system

Abstract

The system according to this embodiment aims to provide each student with a curriculum and teaching materials optimized for them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects student data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates curricula and teaching materials based on the data analyzed by the analysis unit. The provision unit provides the curricula and teaching materials generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide educational content optimized for each individual student, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a curriculum and teaching materials optimized for each individual student.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data of students. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a curriculum and teaching materials based on the data analyzed by the analysis unit. The provision unit provides the curriculum and teaching materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide each student with a curriculum and teaching materials optimized for them. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 5? The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides a personalized education system that generates customized educational content and experiences for each student. This system uses state-of-the-art AI algorithms to analyze individual learning styles, progress, and goals, and generates personalized curricula, learning materials, and interactive lessons. The system adapts to student performance in real time, ensuring optimal learning outcomes while addressing Japan's diverse educational needs. For example, the system provides AI-generated personalized curricula, dynamic content creation, real-time progress tracking, and support multimodal learning. Furthermore, it provides personalized explanations and feedback using natural language processing through AI tutoring. The system creates a more effective and engaging education ecosystem across educational institutions throughout Japan, adapting to rapid social and economic changes. This fosters a new generation of learners with the skills and knowledge to succeed in the 21st century and also supports the continuous skill development of adults. Ultimately, it contributes to Japan's long-term economic competitiveness and social welfare by cultivating a highly skilled, adaptable, and innovative workforce. The system targets public and private schools, universities, vocational training centers, educational technology companies, and publishers throughout Japan, and serves a wide range of students across various age groups. The system addresses issues such as the inability of traditional, uniform approaches to education to meet the needs of individual students, educational disparities due to socioeconomic factors and regional differences, teacher shortages, especially in rural areas, the difficulty of providing individual attention in large class sizes, the pressure on students in Japan's competitive academic environment, and the need for continuous skill updating in a rapidly changing workplace. The system provides a truly personalized learning experience and has the potential to improve student performance by 30%. By providing high-quality, customized content, it bridges educational disparities, complements teachers' efforts, and makes more effective use of class time. The system adapts to each student's pace and style, reduces academic stress, and improves engagement. It provides lifelong learning solutions and supports the continuous skill development of Japan's workforce. The scalable platform can reach millions of students simultaneously.The system utilizes generative AI for content generation, natural language processing for conversational AI tutoring and student response analysis, predictive modeling to predict student performance and suggest preventative interventions, learning style analysis to identify optimal teaching methods, adaptive testing to adjust difficulty levels in real time, and knowledge graph creation to build a comprehensive and interconnected knowledge structure across subject areas. This enables the personalized education system to deliver individualized educational content by collecting and analyzing student data, generating and providing curricula and materials.

[0029] The personalized education system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects student data. The collection unit can collect, for example, a student's learning history, grades, behavioral data, etc. The collection unit can also use AI to collect a student's learning style and progress. For example, the collection unit collects data on a student's use of an online learning platform to identify their learning style. The collection unit can also collect a student's test results to evaluate their learning progress. Furthermore, the collection unit can collect a student's behavioral data and analyze their learning patterns. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes a student's learning style and progress, for example, using statistical analysis or machine learning algorithms. The analysis unit uses AI to analyze a student's learning style and progress based on the collected data. For example, the analysis unit analyzes a student's learning history to identify the optimal learning method. The analysis unit can also analyze a student's grade data to evaluate the effectiveness of their learning. Furthermore, the analysis unit can analyze a student's behavioral data to identify their learning patterns. The generation unit generates curricula and teaching materials based on data analyzed by the analysis unit. For example, the generation unit generates curriculum structure and types of teaching materials. The generation unit uses AI to generate curricula and teaching materials based on the analysis results. For example, the generation unit generates personalized curricula based on students' learning styles. The generation unit can also generate appropriate teaching materials based on students' progress. Furthermore, the generation unit can adjust the curriculum based on students' learning objectives. The delivery unit provides the curricula and teaching materials generated by the generation unit. For example, the delivery unit provides curricula and teaching materials through online platforms or printed materials. The delivery unit can also use AI to provide the generated curricula and teaching materials to students. For example, the delivery unit provides curricula to students through online platforms. The delivery unit can also provide teaching materials to students through printed materials. Furthermore, the delivery unit can adjust the curriculum and teaching materials based on students' learning progress.As a result, the personalized education system according to this embodiment can provide individualized educational content by collecting and analyzing student data, generating and providing curricula and teaching materials.

[0030] The data collection unit collects student data. For example, it can collect student learning history, grades, and behavioral data. Specifically, it collects data on the use of online learning platforms, obtaining detailed information such as which materials students studied, for how long, which problems they struggled with, and the pace at which they are progressing. Furthermore, the data collection unit can collect student test results and evaluate learning progress. For example, it automatically collects the results of regular tests and quizzes to identify students' understanding and strengths / weaknesses in each subject. The data collection unit can also collect student behavioral data and analyze learning patterns. For example, it collects data on when students are most focused on studying, the environment in which they study, and their behaviors while studying. This data is collected in real time using AI and stored in a central database. The AI ​​automatically analyzes students' learning styles and progress, and collects data tailored to individual learning needs. For example, the AI ​​identifies whether students prefer visual or auditory materials and adjusts the data collection method accordingly. This allows the data collection unit to efficiently collect data tailored to each student's learning style and progress, and to provide the information necessary for delivering personalized educational content.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to analyze students' learning styles and progress. Specifically, it analyzes students' learning history to identify the optimal learning method. For example, it analyzes which materials students studied, in what order, which problems they struggled with, and at what pace they are learning to identify the optimal learning pattern. The analysis unit can also analyze students' performance data to evaluate the effectiveness of their learning. For example, it analyzes the results of regular tests and quizzes to identify their level of understanding and their strengths and weaknesses in each subject. Furthermore, the analysis unit can analyze students' behavioral data to identify learning patterns. For example, it analyzes when students are most focused on studying, what kind of environment they study in, and what actions they take while studying to identify the optimal learning environment. The analysis unit uses AI to analyze students' learning styles and progress based on the collected data. The AI ​​uses machine learning algorithms to analyze the collected data and identify the learning style and progress of each individual student. For example, AI can analyze a student's learning history to identify the optimal learning method. It can also analyze student performance data to evaluate the effectiveness of their learning. Furthermore, AI can analyze student behavioral data to identify learning patterns. This allows the analysis unit to quickly and accurately analyze the collected data, understanding each student's learning style and progress.

[0032] The generation unit generates curricula and learning materials based on data analyzed by the analysis unit. For example, the generation unit generates curriculum structure and types of learning materials. Specifically, it generates personalized curricula based on students' learning styles. For instance, it generates materials that heavily utilize videos and diagrams for students who prefer visual learning, and audio materials and podcasts for students who prefer auditory learning. The generation unit can also generate appropriate materials based on students' progress. For example, it generates supplementary materials specifically for students struggling with a particular subject, and advanced materials to promote deeper understanding for subjects they excel in. Furthermore, the generation unit can adjust the curriculum based on students' learning goals. For example, it generates a curriculum specifically for exam preparation for students aiming for university entrance exams, and a curriculum specialized in a particular field for students who want to deepen their hobbies or interests. The generation unit uses AI to generate curricula and learning materials based on the analysis results. The AI ​​uses machine learning algorithms to automatically generate optimal curricula and learning materials based on the analysis results. For example, the AI ​​considers students' learning styles and progress to generate the optimal curriculum and provides learning materials tailored to each individual student. This allows the generation unit to efficiently generate curricula and teaching materials tailored to the individual learning needs of each student, providing personalized educational content.

