system

Abstract

The system according to the embodiment aims to reduce the burden on teachers and provide efficient feedback to students. [Solution] A system according to an embodiment includes a scoring unit, a providing unit, an evaluation unit, and a management unit. The scoring unit grades homework. The providing unit provides feedback based on the results of the grading by the scoring unit. The evaluation unit evaluates playing and singing. The providing unit provides feedback based on the results of the evaluation by the evaluation unit. The management unit manages the student's learning progress. The providing unit provides feedback based on the learning progress managed by the management unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, teachers have to spend a lot of time and effort grading homework and evaluating performances and singing, making it difficult to provide efficient feedback.

[0005] The system according to the embodiment aims to reduce the burden on teachers and provide efficient feedback to students. [Means for solving the problem]

[0006] The system according to the embodiment includes a scoring unit, a providing unit, an evaluation unit, and a management unit. The scoring unit grades homework. The providing unit provides feedback based on the results of the grading by the scoring unit. The evaluation unit evaluates playing and singing. The providing unit provides feedback based on the results of the evaluation by the evaluation unit. The management unit manages the student's learning progress. The providing unit provides feedback based on the learning progress managed by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on teachers and provide feedback to students efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention utilizes AI to reduce teachers' workloads and provide students with more feedback. In this educational support system, AI automatically grades homework assigned by teachers to students, students record themselves playing or singing, and the AI ​​evaluates the recordings. Furthermore, based on the results of the AI's grading and evaluation, the AI ​​provides specific feedback to students, manages students' learning progress, and reports to teachers on their learning status. This system not only reduces teachers' workloads but also provides more feedback to students, potentially improving learning outcomes. For example, AI automatically grades homework assigned by teachers to students. For example, AI accurately grades routine questions, such as math problems and English grammar problems. This significantly reduces the time teachers spend grading. Next, students record themselves playing or singing, and the AI ​​evaluates the recordings. AI analyzes pitch, rhythm, expressiveness, and other factors to provide detailed feedback. This allows students to specifically identify areas for improvement in their playing or singing. Furthermore, the AI ​​provides specific feedback to students based on the results of its grading and evaluation. For example, it provides an explanation of how to solve a math problem when it gets it wrong, and advice on what parts of a musical performance or singing performance need improvement. Finally, the AI ​​manages students' learning progress and reports the students' learning status to teachers. This makes it easier for teachers to grasp the learning status of each student and provide appropriate guidance. This system not only reduces the burden on teachers, but also enables them to provide more feedback to students, which is expected to improve learning effectiveness. This allows the educational support system to reduce the burden on teachers and provide more feedback to students.

[0029] An educational support system according to an embodiment includes a scoring unit, a providing unit, an evaluation unit, and a management unit. The scoring unit automatically scores homework assigned by teachers to students. For example, AI accurately scores standard questions such as math problems and English grammar problems. The scoring unit, for example, analyzes math calculation problems and calculates scores based on the percentage of correct answers. For English grammar questions, it can also evaluate the accuracy of the grammar and award partial credit. Furthermore, the scoring unit can use AI to scan handwritten answer sheets, convert them into digital data using character recognition technology, and grade them. For example, handwritten answer sheets can be scanned and converted into text information using OCR technology, and graded by AI. The providing unit provides feedback based on the results of the grading by the scoring unit. For example, it provides explanations on how to solve math problems where students made mistakes. For example, the providing unit provides step-by-step explanations and specific solutions. For English grammar questions, it can also explain correct grammar usage and provide example sentences. Furthermore, the providing unit can use AI to automatically generate feedback on students' answers and provide it in text or audio format. For example, AI can analyze students' answers, generate explanations for mistakes, and provide audio feedback. The evaluating unit records students playing or singing and evaluates the recording. For example, it analyzes pitch, rhythm, expressiveness, etc. and provides detailed feedback. For example, the evaluating unit evaluates pitch accuracy and rhythm accuracy. Regarding expressiveness, it can also evaluate emotional expression and acting ability and point out specific areas for improvement. Furthermore, the evaluating unit can use AI to analyze the recording data, automatically detect pitch and rhythm errors, and provide feedback. For example, AI can analyze the recording data, detect pitch errors, and suggest specific areas for improvement. The management unit manages students' learning progress and reports their learning status to teachers. For example, it manages test scores, assignment submission status, study time, etc., and reports them to teachers. For example, the management department can visually display learning progress in graphs and charts, allowing teachers to grasp students' learning status at a glance. The management department can also use AI to analyze students' learning data, identify learning trends and weaknesses, and report them to teachers.For example, AI can analyze students' learning data, identify weaknesses in specific areas, and report them to teachers. This reduces the burden on teachers and allows the education support system according to the embodiment to provide more feedback to students.

[0030] The scoring unit can grade standardized questions, such as math problems or English grammar problems. Standardized questions include, but are not limited to, math calculation problems and English grammar problems. For example, the scoring unit can analyze math calculation problems and calculate scores based on the percentage of correct answers. For English grammar questions, the scoring unit can also evaluate the accuracy of the grammar and award partial credit. Furthermore, the scoring unit can use AI to scan handwritten answers, convert them into digital data using character recognition technology, and grade them. For example, handwritten answers can be scanned with a scanner, converted into text information using OCR technology, and graded by AI. This enables accurate grading of standardized questions. Some or all of the above-described processing in the scoring unit can be performed using AI, or can be performed without AI. For example, the scoring unit can scan handwritten answers with a scanner, obtain image data, input the acquired image data, and have the generation AI generate text data from the image data.

[0031] The evaluation unit can analyze pitch, rhythm, and expressiveness to evaluate the performance or singing. The evaluation unit, for example, evaluates the accuracy of pitch and analyzes the accuracy of rhythm. For example, the evaluation unit analyzes the pitch height and accuracy and detects pitch errors. The evaluation unit can also analyze the tempo and accuracy of rhythm and detect rhythm errors. Furthermore, the evaluation unit can evaluate emotional expression and acting ability with respect to expressiveness and point out specific areas for improvement. For example, the evaluation unit analyzes emotional expression and acting ability and presents specific areas for improvement. This enables a detailed evaluation of the performance or singing. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input recording data into a generation AI and have the generation AI analyze pitch and rhythm.

