system

Abstract

The system according to this embodiment aims to efficiently collect video and audio recordings and textbook data from classes held in schools nationwide, and to provide only the necessary parts. [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 video, audio, and textbook data of lessons conducted at schools nationwide. The analysis unit analyzes the data collected by the collection unit. The generation unit generates video, audio, or materials for the desired portion of the lesson based on the data analyzed by the analysis unit. The provision unit provides the video, audio, or 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that it is difficult to efficiently collect video and audio of classes conducted in schools across the country and textbook data, and provide only the necessary parts.

[0005] The system according to the embodiment aims to efficiently collect video and audio of classes conducted in schools across the country and textbook data, and provide only the necessary parts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video, audio, and textbook data of lessons conducted at schools nationwide. The analysis unit analyzes the data collected by the collection unit. The generation unit generates video, audio, or materials for the desired portion of the lesson based on the data analyzed by the analysis unit. The provision unit provides the video, audio, or materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect video and audio recordings of lessons conducted in schools nationwide, as well as textbook data, and provide only the necessary portions. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) The lesson generation system according to an embodiment of the present invention is a system that takes in video and audio recordings and textbook data from actual school lessons and generates video and audio recordings or materials for the portion of the lesson that the user wants to take. The lesson generation system takes in video and audio recordings and textbook data from actual school lessons and generates video and audio recordings or materials for the portion of the lesson that the user wants to take. For example, the lesson generation system collects video and audio recordings and textbook data from lessons conducted in schools nationwide. Next, the lesson generation system trains an AI on the collected data and analyzes the content of each lesson and the information in the textbook. Next, the lesson generation system allows the user to specify the portion of the lesson they want to take, and the AI ​​generates video and audio recordings or materials for the specified portion of the lesson. The generated video and audio recordings or materials are provided to the user and can be used for learning at home. This reduces the workload of teachers and is also useful for supporting the learning of truant children. As a result, the lesson generation system allows users to efficiently learn the portion of the lesson they want to take.

[0029] The lesson generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video, audio, and textbook data of lessons conducted at schools nationwide. The collection unit can collect data from, for example, public schools, private schools, and schools in specific regions nationwide. The collection unit can collect video and audio data, such as video format, audio format, and recording quality. The collection unit can also collect textbook data, such as PDF format, text format, and image format. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data, for example, using data classification methods and analysis algorithms. The analysis unit can extract information on the content of each lesson and textbook information, for example, using AI. The generation unit generates video, audio, or materials for the desired lesson portion based on the data analyzed by the analysis unit. The generation unit can generate the desired lesson portion, for example, using video and audio editing methods or material creation methods. The generation unit can generate video, audio, or materials for the desired lesson portion, for example, using AI. The providing unit provides video, audio, or materials generated by the generating unit. The providing unit can provide the generated video, audio, or materials to the user, for example, through online distribution or download. The providing unit can provide the generated video, audio, or materials using AI, for example. As a result, the lesson generation system according to this embodiment allows the user to efficiently learn the part of the lesson they want to take.

[0030] The data collection unit collects video, audio, and textbook data from classes held at schools nationwide. Specifically, it can collect data from public schools, private schools, and schools in specific regions across the country. The unit collects video and audio data in various formats, including video format, audio format, and recording quality. For example, class videos are filmed with high-resolution cameras, and audio is recorded with clear microphones. This ensures that the content of the classes is clearly recorded. The unit also collects textbook data in formats such as PDF, text, and image. Textbook data is digitized using scanners and OCR technology and stored as text data. This makes the content of the textbooks easily searchable. Furthermore, the unit centrally manages the collected data and stores it in a database. The database is tagged with metadata to enable efficient searching and access. For example, information such as the class title, instructor's name, class date and time, and textbook chapter / section is assigned as metadata. This allows the unit to efficiently collect and manage diverse data.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes the collected data using data classification methods and analysis algorithms. The analysis unit uses AI to extract information about the content of each lesson and textbooks. For example, it uses natural language processing technology to extract important keywords and phrases from the audio data of lessons and saves them as text data. It also uses image recognition technology to extract diagrams and illustrations from image data of textbooks and classify them into appropriate categories. Furthermore, the analysis unit can automatically summarize the content of lessons based on the collected data. For example, the AI ​​analyzes the audio data of lessons, extracts important points and topics, and generates a summary. This allows users to grasp the overall picture of the lesson in a short amount of time. The analysis unit can also use past data and statistical information to evaluate the effectiveness of lessons and the progress of learning. For example, based on past lesson data, it can evaluate students' understanding and interest in specific topics and use this to improve future lesson content. In this way, the analysis unit can analyze the collected data from multiple angles and provide valuable information to improve the quality of lessons.