[0033] The provisioning department provides the curriculum and teaching materials generated by the generation department. The provisioning department provides the curriculum and teaching materials, for example, through online platforms or printed materials. Specifically, the curriculum is provided to students through an online platform. For example, students can log in to the online platform and access their personalized curriculum and teaching materials. The provisioning department can also provide teaching materials to students through printed materials. For example, if a student requests it, the generated teaching materials can be printed and provided. Furthermore, the provisioning department can adjust the curriculum and teaching materials based on the student's learning progress. For example, if a student is struggling with a particular subject, supplementary materials specifically for that subject can be provided, and for subjects they excel in, advanced materials can be provided to promote a deeper understanding. The provisioning department can also use AI to provide the generated curriculum and teaching materials to students. The AI ​​monitors the student's learning progress in real time and adjusts the curriculum and teaching materials as needed. For example, the AI ​​monitors the student's learning progress and, if they are struggling with a particular subject, automatically provides supplementary materials specifically for that subject. The AI ​​also provides the optimal curriculum and teaching materials according to the student's learning style and progress. This allows the service provider to efficiently deliver curricula and materials tailored to the individual learning needs of each student, providing personalized educational content.

[0034] The tracking unit can track progress in real time. For example, the tracking unit collects and analyzes learning data to understand students' learning status in real time. The tracking unit can also use AI to track progress in real time. For example, the tracking unit can collect data on students' use of online learning platforms in real time to understand their learning status. The tracking unit can also collect students' test results in real time to evaluate their learning progress. Furthermore, the tracking unit can collect student behavioral data in real time and analyze learning patterns. This allows for an immediate understanding of students' learning status by tracking progress in real time.

[0035] The tutoring department can provide AI tutoring. For example, the tutoring department can use natural language processing and machine learning algorithms to provide individualized learning support. The tutoring department can also use AI to provide AI tutoring. For example, the tutoring department can use natural language processing to provide personalized answers to students' questions. Furthermore, the tutoring department can use machine learning algorithms to provide appropriate learning support based on students' learning progress. In addition, the tutoring department can provide individualized learning support based on students' learning styles. This makes individualized learning support possible through AI tutoring.

[0036] The data collection unit can analyze students' past learning history and select the optimal data collection method. For example, the data collection unit can select a similar data collection method based on learning methods that were effective for the student in the past. The data collection unit can also collect more detailed data on areas where the student struggled in the past. Furthermore, the data collection unit can select a data collection method that is effective for a specific time period based on the student's past learning history. This enables effective data collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the student's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0037] The data collection unit can filter data based on students' current learning environment and areas of interest during data collection. For example, if a student is studying in a quiet environment, the unit will collect data appropriate for that environment. Furthermore, if a student is focused on a specific area of ​​interest, the unit can prioritize collecting data related to that area. Additionally, if students are engaged in group learning, the unit can collect data for the entire group and compare it with individual data. This allows for the collection of highly relevant data by filtering it based on the learning environment and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input student learning environment data into a generating AI and have the generating AI perform the filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location during data collection. For example, if a student is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if a student is on the move, the data collection unit can also collect data related to their destination. Additionally, if a student is at home, the data collection unit can collect data suitable for home study. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the student's geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0039] The data collection unit can analyze students' social media activities and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on learning content shared by students on social media. It can also collect data on areas of interest based on information about accounts students follow on social media. Furthermore, the data collection unit can analyze the activities of groups students participate in on social media and collect relevant data. Thus, relevant data can be collected by analyzing social media activities. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input students' social media activity data into a generating AI and have the generating AI collect relevant data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a speech recognition algorithm to audio data. This allows for highly accurate analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of recently collected data. It can also prioritize the analysis of data collected during a specific period. Furthermore, the analysis unit can analyze current data while referring to past data. This allows for the prioritization of the latest data by determining the analysis priority based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance next. Furthermore, it may analyze data with low relevance last. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] The generation unit can adjust the difficulty level of the curriculum and materials based on the student's learning progress during the generation process. For example, if a student is making progress, the generation unit can generate a more difficult curriculum and materials. Conversely, if a student is stagnating, the generation unit can also generate a less difficult curriculum and materials. Furthermore, if a student is showing moderate progress, the generation unit can generate a curriculum and materials of appropriate difficulty. This allows for the provision of learning content of appropriate difficulty by adjusting the difficulty level of the curriculum and materials based on learning progress. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input student learning progress data into a generation AI and have the generation AI perform the adjustment of the difficulty level of the curriculum and materials.

[0045] The generation unit can apply different generation algorithms depending on the student's learning style during generation. For example, for visual learners, the generation unit can generate curricula and materials that heavily utilize images and videos. It can also generate curricula and materials that heavily utilize audio and music for auditory learners. Furthermore, for tactile learners, it can generate curricula and materials that heavily utilize interactive simulations. This allows for the provision of effective learning content by applying an appropriate generation algorithm according to the learning style. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input student learning style data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0046] The generation unit can adjust the order of the curriculum and learning materials based on the student's learning history during the generation process. For example, the generation unit can generate the next curriculum and learning materials that the student should study based on what the student has studied in the past. The generation unit can also generate a curriculum and learning materials that prioritize topics the student has struggled with in the past. Furthermore, the generation unit can generate a curriculum and learning materials that review topics the student has excelled at in the past. By adjusting the order of the curriculum and learning materials based on the learning history, effective learning content can be provided. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the student's learning history data into a generation AI and have the generation AI perform the adjustment of the order of the curriculum and learning materials.

[0047] The generation unit can customize the content of the curriculum and teaching materials based on the student's areas of interest during the generation process. For example, if a student is interested in a particular field, the generation unit can generate curriculum and teaching materials related to that field. Furthermore, if a student is interested in multiple fields, the generation unit can generate curriculum and teaching materials that combine those fields. Additionally, if a student develops an interest in a new field, the generation unit can generate curriculum and teaching materials related to that field. This allows for the provision of engaging learning content by customizing the curriculum and teaching materials based on areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input student area of ​​interest data into a generation AI and have the generation AI customize the content of the curriculum and teaching materials.

[0048] The content delivery unit can adjust the content provided based on the student's learning progress at the time of delivery. For example, if the student is making progress, the delivery unit can provide more difficult content. Conversely, if the student is stagnating, the delivery unit can provide easier content. Furthermore, if the student is showing moderate progress, the delivery unit can provide content of appropriate difficulty. This allows for appropriate learning support by adjusting the content provided based on learning progress. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input student learning progress data into a generating AI and have the generating AI perform the adjustment of the content provided.