[0032] The providing unit can provide an explanation of how to solve a mistake in a math problem. For example, the providing unit provides an explanation of how to solve a mistake in a math problem. For example, the providing unit provides a step-by-step explanation and shows a specific solution. The providing unit can also automatically generate an explanation for the mistake using a generation AI and provide it in text or audio format. For example, the providing unit causes the generation AI to analyze the student's answer, generate an explanation for the mistake, and provide audio feedback. This makes it easier for the student to understand the mistake. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input an explanation for the answer to the generation AI and cause the generation AI to generate an explanation.

[0033] The providing unit can provide advice on areas for improvement regarding playing or singing. For example, the providing unit provides advice on which parts of playing or singing need improvement. For example, the providing unit points out areas for improvement in pitch or rhythm and suggests specific practice methods. The providing unit can also provide advice on areas for improvement regarding expressiveness and provide hints for improving emotional expression and acting skills. Furthermore, the providing unit can use a generating AI to automatically generate areas for improvement regarding playing or singing and provide them in text or audio format. For example, the providing unit can have the generating AI analyze recorded data, generate specific areas for improvement, and provide audio feedback. This allows the student to know specific areas for improvement regarding playing or singing. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input advice on areas for improvement into the generating AI and have the generating AI generate the advice.

[0034] The management unit can manage students' learning progress and report the students' learning status to teachers. The management unit can manage, for example, test scores, assignment submission status, study time, etc., and report them to teachers. For example, the management unit can visually display learning progress using graphs and charts, allowing teachers to understand students' learning status at a glance. The management unit can also use AI to analyze students' learning data, identify learning trends and weaknesses, and report them to teachers. For example, AI can analyze students' learning data, identify weaknesses in specific areas, and report them to teachers. This makes it easier for teachers to understand the learning status of each student. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input learning status reports into a generation AI and have the generation AI generate reports.

[0035] The scoring unit can identify individual weaknesses based on the student's past answer history. For example, the scoring unit uses AI to analyze the past answer history and identify the student's weaknesses for specific questions. For example, the scoring unit can identify areas where the same mistakes are repeated based on the past answer history. The scoring unit can also refer to the past answer history to identify the student's weaknesses in specific areas. This makes it easier to identify the student's individual weaknesses. The answer history is referenced based on, for example, past test results and assignment submission status. Some or all of the above-mentioned processing in the scoring unit may be performed using AI, or may be performed without using AI. For example, the scoring unit can input the past answer history into the generation AI and have the generation AI identify individual weaknesses.

[0036] The scoring unit can apply a scoring algorithm depending on the difficulty of the question. For example, the scoring unit uses AI to analyze the difficulty of the question and apply a scoring algorithm depending on the difficulty. For example, the scoring unit applies strict scoring criteria to easy questions and lenient scoring criteria to difficult questions. The scoring unit can also apply an algorithm that awards partial points depending on the difficulty of the question. This enables appropriate scoring depending on the difficulty of the question. The scoring algorithm is applied using, for example, a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input question difficulty data to the generation AI and cause the generation AI to apply the scoring algorithm.

[0037] The scoring unit can customize the feedback content based on the student's learning style. For example, if the student has a visual learning style, the scoring unit provides feedback using graphs or diagrams. For example, if the student has an auditory learning style, the scoring unit provides audio feedback. The scoring unit can also provide practical advice if the student has an experiential learning style. This allows feedback to be provided according to the student's learning style. The learning style is identified based on classifications such as visual, auditory, and experiential. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the student's learning style data into a generation AI and have the generation AI customize the feedback content.

[0038] The grading unit can perform a comprehensive evaluation based on the grade data of other subjects. The grading unit, for example, uses AI to refer to the grade data of other subjects and perform a comprehensive academic evaluation. For example, the grading unit identifies a student's strengths and weaknesses in a specific area based on the grade data of other subjects. The grading unit can also provide comprehensive feedback by referring to the grade data of other subjects. This makes it possible to perform a comprehensive evaluation by referring to the grade data of other subjects. The grade data is referenced based on, for example, past test results and assignment grades. Some or all of the above-mentioned processing in the grading unit may be performed using AI, or may be performed without using AI. For example, the grading unit can input grade data of other subjects into the generation AI and have the generation AI perform a comprehensive evaluation.

[0039] The providing unit can strengthen the individual advice based on the student's past learning history. For example, the providing unit uses AI to analyze the student's past learning history and provide individual advice for a specific problem. For example, the providing unit provides advice to avoid repeating the same mistakes based on the student's past learning history. The providing unit can also refer to the student's past learning history and provide individual advice in a specific field. In this way, the individual advice is strengthened by referring to the student's past learning history. The learning history is referenced based on, for example, the student's past assignment submission status and test scores. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the student's past learning history into the generating AI and have the generating AI strengthen the individual advice.

[0040] The providing unit can apply a feedback format depending on the type of problem. For example, for a math problem, the providing unit provides feedback that explains the solution steps in detail. For example, for an English grammar problem, the providing unit provides feedback that explains correct grammar usage. For playing or singing, the providing unit can also provide feedback that points out areas for improvement in pitch or rhythm. This allows appropriate feedback to be provided depending on the type of problem. The feedback format can be applied in, for example, text format, audio format, video format, or the like. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input problem type data to a generating AI and cause the generating AI to apply the feedback format.

[0041] The providing unit can customize the feedback content based on the student's learning goals. For example, if the student is studying for a specific exam, the providing unit provides feedback related to the exam. For example, if the student wants to improve a specific skill, the providing unit provides feedback related to the skill. Furthermore, if the student wants to achieve a specific goal, the providing unit can also provide feedback aimed at the goal. This allows feedback to be provided according to the student's learning goals. The learning goals are identified based on, for example, short-term goals, long-term goals, specific achievement criteria, etc. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the student's learning goal data into a generating AI and cause the generating AI to customize the feedback content.

