System

Abstract

An object of the system according to the embodiment is to propose an optimal homework assignment according to the level of understanding and learning style of each student.SOLUTION: A system includes a comprehension degree analysis part, a learning style specification part, a skillful subject determination part, and a homework proposal part. The understanding level analysis unit analyzes the understanding level and the progress level of each student. A learning style specification part specifies the learning style of each student on the basis of the data analyzed by the comprehension degree analysis part. A strong subject determination part determines the strong subject / weak subject of each student on the basis of the learning style specified by the learning style specification part. A homework proposal part proposes homework optimum to each student on the basis of the data determined by the strong subject determination part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide individual homework that suited each student's level of understanding and learning style, resulting in a uniform education.

[0005] The system according to the embodiment aims to propose optimal homework according to each student's level of understanding and learning style. [Means for solving the problem]

[0006] The system according to the embodiment includes a comprehension analysis unit, a learning style identification unit, a strong subject determination unit, and a homework suggestion unit. The comprehension analysis unit analyzes each student's level of comprehension and progress. The learning style identification unit identifies each student's learning style based on the data analyzed by the comprehension analysis unit. The strong subject determination unit determines each student's strong and weak subjects based on the learning style identified by the learning style determination unit. The homework suggestion unit suggests optimal homework for each student based on the data determined by the strong subject determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal homework according to each student's level of understanding and learning style. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The homework suggestion system according to an embodiment of the present invention is a system that uses a generative AI to suggest homework (supplementary learning) for elementary and junior high school students that is tailored to each student's level of understanding, progress, learning style, and strengths and weaknesses. As a result, the homework suggestion system can suggest the most suitable homework for each student, maximizing the learning effect.

[0029] The homework suggestion system according to the embodiment includes a comprehension analysis unit, a learning style identification unit, a strong subject determination unit, and a homework suggestion unit. The comprehension analysis unit analyzes each student's level of comprehension and progress. For example, the generation AI evaluates each student's level of comprehension based on test results, comments made in class, the contents of submitted work, etc. The generation AI also analyzes learning data to grasp each student's progress. The learning style identification unit identifies each student's learning style based on the data analyzed by the comprehension analysis unit. For example, the generation AI identifies each student's learning style based on their learning history, survey results, etc. The strong subject determination unit determines each student's strong and weak subjects based on the learning style identified by the learning style identification unit. For example, the generation AI evaluates each student's strong and weak subjects based on test results, comments made in class, the contents of submitted work, etc. The homework suggestion unit suggests optimal homework for each student based on the data determined by the strong subject determination unit. For example, the generation AI may suggest homework that includes many basic questions for subjects with low comprehension levels, and homework that includes applied questions for subjects with high comprehension levels. As a result, the homework suggestion system according to the embodiment can suggest optimal homework for each student, maximizing learning effectiveness.

[0030] The comprehension analysis unit can monitor a student's study time or concentration level in real time and evaluate their progress. For example, the generation AI records the student's study time and evaluates their concentration level at regular intervals. For example, if the study time is long and the concentration level is high, it determines that the progress is good. The comprehension analysis unit also tracks the student's eye movements while studying with a camera to evaluate their concentration level. For example, if the eye movements are frequent, it determines that the concentration level is low, and if the eye movements are fixed, it determines that the concentration level is high. The comprehension analysis unit also analyzes the frequency of the student's mouse or keyboard operations while studying to evaluate their concentration level. For example, if operations are performed frequently, it determines that the concentration level is high, and if operations are performed infrequently, it determines that the concentration level is low. This allows for accurate evaluation of a student's progress by monitoring their study time and concentration level in real time.

[0031] The comprehension analysis unit can evaluate the level of understanding by analyzing images of students' handwritten notes or answer sheets. For example, the comprehension analysis unit uses a generation AI to scan the student's handwritten notes and analyze the content using character recognition technology. For example, if the content of the notes is detailed, it determines that the level of understanding is high. The comprehension analysis unit also analyzes images of the student's answer sheets to evaluate the accuracy and detail of the answers. For example, if the correct answer rate is high and there are many detailed answers, it determines that the level of understanding is high. The comprehension analysis unit also analyzes handwritten diagrams and graphs to evaluate the level of understanding. For example, if the diagrams and graphs are accurate and detailed, it determines that the level of understanding is high. This makes it possible to accurately evaluate the level of understanding by analyzing images of handwritten notes and answer sheets.

[0032] The learning style identification unit can analyze a student's past learning patterns and identify the optimal learning style. For example, the generative AI analyzes a student's past learning history to identify which learning method was most effective. For example, if repetitive learning was effective, that style is recommended. The learning style identification unit also analyzes a student's past test results to identify which learning style produced the highest grades. For example, if visual learning was effective, that style is recommended. The learning style identification unit also analyzes the relationship between a student's past study time and grades to identify the optimal learning style. For example, if a style of studying intensively for short periods of time was effective, that style is recommended. In this way, the most effective learning style can be identified by analyzing past learning patterns.

