System

Abstract

An object of the system according to the embodiment is to automatically generate a teaching material according to academic ability and aptitude of a student and provide an online class.SOLUTION: A system includes an academic aptitude investigation unit, a teaching material generation unit, and a lesson provision unit. An academic ability aptitude investigation part investigates the academic ability and aptitude of the student. The teaching material generation unit generates a teaching material based on the result obtained by the academic aptitude survey unit. The lesson providing unit provides an online lesson using the teaching material generated by the teaching material generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently provide teaching materials that match the academic ability and aptitude of students.

[0005] The system according to the embodiment aims to automatically generate teaching materials that match the academic ability and aptitude of students and provide online classes. [Means for solving the problem]

[0006] The system according to the embodiment includes an academic ability aptitude investigation unit, a teaching material generation unit, and a lesson provision unit. The academic ability aptitude investigation unit investigates the academic ability and aptitude of students. The teaching material generation unit generates teaching materials based on the results obtained by the academic ability aptitude investigation unit. The lesson provision unit provides online lessons using the teaching materials generated by the teaching material generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate teaching materials according to the academic ability and aptitude of students and provide online lessons. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The online class system according to an embodiment of the present invention is a system that investigates a student's current academic ability and aptitude, automatically generates individually tailored learning materials based on the results, and provides online classes. This allows the online class system to provide an optimal learning environment for each student, maximizing learning effectiveness.

[0029] An online lesson system according to an embodiment includes an academic ability and aptitude assessment unit, a teaching material generation unit, and a lesson provision unit. The academic ability and aptitude assessment unit assesses a student's academic ability and aptitude. For example, the generation AI conducts tests and surveys to assess the student's academic ability and aptitude. The generation AI also analyzes the test and survey results to evaluate the student's current academic ability and aptitude. The generation AI, for example, conducts mathematics tests, reading comprehension tests, and interest surveys, and performs analysis based on those results. The teaching material generation unit generates teaching materials based on the results obtained by the academic ability and aptitude assessment unit. For example, the generation AI automatically generates individually tailored teaching materials based on the analysis results of the student's academic ability and aptitude. For example, the generation AI generates teaching materials including advanced math problems for students who are good at math and difficult sentences for students with high reading comprehension. The lesson provision unit provides online lessons using the teaching materials generated by the teaching material generation unit. For example, the teaching materials generated by the generation AI are uploaded to an online platform, and students use them to advance their studies. In addition, the generation AI monitors the student's learning progress and generates additional learning materials or suggests learning methods as needed. As a result, the online class system according to the embodiment generates optimal learning materials based on the student's academic ability and aptitude, and provides online classes, thereby maximizing the learning effect.

[0030] The academic aptitude assessment unit can analyze individual learning patterns using past learning history and grade data and propose the optimal test format. In the academic aptitude assessment unit, for example, the generation AI collects students' past test results and grade data and analyzes their learning patterns. For example, it identifies strong and weak subjects and adjusts the test format based on that. The generation AI also analyzes the student's learning history and identifies their learning pattern. For example, it analyzes the frequency and progress of learning and proposes the optimal test format. The generation AI also evaluates learning progress based on grade data and adjusts the test format. For example, it increases the difficulty if grades are improving and decreases the difficulty if grades are stagnating. This makes it possible to maximize learning effectiveness by proposing the optimal test format based on past learning history and grade data.

[0031] The academic aptitude assessment unit provides game-style tests, making it possible to assess academic ability while having fun. In the academic aptitude assessment unit, for example, the generation AI designs game-style tests, allowing students to have fun while having their academic ability assessed. For example, it may provide quiz-style or puzzle-style questions. The generation AI also assesses students' academic ability through game-style tests. For example, it may assess academic ability based on scores and progress in the game. The generation AI also stimulates students' interest through game-style tests. For example, it may provide questions that incorporate their favorite characters or stories. In this way, by providing game-style tests, students can have fun while their academic ability is assessed.

[0032] The academic aptitude assessment unit can provide multilingual tests to students with different cultural and linguistic backgrounds, enabling global academic assessment. In the academic aptitude assessment unit, for example, the generative AI designs multilingual tests and provides them to students with different linguistic backgrounds. For example, questions are provided in multiple languages, such as English, Spanish, and Chinese. The generative AI also provides tests that take different cultural backgrounds into consideration. For example, questions are provided using culturally appropriate examples and scenarios. The generative AI also performs global academic assessments through multilingual tests. For example, assessments are performed based on international test standards. This makes it possible to provide multilingual tests to students with different cultural and linguistic backgrounds, enabling global academic assessments.

[0033] The teaching material generation unit can analyze a student's learning style and generate teaching materials in the optimal format based on that. In the teaching material generation unit, for example, a generation AI analyzes a student's learning style and generates visual teaching materials. For example, teaching materials that make extensive use of diagrams and graphs are provided. The generation AI also generates teaching materials that cater to auditory learning styles. For example, teaching materials in the form of audio commentary or podcasts are provided. The generation AI also generates teaching materials that cater to tactile learning styles. For example, interactive simulations or experiment kits are provided. This makes it possible to maximize learning effectiveness by generating teaching materials in the optimal format based on the student's learning style.

[0034] The teaching material generation unit can adjust the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude, maximizing the learning effect. For example, the generation AI of the teaching material generation unit adjusts the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude. For example, it dynamically changes the difficulty of the questions. The generation AI also monitors the student's learning progress and adjusts the difficulty of the teaching materials as needed. For example, it lowers the difficulty if the level of understanding is low and raises the difficulty if the level of understanding is high. The generation AI also analyzes the student's responses in real time and adjusts the difficulty of the teaching materials. For example, it adjusts the difficulty based on the answer time and the percentage of correct answers. In this way, the learning effect can be maximized by adjusting the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude.