[0049] The delivery unit can apply different delivery methods depending on the student's learning style at the time of delivery. For example, for visual learners, the delivery unit may select a delivery method that makes extensive use of images and videos. For auditory learners, the delivery unit may also select a delivery method that makes extensive use of audio and music. Furthermore, for tactile learners, the delivery unit may select a delivery method that makes extensive use of interactive simulations. This allows for effective learning support by applying delivery methods according to the learning style. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input student learning style data into a generating AI and have the generating AI apply an appropriate delivery method.

[0050] The content delivery unit can adjust the order of the content provided based on the student's learning history. For example, the delivery unit can provide the next curriculum and materials that the student should study based on what the student has studied in the past. The delivery unit can also provide a curriculum and materials that prioritize learning topics that the student has struggled with in the past. Furthermore, the delivery unit can provide a curriculum and materials that allow the student to review topics that the student has excelled at in the past. By adjusting the order of the content provided based on the learning history, effective learning support becomes possible. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the student's learning history data into a generating AI and have the generating AI perform the adjustment of the order of the content provided.

[0051] The service provider can customize the content offered based on the student's areas of interest at the time of delivery. For example, if a student is interested in a particular field, the service provider can provide curriculum and materials related to that field. If a student is interested in multiple fields, the service provider can also provide curriculum and materials that combine those fields. Furthermore, if a student begins to show interest in a new field, the service provider can provide curriculum and materials related to that field. In this way, by customizing the content based on areas of interest, the service provider can provide learning content that is engaging. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input student areas of interest data into a generating AI and have the generating AI perform the customization of the content offered.

[0052] The tracking unit can select the optimal tracking method by referring to the student's past learning history during tracking. For example, the tracking unit can select a similar method based on a tracking method that was effective for the student in the past. The tracking unit can also perform more detailed tracking for areas where the student has struggled in the past. Furthermore, the tracking unit can select a tracking method that is effective for a specific time period based on the student's past learning history. This enables effective progress management by selecting the optimal tracking method based on past learning history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's past learning history data into a generating AI and have the generating AI select the optimal tracking method.

[0053] The tracking unit can select the optimal tracking method by considering the student's geographical location information during tracking. For example, if a student is in a specific region, the tracking unit will prioritize tracking progress related to that region. Furthermore, if a student is on the move, the tracking unit can also track progress related to their destination. Additionally, if a student is at home, the tracking unit can track progress suitable for home study. This allows the system to select the optimal tracking method by considering geographical location information. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's geographical location data into a generating AI and have the generating AI select the optimal tracking method.

[0054] The tutoring department can select the optimal tutoring method by referring to the student's past learning history during tutoring sessions. For example, the tutoring department can select a similar method based on a tutoring method that was effective for the student in the past. Furthermore, the tutoring department can provide more detailed tutoring for areas where the student has struggled in the past. In addition, the tutoring department can select a tutoring method that is effective for a specific time period based on the student's past learning history. This allows for effective learning support by selecting the optimal tutoring method based on past learning history. Some or all of the above processes in the tutoring department may be performed using AI, or not. For example, the tutoring department can input the student's past learning history data into a generating AI and have the generating AI select the optimal tutoring method.

[0055] The tutoring department can select the optimal tutoring method by considering the student's geographical location during tutoring sessions. For example, if a student is in a specific region, the tutoring department will prioritize tutoring related to that region. Furthermore, if a student is traveling, the tutoring department can provide tutoring relevant to their destination. Additionally, if a student is at home, the tutoring department can provide tutoring suitable for home study. This allows the tutoring department to select the optimal tutoring method by considering geographical location. Some or all of the above processes in the tutoring department may be performed using AI, or not. For example, the tutoring department can input the student's geographical location data into a generating AI and have the generating AI select the optimal tutoring method.

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

[0057] The data collection unit collects audio data of students' learning environments, and the analysis unit analyzes this data to evaluate the quality of the learning environment. For example, the data collection unit collects background noise in the room where students are studying, and the analysis unit analyzes this audio data to evaluate whether the environment is quiet. The data collection unit can also collect data on the music students are listening to while studying, and the analysis unit can evaluate the impact of that music on their learning. Furthermore, the data collection unit can collect the tone of voice students emit while studying, and the analysis unit can evaluate the student's level of concentration based on that tone. In this way, by collecting and analyzing audio data of the learning environment, the quality of the learning environment can be evaluated, and an optimal learning environment can be provided.

[0058] The generation unit can combine a student's learning history and current learning progress to predict future learning plans. For example, it can predict what kind of learning a student will need in the future in areas they have struggled with in the past. It can also create plans for students to further deepen their learning in areas they excel in. Furthermore, based on the student's learning progress, it can predict what they should learn next and generate appropriate learning materials. This enables effective learning support by predicting future learning plans by combining learning history and progress. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student learning history data and progress data into a generation AI and have the generation AI perform predictions of future learning plans.

[0059] The data collection unit collects temperature data of students' learning environments, and the analysis unit analyzes this data to evaluate the comfort level of the learning environment. For example, the data collection unit collects the temperature of the room where students are studying, and the analysis unit analyzes this data to determine if the temperature is appropriate. The data collection unit can also collect temperature changes that students experience while studying, and the analysis unit can evaluate the impact of these changes on their learning. Furthermore, the data collection unit can collect the temperature of devices that students use while studying, and the analysis unit can evaluate the impact of these device temperatures on their learning. By collecting and analyzing temperature data of the learning environment, it is possible to evaluate the comfort level of the learning environment and provide an optimal learning environment.

[0060] The generation unit can combine a student's learning history and current learning progress to generate curricula and learning materials designed to enhance learning motivation. For example, it can generate curricula that incorporate learning methods the student has previously found successful. It can also generate learning materials related to areas of interest to the student. Furthermore, it can generate curricula that clearly outline the steps the student needs to achieve their goals. This enables effective learning support by generating motivational curricula and learning materials that combine learning history and progress. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input student learning history data and progress data into a generation AI, which can then generate motivational curricula and learning materials.

[0061] The data collection unit collects lighting data from students' learning environments, and the analysis unit analyzes this data to evaluate the appropriateness of the learning environment. For example, the data collection unit collects the brightness of the lighting in the room where students are studying, and the analysis unit analyzes this data to evaluate whether the brightness is appropriate. The data collection unit can also collect the brightness of the screen of the device students use while studying, and the analysis unit can evaluate the impact of that brightness on learning. Furthermore, the data collection unit can collect changes in lighting that students perceive while studying, and the analysis unit can evaluate the impact of these changes on learning. In this way, by collecting and analyzing lighting data from the learning environment, the appropriateness of the learning environment can be evaluated, and an optimal learning environment can be provided.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects student data. The data collection unit can collect, for example, students' learning history, grades, and behavioral data. The data collection unit can also use AI to collect students' learning styles and progress. For example, the data collection unit can collect data on students' use of online learning platforms to identify their learning styles. The data collection unit can also collect students' test results to evaluate their learning progress. Furthermore, the data collection unit can collect students' behavioral data and analyze their learning patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes students' learning styles and progress, for example, using statistical analysis and machine learning algorithms. The analysis unit uses AI to analyze students' learning styles and progress based on the collected data. For example, the analysis unit analyzes students' learning history to identify the optimal learning method. The analysis unit can also analyze students' performance data to evaluate the effectiveness of their learning. Furthermore, the analysis unit can analyze students' behavioral data to identify learning patterns. Step 3: The generation unit generates curricula and teaching materials based on the data analyzed by the analysis unit. For example, the generation unit generates the curriculum structure and the types of teaching materials. The generation unit uses AI to generate curricula and teaching materials based on the analysis results. For example, the generation unit generates a personalized curriculum based on the students' learning styles. The generation unit can also generate appropriate teaching materials based on the students' progress. Furthermore, the generation unit can adjust the curriculum based on the students' learning objectives. Step 4: The delivery unit provides the curriculum and materials generated by the generation unit. The delivery unit provides the curriculum and materials, for example, through an online platform or printed materials. The delivery unit can also use AI to provide the generated curriculum and materials to students. For example, the delivery unit provides the curriculum to students through an online platform. The delivery unit can also provide materials to students through printed materials. Furthermore, the delivery unit can adjust the curriculum and materials based on the students' learning progress.