[0033] The learning style identification unit can analyze a student's learning environment and suggest the optimal learning style. For example, the generative AI analyzes a student's learning environment and suggests the optimal learning style. For example, if studying in a quiet place is effective, that environment is recommended. The learning style identification unit also analyzes the effectiveness of a student studying while listening to music and suggests the optimal learning style. For example, if a specific music genre improves concentration, that music is recommended. The learning style identification unit also analyzes the relationship between changes in a student's learning environment and grades and suggests the optimal learning style. For example, if studying under natural light is effective, that environment is recommended. In this way, the optimal learning style can be suggested by analyzing the learning environment.

[0034] The learning style identification unit can analyze a student's physical movements while studying and identify their learning style. For example, the generative AI analyzes how a student sits and identifies the optimal learning style. For example, if sitting in the correct posture improves concentration, that posture is recommended. The learning style identification unit also analyzes how a student writes and identifies the optimal learning style. For example, if taking notes by hand improves comprehension, that method is recommended. The learning style identification unit also analyzes a student's physical movements while studying and identifies the optimal learning style. For example, if studying while standing is effective, that style is recommended. In this way, learning styles can be identified by analyzing physical movements while studying.

[0035] The strong subject determination unit can determine strong and weak subjects by analyzing not only a student's past grade data but also the frequency of comments or questions made during class. The strong subject determination unit, for example, uses a generation AI to analyze a student's past grade data to determine strong and weak subjects. For example, it determines subjects with high grades as strong subjects. The strong subject determination unit also analyzes the frequency of comments and questions made during class to determine strong and weak subjects. For example, it determines subjects with many comments and questions as strong subjects. The strong subject determination unit also integrates grade data and the frequency of comments and questions made during class to comprehensively determine strong and weak subjects. For example, it determines subjects with high grades and many comments and questions as strong subjects. This makes it possible to more accurately determine strong and weak subjects by analyzing not only grade data but also the frequency of comments and questions made during class.

[0036] The strong subject determination unit monitors the stress level of the student while studying and evaluates the stress level for each subject, thereby determining which subjects are strong and weak. The strong subject determination unit, for example, uses a generation AI to monitor the stress level of the student while studying and evaluates the stress level for each subject. For example, it determines a subject with a low stress level as a strong subject. The strong subject determination unit also monitors the student's heart rate and galvanic skin response to evaluate the stress level. For example, it determines a subject with a stable heart rate as a strong subject. The strong subject determination unit also analyzes the student's facial expressions while studying and evaluates the stress level. For example, it determines a subject with a high number of relaxed facial expressions as a strong subject. In this way, by monitoring the stress level during studying, it is possible to accurately evaluate the strengths and weaknesses of each subject.

[0037] The strong subject determination unit can analyze a student's error rate or answer quality during study and evaluate their strengths and weaknesses for each subject. For example, the strong subject determination unit uses a generation AI to analyze a student's error rate during study and evaluate their strengths and weaknesses for each subject. For example, it determines a subject with a low error rate as a strong subject. The strong subject determination unit also analyzes the quality of the student's answers and evaluates their strengths and weaknesses for each subject. For example, it determines a subject with detailed and accurate answers as a strong subject. The strong subject determination unit also integrates the error rate and answer quality to comprehensively evaluate strong and weak subjects. For example, it determines a subject with a low error rate and high answer quality as a strong subject. In this way, by analyzing the error rate and answer quality during study, it is possible to accurately evaluate the strengths and weaknesses of each subject.

[0038] The homework suggestion unit can analyze a student's learning history and suggest homework based on past learning content. For example, the homework suggestion unit uses a generation AI to analyze a student's past learning history and suggest linked homework. For example, it can provide homework to review content previously learned. The homework suggestion unit can also suggest homework to strengthen areas of low understanding based on the student's learning history. For example, it can provide homework that includes questions that were incorrectly answered in past tests. The homework suggestion unit can also analyze learning history and suggest application questions linked to past learning content. For example, it can provide homework that applies content previously learned. In this way, by analyzing learning history, it is possible to suggest homework that is linked to past learning content.

[0039] The homework suggestion unit can adjust the homework format according to the student's learning style. For example, the homework suggestion unit uses a generative AI to analyze the student's learning style and suggest the optimal homework format. For example, it provides video lectures to students who are good at visual learning. The homework suggestion unit also suggests interactive quiz-style homework according to the student's learning style. For example, it provides quiz-style homework to students who are good at repetitive learning. The homework suggestion unit also suggests homework that incorporates real-life experiences based on the student's learning style. For example, it provides homework that includes experiments and observations. This makes it possible to maximize learning effectiveness by customizing the homework format according to the student's learning style.