[0035] The teaching material generation unit can generate teaching materials that incorporate entertainment elements based on the students' interests. For example, the generation AI of the teaching material generation unit analyzes the students' interests and generates teaching materials that incorporate entertainment elements. For example, it provides questions that incorporate favorite characters or stories. The generation AI also generates teaching materials that incorporate game elements. For example, it provides questions in the form of quizzes or puzzles. The generation AI also generates teaching materials that use storytelling. For example, it provides teaching materials that explain learning content in the form of a story. In this way, by generating teaching materials that incorporate entertainment elements based on the students' interests, it is possible to increase their motivation to learn.

[0036] The lesson provision unit monitors students' learning progress in real time and can dynamically change the lesson content as needed. In the lesson provision unit, for example, the generation AI monitors students' learning progress in real time and changes the lesson content according to their progress. For example, if their level of understanding is low, it adds supplementary explanations. The generation AI also dynamically changes the lesson content based on the student's learning progress. For example, if their progress is fast, it provides content to move on to the next step, and if their progress is slow, it provides review content. The generation AI also analyzes students' responses in real time and adjusts the lesson content. For example, it changes the lesson content based on the frequency of questions and the time it takes to answer them. In this way, it is possible to maximize the learning effect by monitoring students' learning progress in real time and dynamically changing the lesson content as needed.

[0037] The lesson provision unit can analyze students' learning data, automatically generate individual learning plans, and customize online lessons. In the lesson provision unit, for example, the generation AI analyzes students' learning data and automatically generates individual learning plans. For example, it provides learning plans based on strong and weak subjects. The generation AI also customizes online lessons based on the students' learning data. For example, it adjusts lesson content based on learning progress and level of understanding. The generation AI also manages the progress of online lessons based on individual learning plans. For example, it sets learning goals and schedules and conducts lessons based on them. In this way, by analyzing students' learning data, automatically generating individual learning plans, and customizing online lessons, it is possible to maximize learning effectiveness.

[0038] The lesson provision unit can provide customized online lessons to students from different regions and cultural backgrounds. For example, the generation AI in the lesson provision unit provides online lessons that take into account different regions and cultural backgrounds. For example, lessons are conducted using culturally appropriate examples and scenarios. The generation AI also provides lesson content that is appropriate for different regions and cultural backgrounds. For example, lessons are conducted that incorporate local examples and history. The generation AI also provides multilingual lessons to students from different linguistic backgrounds. For example, lessons are conducted in multiple languages, such as English, Spanish, and Chinese. This makes it possible to maximize learning effectiveness by providing customized online lessons to students from different regions and cultural backgrounds.

[0039] In the lesson provision unit, the generation AI can automatically generate group work assignments and incorporate them into online classes to promote collaborative learning between students. In the lesson provision unit, for example, the generation AI organizes optimal groups based on students' academic ability and aptitude, and automatically generates collaborative learning assignments. For example, it may pair students with different areas of expertise. The generation AI also automatically generates group work assignments and incorporates them into online classes. For example, it may provide problems or projects to be solved jointly. The generation AI also monitors the progress of collaborative learning and provides feedback as needed. For example, it may evaluate the degree of communication and cooperation within the group. In this way, the generation AI can automatically generate group work assignments and incorporate them into online classes to promote collaborative learning between students, maximizing learning effectiveness.

[0040] The lesson providing unit can analyze students' learning progress in detail and provide individual feedback in real time. In the lesson providing unit, for example, the generation AI analyzes students' learning progress data in real time and provides individual feedback. For example, it provides specific advice on areas where understanding is low. The generation AI also provides feedback based on the student's learning progress. For example, if progress is fast, it provides advice on moving on to the next step, and if progress is slow, it provides advice on review. The generation AI also analyzes students' responses in real time and provides feedback. For example, it provides feedback based on answer time and accuracy rate. In this way, the learning effect can be maximized by analyzing students' learning progress in detail and providing individual feedback in real time.

[0041] The lesson provider allows the generation AI to set long-term learning goals based on the student's learning data and monitor the degree of achievement of those goals. In the lesson provider, for example, the generation AI analyzes the student's learning data and sets long-term learning goals. For example, it may present specific goals to be achieved in one year. The generation AI also monitors the degree of achievement of the long-term learning goals. For example, it may periodically evaluate progress and provide advice on how to achieve the goals. The generation AI also adjusts the learning plan based on the long-term learning goals. For example, it may present specific steps for achieving the goals. In this way, by setting long-term learning goals based on the student's learning data and monitoring the degree of achievement of those goals, it is possible to maximize the learning effect.

[0042] The lesson providing unit can have the generating AI provide an interactive dashboard to visualize students' learning progress. For example, the lesson providing unit provides an interactive dashboard in which the generating AI visualizes students' learning progress. For example, the progress status is displayed in graphs and charts. The generating AI also updates the learning progress in real time and reflects it on the dashboard. For example, the progress is displayed based on test results and study time. The generating AI also provides a dashboard that incorporates interactive elements. For example, the user can click on the progress status to check detailed information. In this way, by providing an interactive dashboard to visualize students' learning progress, it is possible to maximize the learning effect.

[0043] The lesson provision unit can analyze students' learning history in detail and optimize individual learning plans over the long term. In the lesson provision unit, for example, the generation AI analyzes students' learning history in detail and optimizes individual learning plans over the long term. For example, it proposes an optimal plan based on past grades and learning patterns. The generation AI also sets long-term learning goals based on the learning history. For example, it presents specific goals to be achieved in one year. The generation AI also monitors learning progress based on the long-term learning plan. For example, it regularly evaluates progress and provides advice on how to achieve goals. In this way, the learning effect can be maximized by analyzing students' learning history in detail and optimizing individual learning plans over the long term.