[0032] The generation unit generates video, audio, or materials for the desired portion of a lesson based on data analyzed by the analysis unit. Specifically, it generates the desired portion of the lesson using video and audio editing methods and material creation methods. The generation unit uses AI to generate video, audio, and materials for the desired portion of a lesson. For example, the AI ​​can analyze video data of a lesson and automatically extract and edit important parts. It can also analyze audio data and adjust the volume to emphasize important points. Furthermore, the generation unit can automatically create lesson materials based on textbook data. For example, the AI ​​can analyze text data from a textbook, extract important points and diagrams, and create presentation materials and study guides. This allows users to efficiently learn the desired portion of the lesson. The generation unit can also provide customized lesson content according to the user's learning style and needs. For example, if a user is interested in a particular topic, it will prioritize generating lesson portions related to that topic. It can also suggest optimal lesson content based on the user's learning history and feedback. In this way, the generation unit can provide users with a personalized learning experience and maximize learning effectiveness.

[0033] The service provider provides video, audio, or materials generated by the generation unit. Specifically, it provides the generated video, audio, or materials to users through methods such as online distribution or download. The service provider uses AI to provide the generated video, audio, or materials. For example, it provides lesson content in a user-friendly format through an online platform. Users can view the generated lesson content or download materials using devices such as PCs, smartphones, and tablets. Furthermore, the service provider can track users' learning history and progress and provide additional learning resources at the appropriate time. For example, if a user has an insufficient understanding of a particular topic, it can suggest relevant supplementary materials or additional lessons. The service provider can also collect user feedback and continuously improve the quality of the lesson content it provides. For example, by having users leave ratings and comments on the lesson content, the service provider can review the lesson content based on that feedback and reflect areas for improvement. This allows the service provider to provide users with high-quality lesson content and improve the learning experience. Furthermore, the service provider can also provide lesson content that supports multiple languages ​​and cultures. For example, by adding subtitles to the video and audio of lessons or translating textbook data into multiple languages, it can accommodate users with different languages ​​and cultures. This allows the service provider to offer high-quality learning resources to a global user base and expand educational opportunities.

[0034] The reception desk can receive user requests. For example, the reception desk can receive requests specifying which part of a class a user wants to take. The reception desk can receive user requests using, for example, online forms or chatbots. The reception desk can also receive user requests using, for example, AI. This allows the reception desk to receive user requests and specify which part of a class a user wants to take.

[0035] The data collection unit can reflect the progress of lessons and textbook updates in real time during data collection. For example, if a lesson is in progress, the data collection unit can collect video and audio of that lesson in real time. For example, if a textbook is updated, the data collection unit can collect the latest textbook data. For example, the data collection unit can prioritize the collection of necessary data according to the progress of the lesson. This allows for the collection of the latest information by reflecting the progress of lessons and textbook updates in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input lesson progress data into a generating AI and have the generating AI perform real-time data collection.