[0040] The homework suggestion unit can analyze a student's motivation while studying and suggest homework that will increase their motivation. For example, the homework suggestion unit uses a generation AI to analyze a student's motivation while studying and suggest homework that will increase their motivation. For example, it can provide homework that includes topics that interest them. The homework suggestion unit can also suggest homework that will give a sense of accomplishment based on the student's motivation data. For example, it can provide homework that gradually increases in difficulty. The homework suggestion unit can also analyze a student's motivation while studying and suggest homework that incorporates a reward system. For example, it can provide a system where points are accumulated each time homework is completed. In this way, it can suggest homework that will increase motivation by analyzing a student's motivation while studying.

[0041] The homework suggestion unit can share the homework suggestion results with other students or teachers to help improve learning outcomes. For example, the homework suggestion unit shares the optimized homework results suggested by the generation AI with other students to help improve learning outcomes. For example, students who have done the same homework can exchange information with each other. The homework suggestion unit also shares the optimized homework suggestion results with teachers to help with the progress of lessons. For example, the lesson content can be adjusted based on the homework results. The homework suggestion unit also shares the optimized homework suggestion results with parents to help support learning at home. For example, the homework progress can be reported to parents. In this way, sharing the homework suggestion results can help improve learning outcomes.

[0042] The homework suggestion unit can adjust the homework suggestion results according to different learning resources and suggest the optimal learning resources. For example, the homework suggestion unit suggests optimal online learning materials based on the optimized homework results suggested by the generation AI. For example, it recommends basic online learning materials for weak subjects. The homework suggestion unit also suggests optimal video lectures based on the optimized homework suggestion results. For example, it recommends visual video lectures for weak subjects. The homework suggestion unit also customizes and suggests optimal learning resources based on the optimized homework suggestion results. For example, it recommends interactive learning resources for weak subjects. In this way, by customizing the homework suggestion results, it is possible to suggest optimal learning resources.

[0043] The homework suggestion unit can analyze the quality of the student's answers and suggest specific areas for improvement. For example, the homework suggestion unit uses a generative AI to analyze the quality of the student's answers and suggest specific areas for improvement. For example, it evaluates the detail and accuracy of the answers and suggests areas for improvement. The homework suggestion unit also suggests specific areas for improvement to be reflected in the next homework assignment based on the student's answers. For example, it provides an explanation for a question that was answered incorrectly. The homework suggestion unit also analyzes the quality of the answers and suggests specific areas for improvement. For example, it evaluates the structure and logic of the answers and suggests areas for improvement. In this way, specific areas for improvement can be suggested by analyzing the quality of the answers.

[0044] The homework suggestion unit can analyze the student's error rate and answer quality while studying and reflect them in the next homework assignment. For example, the homework suggestion unit uses a generation AI to analyze the student's error rate while studying and reflect them in the next homework assignment. For example, it provides homework that focuses on questions with a high error rate. The homework suggestion unit also analyzes the quality of the student's answers and reflects them in the next homework assignment. For example, it evaluates the detail and accuracy of the answers and reflects them in the next homework assignment. The homework suggestion unit also integrates the error rate and answer quality and reflects them in the next homework assignment. For example, it provides homework that focuses on questions with a high error rate and low answer quality. In this way, the error rate and answer quality can be analyzed and reflected in the next homework assignment.

[0045] The homework suggestion unit can analyze a student's motivation while studying and suggest feedback that will improve their motivation. For example, the homework suggestion unit uses a generation AI to analyze a student's motivation while studying and suggest feedback that will improve their motivation. For example, it can provide feedback that will give a sense of accomplishment. The homework suggestion unit also suggests specific feedback based on the student's motivation data. For example, it can provide feedback that praises their efforts. The homework suggestion unit also analyzes a student's motivation while studying and suggests feedback that incorporates a reward system. For example, it can provide a system that accumulates points each time homework is completed. In this way, it can suggest feedback that will improve motivation by analyzing a student's motivation while studying.

[0046] The homework suggestion unit can share the homework feedback results with other students or teachers to help improve learning outcomes. For example, the generation AI of the homework suggestion unit shares the homework feedback results with other students to help improve learning outcomes. For example, students who have done the same homework can exchange information with each other. The homework suggestion unit also shares the homework feedback results with teachers to help with the progress of lessons. For example, the lesson content can be adjusted based on the feedback results. The homework suggestion unit also shares the homework feedback results with parents to help support learning at home. For example, the home study plan can be adjusted based on the feedback results. In this way, sharing the homework feedback results can help improve learning outcomes.

[0047] The homework suggestion unit can adjust the homework feedback results according to different learning resources and suggest the optimal learning resources. For example, the generation AI in the homework suggestion unit suggests the optimal online learning materials based on the homework feedback results. For example, it recommends basic online learning materials for weak subjects. The homework suggestion unit also suggests the optimal video lectures based on the homework feedback results. For example, it recommends visual video lectures for weak subjects. The homework suggestion unit also customizes and suggests the optimal learning resources based on the homework feedback results. For example, it recommends interactive learning resources for weak subjects. In this way, the optimal learning resources can be suggested by customizing the homework feedback results.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The homework suggestion system can also be equipped with a posture analysis unit that monitors students' posture while studying and suggests improvements. For example, the generative AI can use a camera to analyze how students sit and how upright they are, and provide advice on maintaining good posture. Furthermore, if a student has been studying in the same position for a long time, the posture analysis unit can suggest appropriate breaks or stretches. By improving their posture while studying, this can improve concentration and learning effectiveness.