[0044] The lesson provision unit uses the generation AI to predict future learning trends based on the student's learning history and suggest advanced learning. In the lesson provision unit, for example, the generation AI analyzes the student's learning history and predicts future learning trends. For example, it suggests what to study next based on past grades and learning patterns. The generation AI also suggests advanced learning based on future learning trends. For example, it provides a plan for studying in advance what will be learned in the next school year. The generation AI also monitors the progress of advanced learning and provides feedback as needed. For example, if progress is rapid, it provides advice on moving on to the next step, and if progress is slow, it provides advice on review. In this way, learning effectiveness can be maximized by predicting future learning trends based on the student's learning history and suggesting advanced learning.

[0045] The lesson provision unit can integrate learning histories from different academic fields to improve overall academic ability. For example, the generation AI in the lesson provision unit integrates learning histories from different academic fields to improve overall academic ability. For example, it can integrate learning histories from mathematics and science to reinforce interrelated content. The generation AI also provides learning plans that link knowledge from different academic fields. For example, it can provide a learning plan that combines history and geography. The generation AI also provides learning plans that include interdisciplinary learning content. For example, it can provide a learning plan that combines technology and art. This makes it possible to maximize learning effectiveness by integrating learning histories from different academic fields to improve overall academic ability.

[0046] The lesson provider unit allows the generation AI to propose an individual career plan based on the student's learning history. In the lesson provider unit, for example, the generation AI analyzes the student's learning history and proposes an individual career plan. For example, it proposes a career path based on the student's favorite subjects and areas of interest. The generation AI also adjusts the learning plan based on the career plan. For example, it provides a plan for acquiring the skills necessary for the target occupation. The generation AI also monitors the progress of the career plan and provides feedback as needed. For example, if progress is rapid, it provides advice on moving on to the next step, and if progress is slow, it provides advice on review. In this way, by proposing an individual career plan based on the student's learning history, it is possible to support future career choices.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The Academic Aptitude Assessment Department can monitor students' physical health and adjust the difficulty and content of tests based on their health status. For example, the generation AI measures students' heart rate and blood pressure to detect signs of stress and fatigue. The generation AI also adjusts the difficulty of the test based on their health status. For example, if fatigue is observed, the difficulty of the test is lowered, and if health is good, the difficulty is increased. The generation AI also changes the content of the test according to the student's health status. For example, if fatigue is observed, questions that can be answered in a short time are presented, and if health is good, questions that require long-term concentration are presented. This makes it possible to maximize learning effectiveness by adjusting the difficulty and content of the test according to the student's health status.

[0049] The Academic Aptitude Assessment Department can analyze students' learning styles using past learning history and grade data and suggest optimal learning methods. For example, the generative AI collects students' past test results and grade data and analyzes their learning styles. For example, it suggests materials that make heavy use of diagrams and graphs to students who are good at visual learning, and materials that make heavy use of audio commentary to students who are good at auditory learning. The generative AI also suggests learning methods based on students' learning styles. For example, it suggests learning methods using visual aids to students who are good at visual learning, and learning methods in the form of podcasts to students who are good at auditory learning. The generative AI also adjusts the learning environment according to learning style. For example, it provides bright lighting for students who are good at visual learning, and a quiet environment for students who are good at auditory learning. This maximizes learning effectiveness by suggesting optimal learning methods based on past learning history and grade data.

[0050] The Academic Aptitude Assessment Department can provide game-style tests to assess academic ability while having fun. For example, the generative AI can design game-style tests so that students can have fun while assessing their academic ability. For example, it can provide quiz-style or puzzle-style questions. The generative AI can also assess students' academic ability through game-style tests. For example, it can assess academic ability based on in-game scores and progress. The generative AI can also stimulate students' interest through game-style tests. For example, it can provide questions that incorporate their favorite characters or stories. In this way, by providing game-style tests, students can have fun while their academic ability is assessed.

[0051] The Academic Aptitude Assessment Department can provide multilingual tests to students from different cultural and linguistic backgrounds, enabling global academic assessment. For example, the generative AI designs multilingual tests and provides them to students from different linguistic backgrounds. For example, questions are provided in multiple languages, such as English, Spanish, and Chinese. The generative AI also provides tests that take different cultural backgrounds into account. For example, questions are provided using culturally appropriate examples and scenarios. The generative AI also performs global academic assessments through multilingual tests. For example, assessments are performed based on international test standards. This makes it possible to provide multilingual tests to students from different cultural and linguistic backgrounds, enabling global academic assessments.

[0052] The teaching material generation unit can analyze a student's learning style and generate teaching materials in the optimal format based on that. For example, the generation AI analyzes a student's learning style and generates visual teaching materials. For example, it may provide teaching materials that make extensive use of diagrams and graphs. The generation AI may also generate teaching materials that cater to auditory learning styles. For example, it may provide teaching materials in the form of audio commentary or podcasts. The generation AI may also generate teaching materials that cater to tactile learning styles. For example, it may provide interactive simulations or experiment kits. This makes it possible to maximize learning effectiveness by generating teaching materials in the optimal format based on the student's learning style.

[0053] The teaching material generation unit can adjust the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude, maximizing the learning effect. For example, the generation AI adjusts the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude. For example, it dynamically changes the difficulty of the questions. The generation AI also monitors the student's learning progress and adjusts the difficulty of the teaching materials as needed. For example, it lowers the difficulty if the level of understanding is low and raises the difficulty if the level of understanding is high. The generation AI also analyzes the student's responses in real time and adjusts the difficulty of the teaching materials. For example, it adjusts the difficulty based on the answer time and correct answer rate. In this way, the learning effect can be maximized by adjusting the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude.

