares the learning history recorded by the history recording unit with the child, teacher, and parent. For example, teachers and parents can understand their children's learning status and provide appropriate support. This allows the learning support system to improve the efficiency of learning activities and enable information sharing.

[0052] The scoring unit can provide optimal learning advice to individual students in real time based on the scoring results. For example, the generating AI analyzes the scoring results and provides specific learning advice to each student. For example, it can identify mistakes made in math problems and provide advice encouraging students to review that area. The generating AI also provides advice based on the scoring results according to the student's learning progress. For example, it can suggest additional practice problems for areas where understanding is low. The generating AI also analyzes the scoring results and provides advice tailored to the student's learning style. For example, it can provide explanations using diagrams for students who are good at visual learning. This makes it possible to provide individual learning advice to each student.

[0053] The scoring unit can automatically suggest the next learning assignment to each student based on the scoring results, optimizing their learning progress. For example, the scoring unit uses a generating AI to analyze the scoring results and automatically suggest the next learning assignment based on the student's level of understanding. For example, if the student does not understand the basics, it will suggest basic questions, and if application is required, it will suggest application questions. The scoring unit also uses the generating AI to suggest assignments to optimize the student's learning progress based on the scoring results. For example, if the student's level of understanding in a particular field is low, it will provide assignments specialized for that field. The scoring unit also uses the generating AI to analyze the scoring results and suggest the next assignment that matches the student's learning goals. For example, it will provide assignments that focus on fields that are important for exam preparation. This makes it possible to optimize the student's learning progress.

[0054] The scoring unit can use the emotion estimation function to analyze students' emotional reactions to the scoring results and reinforce positive feedback. For example, the scoring unit uses the generation AI to analyze students' emotional reactions to the scoring results in real time and reinforce positive feedback. For example, it displays words of praise when students get the answer right. The scoring unit also uses the emotion estimation function to analyze students' emotions about the scoring results and provide feedback that increases motivation. For example, it displays encouraging messages for questions they got wrong. The scoring unit also uses the generation AI to analyze students' emotional reactions to the scoring results and provide feedback that elicits positive emotions. For example, it displays a message that makes them feel a sense of accomplishment when their grades improve. This can increase student motivation.

[0055] The scoring unit supports both voice and handwritten input, allowing it to accommodate a variety of students' learning styles. For example, the scoring unit's generation AI supports voice input, allowing students to verbally state their answers and score them. For example, it can score English pronunciation practice or speeches. The scoring unit can also support handwritten input, allowing students to write their answers by hand, which the generation AI can analyze and score. For example, it can score handwritten mathematical calculations or dictation of kanji. The scoring unit can also support both voice and handwritten input, allowing it to score according to the student's learning style. For example, it can support a format where students state their answers by voice and then provide supplementary explanations by hand. This allows it to accommodate a variety of learning styles.

[0056] The scoring unit can link the scoring results with an educational platform to perform comprehensive learning assessment. For example, the generation AI in the scoring unit links the scoring results with other educational platforms to perform comprehensive learning assessment. For example, it links with an online learning platform to consolidate grades. The scoring unit also sends the scoring results to the educational platform and integrates them with other learning data to perform comprehensive assessment. For example, it links with a school's grade management system to centrally manage grades. The scoring unit also links the generation AI in the scoring unit with other educational platforms to comprehensively evaluate students' learning progress. For example, it integrates data from home learning and school learning for assessment. This makes comprehensive learning assessment possible.

[0057] The scoring unit can use the emotion estimation function to collect the emotional reactions of parents and teachers to the scoring results and promote communication. For example, the generation AI in the scoring unit collects the emotional reactions of parents and teachers to the scoring results in real time and promotes communication. For example, if a parent expresses positive feelings about their child's grades, it shares that information with the teacher. The scoring unit also uses the emotion estimation function to analyze the emotions of parents and teachers regarding the scoring results and provide appropriate feedback. For example, it can send a message of congratulations to parents when their grades improve. The generation AI in the scoring unit also analyzes the emotional reactions of parents and teachers to the scoring results and makes suggestions to promote communication. For example, it can suggest ways for parents and teachers to work together to support a child when their grades drop. This promotes communication between parents and teachers.

[0058] The retry instruction unit can provide hints and explanations for questions that have been answered incorrectly, helping students to deepen their understanding. For example, the retry instruction unit can provide specific hints for questions that the generating AI has answered incorrectly. For example, if a math question is answered incorrectly, a hint showing the steps to solving the question can be provided. The retry instruction unit can also provide a detailed explanation for questions that the generating AI has answered incorrectly. For example, if a history question is answered incorrectly, the unit can explain the background and important points of the event. The retry instruction unit can also provide additional materials and reference links to deepen students' understanding of questions that the generating AI has answered incorrectly. For example, if a science question is answered incorrectly, the unit can introduce related videos and articles. This makes it possible to support students in deepening their understanding.