[0064] (Example of form 2) An embodiment of the present invention provides a personalized education system that generates customized educational content and experiences for each student. This system uses state-of-the-art AI algorithms to analyze individual learning styles, progress, and goals, and generates personalized curricula, learning materials, and interactive lessons. The system adapts to student performance in real time, ensuring optimal learning outcomes while addressing Japan's diverse educational needs. For example, the system provides AI-generated personalized curricula, dynamic content creation, real-time progress tracking, and support multimodal learning. Furthermore, it provides personalized explanations and feedback using natural language processing through AI tutoring. The system creates a more effective and engaging education ecosystem across educational institutions throughout Japan, adapting to rapid social and economic changes. This fosters a new generation of learners with the skills and knowledge to succeed in the 21st century and also supports the continuous skill development of adults. Ultimately, it contributes to Japan's long-term economic competitiveness and social welfare by cultivating a highly skilled, adaptable, and innovative workforce. The system targets public and private schools, universities, vocational training centers, educational technology companies, and publishers throughout Japan, and serves a wide range of students across various age groups. The system addresses issues such as the inability of traditional, uniform approaches to education to meet the needs of individual students, educational disparities due to socioeconomic factors and regional differences, teacher shortages, especially in rural areas, the difficulty of providing individual attention in large class sizes, the pressure on students in Japan's competitive academic environment, and the need for continuous skill updating in a rapidly changing workplace. The system provides a truly personalized learning experience and has the potential to improve student performance by 30%. By providing high-quality, customized content, it bridges educational disparities, complements teachers' efforts, and makes more effective use of class time. The system adapts to each student's pace and style, reduces academic stress, and improves engagement. It provides lifelong learning solutions and supports the continuous skill development of Japan's workforce. The scalable platform can reach millions of students simultaneously.The system utilizes generative AI for content generation, natural language processing for conversational AI tutoring and student response analysis, predictive modeling to predict student performance and suggest preventative interventions, learning style analysis to identify optimal teaching methods, adaptive testing to adjust difficulty levels in real time, and knowledge graph creation to build a comprehensive and interconnected knowledge structure across subject areas. This enables the personalized education system to deliver individualized educational content by collecting and analyzing student data, generating and providing curricula and materials.

[0065] The personalized education system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects student data. The collection unit can collect, for example, a student's learning history, grades, behavioral data, etc. The collection unit can also use AI to collect a student's learning style and progress. For example, the collection unit collects data on a student's use of an online learning platform to identify their learning style. The collection unit can also collect a student's test results to evaluate their learning progress. Furthermore, the collection unit can collect a student's behavioral data and analyze their learning patterns. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes a student's learning style and progress, for example, using statistical analysis or machine learning algorithms. The analysis unit uses AI to analyze a student's learning style and progress based on the collected data. For example, the analysis unit analyzes a student's learning history to identify the optimal learning method. The analysis unit can also analyze a student's grade data to evaluate the effectiveness of their learning. Furthermore, the analysis unit can analyze a student's behavioral data to identify their learning patterns. The generation unit generates curricula and teaching materials based on data analyzed by the analysis unit. For example, the generation unit generates curriculum structure and types of teaching materials. The generation unit uses AI to generate curricula and teaching materials based on the analysis results. For example, the generation unit generates personalized curricula based on students' learning styles. The generation unit can also generate appropriate teaching materials based on students' progress. Furthermore, the generation unit can adjust the curriculum based on students' learning objectives. The delivery unit provides the curricula and teaching materials generated by the generation unit. For example, the delivery unit provides curricula and teaching materials through online platforms or printed materials. The delivery unit can also use AI to provide the generated curricula and teaching materials to students. For example, the delivery unit provides curricula to students through online platforms. The delivery unit can also provide teaching materials to students through printed materials. Furthermore, the delivery unit can adjust the curriculum and teaching materials based on students' learning progress.As a result, the personalized education system according to this embodiment can provide individualized educational content by collecting and analyzing student data, generating and providing curricula and teaching materials.

[0066] The data collection unit collects student data. For example, it can collect student learning history, grades, and behavioral data. Specifically, it collects data on the use of online learning platforms, obtaining detailed information such as which materials students studied, for how long, which problems they struggled with, and the pace at which they are progressing. Furthermore, the data collection unit can collect student test results and evaluate learning progress. For example, it automatically collects the results of regular tests and quizzes to identify students' understanding and strengths / weaknesses in each subject. The data collection unit can also collect student behavioral data and analyze learning patterns. For example, it collects data on when students are most focused on studying, the environment in which they study, and their behaviors while studying. This data is collected in real time using AI and stored in a central database. The AI ​​automatically analyzes students' learning styles and progress, and collects data tailored to individual learning needs. For example, the AI ​​identifies whether students prefer visual or auditory materials and adjusts the data collection method accordingly. This allows the data collection unit to efficiently collect data tailored to each student's learning style and progress, and to provide the information necessary for delivering personalized educational content.

[0067] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to analyze students' learning styles and progress. Specifically, it analyzes students' learning history to identify the optimal learning method. For example, it analyzes which materials students studied, in what order, which problems they struggled with, and at what pace they are learning to identify the optimal learning pattern. The analysis unit can also analyze students' performance data to evaluate the effectiveness of their learning. For example, it analyzes the results of regular tests and quizzes to identify their level of understanding and their strengths and weaknesses in each subject. Furthermore, the analysis unit can analyze students' behavioral data to identify learning patterns. For example, it analyzes when students are most focused on studying, what kind of environment they study in, and what actions they take while studying to identify the optimal learning environment. The analysis unit uses AI to analyze students' learning styles and progress based on the collected data. The AI ​​uses machine learning algorithms to analyze the collected data and identify the learning style and progress of each individual student. For example, AI can analyze a student's learning history to identify the optimal learning method. It can also analyze student performance data to evaluate the effectiveness of their learning. Furthermore, AI can analyze student behavioral data to identify learning patterns. This allows the analysis unit to quickly and accurately analyze the collected data, understanding each student's learning style and progress.