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

[0055] Step 1: The academic ability and aptitude assessment unit assesses the student's academic ability and aptitude. For example, the generative AI conducts tests and surveys to assess the student's academic ability and aptitude, and analyzes the results to evaluate the student's current academic ability and aptitude. Specifically, it conducts mathematics tests, reading comprehension tests, and surveys on interests, and performs analysis based on the results. Step 2: The teaching material generation unit generates teaching materials based on the results obtained by the academic ability and aptitude survey unit. For example, the generation AI automatically generates teaching materials that are individually tailored to the student based on the analysis results of their academic ability and aptitude. Specifically, it generates teaching materials that include advanced math problems for students who are good at math, and difficult sentences for students with strong reading comprehension skills. Step 3: The lesson provider provides online lessons using the teaching materials generated by the teaching material generator. For example, the teaching materials generated by the generation AI are uploaded to an online platform, and students use them to advance their studies. The generation AI also monitors students' learning progress and generates additional teaching materials or suggests learning methods as needed.

[0056] (Example 2) The online class system according to an embodiment of the present invention is a system that investigates a student's current academic ability and aptitude, automatically generates individually tailored learning materials based on the results, and provides online classes. This allows the online class system to provide an optimal learning environment for each student, maximizing learning effectiveness.

[0057] An online lesson system according to an embodiment includes an academic ability and aptitude assessment unit, a teaching material generation unit, and a lesson provision unit. The academic ability and aptitude assessment unit assesses a student's academic ability and aptitude. For example, the generation AI conducts tests and surveys to assess the student's academic ability and aptitude. The generation AI also analyzes the test and survey results to evaluate the student's current academic ability and aptitude. The generation AI, for example, conducts mathematics tests, reading comprehension tests, and interest surveys, and performs analysis based on those results. The teaching material generation unit generates teaching materials based on the results obtained by the academic ability and aptitude assessment unit. For example, the generation AI automatically generates individually tailored teaching materials based on the analysis results of the student's academic ability and aptitude. For example, the generation AI generates teaching materials including advanced math problems for students who are good at math and difficult sentences for students with high reading comprehension. The lesson provision unit provides online lessons using the teaching materials generated by the teaching material generation unit. For example, the teaching materials generated by the generation AI are uploaded to an online platform, and students use them to advance their studies. In addition, the generation AI monitors the student's learning progress and generates additional learning materials or suggests learning methods as needed. As a result, the online class system according to the embodiment generates optimal learning materials based on the student's academic ability and aptitude, and provides online classes, thereby maximizing the learning effect.

[0058] The academic aptitude assessment unit can analyze a student's facial expressions and tone of voice and adjust the difficulty of the test based on their emotional state. For example, in the academic aptitude assessment unit, the generation AI analyzes a student's facial expressions in real time using a camera to detect signs of stress or anxiety. For example, it analyzes wrinkles between the eyebrows and the degree to which the corners of the mouth are turned down to calculate an emotional score. The generation AI also analyzes the tone of the student's voice to evaluate their emotional state. For example, it analyzes the pitch and speed of the voice to calculate an emotional score. The generation AI also adjusts the difficulty of the test based on their emotional state. For example, it lowers the difficulty of the test if the student is stressed and raises the difficulty if the student is relaxed. In this way, adjusting the difficulty of the test according to the student's emotional state can reduce stress and improve learning outcomes.

[0059] The academic aptitude assessment unit can analyze individual learning patterns using past learning history and grade data and propose the optimal test format. In the academic aptitude assessment unit, for example, the generation AI collects students' past test results and grade data and analyzes their learning patterns. For example, it identifies strong and weak subjects and adjusts the test format based on that. The generation AI also analyzes the student's learning history and identifies their learning pattern. For example, it analyzes the frequency and progress of learning and proposes the optimal test format. The generation AI also evaluates learning progress based on grade data and adjusts the test format. For example, it increases the difficulty if grades are improving and decreases the difficulty if grades are stagnating. This makes it possible to maximize learning effectiveness by proposing the optimal test format based on past learning history and grade data.

[0060] The academic aptitude assessment unit can use the emotion estimation function to detect stress and anxiety felt by students during tests in real time and provide interactive content to help them relax. For example, in the academic aptitude assessment unit, the generation AI analyzes students' facial expressions and tone of voice to detect stress and anxiety. For example, facial recognition technology is used to monitor changes in facial expressions in real time. The generation AI also analyzes the tone of the student's voice to evaluate their emotional state. For example, it analyzes the pitch and speed of the voice to calculate an emotion score. If stress or anxiety is detected, the generation AI provides interactive content to help them relax. For example, it plays relaxing music or relaxation videos. The generation AI also provides interactive games and quizzes to help students change their mood. This allows the system to detect stress and anxiety felt by students during tests in real time and provide interactive content to help them relax, thereby improving learning effectiveness.

[0061] The academic aptitude assessment unit provides game-style tests, making it possible to assess academic ability while having fun. In the academic aptitude assessment unit, for example, the generation AI designs game-style tests, allowing students to have fun while having their academic ability assessed. For example, it may provide quiz-style or puzzle-style questions. The generation AI also assesses students' academic ability through game-style tests. For example, it may assess academic ability based on scores and progress in the game. The generation AI also stimulates students' interest through game-style tests. For example, it may provide questions that incorporate their favorite characters or stories. In this way, by providing game-style tests, students can have fun while their academic ability is assessed.

[0062] The academic aptitude assessment unit can provide multilingual tests to students with different cultural and linguistic backgrounds, enabling global academic assessment. In the academic aptitude assessment unit, for example, the generative AI designs multilingual tests and provides them to students with different linguistic backgrounds. For example, questions are provided in multiple languages, such as English, Spanish, and Chinese. The generative AI also provides tests that take different cultural backgrounds into consideration. For example, questions are provided using culturally appropriate examples and scenarios. The generative AI also performs global academic assessments through multilingual tests. For example, assessments are performed based on international test standards. This makes it possible to provide multilingual tests to students with different cultural and linguistic backgrounds, enabling global academic assessments.