[0050] The homework suggestion system can also be equipped with a nutritional analysis unit that monitors students' diet and nutritional status while studying and proposes optimal meal plans. For example, the generative AI records students' dietary habits and evaluates nutritional balance. The nutritional analysis unit can also analyze the relationship between students' learning performance and diet and propose meal plans to improve concentration and memory. This can maximize learning outcomes through proper nutritional intake.

[0051] The homework suggestion system can also be equipped with a sleep analysis unit that monitors students' sleep patterns while studying and suggests optimal sleep schedules. For example, the generative AI can record the student's sleep duration and quality and analyze the correlation with learning performance. The sleep analysis unit can also provide advice on how to help students get enough rest and suggest optimal sleep schedules to improve learning outcomes. This can maximize learning outcomes through proper sleep.

[0052] The homework suggestion system can also be equipped with an exercise analysis unit that monitors students' exercise volume while studying and proposes appropriate exercise plans. For example, the generative AI records students' exercise habits and analyzes the correlation with their learning performance. The exercise analysis unit can also provide advice on how students can exercise moderately and propose exercise plans for improving concentration and stress relief. This can maximize learning outcomes through appropriate exercise.

[0053] The homework suggestion system can also be equipped with an environmental sound analysis unit that monitors the ambient sounds a student is making while studying and suggests the optimal learning environment. For example, the generation AI can record the sounds around a student while they are studying and analyze the factors that affect their concentration. The environmental sound analysis unit can also provide advice to help students create the optimal learning environment, suggesting a quiet environment or appropriate music. This can maximize learning effectiveness by optimizing the learning environment.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The comprehension analysis unit analyzes each student's level of comprehension and progress. For example, the generation AI evaluates each student's level of comprehension based on test results, comments made during class, and the content of submitted work. The generation AI also analyzes learning data to understand each student's progress. Step 2: The learning style identification unit identifies each student's learning style based on the data analyzed by the comprehension analysis unit. For example, the generation AI identifies each student's learning style based on their learning history, survey results, etc. Step 3: The strong subject determination unit determines each student's strong and weak subjects based on the learning style identified by the learning style identification unit. For example, the generation AI evaluates each student's strong and weak subjects based on test results, comments made in class, the content of submitted work, etc. Step 4: The homework suggestion unit suggests the most suitable homework for each student based on the data determined by the strong subject determination unit. For example, the generation AI will suggest homework that includes many basic questions for subjects with low comprehension, and homework that includes applied questions for subjects with high comprehension.

[0056] (Example 2) The homework suggestion system according to an embodiment of the present invention is a system that uses a generative AI to suggest homework (supplementary learning) for elementary and junior high school students that is tailored to each student's level of understanding, progress, learning style, and strengths and weaknesses. As a result, the homework suggestion system can suggest the most suitable homework for each student, maximizing the learning effect.

[0057] The homework suggestion system according to the embodiment includes a comprehension analysis unit, a learning style identification unit, a strong subject determination unit, and a homework suggestion unit. The comprehension analysis unit analyzes each student's level of comprehension and progress. For example, the generation AI evaluates each student's level of comprehension based on test results, comments made in class, the contents of submitted work, etc. The generation AI also analyzes learning data to grasp each student's progress. The learning style identification unit identifies each student's learning style based on the data analyzed by the comprehension analysis unit. For example, the generation AI identifies each student's learning style based on their learning history, survey results, etc. The strong subject determination unit determines each student's strong and weak subjects based on the learning style identified by the learning style identification unit. For example, the generation AI evaluates each student's strong and weak subjects based on test results, comments made in class, the contents of submitted work, etc. The homework suggestion unit suggests optimal homework for each student based on the data determined by the strong subject determination unit. For example, the generation AI may suggest homework that includes many basic questions for subjects with low comprehension levels, and homework that includes applied questions for subjects with high comprehension levels. As a result, the homework suggestion system according to the embodiment can suggest optimal homework for each student, maximizing learning effectiveness.

[0058] The comprehension analysis unit can analyze students' facial expressions and tone of voice and evaluate their level of comprehension based on their emotional state. For example, the comprehension analysis unit evaluates students' comprehension by using a generation AI to capture their facial expressions with a camera and analyze subtle changes in their facial expressions. For example, it analyzes eyebrow movements and the degree to which the corners of the mouth are turned up to determine the level of comprehension. The comprehension analysis unit also collects students' vocal tones with a microphone and evaluates their emotional state using voice analysis technology. For example, it analyzes changes in the pitch, intensity, and rhythm of their voices and reflects this in the evaluation of their comprehension. The comprehension analysis unit also analyzes the words and phrases students use in class in real time to estimate their emotional state. For example, it determines that a student's level of comprehension is high if they use a lot of positive words, and low if they use a lot of negative words. This allows for more accurate assessment of their comprehension by taking their emotional state into account.