[0068] The generation unit generates curricula and learning materials based on data analyzed by the analysis unit. For example, the generation unit generates curriculum structure and types of learning materials. Specifically, it generates personalized curricula based on students' learning styles. For instance, it generates materials that heavily utilize videos and diagrams for students who prefer visual learning, and audio materials and podcasts for students who prefer auditory learning. The generation unit can also generate appropriate materials based on students' progress. For example, it generates supplementary materials specifically for students struggling with a particular subject, and advanced materials to promote deeper understanding for subjects they excel in. Furthermore, the generation unit can adjust the curriculum based on students' learning goals. For example, it generates a curriculum specifically for exam preparation for students aiming for university entrance exams, and a curriculum specialized in a particular field for students who want to deepen their hobbies or interests. The generation unit uses AI to generate curricula and learning materials based on the analysis results. The AI ​​uses machine learning algorithms to automatically generate optimal curricula and learning materials based on the analysis results. For example, the AI ​​considers students' learning styles and progress to generate the optimal curriculum and provides learning materials tailored to each individual student. This allows the generation unit to efficiently generate curricula and teaching materials tailored to the individual learning needs of each student, providing personalized educational content.

[0069] The provisioning department provides the curriculum and teaching materials generated by the generation department. The provisioning department provides the curriculum and teaching materials, for example, through online platforms or printed materials. Specifically, the curriculum is provided to students through an online platform. For example, students can log in to the online platform and access their personalized curriculum and teaching materials. The provisioning department can also provide teaching materials to students through printed materials. For example, if a student requests it, the generated teaching materials can be printed and provided. Furthermore, the provisioning department can adjust the curriculum and teaching materials based on the student's learning progress. For example, if a student is struggling with a particular subject, supplementary materials specifically for that subject can be provided, and for subjects they excel in, advanced materials can be provided to promote a deeper understanding. The provisioning department can also use AI to provide the generated curriculum and teaching materials to students. The AI ​​monitors the student's learning progress in real time and adjusts the curriculum and teaching materials as needed. For example, the AI ​​monitors the student's learning progress and, if they are struggling with a particular subject, automatically provides supplementary materials specifically for that subject. The AI ​​also provides the optimal curriculum and teaching materials according to the student's learning style and progress. This allows the service provider to efficiently deliver curricula and materials tailored to the individual learning needs of each student, providing personalized educational content.

[0070] The tracking unit can track progress in real time. For example, the tracking unit collects and analyzes learning data to understand students' learning status in real time. The tracking unit can also use AI to track progress in real time. For example, the tracking unit can collect data on students' use of online learning platforms in real time to understand their learning status. The tracking unit can also collect students' test results in real time to evaluate their learning progress. Furthermore, the tracking unit can collect student behavioral data in real time and analyze learning patterns. This allows for an immediate understanding of students' learning status by tracking progress in real time.

[0071] The tutoring department can provide AI tutoring. For example, the tutoring department can use natural language processing and machine learning algorithms to provide individualized learning support. The tutoring department can also use AI to provide AI tutoring. For example, the tutoring department can use natural language processing to provide personalized answers to students' questions. Furthermore, the tutoring department can use machine learning algorithms to provide appropriate learning support based on students' learning progress. In addition, the tutoring department can provide individualized learning support based on students' learning styles. This makes individualized learning support possible through AI tutoring.

[0072] The data collection unit can estimate students' emotions and adjust the timing of data collection based on the estimated emotions. For example, if a student is stressed, the data collection unit can temporarily stop data collection and wait until the student is relaxed. Alternatively, if a student is focused, the data collection unit can collect detailed data at that time. Furthermore, if a student is tired, the data collection unit can minimize data collection and resume after a break. This allows for more appropriate data collection by adjusting the timing of data collection according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data of students captured by a camera into a generative AI and have the generative AI perform the estimation of the students' emotions.

[0073] The data collection unit can analyze students' past learning history and select the optimal data collection method. For example, the data collection unit can select a similar data collection method based on learning methods that were effective for the student in the past. The data collection unit can also collect more detailed data on areas where the student struggled in the past. Furthermore, the data collection unit can select a data collection method that is effective for a specific time period based on the student's past learning history. This enables effective data collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the student's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter data based on students' current learning environment and areas of interest during data collection. For example, if a student is studying in a quiet environment, the unit will collect data appropriate for that environment. Furthermore, if a student is focused on a specific area of ​​interest, the unit can prioritize collecting data related to that area. Additionally, if students are engaged in group learning, the unit can collect data for the entire group and compare it with individual data. This allows for the collection of highly relevant data by filtering it based on the learning environment and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input student learning environment data into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate students' emotions and prioritize the data to be collected based on the estimated emotions. For example, if a student is excited, the data collection unit will prioritize collecting data related to that emotion. It can also collect data for longer periods if the student is relaxed. Furthermore, if a student is anxious, the data collection unit can prioritize collecting data to identify the cause of that anxiety. This allows for the priority collection of important data by prioritizing data based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data of students captured by a camera into a generative AI and have the generative AI perform the estimation of the students' emotions.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location during data collection. For example, if a student is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if a student is on the move, the data collection unit can also collect data related to their destination. Additionally, if a student is at home, the data collection unit can collect data suitable for home study. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the student's geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0077] The data collection unit can analyze students' social media activities and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on learning content shared by students on social media. It can also collect data on areas of interest based on information about accounts students follow on social media. Furthermore, the data collection unit can analyze the activities of groups students participate in on social media and collect relevant data. Thus, relevant data can be collected by analyzing social media activities. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input students' social media activity data into a generating AI and have the generating AI collect relevant data.

[0078] The analysis unit can estimate the student's emotions and adjust the analysis method based on the estimated emotions. For example, if the student is relaxed, the analysis unit can perform a detailed analysis. If the student is in a hurry, the analysis unit can perform a simplified analysis. Furthermore, if the student is excited, the analysis unit can provide visually stimulating analysis results. This allows for more appropriate analysis by adjusting the analysis method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input image data of the student captured by a camera into the generative AI and have the generative AI perform the estimation of the student's emotions.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a speech recognition algorithm to audio data. This allows for highly accurate analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0081] The analysis unit can estimate a student's emotions and determine the priority of analysis based on the estimated emotions. For example, if a student is excited, the analysis unit will prioritize analyzing data related to that emotion. It can also perform longer analyses if the student is relaxed. Furthermore, if a student is anxious, the analysis unit can prioritize analyzing data to identify the cause of that anxiety. This allows for the prioritization of important data by determining the analysis priority based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input image data of students captured by a camera into a generative AI and have the generative AI perform the estimation of the students' emotions.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of recently collected data. It can also prioritize the analysis of data collected during a specific period. Furthermore, the analysis unit can analyze current data while referring to past data. This allows for the prioritization of the latest data by determining the analysis priority based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance next. Furthermore, it may analyze data with low relevance last. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The generation unit can estimate students' emotions and adjust the content of the curriculum and materials it generates based on the estimated emotions. For example, if a student is relaxed, the generation unit can generate a detailed curriculum and materials. If a student is in a hurry, the generation unit can also generate a simplified curriculum and materials. Furthermore, if a student is excited, the generation unit can generate a visually stimulating curriculum and materials. This allows for the provision of more appropriate learning content by adjusting the curriculum and materials based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input image data of students captured by a camera into the generation AI and have the generation AI perform the estimation of the students' emotions.