[0036] The data collection unit can apply different collection algorithms depending on the content of the lesson. For example, in a science lesson, the collection unit can apply an algorithm that prioritizes the collection of experimental videos. For example, in a mathematics lesson, the collection unit can apply an algorithm that prioritizes the collection of videos of blackboard writing. For example, in an English lesson, the collection unit can apply an algorithm that prioritizes the collection of pronunciation practice audio. By applying different collection algorithms depending on the content of the lesson, the optimal data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input lesson content data into a generating AI and have the generating AI execute the application of the collection algorithm.

[0037] The data collection unit can prioritize data collection by considering the geographical distribution of classes. The data collection unit can, for example, collect class data based on regional educational curricula. The data collection unit can, for example, prioritize data collection from schools that are geographically close. The data collection unit can, for example, prioritize data collection that includes region-specific class content. By prioritizing data collection while considering the geographical distribution of classes, it is possible to collect data that includes region-specific class content. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical distribution data into a generating AI and have the generating AI perform priority data collection.

[0038] The collection unit can simultaneously collect relevant literature and supplementary materials for the course. For example, the collection unit can collect relevant literature for textbooks used in the course. For example, the collection unit can collect supplementary materials used in the course. For example, the collection unit can collect academic papers related to the course content. By simultaneously collecting relevant literature and supplementary materials for the course, all the information necessary for learning can be collected at once. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input relevant literature data into a generating AI and have the generating AI perform the collection of literature and materials.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the lesson content during the analysis. For example, the analysis unit can perform a detailed analysis of important lesson content. For example, the analysis unit can perform a simplified analysis of supplementary lesson content. For example, the analysis unit can perform a highly accurate analysis of content that is likely to appear on an exam. In this way, by adjusting the level of detail of the analysis based on the importance of the lesson content, important content can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson content importance data into a generating AI and have the generating AI perform the analysis with a specified level of detail.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the lesson during analysis. For example, in a science lesson, the analysis unit can apply an experimental data analysis algorithm. For example, in a mathematics lesson, the analysis unit can apply a mathematical formula analysis algorithm. For example, in an English lesson, the analysis unit can apply a speech analysis algorithm. By applying different analysis algorithms depending on the category of the lesson, the analysis unit can provide optimal analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the progress of the lesson during the analysis. For example, the analysis unit can perform analysis in real time if the lesson is in progress. For example, the analysis unit can perform analysis after the lesson has finished. For example, the analysis unit can prioritize the analysis of necessary data according to the progress of the lesson. This allows for real-time analysis of necessary data by determining the priority of analysis based on the progress of the lesson. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson progress data into a generating AI and have the generating AI execute the analysis prioritization.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the lessons during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant lesson content. For example, the analysis unit can postpone the analysis of less relevant lesson content. The analysis unit can adjust the order of analysis based on the relevance of the lessons. By adjusting the order of analysis based on the relevance of the lessons, highly relevant content can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson relevance data into a generating AI and have the generating AI execute the analysis order.

[0043] The generation unit can adjust the level of detail of the generated content based on the importance of the lesson content during generation. For example, the generation unit can generate detailed video and audio for important lesson content. For example, the generation unit can generate simplified video and audio for supplementary lesson content. For example, the generation unit can generate highly accurate video and audio for content that is likely to appear on an exam. In this way, by adjusting the level of detail of the generated content based on the importance of the lesson content, important content can be generated in detail. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson content importance data into a generation AI and have the generation AI perform the level of detail of the generated content.

[0044] The generation unit can apply different generation algorithms depending on the category of the lesson during generation. For example, in a science lesson, the generation unit can apply an algorithm that generates experimental videos. For example, in a mathematics lesson, the generation unit can apply an algorithm that generates videos explaining mathematical formulas. For example, in an English lesson, the generation unit can apply an algorithm that generates audio for pronunciation practice. By applying different generation algorithms depending on the category of the lesson, the optimal learning content can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0045] The generation unit can determine the generation priority based on the progress of the lesson during generation. For example, the generation unit can generate video and audio in real time if the lesson is in progress. For example, the generation unit can generate video and audio after the lesson has finished. For example, the generation unit can prioritize the generation of necessary data according to the progress of the lesson. This allows for the generation of necessary data in real time by determining the generation priority based on the progress of the lesson. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson progress data into a generation AI and have the generation AI execute the generation priority.