[0042] The provision unit can propose specific improvement measures based on success stories of other students. For example, the provision unit uses AI to analyze success stories of other students and propose specific improvement measures. For example, the provision unit proposes improvement measures for the same problem based on success stories of other students. The provision unit can also refer to success stories of other students and propose improvement measures in specific areas. In this way, specific improvement measures are proposed by referring to success stories of other students. Success stories are referenced based on, for example, the success experiences and specific improvement measures of other students. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input data on success stories of other students into the generation AI and have the generation AI execute the proposed improvement measures.

[0043] The evaluation unit can identify individual areas for improvement based on past performance and singing data. The evaluation unit, for example, uses AI to analyze past performance data and identify areas for improvement in specific sections. For example, the evaluation unit can identify parts where the same mistakes are repeated based on past singing data. The evaluation unit can also refer to past performance and singing data to identify areas for improvement in specific techniques. This makes it easier to identify individual areas for improvement by referring to past data. The performance and singing data can be referenced based on, for example, audio recording data or video data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past performance and singing data into the generation AI and have the generation AI identify individual areas for improvement.

[0044] The evaluation unit can apply an evaluation algorithm depending on the genre of the performance or singing. For example, for classical music, the evaluation unit applies an evaluation algorithm that emphasizes accuracy of pitch and rhythm. For example, for pop music, the evaluation unit can apply an evaluation algorithm that emphasizes expressiveness and emotional expression. For jazz, the evaluation unit can also apply an evaluation algorithm that emphasizes improvisational performance techniques. This enables appropriate evaluation depending on the genre. The evaluation algorithm is applied using, for example, a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input genre data of the performance or singing to the generation AI and cause the generation AI to apply the evaluation algorithm.

[0045] The evaluation unit can customize the feedback content based on the student's practice history. For example, the evaluation unit uses AI to analyze the student's practice history and provide individual feedback for a specific problem. For example, the evaluation unit provides feedback to prevent the student from making the same mistake again based on the student's practice history. The evaluation unit can also refer to the student's practice history and provide individual feedback for a specific technique. This allows individual feedback to be provided based on the student's practice history. The practice history is referenced based on, for example, past practice records and practice time. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the student's practice history data into the generation AI and have the generation AI customize the feedback content.

[0046] The evaluation unit can perform comparative evaluation based on the performance and singing data of other students. The evaluation unit, for example, uses AI to analyze the performance data of other students and perform comparative evaluation. For example, the evaluation unit performs comparative evaluation for the same question based on the singing data of other students. The evaluation unit can also perform comparative evaluation of specific techniques by referring to the performance and singing data of other students. This makes comparative evaluation possible by referring to the data of other students. The comparative evaluation method is performed based on, for example, comparison criteria with other students and evaluation scales. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the performance and singing data of other students into the generation AI and have the generation AI perform comparative evaluation.

[0047] The management unit can optimize individual study plans based on past learning data. For example, the management unit uses AI to analyze past learning data and optimize study plans in specific fields. For example, the management unit proposes a study plan based on past learning data to prevent the same mistakes from being repeated. The management unit can also refer to past learning data to optimize a study plan aimed at a specific goal. This provides an optimal study plan based on past learning data. The study plan is optimized based on, for example, the allocation of study time and the priority of tasks. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input past learning data into a generation AI and have the generation AI optimize the study plan.

[0048] The management unit can apply a progress management algorithm for each subject. For example, to manage progress in mathematics, the management unit can apply an algorithm that emphasizes the speed and accuracy of problem-solving. For example, to manage progress in English, the management unit can apply an algorithm that emphasizes the level of grammar and vocabulary acquisition. Furthermore, to manage progress in music, the management unit can apply an algorithm that emphasizes the improvement of performance technique and expressiveness. This enables optimal progress management for each subject. The progress management algorithm is applied using, for example, a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input progress data for each subject into a generation AI and have the generation AI apply the progress management algorithm.

[0049] The management unit can customize a study plan based on the student's lifestyle data. For example, the management unit uses AI to analyze the student's lifestyle data and suggest optimal study times. For example, the management unit can suggest a study plan that takes into account the balance between study and rest based on the student's lifestyle data. The management unit can also refer to the student's lifestyle data and suggest a study plan that allows the student to focus during specific time periods. This provides an optimal study plan based on the student's lifestyle. The lifestyle data can be referenced based on, for example, sleep time, eating habits, exercise frequency, etc. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the student's lifestyle data into a generation AI and have the generation AI customize the study plan.

[0050] The management unit can perform comparative analysis based on the progress data of other students. The management unit, for example, uses AI to analyze the progress data of other students and perform the comparative analysis. For example, the management unit compares progress on the same assignment based on the progress data of other students. The management unit can also refer to the progress data of other students and perform comparative analysis of progress in a specific area. This makes it possible to perform comparative analysis by referring to the data of other students. The comparative analysis method is performed based on, for example, a comparison standard with other students or an analytical scale. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the progress data of other students into the generation AI and have the generation AI perform the comparative analysis.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The educational support system may further include a learning style adaptation unit that customizes feedback based on a student's learning style. For example, a student with a visual learning style may be provided with feedback using graphs and diagrams. A student with an auditory learning style may be provided with audio feedback. A student with an experiential learning style may be provided with practical advice. This allows optimal feedback to be provided according to each student's learning style.

[0053] The scoring department can identify individual weaknesses based on a student's past answer history. For example, AI can analyze past answer history to identify a student's weaknesses for a specific question. Based on past answer history, it can identify areas where the same mistakes are repeated. It can also identify a student's weaknesses in specific areas. This makes it easier to identify individual weaknesses for students.