[0085] The generation unit can adjust the difficulty level of the curriculum and materials based on the student's learning progress during the generation process. For example, if a student is making progress, the generation unit can generate a more difficult curriculum and materials. Conversely, if a student is stagnating, the generation unit can also generate a less difficult curriculum and materials. Furthermore, if a student is showing moderate progress, the generation unit can generate a curriculum and materials of appropriate difficulty. This allows for the provision of learning content of appropriate difficulty by adjusting the difficulty level of the curriculum and materials based on learning progress. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input student learning progress data into a generation AI and have the generation AI perform the adjustment of the difficulty level of the curriculum and materials.

[0086] The generation unit can apply different generation algorithms depending on the student's learning style during generation. For example, for visual learners, the generation unit can generate curricula and materials that heavily utilize images and videos. It can also generate curricula and materials that heavily utilize audio and music for auditory learners. Furthermore, for tactile learners, it can generate curricula and materials that heavily utilize interactive simulations. This allows for the provision of effective learning content by applying an appropriate generation algorithm according to the learning style. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input student learning style data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0087] The generation unit can estimate students' emotions and prioritize the curriculum and materials it generates based on those estimated emotions. For example, if a student is excited, the generation unit will prioritize generating curriculum and materials related to that emotion. It can also generate longer curriculum and materials if a student is relaxed. Furthermore, if a student is anxious, the generation unit can prioritize generating curriculum and materials to identify the cause of that anxiety. This allows for the priority delivery of important learning content by prioritizing curriculum and materials based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input image data of students captured by a camera into a generative AI and have the generative AI perform the estimation of the students' emotions.

[0088] The generation unit can adjust the order of the curriculum and learning materials based on the student's learning history during the generation process. For example, the generation unit can generate the next curriculum and learning materials that the student should study based on what the student has studied in the past. The generation unit can also generate a curriculum and learning materials that prioritize topics the student has struggled with in the past. Furthermore, the generation unit can generate a curriculum and learning materials that review topics the student has excelled at in the past. By adjusting the order of the curriculum and learning materials based on the learning history, effective learning content can be provided. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the student's learning history data into a generation AI and have the generation AI perform the adjustment of the order of the curriculum and learning materials.

[0089] The generation unit can customize the content of the curriculum and teaching materials based on the student's areas of interest during the generation process. For example, if a student is interested in a particular field, the generation unit can generate curriculum and teaching materials related to that field. Furthermore, if a student is interested in multiple fields, the generation unit can generate curriculum and teaching materials that combine those fields. Additionally, if a student develops an interest in a new field, the generation unit can generate curriculum and teaching materials related to that field. This allows for the provision of engaging learning content by customizing the curriculum and teaching materials based on areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input student area of ​​interest data into a generation AI and have the generation AI customize the content of the curriculum and teaching materials.

[0090] The delivery unit can estimate students' emotions and adjust the curriculum and material delivery methods based on the estimated emotions. For example, if a student is relaxed, the delivery unit may select a delivery method that includes detailed explanations. If a student is in a hurry, the delivery unit may select a simplified delivery method. Furthermore, if a student is excited, the delivery unit may select a visually stimulating delivery method. By adjusting the delivery method based on students' emotions, more effective learning support becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input image data of students captured by a camera into a generative AI and have the generative AI perform the estimation of students' emotions.

[0091] The content delivery unit can adjust the content provided based on the student's learning progress at the time of delivery. For example, if the student is making progress, the delivery unit can provide more difficult content. Conversely, if the student is stagnating, the delivery unit can provide easier content. Furthermore, if the student is showing moderate progress, the delivery unit can provide content of appropriate difficulty. This allows for appropriate learning support by adjusting the content provided based on learning progress. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input student learning progress data into a generating AI and have the generating AI perform the adjustment of the content provided.

[0092] The delivery unit can apply different delivery methods depending on the student's learning style at the time of delivery. For example, for visual learners, the delivery unit may select a delivery method that makes extensive use of images and videos. For auditory learners, the delivery unit may also select a delivery method that makes extensive use of audio and music. Furthermore, for tactile learners, the delivery unit may select a delivery method that makes extensive use of interactive simulations. This allows for effective learning support by applying delivery methods according to the learning style. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input student learning style data into a generating AI and have the generating AI apply an appropriate delivery method.

[0093] The service provider can estimate students' emotions and prioritize the curriculum and materials they provide based on those estimated emotions. For example, if a student is excited, the service provider will prioritize providing curriculum and materials related to that emotion. If a student is relaxed, the service provider may also provide longer curriculum and materials. Furthermore, if a student is anxious, the service provider may prioritize providing curriculum and materials that help identify the cause of that anxiety. This allows for the prioritization of important learning content based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input image data of students captured by a camera into a generative AI and have the generative AI perform the estimation of the students' emotions.

[0094] The content delivery unit can adjust the order of the content provided based on the student's learning history. For example, the delivery unit can provide the next curriculum and materials that the student should study based on what the student has studied in the past. The delivery unit can also provide a curriculum and materials that prioritize learning topics that the student has struggled with in the past. Furthermore, the delivery unit can provide a curriculum and materials that allow the student to review topics that the student has excelled at in the past. By adjusting the order of the content provided based on the learning history, effective learning support becomes possible. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the student's learning history data into a generating AI and have the generating AI perform the adjustment of the order of the content provided.

[0095] The service provider can customize the content offered based on the student's areas of interest at the time of delivery. For example, if a student is interested in a particular field, the service provider can provide curriculum and materials related to that field. If a student is interested in multiple fields, the service provider can also provide curriculum and materials that combine those fields. Furthermore, if a student begins to show interest in a new field, the service provider can provide curriculum and materials related to that field. In this way, by customizing the content based on areas of interest, the service provider can provide learning content that is engaging. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input student areas of interest data into a generating AI and have the generating AI perform the customization of the content offered.

[0096] The tracking unit can estimate the student's emotions and adjust the progress tracking method based on the estimated emotions. For example, if the student is relaxed, the tracking unit can perform detailed progress tracking. If the student is in a hurry, the tracking unit can perform simplified progress tracking. Furthermore, if the student is excited, the tracking unit can perform visually stimulating progress tracking. This allows for more appropriate progress management by adjusting the progress tracking method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input image data of the student taken by a camera into the generative AI and have the generative AI perform the estimation of the student's emotions.

[0097] The tracking unit can select the optimal tracking method by referring to the student's past learning history during tracking. For example, the tracking unit can select a similar method based on a tracking method that was effective for the student in the past. The tracking unit can also perform more detailed tracking for areas where the student has struggled in the past. Furthermore, the tracking unit can select a tracking method that is effective for a specific time period based on the student's past learning history. This enables effective progress management by selecting the optimal tracking method based on past learning history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's past learning history data into a generating AI and have the generating AI select the optimal tracking method.