[0046] The generation unit can adjust the generation order based on the relevance of the lessons during generation. For example, the generation unit can prioritize the generation of highly relevant lesson content. For example, the generation unit can postpone the generation of less relevant lesson content. The generation unit can adjust the generation order based on the relevance of the lessons. By adjusting the generation order based on the relevance of the lessons, it is possible to prioritize the generation of highly relevant content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson relevance data into a generation AI and have the generation AI execute the generation order.

[0047] The service provider can select the optimal service delivery method by referring to the user's past learning history at the time of delivery. For example, the service provider can provide relevant video, audio, and materials based on what the user has previously learned. For example, the service provider can select the optimal display method from the user's learning history. For example, the service provider can analyze the user's learning history and select the most efficient service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning history data into a generating AI and have the generating AI select the optimal service delivery method.

[0048] The delivery unit can customize the means of delivery based on the user's current learning status at the time of delivery. For example, the delivery unit can provide relevant video, audio, and materials based on the content the user is currently learning. For example, the delivery unit can select the optimal delivery method according to the user's learning progress. For example, the delivery unit can analyze the user's learning status and select the most efficient delivery method. This allows the delivery unit to provide optimal learning content by customizing the means of delivery based on the user's current learning status. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's learning status data into a generating AI and have the generating AI execute the means of delivery.

[0049] The delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the delivery unit can provide a method that is easy to learn at home. For example, if the user is at school, the delivery unit can provide a method that is easy to learn at school. For example, if the user is on the move, the delivery unit can provide a method that is easy to learn on a mobile device. In this way, the optimal delivery method can be selected by considering the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0050] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide relevant videos, audio, and materials based on learning content shared by the user on social media. For example, the service provider can analyze the user's interests from their social media activity and propose the optimal delivery method. For example, the service provider can provide relevant videos, audio, and materials based on information from educational accounts that the user follows on social media. In this way, the service provider can propose the optimal means of delivery by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute a proposal for a means of delivery.

[0051] The reception unit can select the optimal reception method by referring to the user's past request history when a request is received. For example, the reception unit can automatically display as suggestions the content of requests the user has frequently made in the past. For example, the reception unit can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest the content of requests to be used during a specific time period based on the user's past request history. This allows the reception unit to select the optimal reception method by referring to the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's request history data into a generating AI and have the generating AI select the optimal reception method.

[0052] The reception unit can automatically acquire the user's current location information and simplify the request upon receiving it. For example, when a user opens the app, the reception unit can automatically acquire the current location and reflect it in the request. For example, when a user enters a request, the reception unit can suggest the best option considering the distance from the current location. For example, if a user uses the app while on the move, the reception unit can update the current location in real time and reflect it in the request. This simplifies the request by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI perform the request simplification.

[0053] The reception unit can suggest the most suitable reception method by referring to the user's past request history when a request is received. For example, the reception unit can automatically display as suggestions the content of requests the user has frequently made in the past. For example, the reception unit can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest the content of requests to be used during a specific time period based on the user's past request history. In this way, the reception unit can suggest the most suitable reception method by referring to the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's request history data into a generating AI and have the generating AI suggest the most suitable reception method.

[0054] The reception unit can select the optimal reception method at the time of reception, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that is adapted to the screen size. For example, if the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. For example, if the user is using a smartwatch, the reception unit can provide a simple and highly visible reception method. This allows the reception unit to select the optimal reception method by taking into account the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user device information data into a generating AI and have the generating AI select the optimal reception method.

[0055] The reception desk can make suggestions based on the user's schedule by referring to the user's calendar information at the time of reception. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the request content. For example, the reception desk can suggest a request content related to a specific event from the user's calendar information. For example, the reception desk can suggest the most suitable request content based on the user's schedule based on the user's calendar information. In this way, by referring to the user's calendar information, the reception desk can make the best suggestions based on the schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information data into a generating AI and have the generating AI execute suggestions based on the schedule.