[0059] The retry instruction unit can adjust the difficulty of the problem when retrying, encouraging the student to try again according to their level of understanding. For example, the generation AI adjusts the difficulty of the problem to be retried according to the student's level of understanding. For example, it provides easy problems if the student does not understand the basics, and difficult problems if application is required. The retry instruction unit also allows the generation AI to gradually increase the difficulty of the problem when retrying. For example, starting with easy problems and gradually increasing the difficulty deepens the student's understanding. The retry instruction unit also allows the generation AI to analyze the student's level of understanding and individually adjust the difficulty of the problem to be retried. For example, if the student's level of understanding in a particular field is low, it provides problems specialized in that field. This allows the student to try again according to their level of understanding.

[0060] The retry instruction unit can use the emotion estimation function to analyze the student's emotions when retrying and provide feedback to maintain motivation. For example, the generation AI in the retry instruction unit analyzes the student's emotions when retrying in real time and provides feedback to maintain motivation. For example, it displays words of praise when the retry is successful. The retry instruction unit also uses the emotion estimation function to analyze the student's emotions when retrying and provides an appropriate message of encouragement. For example, it displays words of encouragement when the retry is unsuccessful. The retry instruction unit also analyzes the student's emotional response when retrying and provides feedback to increase motivation. For example, it displays messages that make the student feel a sense of accomplishment according to the progress of the retry. This makes it possible to provide feedback to maintain the student's motivation.

[0061] The retry instruction unit can expand the retry function so that it can also be used in group learning and pair work formats. For example, the generation AI makes the retry function compatible with group learning, providing a format in which multiple students solve problems collaboratively. For example, solving problems while discussing in a group. The retry instruction unit also makes the retry function available in a pair work format, in which students solve problems while teaching each other. For example, solving problems in pairs and providing feedback to each other. The retry instruction unit also provides real-time feedback during group learning and pair work, improving learning effectiveness. For example, the generation AI can monitor the progress of the entire group and provide appropriate advice. This allows it to be used in group learning and pair work formats.

[0062] The retry instruction unit can link the results of the retry with a learning application to provide comprehensive learning support. For example, the retry instruction unit links the results of the retry by the generation AI with other learning applications to provide comprehensive learning support. For example, it links with an online learning platform to integrate grades. The retry instruction unit also sends the results of the retry to an educational application and integrates them with other learning data to provide a comprehensive evaluation. For example, it links with a school's grade management system to centrally manage grades. The retry instruction unit also links the results of the retry by the generation AI with other learning applications to comprehensively evaluate the student's learning progress. For example, it integrates data from home learning and school learning for evaluation. This makes it possible to provide comprehensive learning support.

[0063] The retry instruction unit uses the emotion estimation function to analyze the emotions of parents and teachers when a child is retrying and can provide appropriate support. For example, the retry instruction unit uses the generation AI to analyze the emotions of parents and teachers when a child is retrying in real time and provide appropriate support. For example, if a parent expresses positive feelings about their child's grades, the retry instruction unit shares that information with the teacher. The retry instruction unit also uses the emotion estimation function to analyze the emotions of parents and teachers when a child is retrying and provide appropriate feedback. For example, it can send a message of joy to parents when their grades improve. The retry instruction unit also analyzes the emotional reactions of parents and teachers when a child is retrying and makes suggestions for providing appropriate support. For example, it can suggest ways for parents and teachers to work together to provide support when their grades drop. This makes it possible to provide appropriate support to parents and teachers.

[0064] The history recording unit analyzes learning history, understands long-term learning trends, and can propose individualized learning plans. In the history recording unit, for example, the generation AI analyzes learning history and understands the student's long-term learning trends. For example, learning trends are analyzed based on past grades and frequency of effort. The history recording unit also uses the generation AI to understand the student's learning trends based on the learning history and propose individualized learning plans. For example, if a student's grades are low in a particular subject, a learning plan specialized for that subject is provided. The history recording unit also uses the generation AI to analyze learning history and propose long-term learning plans based on the student's learning trends. For example, a learning plan is provided that focuses on subjects important for exam preparation. This makes it possible to propose individualized learning plans.