[0054] The evaluation unit can apply an evaluation algorithm depending on the genre of the performance or singing. For example, for classical music, an evaluation algorithm that emphasizes accuracy of pitch and rhythm can be applied. For pop music, an evaluation algorithm that emphasizes expressiveness and emotional expression can be applied. Furthermore, for jazz, an evaluation algorithm that emphasizes improvisational performance techniques can be applied. This makes it possible to perform appropriate evaluations depending on the genre.

[0055] The provider can propose specific improvement measures based on the success stories of other students. For example, AI can analyze the success stories of other students and propose specific improvement measures. It can propose improvement measures for the same problem based on the success stories of other students. It can also propose improvement measures in specific areas. This allows specific improvement measures to be proposed by referring to the success stories of other students.

[0056] The management department can apply a progress management algorithm to each subject. For example, an algorithm that emphasizes the speed and accuracy of problem-solving can be applied to progress management in mathematics. An algorithm that emphasizes the level of grammar and vocabulary acquisition can be applied to progress management in English. Furthermore, an algorithm that emphasizes the improvement of performance technique and expressiveness can be applied to progress management in music. This makes it possible to optimally manage progress for each subject.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The grading unit automatically grades homework assigned by teachers to students. For example, AI accurately grades standard questions such as math problems and English grammar questions. The grading unit analyzes math calculation problems and calculates scores based on the percentage of correct answers. For English grammar questions, it can also evaluate the accuracy of the grammar and award partial credit. Furthermore, the grading unit can use AI to scan handwritten answer sheets, convert them into digital data using character recognition technology, and grade them. For example, handwritten answer sheets can be read with a scanner, converted into text information using OCR technology, and graded by AI. Step 2: The providing unit provides feedback based on the results of the grading unit. For example, it provides an explanation of how to solve a math problem where the student made a mistake. The providing unit provides a step-by-step explanation and shows a specific solution. For English grammar questions, it can also explain the correct use of grammar and provide example sentences. Furthermore, the providing unit can use AI to automatically generate feedback on the student's answers and provide it in text or audio format. For example, AI can analyze the student's answers, generate explanations for the mistakes, and provide audio feedback. Step 3: The evaluation unit records the student's performance or singing and evaluates the recording. For example, it analyzes pitch, rhythm, expressiveness, etc. and provides detailed feedback. The evaluation unit evaluates pitch accuracy and analyzes rhythm accuracy. Regarding expressiveness, it can also evaluate emotional expression and acting ability and point out specific areas for improvement. Furthermore, the evaluation unit can use AI to analyze the recording data, automatically detect pitch and rhythm errors, and provide feedback. For example, AI can analyze the recording data, detect pitch errors, and suggest specific areas for improvement. Step 4: The management department manages students' learning progress and reports their learning status to teachers. For example, it manages test scores, assignment submission status, study time, etc. and reports them to teachers. The management department visually displays learning progress in graphs and charts, allowing teachers to understand students' learning status at a glance. The management department can also use AI to analyze students' learning data, identify learning trends and weaknesses, and report them to teachers. For example, AI can analyze students' learning data, identify weaknesses in specific areas, and report them to teachers.

[0059] (Example 2) An educational support system according to an embodiment of the present invention utilizes AI to reduce teachers' workloads and provide students with more feedback. In this educational support system, AI automatically grades homework assigned by teachers to students, students record themselves playing or singing, and the AI ​​evaluates the recordings. Furthermore, based on the results of the AI's grading and evaluation, the AI ​​provides specific feedback to students, manages students' learning progress, and reports to teachers on their learning status. This system not only reduces teachers' workloads but also provides more feedback to students, potentially improving learning outcomes. For example, AI automatically grades homework assigned by teachers to students. For example, AI accurately grades routine questions, such as math problems and English grammar problems. This significantly reduces the time teachers spend grading. Next, students record themselves playing or singing, and the AI ​​evaluates the recordings. AI analyzes pitch, rhythm, expressiveness, and other factors to provide detailed feedback. This allows students to specifically identify areas for improvement in their playing or singing. Furthermore, the AI ​​provides specific feedback to students based on the results of its grading and evaluation. For example, it provides an explanation of how to solve a math problem when it gets it wrong, and advice on what parts of a musical performance or singing performance need improvement. Finally, the AI ​​manages students' learning progress and reports the students' learning status to teachers. This makes it easier for teachers to grasp the learning status of each student and provide appropriate guidance. This system not only reduces the burden on teachers, but also enables them to provide more feedback to students, which is expected to improve learning effectiveness. This allows the educational support system to reduce the burden on teachers and provide more feedback to students.

[0060] An educational support system according to an embodiment includes a scoring unit, a providing unit, an evaluation unit, and a management unit. The scoring unit automatically scores homework assigned by teachers to students. For example, AI accurately scores standard questions such as math problems and English grammar problems. The scoring unit, for example, analyzes math calculation problems and calculates scores based on the percentage of correct answers. For English grammar questions, it can also evaluate the accuracy of the grammar and award partial credit. Furthermore, the scoring unit can use AI to scan handwritten answer sheets, convert them into digital data using character recognition technology, and grade them. For example, handwritten answer sheets can be scanned and converted into text information using OCR technology, and graded by AI. The providing unit provides feedback based on the results of the grading by the scoring unit. For example, it provides explanations on how to solve math problems where students made mistakes. For example, the providing unit provides step-by-step explanations and specific solutions. For English grammar questions, it can also explain correct grammar usage and provide example sentences. Furthermore, the providing unit can use AI to automatically generate feedback on students' answers and provide it in text or audio format. For example, AI can analyze students' answers, generate explanations for mistakes, and provide audio feedback. The evaluating unit records students playing or singing and evaluates the recording. For example, it analyzes pitch, rhythm, expressiveness, etc. and provides detailed feedback. For example, the evaluating unit evaluates pitch accuracy and rhythm accuracy. Regarding expressiveness, it can also evaluate emotional expression and acting ability and point out specific areas for improvement. Furthermore, the evaluating unit can use AI to analyze the recording data, automatically detect pitch and rhythm errors, and provide feedback. For example, AI can analyze the recording data, detect pitch errors, and suggest specific areas for improvement. The management unit manages students' learning progress and reports their learning status to teachers. For example, it manages test scores, assignment submission status, study time, etc., and reports them to teachers. For example, the management department can visually display learning progress in graphs and charts, allowing teachers to grasp students' learning status at a glance. The management department can also use AI to analyze students' learning data, identify learning trends and weaknesses, and report them to teachers.For example, AI can analyze students' learning data, identify weaknesses in specific areas, and report them to teachers. This reduces the burden on teachers and allows the education support system according to the embodiment to provide more feedback to students.