[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 lesson generation system can also include a learning style analysis unit that analyzes the user's learning style. This unit analyzes the user's past learning data and learning history to identify their preferred learning style. For example, for users who prefer visual information, the system can generate materials that heavily utilize videos and diagrams. For users who prefer auditory information, it can generate video and audio content that emphasizes audio explanations. Furthermore, for users who prefer practical learning, it can provide interactive learning materials and experimental videos. This allows the system to provide optimal learning content tailored to the user's learning style.

[0058] The lesson generation system can also include a progress monitoring unit that monitors the user's learning progress. The progress monitoring unit can grasp the user's learning progress in real time and adjust the learning plan as needed. For example, if a user is behind schedule, the progress monitoring unit can simplify the learning content or provide advice to increase the learning pace. Conversely, if a user is ahead of schedule, the progress monitoring unit can provide additional learning materials or challenge problems. This allows for flexible support tailored to the user's learning progress.

[0059] The lesson generation system may also include an environment optimization unit to further optimize the user's learning environment. This unit collects data about the user's learning environment and can provide advice to ensure an optimal learning environment. For example, if a user is studying in a noisy environment, the environment optimization unit can recommend the use of noise-canceling headphones. It can also suggest appropriate break times if the user is studying for extended periods. Furthermore, if the user's learning environment is dark, it can advise the use of appropriate lighting. This allows the user to study in an optimal environment.

[0060] The lesson generation system can also include an evaluation unit to assess the user's learning outcomes. This evaluation unit can evaluate the user's learning content through tests and quizzes and provide feedback on the results. For example, it can periodically issue quizzes based on the user's learning content and evaluate their accuracy. It can also identify areas where the user struggles and provide additional learning materials for those areas. Furthermore, it can visually display the user's learning outcomes using graphs and charts, allowing users to grasp their progress at a glance. This enables users to objectively evaluate their learning outcomes and create effective learning plans.

[0061] The lesson generation system can also include a planning suggestion unit that proposes future learning plans based on the user's learning history. This unit analyzes the user's past learning content and progress to propose an optimal learning plan. For example, if a user is falling behind in a particular subject, it can propose a learning plan that focuses on that subject. Furthermore, if a user has set a specific goal, it can provide a step-by-step plan to achieve that goal. It can also propose flexible plans tailored to the user's learning pace and style. This allows users to create effective learning plans.

[0062] The lesson generation system can also include a community building section that forms user learning communities. This section connects users with similar subjects or interests, facilitating the exchange of learning-related information and discussions. For example, if a user has a question about a particular subject, they can ask other users within the community. Users can also share their learning progress and encourage each other. Furthermore, learning events and challenges can be held within the community to enhance user motivation. This allows users to progress in their learning without feeling isolated.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects video, audio, and textbook data from lessons conducted in schools nationwide. The collection unit can collect data from public schools, private schools, and schools in specific regions across the country, for example. The collection unit can collect video and audio data in various formats, including video format, audio format, and recording quality, as well as textbook data in formats such as PDF, text, and image. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using data classification methods and analysis algorithms. For example, it can use AI to extract information about the content of each lesson and textbooks. Step 3: The generation unit generates video, audio, or materials for the desired portion of the lesson based on the data analyzed by the analysis unit. The generation unit can generate the desired portion of the lesson using video and audio editing methods or material creation methods. For example, AI can be used to generate video, audio, or materials for the desired portion of the lesson. Step 4: The provider unit provides the video, audio, or materials generated by the generator unit. The provider unit can provide the generated video, audio, or materials to the user through methods such as online distribution or download. For example, the provider unit can provide the generated video, audio, or materials using AI.