[0065] The history recording unit visualizes a student's progress based on their learning history and can display the degree of goal achievement in real time. For example, the generation AI visualizes a student's progress in graphs and charts based on their learning history. For example, it displays the progress of grades in each subject and the frequency of effort. The history recording unit also displays a student's degree of goal achievement in real time based on their learning history. For example, it displays the degree of achievement of set learning goals as a percentage. The history recording unit also provides a dashboard for the generation AI to analyze the learning history and visualize the student's progress. For example, it allows the progress and goal achievement of each subject to be checked at a glance. This makes it possible to display a student's progress and goal achievement in real time.

[0066] The history recording unit can use the emotion estimation function to analyze students' emotions regarding their learning history and provide feedback to increase their motivation to learn. For example, the generation AI in the history recording unit analyzes students' emotions regarding their learning history in real time and provides feedback to increase their motivation to learn. For example, it displays words of praise when their grades improve. The history recording unit also uses the emotion estimation function to analyze students' emotions regarding their learning history and provides appropriate messages of encouragement. For example, it displays words of encouragement when their grades drop. The history recording unit also uses the generation AI to analyze students' emotional responses to their learning history and provides feedback to increase their motivation to learn. For example, it displays messages that make them feel a sense of accomplishment according to their learning progress. This makes it possible to provide feedback to increase students' motivation to learn.

[0067] The history recording unit can link the learning history with educational institutions and platforms to perform comprehensive learning assessments. In the history recording unit, for example, the generation AI links the learning history with other educational institutions to perform comprehensive learning assessments. For example, it links with a school's grade management system to centrally manage grades. The history recording unit also sends the learning history to an educational platform and integrates it with other learning data to perform comprehensive assessments. For example, it links with an online learning platform to integrate grades. In addition, the history recording unit can link the learning history with other educational institutions and platforms to comprehensively evaluate the student's learning progress. For example, it can integrate data on home learning and school learning to perform assessments. This makes comprehensive learning assessments possible.

[0068] The history recording unit can suggest future career paths and plans for students based on their learning history. For example, the generation AI in the history recording unit analyzes learning history to suggest future career paths and plans for students. For example, if students have excellent grades in a particular field, it will suggest career paths related to that field. The generation AI in the history recording unit also suggests career plans based on the student's interests and aptitudes based on their learning history. For example, it will introduce occupations and universities related to their field of interest. The generation AI in the history recording unit also analyzes learning history to suggest career paths and plans that match the student's future goals. For example, it will suggest the skills and qualifications required for the desired occupation. This makes it possible to suggest future career paths and career plans for students.

[0069] The history recording unit uses the emotion estimation function to analyze the emotions of parents and teachers regarding the learning history and provide appropriate support. For example, the generation AI in the history recording unit analyzes the emotions of parents and teachers regarding the learning history in real time and provides appropriate support. For example, if a parent expresses positive emotions about their child's grades, it shares that information with the teacher. The history recording unit also uses the emotion estimation function to analyze the emotions of parents and teachers regarding the learning history and provide appropriate feedback. For example, it sends a message of congratulations to parents when grades improve. The generation AI in the history recording unit also analyzes the emotional reactions of parents and teachers to the learning history and makes suggestions for providing appropriate support. For example, it suggests ways for parents and teachers to work together to provide support when grades drop. This enables appropriate support to be provided to parents and teachers.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The learning support system can also be equipped with customization functions according to the student's learning style. For example, visually-inclined students can be provided with educational materials using diagrams and videos. Auditory-inclined students can be provided with educational materials in the form of audio commentary or podcasts. Furthermore, tactile-inclined students can be provided with interactive simulations and experiment kits. This allows the system to provide the optimal learning environment according to each student's learning style.

[0072] The learning support system can also be equipped with a reward system that corresponds to the student's learning progress. For example, badges or points can be awarded when a specific goal is achieved. Also, benefits or rewards can be provided according to accumulated points. Furthermore, the reward system can increase the difficulty level in stages according to the student's learning progress, thereby increasing the student's willingness to take on new challenges. This can maintain the student's desire to learn and increase their motivation.

[0073] The learning support system can also suggest future career paths and plans based on a student's learning history. For example, if a student has excellent grades in a particular field, it can suggest a career path related to that field. It can also suggest career plans that match the student's interests and aptitudes. Furthermore, by suggesting career paths and plans that match their future goals, it can increase students' motivation to learn. This can support students' future career paths and plans.

[0074] The learning support system can also propose individualized learning plans based on the student's learning history. For example, if a student's grades are low in a particular subject, a learning plan specialized for that subject can be provided. It can also propose long-term learning plans based on the student's learning tendencies. Furthermore, by providing learning plans that focus on subjects that are important for exam preparation, the student's learning effectiveness can be improved. This makes it possible to propose individualized learning plans.