[0061] The scoring unit can grade standardized questions, such as math problems or English grammar problems. Standardized questions include, but are not limited to, math calculation problems and English grammar problems. For example, the scoring unit can analyze math calculation problems and calculate scores based on the percentage of correct answers. For English grammar questions, the scoring unit can also evaluate the accuracy of the grammar and award partial credit. Furthermore, the scoring unit can use AI to scan handwritten answers, convert them into digital data using character recognition technology, and grade them. For example, handwritten answers can be scanned with a scanner, converted into text information using OCR technology, and graded by AI. This enables accurate grading of standardized questions. Some or all of the above-described processing in the scoring unit can be performed using AI, or can be performed without AI. For example, the scoring unit can scan handwritten answers with a scanner, obtain image data, input the acquired image data, and have the generation AI generate text data from the image data.

[0062] The evaluation unit can analyze pitch, rhythm, and expressiveness to evaluate the performance or singing. The evaluation unit, for example, evaluates the accuracy of pitch and analyzes the accuracy of rhythm. For example, the evaluation unit analyzes the pitch height and accuracy and detects pitch errors. The evaluation unit can also analyze the tempo and accuracy of rhythm and detect rhythm errors. Furthermore, the evaluation unit can evaluate emotional expression and acting ability with respect to expressiveness and point out specific areas for improvement. For example, the evaluation unit analyzes emotional expression and acting ability and presents specific areas for improvement. This enables a detailed evaluation of the performance or singing. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input recording data into a generation AI and have the generation AI analyze pitch and rhythm.

[0063] The providing unit can provide an explanation of how to solve a mistake in a math problem. For example, the providing unit provides an explanation of how to solve a mistake in a math problem. For example, the providing unit provides a step-by-step explanation and shows a specific solution. The providing unit can also automatically generate an explanation for the mistake using a generation AI and provide it in text or audio format. For example, the providing unit causes the generation AI to analyze the student's answer, generate an explanation for the mistake, and provide audio feedback. This makes it easier for the student to understand the mistake. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input an explanation for the answer to the generation AI and cause the generation AI to generate an explanation.

[0064] The providing unit can provide advice on areas for improvement regarding playing or singing. For example, the providing unit provides advice on which parts of playing or singing need improvement. For example, the providing unit points out areas for improvement in pitch or rhythm and suggests specific practice methods. The providing unit can also provide advice on areas for improvement regarding expressiveness and provide hints for improving emotional expression and acting skills. Furthermore, the providing unit can use a generating AI to automatically generate areas for improvement regarding playing or singing and provide them in text or audio format. For example, the providing unit can have the generating AI analyze recorded data, generate specific areas for improvement, and provide audio feedback. This allows the student to know specific areas for improvement regarding playing or singing. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input advice on areas for improvement into the generating AI and have the generating AI generate the advice.

[0065] The management unit can manage students' learning progress and report the students' learning status to teachers. The management unit can manage, for example, test scores, assignment submission status, study time, etc., and report them to teachers. For example, the management unit can visually display learning progress using graphs and charts, allowing teachers to understand students' learning status at a glance. The management unit can also use AI to analyze students' learning data, identify learning trends and weaknesses, and report them to teachers. For example, AI can analyze students' learning data, identify weaknesses in specific areas, and report them to teachers. This makes it easier for teachers to understand the learning status of each student. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input learning status reports into a generation AI and have the generation AI generate reports.

[0066] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated user emotions. For example, if the user is feeling stressed, the scoring unit uses the AI ​​to ease the strictness of the scoring and provide gentle feedback. For example, if the user is relaxed, the scoring unit applies normal scoring criteria and provides standard feedback. Furthermore, if the user is confident, the scoring unit can use the AI ​​to tighten the strictness of the scoring and provide detailed feedback. This enables flexible scoring according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the scoring unit may be performed using an AI, for example, or without an AI. For example, the scoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the scoring criteria based on the emotion.

[0067] The scoring unit can identify individual weaknesses based on the student's past answer history. For example, the scoring unit uses AI to analyze the past answer history and identify the student's weaknesses for specific questions. For example, the scoring unit can identify areas where the same mistakes are repeated based on the past answer history. The scoring unit can also refer to the past answer history to identify the student's weaknesses in specific areas. This makes it easier to identify the student's individual weaknesses. The answer history is referenced based on, for example, past test results and assignment submission status. Some or all of the above-mentioned processing in the scoring unit may be performed using AI, or may be performed without using AI. For example, the scoring unit can input the past answer history into the generation AI and have the generation AI identify individual weaknesses.

[0068] The scoring unit can apply a scoring algorithm depending on the difficulty of the question. For example, the scoring unit uses AI to analyze the difficulty of the question and apply a scoring algorithm depending on the difficulty. For example, the scoring unit applies strict scoring criteria to easy questions and lenient scoring criteria to difficult questions. The scoring unit can also apply an algorithm that awards partial points depending on the difficulty of the question. This enables appropriate scoring depending on the difficulty of the question. The scoring algorithm is applied using, for example, a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input question difficulty data to the generation AI and cause the generation AI to apply the scoring algorithm.