[0065] (Example of form 2) The lesson generation system according to an embodiment of the present invention is a system that takes in video and audio recordings and textbook data from actual school lessons and generates video and audio recordings or materials for the portion of the lesson that the user wants to take. The lesson generation system takes in video and audio recordings and textbook data from actual school lessons and generates video and audio recordings or materials for the portion of the lesson that the user wants to take. For example, the lesson generation system collects video and audio recordings and textbook data from lessons conducted in schools nationwide. Next, the lesson generation system trains an AI on the collected data and analyzes the content of each lesson and the information in the textbook. Next, the lesson generation system allows the user to specify the portion of the lesson they want to take, and the AI ​​generates video and audio recordings or materials for the specified portion of the lesson. The generated video and audio recordings or materials are provided to the user and can be used for learning at home. This reduces the workload of teachers and is also useful for supporting the learning of truant children. As a result, the lesson generation system allows users to efficiently learn the portion of the lesson they want to take.

[0066] The lesson generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video, audio, and textbook data of lessons conducted at schools nationwide. The collection unit can collect data from, for example, public schools, private schools, and schools in specific regions nationwide. The collection unit can collect video and audio data, such as video format, audio format, and recording quality. The collection unit can also collect textbook data, such as PDF format, text format, and image format. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data, for example, using data classification methods and analysis algorithms. The analysis unit can extract information on the content of each lesson and textbook information, for example, using AI. The generation unit generates video, audio, or materials for the desired lesson portion based on the data analyzed by the analysis unit. The generation unit can generate the desired lesson portion, for example, using video and audio editing methods or material creation methods. The generation unit can generate video, audio, or materials for the desired lesson portion, for example, using AI. The providing unit provides video, audio, or materials generated by the generating unit. The providing unit can provide the generated video, audio, or materials to the user, for example, through online distribution or download. The providing unit can provide the generated video, audio, or materials using AI, for example. As a result, the lesson generation system according to this embodiment allows the user to efficiently learn the part of the lesson they want to take.

[0067] The data collection unit collects video, audio, and textbook data from classes held at schools nationwide. Specifically, it can collect data from public schools, private schools, and schools in specific regions across the country. The unit collects video and audio data in various formats, including video format, audio format, and recording quality. For example, class videos are filmed with high-resolution cameras, and audio is recorded with clear microphones. This ensures that the content of the classes is clearly recorded. The unit also collects textbook data in formats such as PDF, text, and image. Textbook data is digitized using scanners and OCR technology and stored as text data. This makes the content of the textbooks easily searchable. Furthermore, the unit centrally manages the collected data and stores it in a database. The database is tagged with metadata to enable efficient searching and access. For example, information such as the class title, instructor's name, class date and time, and textbook chapter / section is assigned as metadata. This allows the unit to efficiently collect and manage diverse data.

[0068] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes the collected data using data classification methods and analysis algorithms. The analysis unit uses AI to extract information about the content of each lesson and textbooks. For example, it uses natural language processing technology to extract important keywords and phrases from the audio data of lessons and saves them as text data. It also uses image recognition technology to extract diagrams and illustrations from image data of textbooks and classify them into appropriate categories. Furthermore, the analysis unit can automatically summarize the content of lessons based on the collected data. For example, the AI ​​analyzes the audio data of lessons, extracts important points and topics, and generates a summary. This allows users to grasp the overall picture of the lesson in a short amount of time. The analysis unit can also use past data and statistical information to evaluate the effectiveness of lessons and the progress of learning. For example, based on past lesson data, it can evaluate students' understanding and interest in specific topics and use this to improve future lesson content. In this way, the analysis unit can analyze the collected data from multiple angles and provide valuable information to improve the quality of lessons.