[0063] The academic aptitude assessment unit can use the emotion estimation function to identify areas in which students are most interested and provide tests related to those areas. In the academic aptitude assessment unit, for example, the generation AI analyzes a student's facial expressions and tone of voice to identify areas in which they are interested. For example, it calculates the interest level based on their reaction to a specific topic. The generation AI also provides tests related to the student's areas of interest. For example, it poses questions related to topics of interest. The generation AI also stimulates students' motivation to learn through tests related to areas of interest. For example, solving questions on topics of interest increases motivation to learn. This makes it possible to increase students' motivation to learn by providing them with tests related to the areas in which they are most interested.

[0064] The teaching material generation unit can analyze a student's learning style and generate teaching materials in the optimal format based on that. In the teaching material generation unit, for example, a generation AI analyzes a student's learning style and generates visual teaching materials. For example, teaching materials that make extensive use of diagrams and graphs are provided. The generation AI also generates teaching materials that cater to auditory learning styles. For example, teaching materials in the form of audio commentary or podcasts are provided. The generation AI also generates teaching materials that cater to tactile learning styles. For example, interactive simulations or experiment kits are provided. This makes it possible to maximize learning effectiveness by generating teaching materials in the optimal format based on the student's learning style.

[0065] The teaching material generation unit can adjust the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude, maximizing the learning effect. For example, the generation AI of the teaching material generation unit adjusts the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude. For example, it dynamically changes the difficulty of the questions. The generation AI also monitors the student's learning progress and adjusts the difficulty of the teaching materials as needed. For example, it lowers the difficulty if the level of understanding is low and raises the difficulty if the level of understanding is high. The generation AI also analyzes the student's responses in real time and adjusts the difficulty of the teaching materials. For example, it adjusts the difficulty based on the answer time and the percentage of correct answers. In this way, the learning effect can be maximized by adjusting the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude.

[0066] The teaching material generation unit can use the emotion estimation function to identify the teaching material format that evokes the most positive emotions in students and generate teaching materials in that format. In the teaching material generation unit, for example, the generation AI analyzes students' facial expressions and tone of voice to identify the teaching material format that evokes the most positive emotions. For example, the teaching material format is selected based on signs of smiling or excitement. The generation AI also generates teaching materials that elicit positive emotions. For example, it provides teaching materials that incorporate fun stories and interesting topics. The generation AI also generates teaching materials that incorporate interactive elements to maintain positive emotions. For example, it provides teaching materials in the form of quizzes or games. In this way, by identifying the teaching material format that evokes the most positive emotions in students and generating teaching materials in that format, it is possible to increase motivation to learn.

[0067] The teaching material generation unit can generate teaching materials that incorporate entertainment elements based on the students' interests. For example, the generation AI of the teaching material generation unit analyzes the students' interests and generates teaching materials that incorporate entertainment elements. For example, it provides questions that incorporate favorite characters or stories. The generation AI also generates teaching materials that incorporate game elements. For example, it provides questions in the form of quizzes or puzzles. The generation AI also generates teaching materials that use storytelling. For example, it provides teaching materials that explain learning content in the form of a story. In this way, by generating teaching materials that incorporate entertainment elements based on the students' interests, it is possible to increase their motivation to learn.

[0068] The teaching material generation unit can use the emotion estimation function to identify the topic in which a student is most interested and generate teaching materials related to that topic. In the teaching material generation unit, for example, the generation AI analyzes a student's facial expressions and tone of voice to identify the topic in which the student is most interested. For example, it calculates the interest level based on the student's reaction to a specific topic. The generation AI also generates teaching materials related to the topic of interest. For example, it poses questions related to the topic of interest. The generation AI also stimulates students' motivation to learn through teaching materials related to the topic of interest. For example, solving questions related to the topic of interest increases motivation to learn. In this way, the generation AI can increase students' motivation to learn by generating teaching materials related to the topic in which the student is most interested.

[0069] The lesson provision unit monitors students' learning progress in real time and can dynamically change the lesson content as needed. In the lesson provision unit, for example, the generation AI monitors students' learning progress in real time and changes the lesson content according to their progress. For example, if their level of understanding is low, it adds supplementary explanations. The generation AI also dynamically changes the lesson content based on the student's learning progress. For example, if their progress is fast, it provides content to move on to the next step, and if their progress is slow, it provides review content. The generation AI also analyzes students' responses in real time and adjusts the lesson content. For example, it changes the lesson content based on the frequency of questions and the time it takes to answer them. In this way, it is possible to maximize the learning effect by monitoring students' learning progress in real time and dynamically changing the lesson content as needed.

[0070] The lesson provision unit can analyze students' learning data, automatically generate individual learning plans, and customize online lessons. In the lesson provision unit, for example, the generation AI analyzes students' learning data and automatically generates individual learning plans. For example, it provides learning plans based on strong and weak subjects. The generation AI also customizes online lessons based on the students' learning data. For example, it adjusts lesson content based on learning progress and level of understanding. The generation AI also manages the progress of online lessons based on individual learning plans. For example, it sets learning goals and schedules and conducts lessons based on them. In this way, by analyzing students' learning data, automatically generating individual learning plans, and customizing online lessons, it is possible to maximize learning effectiveness.

[0071] The lesson provider can use the emotion estimation function to adjust the pace and content of the lesson according to the student's emotional state. For example, the generation AI in the lesson provider analyzes the student's facial expressions and tone of voice and adjusts the pace of the lesson according to the student's emotional state. For example, it slows down the pace if the student is stressed and speeds up if the student is relaxed. The generation AI also adjusts the content of the lesson based on the student's emotional state. For example, it adds detailed explanations if the student is interested and provides concise explanations if the student has lost interest. The generation AI also analyzes the student's emotional state in real time and adjusts the pace and content of the lesson. For example, it manages the progress of the lesson based on the emotion score. This makes it possible to maximize learning effectiveness by adjusting the pace and content of the lesson according to the student's emotional state.