[0059] The comprehension analysis unit can monitor a student's study time or concentration level in real time and evaluate their progress. For example, the generation AI records the student's study time and evaluates their concentration level at regular intervals. For example, if the study time is long and the concentration level is high, it determines that the progress is good. The comprehension analysis unit also tracks the student's eye movements while studying with a camera to evaluate their concentration level. For example, if the eye movements are frequent, it determines that the concentration level is low, and if the eye movements are fixed, it determines that the concentration level is high. The comprehension analysis unit also analyzes the frequency of the student's mouse or keyboard operations while studying to evaluate their concentration level. For example, if operations are performed frequently, it determines that the concentration level is high, and if operations are performed infrequently, it determines that the concentration level is low. This allows for accurate evaluation of a student's progress by monitoring their study time and concentration level in real time.

[0060] The comprehension analysis unit can evaluate the level of understanding by analyzing images of students' handwritten notes or answer sheets. For example, the comprehension analysis unit uses a generation AI to scan the student's handwritten notes and analyze the content using character recognition technology. For example, if the content of the notes is detailed, it determines that the level of understanding is high. The comprehension analysis unit also analyzes images of the student's answer sheets to evaluate the accuracy and detail of the answers. For example, if the correct answer rate is high and there are many detailed answers, it determines that the level of understanding is high. The comprehension analysis unit also analyzes handwritten diagrams and graphs to evaluate the level of understanding. For example, if the diagrams and graphs are accurate and detailed, it determines that the level of understanding is high. This makes it possible to accurately evaluate the level of understanding by analyzing images of handwritten notes and answer sheets.

[0061] The learning style identification unit can analyze a student's past learning patterns and identify the optimal learning style. For example, the generative AI analyzes a student's past learning history to identify which learning method was most effective. For example, if repetitive learning was effective, that style is recommended. The learning style identification unit also analyzes a student's past test results to identify which learning style produced the highest grades. For example, if visual learning was effective, that style is recommended. The learning style identification unit also analyzes the relationship between a student's past study time and grades to identify the optimal learning style. For example, if a style of studying intensively for short periods of time was effective, that style is recommended. In this way, the most effective learning style can be identified by analyzing past learning patterns.

[0062] The learning style identification unit can analyze a student's learning environment and suggest the optimal learning style. For example, the generative AI analyzes a student's learning environment and suggests the optimal learning style. For example, if studying in a quiet place is effective, that environment is recommended. The learning style identification unit also analyzes the effectiveness of a student studying while listening to music and suggests the optimal learning style. For example, if a specific music genre improves concentration, that music is recommended. The learning style identification unit also analyzes the relationship between changes in a student's learning environment and grades and suggests the optimal learning style. For example, if studying under natural light is effective, that environment is recommended. In this way, the optimal learning style can be suggested by analyzing the learning environment.

[0063] The learning style identification unit can analyze a student's physical movements while studying and identify their learning style. For example, the generative AI analyzes how a student sits and identifies the optimal learning style. For example, if sitting in the correct posture improves concentration, that posture is recommended. The learning style identification unit also analyzes how a student writes and identifies the optimal learning style. For example, if taking notes by hand improves comprehension, that method is recommended. The learning style identification unit also analyzes a student's physical movements while studying and identifies the optimal learning style. For example, if studying while standing is effective, that style is recommended. In this way, learning styles can be identified by analyzing physical movements while studying.

[0064] The strong subject determination unit can determine strong and weak subjects by analyzing not only a student's past grade data but also the frequency of comments or questions made during class. The strong subject determination unit, for example, uses a generation AI to analyze a student's past grade data to determine strong and weak subjects. For example, it determines subjects with high grades as strong subjects. The strong subject determination unit also analyzes the frequency of comments and questions made during class to determine strong and weak subjects. For example, it determines subjects with many comments and questions as strong subjects. The strong subject determination unit also integrates grade data and the frequency of comments and questions made during class to comprehensively determine strong and weak subjects. For example, it determines subjects with high grades and many comments and questions as strong subjects. This makes it possible to more accurately determine strong and weak subjects by analyzing not only grade data but also the frequency of comments and questions made during class.

[0065] The strong subject determination unit monitors the stress level of the student while studying and evaluates the stress level for each subject, thereby determining which subjects are strong and weak. The strong subject determination unit, for example, uses a generation AI to monitor the stress level of the student while studying and evaluates the stress level for each subject. For example, it determines a subject with a low stress level as a strong subject. The strong subject determination unit also monitors the student's heart rate and galvanic skin response to evaluate the stress level. For example, it determines a subject with a stable heart rate as a strong subject. The strong subject determination unit also analyzes the student's facial expressions while studying and evaluates the stress level. For example, it determines a subject with a high number of relaxed facial expressions as a strong subject. In this way, by monitoring the stress level during studying, it is possible to accurately evaluate the strengths and weaknesses of each subject.