[0098] The tracking unit can estimate a student's emotions and prioritize progress based on the estimated emotions. For example, if a student is excited, the tracking unit will prioritize tracking progress related to that emotion. It can also track progress for longer periods if the student is relaxed. Furthermore, if a student is anxious, the tracking unit can prioritize tracking progress that identifies the cause of that anxiety. This allows for the priority management of important progress by prioritizing it based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input student image data captured by a camera into a generative AI and have the generative AI perform the estimation of the student's emotions.

[0099] The tracking unit can select the optimal tracking method by considering the student's geographical location information during tracking. For example, if a student is in a specific region, the tracking unit will prioritize tracking progress related to that region. Furthermore, if a student is on the move, the tracking unit can also track progress related to their destination. Additionally, if a student is at home, the tracking unit can track progress suitable for home study. This allows the system to select the optimal tracking method by considering geographical location information. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's geographical location data into a generating AI and have the generating AI select the optimal tracking method.

[0100] The tutoring unit can estimate a student's emotions and adjust its tutoring methods based on those estimates. For example, if a student is relaxed, the tutoring unit can provide detailed explanations. If a student is in a hurry, the tutoring unit can provide simplified tutoring. Furthermore, if a student is excited, the tutoring unit can provide visually stimulating tutoring. By adjusting the tutoring method based on the student's emotions, more effective learning support becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tutoring unit may be performed using AI or not. For example, the tutoring unit can input image data of a student taken by a camera into a generative AI and have the generative AI perform the estimation of the student's emotions.

[0101] The tutoring department can select the optimal tutoring method by referring to the student's past learning history during tutoring sessions. For example, the tutoring department can select a similar method based on a tutoring method that was effective for the student in the past. Furthermore, the tutoring department can provide more detailed tutoring for areas where the student has struggled in the past. In addition, the tutoring department can select a tutoring method that is effective for a specific time period based on the student's past learning history. This allows for effective learning support by selecting the optimal tutoring method based on past learning history. Some or all of the above processes in the tutoring department may be performed using AI, or not. For example, the tutoring department can input the student's past learning history data into a generating AI and have the generating AI select the optimal tutoring method.

[0102] The tutoring department can estimate a student's emotions and prioritize tutoring sessions based on those emotions. For example, if a student is excited, the department will prioritize tutoring sessions related to that emotion. Conversely, if a student is relaxed, the department may conduct longer tutoring sessions. Furthermore, if a student is feeling anxious, the department may prioritize tutoring sessions aimed at identifying the cause of that anxiety. This allows for prioritizing important learning support by determining tutoring priorities based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the tutoring department may be performed using AI or not. For example, the tutoring department can input image data of students captured by a camera into a generative AI and have the generative AI perform the estimation of the students' emotions.

[0103] The tutoring department can select the optimal tutoring method by considering the student's geographical location during tutoring sessions. For example, if a student is in a specific region, the tutoring department will prioritize tutoring related to that region. Furthermore, if a student is traveling, the tutoring department can provide tutoring relevant to their destination. Additionally, if a student is at home, the tutoring department can provide tutoring suitable for home study. This allows the tutoring department to select the optimal tutoring method by considering geographical location. Some or all of the above processes in the tutoring department may be performed using AI, or not. For example, the tutoring department can input the student's geographical location data into a generating AI and have the generating AI select the optimal tutoring method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The data collection unit collects audio data of students' learning environments, and the analysis unit analyzes this data to evaluate the quality of the learning environment. For example, the data collection unit collects background noise in the room where students are studying, and the analysis unit analyzes this audio data to evaluate whether the environment is quiet. The data collection unit can also collect data on the music students are listening to while studying, and the analysis unit can evaluate the impact of that music on their learning. Furthermore, the data collection unit can collect the tone of voice students emit while studying, and the analysis unit can evaluate the student's level of concentration based on that tone. In this way, by collecting and analyzing audio data of the learning environment, the quality of the learning environment can be evaluated, and an optimal learning environment can be provided.

[0106] The analysis unit can estimate the student's emotions and adjust the feedback method of the analysis results based on the estimated emotions of the student. For example, if the student is relaxed, detailed feedback can be provided. If the student is in a hurry, concise feedback can be provided. Furthermore, if the student is excited, visually stimulating feedback can be provided. This allows for more effective learning support by adjusting the feedback method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input image data of the student taken by a camera into the generative AI and have the generative AI perform the estimation of the student's emotions.

[0107] The generation unit can combine a student's learning history and current learning progress to predict future learning plans. For example, it can predict what kind of learning a student will need in the future in areas they have struggled with in the past. It can also create plans for students to further deepen their learning in areas they excel in. Furthermore, based on the student's learning progress, it can predict what they should learn next and generate appropriate learning materials. This enables effective learning support by predicting future learning plans by combining learning history and progress. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student learning history data and progress data into a generation AI and have the generation AI perform predictions of future learning plans.

[0108] The content delivery unit can estimate a student's emotions and adjust the timing of content delivery based on the estimated emotions. For example, if a student is stressed, the delivery of content can be temporarily delayed. Conversely, if a student is focused, new content can be delivered at that time. Furthermore, if a student is tired, content delivery can be minimized and resumed after a break. This allows for more effective learning support by adjusting the timing of content delivery according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content delivery unit may be performed using AI or not. For example, the content delivery unit can input image data of students captured by a camera into a generative AI and have the generative AI perform emotion estimation of the students.

[0109] The data collection unit collects temperature data of students' learning environments, and the analysis unit analyzes this data to evaluate the comfort level of the learning environment. For example, the data collection unit collects the temperature of the room where students are studying, and the analysis unit analyzes this data to determine if the temperature is appropriate. The data collection unit can also collect temperature changes that students experience while studying, and the analysis unit can evaluate the impact of these changes on their learning. Furthermore, the data collection unit can collect the temperature of devices that students use while studying, and the analysis unit can evaluate the impact of these device temperatures on their learning. By collecting and analyzing temperature data of the learning environment, it is possible to evaluate the comfort level of the learning environment and provide an optimal learning environment.

[0110] The analysis unit can estimate the student's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the student is relaxed, detailed analysis results can be displayed. If the student is in a hurry, concise analysis results can be displayed. Furthermore, if the student is excited, visually stimulating analysis results can be displayed. By adjusting how the analysis results are displayed based on the student's emotions, more effective learning support becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input image data of the student taken by a camera into the generative AI and have the generative AI perform the estimation of the student's emotions.

[0111] The generation unit can combine a student's learning history and current learning progress to generate curricula and learning materials designed to enhance learning motivation. For example, it can generate curricula that incorporate learning methods the student has previously found successful. It can also generate learning materials related to areas of interest to the student. Furthermore, it can generate curricula that clearly outline the steps the student needs to achieve their goals. This enables effective learning support by generating motivational curricula and learning materials that combine learning history and progress. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input student learning history data and progress data into a generation AI, which can then generate motivational curricula and learning materials.

[0112] The delivery unit can estimate the student's emotions and adjust the format of the learning content based on the estimated emotions. For example, if the student is relaxed, detailed text-based content can be provided. If the student is in a hurry, concise video-based content can be provided. Furthermore, if the student is excited, interactive game-based content can be provided. This allows for more effective learning support by adjusting the format of the learning content according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input image data of the student captured by a camera into a generative AI and have the generative AI perform the estimation of the student's emotions.