[0072] The lesson provision unit can provide customized online lessons to students from different regions and cultural backgrounds. For example, the generation AI in the lesson provision unit provides online lessons that take into account different regions and cultural backgrounds. For example, lessons are conducted using culturally appropriate examples and scenarios. The generation AI also provides lesson content that is appropriate for different regions and cultural backgrounds. For example, lessons are conducted that incorporate local examples and history. The generation AI also provides multilingual lessons to students from different linguistic backgrounds. For example, lessons are conducted in multiple languages, such as English, Spanish, and Chinese. This makes it possible to maximize learning effectiveness by providing customized online lessons to students from different regions and cultural backgrounds.

[0073] In the lesson provision unit, the generation AI can automatically generate group work assignments and incorporate them into online classes to promote collaborative learning between students. In the lesson provision unit, for example, the generation AI organizes optimal groups based on students' academic ability and aptitude, and automatically generates collaborative learning assignments. For example, it may pair students with different areas of expertise. The generation AI also automatically generates group work assignments and incorporates them into online classes. For example, it may provide problems or projects to be solved jointly. The generation AI also monitors the progress of collaborative learning and provides feedback as needed. For example, it may evaluate the degree of communication and cooperation within the group. In this way, the generation AI can automatically generate group work assignments and incorporate them into online classes to promote collaborative learning between students, maximizing learning effectiveness.

[0074] The lesson providing unit can use the emotion estimation function to identify the time periods when students can concentrate best and provide online lessons during those time periods. In the lesson providing unit, for example, the generation AI analyzes students' emotion data and identifies the time periods when they can concentrate best. For example, it calculates the time periods when students are most likely to concentrate based on past learning data. The generation AI also provides online lessons during time periods when students can concentrate best. For example, it teaches important content during time periods when students are most likely to concentrate best. The generation AI also adjusts the lesson schedule to match the time periods when students are most likely to concentrate best. For example, it teaches lighter content during time periods when students are least likely to concentrate best. This makes it possible to maximize learning effectiveness by providing online lessons during time periods when students are most likely to concentrate best.

[0075] The lesson providing unit can analyze students' learning progress in detail and provide individual feedback in real time. In the lesson providing unit, for example, the generation AI analyzes students' learning progress data in real time and provides individual feedback. For example, it provides specific advice on areas where understanding is low. The generation AI also provides feedback based on the student's learning progress. For example, if progress is fast, it provides advice on moving on to the next step, and if progress is slow, it provides advice on review. The generation AI also analyzes students' responses in real time and provides feedback. For example, it provides feedback based on answer time and accuracy rate. In this way, the learning effect can be maximized by analyzing students' learning progress in detail and providing individual feedback in real time.

[0076] The lesson provider allows the generation AI to set long-term learning goals based on the student's learning data and monitor the degree of achievement of those goals. In the lesson provider, for example, the generation AI analyzes the student's learning data and sets long-term learning goals. For example, it may present specific goals to be achieved in one year. The generation AI also monitors the degree of achievement of the long-term learning goals. For example, it may periodically evaluate progress and provide advice on how to achieve the goals. The generation AI also adjusts the learning plan based on the long-term learning goals. For example, it may present specific steps for achieving the goals. In this way, by setting long-term learning goals based on the student's learning data and monitoring the degree of achievement of those goals, it is possible to maximize the learning effect.

[0077] The lesson provider can use the emotion estimation function to analyze the emotional state of students when they receive feedback and suggest the optimal feedback method. For example, the generation AI in the lesson provider analyzes the student's facial expressions and tone of voice to evaluate the student's emotional state when receiving feedback. For example, if the student has strong positive emotions, it will use a lot of praise. The generation AI also suggests a feedback method depending on the student's emotional state. For example, if the student is under high stress, it will give gentle feedback, and if the student is relaxed, it will give specific advice. The generation AI also analyzes the student's emotional state in real time and suggests the optimal feedback method. For example, it adjusts the content of the feedback based on the emotion score. This makes it possible to maximize learning effectiveness by analyzing the student's emotional state when receiving feedback and suggesting the optimal feedback method.

[0078] The lesson providing unit can have the generating AI provide an interactive dashboard to visualize students' learning progress. For example, the lesson providing unit provides an interactive dashboard in which the generating AI visualizes students' learning progress. For example, the progress status is displayed in graphs and charts. The generating AI also updates the learning progress in real time and reflects it on the dashboard. For example, the progress is displayed based on test results and study time. The generating AI also provides a dashboard that incorporates interactive elements. For example, the user can click on the progress status to check detailed information. In this way, by providing an interactive dashboard to visualize students' learning progress, it is possible to maximize the learning effect.

[0079] The lesson providing unit can use the emotion estimation function to identify the feedback format that students find most motivating and provide feedback in that format. For example, the lesson providing unit uses the generation AI to analyze the student's facial expressions and tone of voice to identify the feedback format that students find most motivating. For example, it selects a format that evokes strong positive emotions. The generation AI also provides feedback that elicits motivation. For example, it makes extensive use of words of praise and encouragement. The generation AI also analyzes the student's emotional state in real time and provides the optimal feedback format. For example, it adjusts the content of the feedback based on the emotion score. This makes it possible to identify the feedback format that students find most motivating and provide feedback in that format, thereby maximizing learning effectiveness.

[0080] The lesson provision unit can analyze students' learning history in detail and optimize individual learning plans over the long term. In the lesson provision unit, for example, the generation AI analyzes students' learning history in detail and optimizes individual learning plans over the long term. For example, it proposes an optimal plan based on past grades and learning patterns. The generation AI also sets long-term learning goals based on the learning history. For example, it presents specific goals to be achieved in one year. The generation AI also monitors learning progress based on the long-term learning plan. For example, it regularly evaluates progress and provides advice on how to achieve goals. In this way, the learning effect can be maximized by analyzing students' learning history in detail and optimizing individual learning plans over the long term.