[0066] The strong subject determination unit can analyze a student's error rate or answer quality during study and evaluate their strengths and weaknesses for each subject. For example, the strong subject determination unit uses a generation AI to analyze a student's error rate during study and evaluate their strengths and weaknesses for each subject. For example, it determines a subject with a low error rate as a strong subject. The strong subject determination unit also analyzes the quality of the student's answers and evaluates their strengths and weaknesses for each subject. For example, it determines a subject with detailed and accurate answers as a strong subject. The strong subject determination unit also integrates the error rate and answer quality to comprehensively evaluate strong and weak subjects. For example, it determines a subject with a low error rate and high answer quality as a strong subject. In this way, by analyzing the error rate and answer quality during study, it is possible to accurately evaluate the strengths and weaknesses of each subject.

[0067] The homework suggestion unit can analyze a student's learning history and suggest homework based on past learning content. For example, the homework suggestion unit uses a generation AI to analyze a student's past learning history and suggest linked homework. For example, it can provide homework to review content previously learned. The homework suggestion unit can also suggest homework to strengthen areas of low understanding based on the student's learning history. For example, it can provide homework that includes questions that were incorrectly answered in past tests. The homework suggestion unit can also analyze learning history and suggest application questions linked to past learning content. For example, it can provide homework that applies content previously learned. In this way, by analyzing learning history, it is possible to suggest homework that is linked to past learning content.

[0068] The homework suggestion unit can adjust the homework format according to the student's learning style. For example, the homework suggestion unit uses a generative AI to analyze the student's learning style and suggest the optimal homework format. For example, it provides video lectures to students who are good at visual learning. The homework suggestion unit also suggests interactive quiz-style homework according to the student's learning style. For example, it provides quiz-style homework to students who are good at repetitive learning. The homework suggestion unit also suggests homework that incorporates real-life experiences based on the student's learning style. For example, it provides homework that includes experiments and observations. This makes it possible to maximize learning effectiveness by customizing the homework format according to the student's learning style.

[0069] The homework suggestion unit can analyze a student's motivation while studying and suggest homework that will increase their motivation. For example, the homework suggestion unit uses a generation AI to analyze a student's motivation while studying and suggest homework that will increase their motivation. For example, it can provide homework that includes topics that interest them. The homework suggestion unit can also suggest homework that will give a sense of accomplishment based on the student's motivation data. For example, it can provide homework that gradually increases in difficulty. The homework suggestion unit can also analyze a student's motivation while studying and suggest homework that incorporates a reward system. For example, it can provide a system where points are accumulated each time homework is completed. In this way, it can suggest homework that will increase motivation by analyzing a student's motivation while studying.

[0070] The homework suggestion unit can share the homework suggestion results with other students or teachers to help improve learning outcomes. For example, the homework suggestion unit shares the optimized homework results suggested by the generation AI with other students to help improve learning outcomes. For example, students who have done the same homework can exchange information with each other. The homework suggestion unit also shares the optimized homework suggestion results with teachers to help with the progress of lessons. For example, the lesson content can be adjusted based on the homework results. The homework suggestion unit also shares the optimized homework suggestion results with parents to help support learning at home. For example, the homework progress can be reported to parents. In this way, sharing the homework suggestion results can help improve learning outcomes.

[0071] The homework suggestion unit can adjust the homework suggestion results according to different learning resources and suggest the optimal learning resources. For example, the homework suggestion unit suggests optimal online learning materials based on the optimized homework results suggested by the generation AI. For example, it recommends basic online learning materials for weak subjects. The homework suggestion unit also suggests optimal video lectures based on the optimized homework suggestion results. For example, it recommends visual video lectures for weak subjects. The homework suggestion unit also customizes and suggests optimal learning resources based on the optimized homework suggestion results. For example, it recommends interactive learning resources for weak subjects. In this way, by customizing the homework suggestion results, it is possible to suggest optimal learning resources.

[0072] The homework suggestion unit uses the emotion estimation function to suggest homework that students can enjoy working on, thereby improving learning effectiveness. For example, the homework suggestion unit uses a generation AI to analyze the student's emotional state and suggest homework that they can enjoy working on the most. For example, it provides homework that involves a lot of smiling faces. The homework suggestion unit also suggests homework that students can enjoy working on based on the student's emotion estimation data. For example, it provides homework that involves a lot of positive emotions. The homework suggestion unit also uses the emotion estimation function to suggest homework that students can enjoy working on, thereby maximizing learning effectiveness. For example, it provides homework that they can enjoy working on. In this way, by using the emotion estimation function, it is possible to suggest homework that students can enjoy working on, thereby maximizing learning effectiveness.