[0069] The scoring unit can estimate the user's emotions and adjust the display format of the scoring results based on the estimated user emotions. For example, if the user is nervous, the scoring unit provides a simple, highly visible display method. For example, if the user is relaxed, the scoring unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the scoring unit can also provide a display method that focuses on the main points. This enables flexible display methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the scoring unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the scoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the display format.

[0070] The scoring unit can customize the feedback content based on the student's learning style. For example, if the student has a visual learning style, the scoring unit provides feedback using graphs or diagrams. For example, if the student has an auditory learning style, the scoring unit provides audio feedback. The scoring unit can also provide practical advice if the student has an experiential learning style. This allows feedback to be provided according to the student's learning style. The learning style is identified based on classifications such as visual, auditory, and experiential. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the student's learning style data into a generation AI and have the generation AI customize the feedback content.

[0071] The grading unit can perform a comprehensive evaluation based on the grade data of other subjects. The grading unit, for example, uses AI to refer to the grade data of other subjects and perform a comprehensive academic evaluation. For example, the grading unit identifies a student's strengths and weaknesses in a specific area based on the grade data of other subjects. The grading unit can also provide comprehensive feedback by referring to the grade data of other subjects. This makes it possible to perform a comprehensive evaluation by referring to the grade data of other subjects. The grade data is referenced based on, for example, past test results and assignment grades. Some or all of the above-mentioned processing in the grading unit may be performed using AI, or may be performed without using AI. For example, the grading unit can input grade data of other subjects into the generation AI and have the generation AI perform a comprehensive evaluation.

[0072] The providing unit can estimate the user's emotions and adjust the feedback tone based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide feedback in a gentle tone. For example, if the user is relaxed, the providing unit can provide feedback in a normal tone. Furthermore, if the user is confident, the providing unit can provide feedback in a strict tone. This enables flexible feedback according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback tone.

[0073] The providing unit can strengthen the individual advice based on the student's past learning history. For example, the providing unit uses AI to analyze the student's past learning history and provide individual advice for a specific problem. For example, the providing unit provides advice to avoid repeating the same mistakes based on the student's past learning history. The providing unit can also refer to the student's past learning history and provide individual advice in a specific field. In this way, the individual advice is strengthened by referring to the student's past learning history. The learning history is referenced based on, for example, the student's past assignment submission status and test scores. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the student's past learning history into the generating AI and have the generating AI strengthen the individual advice.

[0074] The providing unit can apply a feedback format depending on the type of problem. For example, for a math problem, the providing unit provides feedback that explains the solution steps in detail. For example, for an English grammar problem, the providing unit provides feedback that explains correct grammar usage. For playing or singing, the providing unit can also provide feedback that points out areas for improvement in pitch or rhythm. This allows appropriate feedback to be provided depending on the type of problem. The feedback format can be applied in, for example, text format, audio format, video format, or the like. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input problem type data to a generating AI and cause the generating AI to apply the feedback format.

[0075] The providing unit can estimate the user's emotions and adjust the level of feedback detail based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide concise and to-the-point feedback. For example, if the user is relaxed, the providing unit can provide detailed feedback. Furthermore, if the user is confident, the providing unit can provide harsh feedback. This enables flexible feedback according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level of feedback detail.

[0076] The providing unit can customize the feedback content based on the student's learning goals. For example, if the student is studying for a specific exam, the providing unit provides feedback related to the exam. For example, if the student wants to improve a specific skill, the providing unit provides feedback related to the skill. Furthermore, if the student wants to achieve a specific goal, the providing unit can also provide feedback aimed at the goal. This allows feedback to be provided according to the student's learning goals. The learning goals are identified based on, for example, short-term goals, long-term goals, specific achievement criteria, etc. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the student's learning goal data into a generating AI and cause the generating AI to customize the feedback content.

[0077] The provision unit can propose specific improvement measures based on success stories of other students. For example, the provision unit uses AI to analyze success stories of other students and propose specific improvement measures. For example, the provision unit proposes improvement measures for the same problem based on success stories of other students. The provision unit can also refer to success stories of other students and propose improvement measures in specific areas. In this way, specific improvement measures are proposed by referring to success stories of other students. Success stories are referenced based on, for example, the success experiences and specific improvement measures of other students. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input data on success stories of other students into the generation AI and have the generation AI execute the proposed improvement measures.

[0078] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can relax the evaluation criteria and provide a gentle evaluation. For example, if the user is relaxed, the evaluation unit can apply normal evaluation criteria and provide a standard evaluation. Furthermore, if the user is confident, the evaluation unit can tighten the evaluation criteria and provide a detailed evaluation. This enables flexible evaluation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using an AI, for example, or without an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the evaluation criteria.

[0079] The evaluation unit can identify individual areas for improvement based on past performance and singing data. The evaluation unit, for example, uses AI to analyze past performance data and identify areas for improvement in specific sections. For example, the evaluation unit can identify parts where the same mistakes are repeated based on past singing data. The evaluation unit can also refer to past performance and singing data to identify areas for improvement in specific techniques. This makes it easier to identify individual areas for improvement by referring to past data. The performance and singing data can be referenced based on, for example, audio recording data or video data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past performance and singing data into the generation AI and have the generation AI identify individual areas for improvement.

[0080] The evaluation unit can apply an evaluation algorithm depending on the genre of the performance or singing. For example, for classical music, the evaluation unit applies an evaluation algorithm that emphasizes accuracy of pitch and rhythm. For example, for pop music, the evaluation unit can apply an evaluation algorithm that emphasizes expressiveness and emotional expression. For jazz, the evaluation unit can also apply an evaluation algorithm that emphasizes improvisational performance techniques. This enables appropriate evaluation depending on the genre. The evaluation algorithm is applied using, for example, a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input genre data of the performance or singing to the generation AI and cause the generation AI to apply the evaluation algorithm.

[0081] The evaluation unit can estimate the user's emotions and adjust the display format of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit provides a simple, highly visible display method. For example, if the user is relaxed, the evaluation unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the evaluation unit can also provide a display method that focuses on the main points. This enables flexible display methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display format.