[0081] The lesson provision unit uses the generation AI to predict future learning trends based on the student's learning history and suggest advanced learning. In the lesson provision unit, for example, the generation AI analyzes the student's learning history and predicts future learning trends. For example, it suggests what to study next based on past grades and learning patterns. The generation AI also suggests advanced learning based on future learning trends. For example, it provides a plan for studying in advance what will be learned in the next school year. The generation AI also monitors the progress of advanced learning and provides feedback as needed. For example, if progress is rapid, it provides advice on moving on to the next step, and if progress is slow, it provides advice on review. In this way, learning effectiveness can be maximized by predicting future learning trends based on the student's learning history and suggesting advanced learning.

[0082] The lesson providing unit can use the emotion estimation function to analyze changes in emotions based on the student's learning history and reflect them in the learning plan. In the lesson providing unit, for example, the generation AI analyzes the student's learning history and evaluates changes in emotions. For example, it identifies periods when positive emotions were strong based on past learning data. The generation AI also adjusts the learning plan based on changes in emotions. For example, it provides more difficult content when positive emotions are strong, and provides less difficult content when negative emotions are strong. The generation AI also analyzes the student's emotional state in real time and reflects this in the learning plan. For example, it adjusts the learning content based on the emotion score. In this way, it is possible to maximize learning effectiveness by analyzing changes in emotions based on the student's learning history and reflecting them in the learning plan.

[0083] The lesson provision unit can integrate learning histories from different academic fields to improve overall academic ability. For example, the generation AI in the lesson provision unit integrates learning histories from different academic fields to improve overall academic ability. For example, it can integrate learning histories from mathematics and science to reinforce interrelated content. The generation AI also provides learning plans that link knowledge from different academic fields. For example, it can provide a learning plan that combines history and geography. The generation AI also provides learning plans that include interdisciplinary learning content. For example, it can provide a learning plan that combines technology and art. This makes it possible to maximize learning effectiveness by integrating learning histories from different academic fields to improve overall academic ability.

[0084] The lesson provider unit allows the generation AI to propose an individual career plan based on the student's learning history. In the lesson provider unit, for example, the generation AI analyzes the student's learning history and proposes an individual career plan. For example, it proposes a career path based on the student's favorite subjects and areas of interest. The generation AI also adjusts the learning plan based on the career plan. For example, it provides a plan for acquiring the skills necessary for the target occupation. The generation AI also monitors the progress of the career plan and provides feedback as needed. For example, if progress is rapid, it provides advice on moving on to the next step, and if progress is slow, it provides advice on review. In this way, by proposing an individual career plan based on the student's learning history, it is possible to support future career choices.

[0085] The lesson providing unit can use the emotion estimation function to identify the learning history in which the student feels the most positive emotions and propose a learning plan based on that history. In the lesson providing unit, for example, the generation AI analyzes the student's learning history and identifies the learning history in which the student feels the most positive emotions. For example, it identifies periods when positive emotions were strongest based on past learning data. The generation AI also proposes a learning plan that elicits positive emotions. For example, it creates a learning plan based on content learned during periods when positive emotions were strongest. The generation AI also analyzes the student's emotional state in real time and reflects this in the learning plan. For example, it adjusts the learning content based on the emotion score. In this way, it is possible to identify the learning history in which the student feels the most positive emotions and propose a learning plan based on that history, thereby maximizing the learning effect.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The Academic Aptitude Assessment Department can monitor students' physical health and adjust the difficulty and content of tests based on their health status. For example, the generation AI measures students' heart rate and blood pressure to detect signs of stress and fatigue. The generation AI also adjusts the difficulty of the test based on their health status. For example, if fatigue is observed, the difficulty of the test is lowered, and if health is good, the difficulty is increased. The generation AI also changes the content of the test according to the student's health status. For example, if fatigue is observed, questions that can be answered in a short time are presented, and if health is good, questions that require long-term concentration are presented. This makes it possible to maximize learning effectiveness by adjusting the difficulty and content of the test according to the student's health status.

[0088] The Academic Aptitude Assessment Department can use the emotion estimation function to identify the environment in which a student is most relaxed and administer the test in that environment. For example, the generation AI analyzes the student's facial expressions and tone of voice to identify a relaxing environment. For example, it selects a quiet place or a place where natural sounds can be heard. The generation AI also administers the test in a relaxing environment. For example, it plays natural sounds in the background while the test is administered. The generation AI also adjusts the ambient sounds and lighting to maintain a relaxing environment. For example, it sets the ambient sounds to an appropriate volume and softens the lighting. This allows the test to be administered in an environment where the student is most relaxed, maximizing learning effectiveness.

[0089] The Academic Aptitude Assessment Department can analyze students' learning styles using past learning history and grade data and suggest optimal learning methods. For example, the generative AI collects students' past test results and grade data and analyzes their learning styles. For example, it suggests materials that make heavy use of diagrams and graphs to students who are good at visual learning, and materials that make heavy use of audio commentary to students who are good at auditory learning. The generative AI also suggests learning methods based on students' learning styles. For example, it suggests learning methods using visual aids to students who are good at visual learning, and learning methods in the form of podcasts to students who are good at auditory learning. The generative AI also adjusts the learning environment according to learning style. For example, it provides bright lighting for students who are good at visual learning, and a quiet environment for students who are good at auditory learning. This maximizes learning effectiveness by suggesting optimal learning methods based on past learning history and grade data.