[0073] The homework suggestion unit can analyze the quality of the student's answers and suggest specific areas for improvement. For example, the homework suggestion unit uses a generative AI to analyze the quality of the student's answers and suggest specific areas for improvement. For example, it evaluates the detail and accuracy of the answers and suggests areas for improvement. The homework suggestion unit also suggests specific areas for improvement to be reflected in the next homework assignment based on the student's answers. For example, it provides an explanation for a question that was answered incorrectly. The homework suggestion unit also analyzes the quality of the answers and suggests specific areas for improvement. For example, it evaluates the structure and logic of the answers and suggests areas for improvement. In this way, specific areas for improvement can be suggested by analyzing the quality of the answers.

[0074] The homework suggestion unit can analyze the student's error rate and answer quality while studying and reflect them in the next homework assignment. For example, the homework suggestion unit uses a generation AI to analyze the student's error rate while studying and reflect them in the next homework assignment. For example, it provides homework that focuses on questions with a high error rate. The homework suggestion unit also analyzes the quality of the student's answers and reflects them in the next homework assignment. For example, it evaluates the detail and accuracy of the answers and reflects them in the next homework assignment. The homework suggestion unit also integrates the error rate and answer quality and reflects them in the next homework assignment. For example, it provides homework that focuses on questions with a high error rate and low answer quality. In this way, the error rate and answer quality can be analyzed and reflected in the next homework assignment.

[0075] The homework suggestion unit can analyze a student's motivation while studying and suggest feedback that will improve their motivation. For example, the homework suggestion unit uses a generation AI to analyze a student's motivation while studying and suggest feedback that will improve their motivation. For example, it can provide feedback that will give a sense of accomplishment. The homework suggestion unit also suggests specific feedback based on the student's motivation data. For example, it can provide feedback that praises their efforts. The homework suggestion unit also analyzes a student's motivation while studying and suggests feedback that incorporates a reward system. For example, it can provide a system that accumulates points each time homework is completed. In this way, it can suggest feedback that will improve motivation by analyzing a student's motivation while studying.

[0076] The homework suggestion unit can share the homework feedback results with other students or teachers to help improve learning outcomes. For example, the generation AI of the homework suggestion unit shares the homework feedback results with other students to help improve learning outcomes. For example, students who have done the same homework can exchange information with each other. The homework suggestion unit also shares the homework feedback results with teachers to help with the progress of lessons. For example, the lesson content can be adjusted based on the feedback results. The homework suggestion unit also shares the homework feedback results with parents to help support learning at home. For example, the home study plan can be adjusted based on the feedback results. In this way, sharing the homework feedback results can help improve learning outcomes.

[0077] The homework suggestion unit can adjust the homework feedback results according to different learning resources and suggest the optimal learning resources. For example, the generation AI in the homework suggestion unit suggests the optimal online learning materials based on the homework feedback results. For example, it recommends basic online learning materials for weak subjects. The homework suggestion unit also suggests the optimal video lectures based on the homework feedback results. For example, it recommends visual video lectures for weak subjects. The homework suggestion unit also customizes and suggests the optimal learning resources based on the homework feedback results. For example, it recommends interactive learning resources for weak subjects. In this way, the optimal learning resources can be suggested by customizing the homework feedback results.

[0078] The homework suggestion unit uses the emotion estimation function to suggest feedback that will evoke positive emotions in students, thereby improving learning outcomes. For example, the homework suggestion unit uses a generation AI to analyze a student's emotional state and suggest feedback that will evoke the most positive emotions. For example, feedback that includes a lot of smiling faces is provided. The homework suggestion unit also uses the emotion estimation function to suggest feedback that will evoke the most positive emotions in students, thereby maximizing learning outcomes. For example, feedback that elicits positive emotions is provided. In this way, the emotion estimation function can be used to suggest feedback that will evoke positive emotions in students, thereby maximizing learning outcomes.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The homework suggestion system can also be equipped with a posture analysis unit that monitors students' posture while studying and suggests improvements. For example, the generative AI can use a camera to analyze how students sit and how upright they are, and provide advice on maintaining good posture. Furthermore, if a student has been studying in the same position for a long time, the posture analysis unit can suggest appropriate breaks or stretches. By improving their posture while studying, this can improve concentration and learning effectiveness.

[0081] The homework suggestion system can also be equipped with a nutritional analysis unit that monitors students' diet and nutritional status while studying and proposes optimal meal plans. For example, the generative AI records students' dietary habits and evaluates nutritional balance. The nutritional analysis unit can also analyze the relationship between students' learning performance and diet and propose meal plans to improve concentration and memory. This can maximize learning outcomes through proper nutritional intake.

[0082] The homework suggestion system can also be equipped with a sleep analysis unit that monitors students' sleep patterns while studying and suggests optimal sleep schedules. For example, the generative AI can record the student's sleep duration and quality and analyze the correlation with learning performance. The sleep analysis unit can also provide advice on how to help students get enough rest and suggest optimal sleep schedules to improve learning outcomes. This can maximize learning outcomes through proper sleep.