[0082] The evaluation unit can customize the feedback content based on the student's practice history. For example, the evaluation unit uses AI to analyze the student's practice history and provide individual feedback for a specific problem. For example, the evaluation unit provides feedback to prevent the student from making the same mistake again based on the student's practice history. The evaluation unit can also refer to the student's practice history and provide individual feedback for a specific technique. This allows individual feedback to be provided based on the student's practice history. The practice history is referenced based on, for example, past practice records and practice time. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the student's practice history data into the generation AI and have the generation AI customize the feedback content.

[0083] The evaluation unit can perform comparative evaluation based on the performance and singing data of other students. The evaluation unit, for example, uses AI to analyze the performance data of other students and perform comparative evaluation. For example, the evaluation unit performs comparative evaluation for the same question based on the singing data of other students. The evaluation unit can also perform comparative evaluation of specific techniques by referring to the performance and singing data of other students. This makes comparative evaluation possible by referring to the data of other students. The comparative evaluation method is performed based on, for example, comparison criteria with other students and evaluation scales. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the performance and singing data of other students into the generation AI and have the generation AI perform comparative evaluation.

[0084] The management unit can estimate the user's emotions and adjust the reporting format of the learning progress based on the estimated user emotions. For example, if the user is feeling stressed, the management unit provides a concise and to-the-point reporting method. For example, if the user is relaxed, the management unit can provide a detailed reporting method. Furthermore, if the user is confident, the management unit can provide a strict reporting method. This enables flexible reporting methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the reporting format.

[0085] The management unit can optimize individual study plans based on past learning data. For example, the management unit uses AI to analyze past learning data and optimize study plans in specific fields. For example, the management unit proposes a study plan based on past learning data to prevent the same mistakes from being repeated. The management unit can also refer to past learning data to optimize a study plan aimed at a specific goal. This provides an optimal study plan based on past learning data. The study plan is optimized based on, for example, the allocation of study time and the priority of tasks. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input past learning data into a generation AI and have the generation AI optimize the study plan.

[0086] The management unit can apply a progress management algorithm for each subject. For example, to manage progress in mathematics, the management unit can apply an algorithm that emphasizes the speed and accuracy of problem-solving. For example, to manage progress in English, the management unit can apply an algorithm that emphasizes the level of grammar and vocabulary acquisition. Furthermore, to manage progress in music, the management unit can apply an algorithm that emphasizes the improvement of performance technique and expressiveness. This enables optimal progress management for each subject. The progress management algorithm is applied using, for example, a machine learning algorithm or a rule-based algorithm. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input progress data for each subject into a generation AI and have the generation AI apply the progress management algorithm.

[0087] The management unit can estimate the user's emotions and set priorities for learning progress based on the estimated user emotions. For example, if the user is feeling stressed, the management unit reports progress by prioritizing easy tasks. For example, if the user is relaxed, the management unit reports progress by normal priority. Furthermore, if the user is confident, the management unit can also report progress by prioritizing difficult tasks. This enables flexible prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using AI, or without AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI set the priorities.

[0088] The management unit can customize a study plan based on the student's lifestyle data. For example, the management unit uses AI to analyze the student's lifestyle data and suggest optimal study times. For example, the management unit can suggest a study plan that takes into account the balance between study and rest based on the student's lifestyle data. The management unit can also refer to the student's lifestyle data and suggest a study plan that allows the student to focus during specific time periods. This provides an optimal study plan based on the student's lifestyle. The lifestyle data can be referenced based on, for example, sleep time, eating habits, exercise frequency, etc. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the student's lifestyle data into a generation AI and have the generation AI customize the study plan.

[0089] The management unit can perform comparative analysis based on the progress data of other students. The management unit, for example, uses AI to analyze the progress data of other students and perform the comparative analysis. For example, the management unit compares progress on the same assignment based on the progress data of other students. The management unit can also refer to the progress data of other students and perform comparative analysis of progress in a specific area. This makes it possible to perform comparative analysis by referring to the data of other students. The comparative analysis method is performed based on, for example, a comparison standard with other students or an analytical scale. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the progress data of other students into the generation AI and have the generation AI perform the comparative analysis. === Hard Collateral 1-1 === Each of the multiple elements including the scoring unit, providing unit, evaluation unit, and management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the scoring unit is realized by the control unit 46A of the smart device 14 and automatically grades homework assigned by a teacher to a student. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the grading results. The evaluation unit is realized, for example, by the control unit 46A of the smart device 14 and evaluates performance or singing data recorded by a student. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the student's learning progress and reports it to the teacher. === Hard Collateral 1-2 === Each of the multiple elements, including the scoring unit, providing unit, evaluation unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the scoring unit is realized by the control unit 46A of the smart glasses 214 and automatically grades homework assigned by a teacher to a student. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the grading results. The evaluation unit is realized, for example, by the control unit 46A of the smart glasses 214 and evaluates performance or singing data recorded by a student. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the student's learning progress and reports it to the teacher. === Hard Collateral 1-3 === Each of the multiple elements including the scoring unit, providing unit, evaluation unit, and management unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the scoring unit is realized by the control unit 46A of the headset type terminal 314 and automatically grades homework assigned by a teacher to a student. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the grading results. The evaluation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and evaluates performance or singing data recorded by a student. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the student's learning progress and reports it to the teacher. === Hard Collateral 1-4 === Each of the multiple elements including the scoring unit, providing unit, evaluation unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the scoring unit is realized by the control unit 46A of the robot 414 and automatically grades homework assigned by a teacher to a student. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the grading results. The evaluation unit is realized, for example, by the control unit 46A of the robot 414 and evaluates performance or singing data recorded by a student. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the student's learning progress and reports it to the teacher.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The educational support system may further include a learning style adaptation unit that customizes feedback based on a student's learning style. For example, a student with a visual learning style may be provided with feedback using graphs and diagrams. A student with an auditory learning style may be provided with audio feedback. A student with an experiential learning style may be provided with practical advice. This allows optimal feedback to be provided according to each student's learning style.