[0090] The Academic Aptitude Survey Department can use the emotion estimation function to identify the time periods when students are most able to concentrate and administer tests at those times. For example, the generation AI can analyze students' facial expressions and tone of voice to identify the time periods when they are most able to concentrate. For example, it can calculate the time periods when students are most able to concentrate based on past learning data. The generation AI can also administer tests at times when students are most able to concentrate. For example, it can administer important tests at times when students are most able to concentrate. The generation AI can also adjust the test schedule to match the time periods when students are most able to concentrate. For example, it can administer tests with lighter content at times when students are least able to concentrate. This maximizes the learning effect by administering tests at times when students are most able to concentrate.

[0091] The Academic Aptitude Assessment Department can provide game-style tests to assess academic ability while having fun. For example, the generative AI can design game-style tests so that students can have fun while assessing their academic ability. For example, it can provide quiz-style or puzzle-style questions. The generative AI can also assess students' academic ability through game-style tests. For example, it can assess academic ability based on in-game scores and progress. The generative AI can also stimulate students' interest through game-style tests. For example, it can provide questions that incorporate their favorite characters or stories. In this way, by providing game-style tests, students can have fun while their academic ability is assessed.

[0092] The Academic Aptitude Assessment Department can provide multilingual tests to students from different cultural and linguistic backgrounds, enabling global academic assessment. For example, the generative AI designs multilingual tests and provides them to students from different linguistic backgrounds. For example, questions are provided in multiple languages, such as English, Spanish, and Chinese. The generative AI also provides tests that take different cultural backgrounds into account. For example, questions are provided using culturally appropriate examples and scenarios. The generative AI also performs global academic assessments through multilingual tests. For example, assessments are performed based on international test standards. This makes it possible to provide multilingual tests to students from different cultural and linguistic backgrounds, enabling global academic assessments.

[0093] The academic aptitude assessment department can use the emotion estimation function to identify the areas in which students are most interested and provide tests related to those areas. For example, the generation AI analyzes a student's facial expressions and tone of voice to identify the areas in which they are interested. For example, it calculates the interest level based on their reaction to a specific topic. The generation AI then provides tests related to the students' areas of interest. For example, it poses questions related to topics of interest. The generation AI also stimulates students' motivation to learn through tests related to their areas of interest. For example, solving questions on topics of interest increases their motivation to learn. This makes it possible to increase students' motivation to learn by providing them with tests related to the areas in which they are most interested.

[0094] The teaching material generation unit can analyze a student's learning style and generate teaching materials in the optimal format based on that. For example, the generation AI analyzes a student's learning style and generates visual teaching materials. For example, it may provide teaching materials that make extensive use of diagrams and graphs. The generation AI may also generate teaching materials that cater to auditory learning styles. For example, it may provide teaching materials in the form of audio commentary or podcasts. The generation AI may also generate teaching materials that cater to tactile learning styles. For example, it may provide interactive simulations or experiment kits. This makes it possible to maximize learning effectiveness by generating teaching materials in the optimal format based on the student's learning style.

[0095] The teaching material generation unit can adjust the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude, maximizing the learning effect. For example, the generation AI adjusts the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude. For example, it dynamically changes the difficulty of the questions. The generation AI also monitors the student's learning progress and adjusts the difficulty of the teaching materials as needed. For example, it lowers the difficulty if the level of understanding is low and raises the difficulty if the level of understanding is high. The generation AI also analyzes the student's responses in real time and adjusts the difficulty of the teaching materials. For example, it adjusts the difficulty based on the answer time and correct answer rate. In this way, the learning effect can be maximized by adjusting the difficulty of the teaching materials in real time based on the analysis results of the student's academic ability and aptitude.

[0096] The teaching material generation unit can use the emotion estimation function to identify the teaching material format that evokes the most positive emotions in students and generate teaching materials in that format. For example, the generation AI analyzes students' facial expressions and tone of voice to identify the teaching material format that evokes the most positive emotions. For example, it selects a teaching material format based on smiles and signs of excitement. The generation AI also generates teaching materials that elicit positive emotions. For example, it provides teaching materials that incorporate fun stories and interesting topics. The generation AI also generates teaching materials that incorporate interactive elements to maintain positive emotions. For example, it provides teaching materials in the form of quizzes or games. In this way, by identifying the teaching material format that evokes the most positive emotions in students and generating teaching materials in that format, it is possible to increase motivation to learn.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The academic ability and aptitude assessment unit assesses the student's academic ability and aptitude. For example, the generative AI conducts tests and surveys to assess the student's academic ability and aptitude, and analyzes the results to evaluate the student's current academic ability and aptitude. Specifically, it conducts mathematics tests, reading comprehension tests, and surveys on interests, and performs analysis based on the results. Step 2: The teaching material generation unit generates teaching materials based on the results obtained by the academic ability and aptitude survey unit. For example, the generation AI automatically generates teaching materials that are individually tailored to the student based on the analysis results of their academic ability and aptitude. Specifically, it generates teaching materials that include advanced math problems for students who are good at math, and difficult sentences for students with strong reading comprehension skills. Step 3: The lesson provider provides online lessons using the teaching materials generated by the teaching material generator. For example, the teaching materials generated by the generation AI are uploaded to an online platform, and students use them to advance their studies. The generation AI also monitors students' learning progress and generates additional teaching materials or suggests learning methods as needed.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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. The Academic Aptitude Research Department investigates students' academic abilities and aptitudes, a teaching material generation unit that generates teaching materials based on the results obtained by the scholastic aptitude survey unit; a lesson providing unit that provides online lessons using the teaching materials generated by the teaching material generating unit. A system characterized by:

2. The academic aptitude investigation department Providing game-style tests to assess academic ability in a fun way 2. The system of claim 1.

3. The teaching material generation unit Analyzing the student's learning style and generating the learning materials in the most suitable format based on that analysis 2. The system of claim 1.

4. The lesson provider: Monitor student progress in real time and dynamically change lesson content as needed 2. The system of claim 1.

5. The lesson provider: Using emotion estimation, we analyze the emotional state of students when they receive feedback and suggest the most appropriate feedback method.

2. The system of claim 1.

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