[0083] The homework suggestion system can also be equipped with an exercise analysis unit that monitors students' exercise volume while studying and proposes appropriate exercise plans. For example, the generative AI records students' exercise habits and analyzes the correlation with their learning performance. The exercise analysis unit can also provide advice on how students can exercise moderately and propose exercise plans for improving concentration and stress relief. This can maximize learning outcomes through appropriate exercise.

[0084] The homework suggestion system can also be equipped with an environmental sound analysis unit that monitors the ambient sounds a student is making while studying and suggests the optimal learning environment. For example, the generation AI can record the sounds around a student while they are studying and analyze the factors that affect their concentration. The environmental sound analysis unit can also provide advice to help students create the optimal learning environment, suggesting a quiet environment or appropriate music. This can maximize learning effectiveness by optimizing the learning environment.

[0085] The homework suggestion system can also analyze a student's emotional state and suggest learning approaches based on their emotions. For example, the generative AI can analyze a student's facial expressions and tone of voice to assess their emotional state. Depending on their emotional state, it can suggest approaches that allow them to study in a relaxed state. For example, if stress levels are high, it can suggest short breaks or relaxation exercises to help them relax, while if positive emotions are prevalent, it can suggest challenging assignments. This maximizes learning effectiveness by providing learning approaches that are tailored to the student's emotional state.

[0086] The homework suggestion system can also analyze students' emotional state and provide emotion-based feedback. For example, the generative AI can analyze a student's facial expressions and tone of voice to evaluate their emotional state. Depending on their emotional state, it can provide positive feedback or words of encouragement. For example, if students have a lot of negative emotions, it can provide words of encouragement, and if they have a lot of positive emotions, it can provide words of praise. This allows for feedback based on their emotional state, which can increase students' motivation and maximize their learning effectiveness.

[0087] The homework suggestion system can also analyze a student's emotional state and suggest learning resources based on their emotions. For example, the generative AI can analyze a student's facial expressions and tone of voice to assess their emotional state. The system can then suggest optimal learning resources based on their emotional state. For example, if a student is feeling highly stressed, the system can suggest relaxing video lectures or interactive games, while if they are feeling mostly positive, the system can suggest challenging workbooks. This maximizes learning effectiveness by providing learning resources tailored to their emotional state.

[0088] The homework suggestion system can also analyze a student's emotional state and suggest a study schedule based on their emotions. For example, the generative AI can analyze a student's facial expressions and tone of voice to assess their emotional state. The system can then suggest an optimal study schedule based on their emotional state. For example, if stress levels are high, the system can suggest short study sessions with frequent breaks, while if positive emotions are prevalent, it can suggest long, intensive study sessions. This maximizes learning effectiveness by providing a study schedule tailored to the student's emotional state.

[0089] The homework suggestion system can also analyze students' emotional state and set learning goals based on their emotions. For example, the generative AI can analyze a student's facial expressions and tone of voice to assess their emotional state. Achievable learning goals can then be set based on their emotional state. For example, if stress levels are high, a small goal can be set, whereas if positive emotions are prevalent, a challenging goal can be set. This allows for learning goals tailored to a student's emotional state to increase motivation and maximize learning outcomes.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The comprehension analysis unit analyzes each student's level of comprehension and progress. For example, the generation AI evaluates each student's level of comprehension based on test results, comments made during class, and the content of submitted work. The generation AI also analyzes learning data to understand each student's progress. Step 2: The learning style identification unit identifies each student's learning style based on the data analyzed by the comprehension analysis unit. For example, the generation AI identifies each student's learning style based on their learning history, survey results, etc. Step 3: The strong subject determination unit determines each student's strong and weak subjects based on the learning style identified by the learning style identification unit. For example, the generation AI evaluates each student's strong and weak subjects based on test results, comments made in class, the content of submitted work, etc. Step 4: The homework suggestion unit suggests the most suitable homework for each student based on the data determined by the strong subject determination unit. For example, the generation AI will suggest homework that includes many basic questions for subjects with low comprehension, and homework that includes applied questions for subjects with high comprehension.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 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 comprehension analysis unit that analyzes each student's comprehension and progress; a learning style identification unit that identifies the learning style of each student based on the data analyzed by the comprehension analysis unit; a strong subject determination unit that determines strong and weak subjects of each student based on the learning style identified by the learning style identification unit; a homework suggestion unit that suggests optimal homework for each student based on the data determined by the strong subject determination unit; A system characterized by:

2. The understanding level analysis unit Analyzing the student's facial expressions and tone of voice to assess the student's understanding based on their emotional state 2. The system of claim 1.

3. The understanding level analysis unit Monitoring the student's learning time or concentration in real time and assessing the student's progress 2. The system of claim 1.

4. The understanding level analysis unit The student's handwritten notes or answer sheets are image-analyzed to evaluate the student's level of understanding.

2. The system of claim 1.

5. The learning style identification unit Analyzing the student's past learning patterns and identifying the most suitable learning style 2. The system of claim 1.

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