[0092] The scoring department can identify individual weaknesses based on a student's past answer history. For example, AI can analyze past answer history to identify a student's weaknesses for a specific question. Based on past answer history, it can identify areas where the same mistakes are repeated. It can also identify a student's weaknesses in specific areas. This makes it easier to identify individual weaknesses for students.

[0093] The evaluation unit can apply an evaluation algorithm depending on the genre of the performance or singing. For example, for classical music, an evaluation algorithm that emphasizes accuracy of pitch and rhythm can be applied. For pop music, an evaluation algorithm that emphasizes expressiveness and emotional expression can be applied. Furthermore, for jazz, an evaluation algorithm that emphasizes improvisational performance techniques can be applied. This makes it possible to perform appropriate evaluations depending on the genre.

[0094] The provider can propose specific improvement measures based on the success stories of other students. For example, AI can analyze the success stories of other students and propose specific improvement measures. It can propose improvement measures for the same problem based on the success stories of other students. It can also propose improvement measures in specific areas. This allows specific improvement measures to be proposed by referring to the success stories of other students.

[0095] The management department can apply a progress management algorithm to each subject. For example, an algorithm that emphasizes the speed and accuracy of problem-solving can be applied to progress management in mathematics. An algorithm that emphasizes the level of grammar and vocabulary acquisition can be applied to progress management in English. Furthermore, an algorithm that emphasizes the improvement of performance technique and expressiveness can be applied to progress management in music. This makes it possible to optimally manage progress for each subject.

[0096] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can ease the strictness of the scoring and provide gentle feedback. If the user is relaxed, the normal scoring criteria can be applied and standard feedback can be provided. Also, if the user is confident, the AI ​​can strengthen the strictness of the scoring and provide detailed feedback. This enables flexible scoring according to the user's emotions.

[0097] The providing unit can estimate the user's emotion and adjust the feedback tone based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback can be provided in a gentle tone. If the user is relaxed, the feedback can be provided in a normal tone. Also, if the user is confident, the feedback can be provided in a strict tone. This enables flexible feedback according to the user's emotion.

[0098] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the evaluation criteria can be relaxed and a gentle evaluation can be provided. If the user is relaxed, normal evaluation criteria can be applied and a standard evaluation can be provided. Also, if the user is confident, the evaluation criteria can be tightened and a detailed evaluation can be provided. This enables flexible evaluation according to the user's emotions.

[0099] The evaluation unit can estimate the user's emotions and adjust the display format of the evaluation results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. This enables flexible display methods according to the user's emotions.

[0100] The management unit can estimate the user's emotions and adjust the reporting format of the learning progress based on the estimated user emotions. For example, if the user is feeling stressed, a brief and to-the-point reporting method can be provided. If the user is relaxed, a detailed reporting method can be provided. If the user is confident, a strict reporting method can be provided. This enables flexible reporting methods according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The grading unit automatically grades homework assigned by teachers to students. For example, AI accurately grades standard questions such as math problems and English grammar questions. The grading unit analyzes math calculation problems and calculates scores based on the percentage of correct answers. For English grammar questions, it can also evaluate the accuracy of the grammar and award partial credit. Furthermore, the grading unit can use AI to scan handwritten answer sheets, convert them into digital data using character recognition technology, and grade them. For example, handwritten answer sheets can be read with a scanner, converted into text information using OCR technology, and graded by AI. Step 2: The providing unit provides feedback based on the results of the grading unit. For example, it provides an explanation of how to solve a math problem where the student made a mistake. The providing unit provides a step-by-step explanation and shows a specific solution. For English grammar questions, it can also explain the correct use of grammar and provide example sentences. Furthermore, the providing unit can use AI to automatically generate feedback on the student's answers and provide it in text or audio format. For example, AI can analyze the student's answers, generate explanations for the mistakes, and provide audio feedback. Step 3: The evaluation unit records the student's performance or singing and evaluates the recording. For example, it analyzes pitch, rhythm, expressiveness, etc. and provides detailed feedback. The evaluation unit evaluates pitch accuracy and analyzes rhythm accuracy. Regarding expressiveness, it can also evaluate emotional expression and acting ability and point out specific areas for improvement. Furthermore, the evaluation unit can use AI to analyze the recording data, automatically detect pitch and rhythm errors, and provide feedback. For example, AI can analyze the recording data, detect pitch errors, and suggest specific areas for improvement. Step 4: The management department manages students' learning progress and reports their learning status to teachers. For example, it manages test scores, assignment submission status, study time, etc. and reports them to teachers. The management department visually displays learning progress in graphs and charts, allowing teachers to understand students' learning status at a glance. The management department can also use AI to analyze students' learning data, identify learning trends and weaknesses, and report them to teachers. For example, AI can analyze students' learning data, identify weaknesses in specific areas, and report them to teachers.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

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

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 grading department that grades homework; a providing unit that provides feedback based on the results of the scoring by the scoring unit; an evaluation department that evaluates the performance and singing; a providing unit that provides feedback based on the result of the evaluation by the evaluating unit; The administrative department manages students' learning progress, a providing unit that provides feedback based on the learning progress managed by the managing unit. A system characterized by:

2. The scoring unit Marking routine math or English grammar questions The system of claim 1 .

3. The evaluation unit Analyze pitch, rhythm, and expressiveness to evaluate performance and singing The system of claim 1 .

4. The providing unit Providing explanations on how to solve math problems when they are wrong The system of claim 1 .

5. The providing unit Providing advice on how to improve your playing and singing The system of claim 1 .

6. The management unit Track student progress and report student progress to teachers The system of claim 1 .

7. The scoring unit Inferring user emotions and adjusting scoring criteria based on the estimated user emotions The system of claim 1 .

8. The scoring unit Identify individual weaknesses based on past student answer history The system of claim 1 .

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