[0113] The data collection unit collects lighting data from students' learning environments, and the analysis unit analyzes this data to evaluate the appropriateness of the learning environment. For example, the data collection unit collects the brightness of the lighting in the room where students are studying, and the analysis unit analyzes this data to evaluate whether the brightness is appropriate. The data collection unit can also collect the brightness of the screen of the device students use while studying, and the analysis unit can evaluate the impact of that brightness on learning. Furthermore, the data collection unit can collect changes in lighting that students perceive while studying, and the analysis unit can evaluate the impact of these changes on learning. In this way, by collecting and analyzing lighting data from the learning environment, the appropriateness of the learning environment can be evaluated, and an optimal learning environment can be provided.

[0114] The analysis unit can estimate the student's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the student is relaxed, a detailed notification can be given. If the student is in a hurry, a concise notification can be given. Furthermore, if the student is excited, a visually stimulating notification can be given. By adjusting the notification method based on the student's emotions, more effective learning support becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input image data of the student taken by a camera into the generative AI and have the generative AI perform the estimation of the student's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection unit collects student data. The data collection unit can collect, for example, students' learning history, grades, and behavioral data. The data collection unit can also use AI to collect students' learning styles and progress. For example, the data collection unit can collect data on students' use of online learning platforms to identify their learning styles. The data collection unit can also collect students' test results to evaluate their learning progress. Furthermore, the data collection unit can collect students' behavioral data and analyze their learning patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes students' learning styles and progress, for example, using statistical analysis and machine learning algorithms. The analysis unit uses AI to analyze students' learning styles and progress based on the collected data. For example, the analysis unit analyzes students' learning history to identify the optimal learning method. The analysis unit can also analyze students' performance data to evaluate the effectiveness of their learning. Furthermore, the analysis unit can analyze students' behavioral data to identify learning patterns. Step 3: The generation unit generates curricula and teaching materials based on the data analyzed by the analysis unit. For example, the generation unit generates the curriculum structure and the types of teaching materials. The generation unit uses AI to generate curricula and teaching materials based on the analysis results. For example, the generation unit generates a personalized curriculum based on the students' learning styles. The generation unit can also generate appropriate teaching materials based on the students' progress. Furthermore, the generation unit can adjust the curriculum based on the students' learning objectives. Step 4: The delivery unit provides the curriculum and materials generated by the generation unit. The delivery unit provides the curriculum and materials, for example, through an online platform or printed materials. The delivery unit can also use AI to provide the generated curriculum and materials to students. For example, the delivery unit provides the curriculum to students through an online platform. The delivery unit can also provide materials to students through printed materials. Furthermore, the delivery unit can adjust the curriculum and materials based on the students' learning progress.

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, tracking unit, and tutoring unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects student data using the camera 42 and microphone 38B of the smart device 14 and identifies the learning style and progress using the control unit 46A. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and identifies the optimal learning method. The generation unit generates curriculum and teaching materials using the identification processing unit 290 of the data processing unit 12, and the provision unit provides them to students through the control unit 46A of the smart device 14. The tracking unit tracks progress in real time using the control unit 46A of the smart device 14, and the tutoring unit performs AI tutoring using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 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.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, tracking unit, and tutoring unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects student data using the camera 42 and microphone 238 of the smart glasses 214 and identifies the learning style and progress using the control unit 46A. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and identifies the optimal learning method. The generation unit generates curriculum and teaching materials using the identification processing unit 290 of the data processing unit 12, and the provision unit provides them to students through the control unit 46A of the smart glasses 214. The tracking unit tracks progress in real time using the control unit 46A of the smart glasses 214, and the tutoring unit performs AI tutoring using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, tracking unit, and tutoring unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects student data using the camera 42 and microphone 238 of the headset terminal 314 and identifies the learning style and progress using the control unit 46A. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and identifies the optimal learning method. The generation unit generates curriculum and teaching materials using the identification processing unit 290 of the data processing unit 12, and the provision unit provides them to students through the control unit 46A of the headset terminal 314. The tracking unit tracks progress in real time using the control unit 46A of the headset terminal 314, and the tutoring unit performs AI tutoring using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, tracking unit, and tutoring unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects student data using the camera 42 and microphone 238 of the robot 414 and identifies the learning style and progress using the control unit 46A. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and identifies the optimal learning method. The generation unit generates curriculum and teaching materials using the identification processing unit 290 of the data processing unit 12, and the provision unit provides them to students through the control unit 46A of the robot 414. The tracking unit tracks progress in real time using the control unit 46A of the robot 414, and the tutoring unit performs AI tutoring using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) The data collection department collects student data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a curriculum and teaching materials based on the data analyzed by the analysis unit, The system comprises a providing unit that provides the curriculum and teaching materials generated by the generation unit. A system characterized by the following features. (Note 2) It also includes a tracking unit to track progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) We will further enhance our tutoring department by providing AI tutoring. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate students' emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, filtering is performed based on students' current learning environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, analyze students' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate the students' emotions and adjust the analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates students' emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate students' emotions and adjust the content of the curriculum and teaching materials generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the difficulty level of the curriculum and materials is adjusted based on the students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different generation algorithms are applied depending on the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates students' emotions and determines the priority of the curriculum and teaching materials generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the curriculum and the order of learning materials are adjusted based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the curriculum and teaching materials are customized based on students' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We estimate students' emotions and adjust the curriculum and teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the content, we adjust it based on the students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, different delivery methods will be applied depending on the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate students' emotions and determine the priority of the curriculum and materials to be provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing content, the order of the content provided will be adjusted based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we customize the content based on the students' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tracking unit is We estimate students' emotions and adjust progress tracking methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned tracking unit is During tracking, the system selects the optimal tracking method by referring to the student's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned tracking unit is Estimate students' emotions and prioritize progress based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned tracking unit is During tracking, the optimal tracking method is selected considering the student's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned tutoring unit is The system estimates the students' emotions and adjusts the tutoring method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned tutoring unit is During tutoring sessions, the optimal tutoring method is selected by referring to the student's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned tutoring unit is The system estimates students' emotions and determines tutoring priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned tutoring unit is During tutoring sessions, the most suitable tutoring method is selected considering the student's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects student data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a curriculum and teaching materials based on the data analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the curriculum and teaching materials generated by the generation unit. A system characterized by the following features.

2. It also includes a tracking unit to track progress in real time. The system according to feature 1.

3. We will further enhance our tutoring department by providing AI tutoring. The system according to feature 1.

4. The aforementioned collection unit is We estimate students' emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, filtering is performed based on students' current learning environment and areas of interest. The system according to feature 1.

7. The aforementioned collection unit is We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account students' geographical location information. The system according to feature 1.

9. The aforementioned collection unit is During data collection, analyze students' social media activity and collect relevant data. The system according to feature 1.

10. The aforementioned analysis unit, We estimate the students' emotions and adjust the analysis method based on the estimated emotions. The system according to feature 1.

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