[0075] The learning support system can also visualize learning progress based on the student's learning history and display the degree of goal achievement in real time. For example, it can display the progress of grades in each subject and the frequency of efforts in graphs and charts. It can also display the degree of achievement toward set learning goals as a percentage. Furthermore, it can analyze learning history and provide a dashboard to visualize the student's progress. This makes it possible to display the student's progress and degree of goal achievement in real time.

[0076] The learning support system can further use the emotion estimation function to provide feedback to increase students' motivation to learn. For example, it can display words of praise when students' grades improve, or words of encouragement when their grades drop. Furthermore, it can increase students' motivation to learn by displaying messages that give them a sense of accomplishment according to their learning progress. In this way, it is possible to provide feedback to increase students' motivation to learn.

[0077] The learning support system can also use the emotion estimation function to collect the emotional responses of parents and teachers and promote communication. For example, if a parent expresses positive feelings about their child's grades, that information can be shared with the teacher. Also, if the child's grades improve, a congratulatory message can be sent to the parent. Furthermore, if the child's grades drop, the system can suggest ways for the parent and teacher to work together to support the child, thereby promoting communication with the parent and teacher.

[0078] The learning support system can further use the emotion estimation function to analyze the student's emotions during redoing and provide feedback to maintain motivation. For example, it can display words of praise when the redoing is successful, or words of encouragement when the redoing is unsuccessful. Furthermore, it can maintain the student's motivation by displaying messages that give a sense of accomplishment according to the progress of the redo. In this way, it is possible to provide feedback to maintain the student's motivation.

[0079] The learning support system can also use its emotion estimation function to analyze the emotions of parents and teachers regarding their child's learning history and provide appropriate support. For example, if a parent expresses positive emotions about their child's grades, that information can be shared with the teacher. Also, if the child's grades improve, a congratulatory message can be sent to the parent. Furthermore, if the child's grades drop, the system can suggest ways for parents and teachers to work together to provide support, thereby providing appropriate support to parents and teachers.

[0080] The learning support system can further use the emotion estimation function to analyze students' emotions regarding their learning history and provide feedback to increase their motivation to learn. For example, it can display words of praise when their grades improve, or words of encouragement when their grades drop. Furthermore, it can increase students' motivation to learn by displaying messages that give them a sense of accomplishment according to their learning progress. This makes it possible to provide feedback to increase students' motivation to learn.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The scoring unit uses the generative AI to instantly score learning activities. For example, the generative AI determines whether a problem is correct or incorrect and displays the results in real time. After solving a math problem, the generative AI also provides immediate feedback such as "That's correct" or "That's incorrect." The generative AI receives a prompt containing the answer as input and scores based on that prompt. Step 2: The retry instruction section gives instructions to retry problems that were incorrectly marked by the marking section. For example, it encourages children to try again by saying, "Try solving this problem again." This allows children to understand their mistakes and improve their learning by trying again. Step 3: The history recording unit records the learning history created by the grading unit and the retry instruction unit. For example, it records information such as how many times each problem was tackled, and which problems the student is good at and which problems he or she is not good at. Step 4: The sharing unit shares the learning history recorded by the history recording unit with the child, teacher, and guardian. For example, teachers and guardians can understand the child's learning situation and provide appropriate support. This enables the learning support system to improve the efficiency of learning activities and share information.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 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.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the 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.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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, in order to avoid confusion and to 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A scoring department that uses generative AI to instantly score learning activities; a retry instruction unit that issues instructions to retry questions that have been answered incorrectly by the marking unit; a history recording unit that records a learning history of the grading unit and the retry instruction unit; A sharing unit is provided for sharing the learning history recorded by the history recording unit with the child, teacher, and guardian. A system characterized by:

2. The scoring unit Supports voice and handwriting input to accommodate diverse student learning styles 2. The system of claim 1.

3. The retry instruction unit Providing hints and explanations for the incorrect questions to help students deepen their understanding 2. The system of claim 1.

4. The history recording unit Analyze the learning history, understand long-term learning trends, and propose individual learning plans 2. The system of claim 1.

5. The scoring unit Analyze students' emotional reactions to their grades and reinforce positive feedback 2. The system of claim 1.

6. The retry instruction unit Analyze students' feelings during the retakes and provide feedback to maintain motivation 2. The system of claim 1.

7. The history recording unit Analyze students' feelings about the learning history and provide feedback to increase their motivation to learn 2. The system of claim 1.

8. The scoring unit Collecting emotional responses from parents and teachers to the results of the assessment and promoting communication 2. The system of claim 1.

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