[0069] The generation unit generates video, audio, or materials for the desired portion of a lesson based on data analyzed by the analysis unit. Specifically, it generates the desired portion of the lesson using video and audio editing methods and material creation methods. The generation unit uses AI to generate video, audio, and materials for the desired portion of a lesson. For example, the AI ​​can analyze video data of a lesson and automatically extract and edit important parts. It can also analyze audio data and adjust the volume to emphasize important points. Furthermore, the generation unit can automatically create lesson materials based on textbook data. For example, the AI ​​can analyze text data from a textbook, extract important points and diagrams, and create presentation materials and study guides. This allows users to efficiently learn the desired portion of the lesson. The generation unit can also provide customized lesson content according to the user's learning style and needs. For example, if a user is interested in a particular topic, it will prioritize generating lesson portions related to that topic. It can also suggest optimal lesson content based on the user's learning history and feedback. In this way, the generation unit can provide users with a personalized learning experience and maximize learning effectiveness.

[0070] The service provider provides video, audio, or materials generated by the generation unit. Specifically, it provides the generated video, audio, or materials to users through methods such as online distribution or download. The service provider uses AI to provide the generated video, audio, or materials. For example, it provides lesson content in a user-friendly format through an online platform. Users can view the generated lesson content or download materials using devices such as PCs, smartphones, and tablets. Furthermore, the service provider can track users' learning history and progress and provide additional learning resources at the appropriate time. For example, if a user has an insufficient understanding of a particular topic, it can suggest relevant supplementary materials or additional lessons. The service provider can also collect user feedback and continuously improve the quality of the lesson content it provides. For example, by having users leave ratings and comments on the lesson content, the service provider can review the lesson content based on that feedback and reflect areas for improvement. This allows the service provider to provide users with high-quality lesson content and improve the learning experience. Furthermore, the service provider can also provide lesson content that supports multiple languages ​​and cultures. For example, by adding subtitles to the video and audio of lessons or translating textbook data into multiple languages, it can accommodate users with different languages ​​and cultures. This allows the service provider to offer high-quality learning resources to a global user base and expand educational opportunities.

[0071] The reception desk can receive user requests. For example, the reception desk can receive requests specifying which part of a class a user wants to take. The reception desk can receive user requests using, for example, online forms or chatbots. The reception desk can also receive user requests using, for example, AI. This allows the reception desk to receive user requests and specify which part of a class a user wants to take.

[0072] The data collection unit can estimate the user's emotions and determine the priority of the lesson data to collect based on the estimated user emotions. For example, the data collection unit can prioritize collecting lesson data for subjects the user is interested in. For example, the data collection unit can prioritize collecting lesson data for subjects the user finds difficult. For example, if the user is relaxed, the data collection unit can prioritize collecting lesson data that allows for learning in a relaxed state. In this way, by prioritizing lesson data based on the user's emotions, the optimal lesson data for the user can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can reflect the progress of lessons and textbook updates in real time during data collection. For example, if a lesson is in progress, the data collection unit can collect video and audio of that lesson in real time. For example, if a textbook is updated, the data collection unit can collect the latest textbook data. For example, the data collection unit can prioritize the collection of necessary data according to the progress of the lesson. This allows for the collection of the latest information by reflecting the progress of lessons and textbook updates in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input lesson progress data into a generating AI and have the generating AI perform real-time data collection.

[0074] The data collection unit can apply different collection algorithms depending on the content of the lesson. For example, in a science lesson, the collection unit can apply an algorithm that prioritizes the collection of experimental videos. For example, in a mathematics lesson, the collection unit can apply an algorithm that prioritizes the collection of videos of blackboard writing. For example, in an English lesson, the collection unit can apply an algorithm that prioritizes the collection of pronunciation practice audio. By applying different collection algorithms depending on the content of the lesson, the optimal data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input lesson content data into a generating AI and have the generating AI execute the application of the collection algorithm.

[0075] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is interested, the data collection unit can prioritize collecting relevant video and audio data. For example, if the user is tired, the data collection unit can prioritize collecting materials that can be learned in a short amount of time. For example, if the user is focused, the data collection unit can prioritize collecting detailed materials. In this way, by adjusting the type of data collected based on the user's emotions, the data best suited to the user can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can prioritize data collection by considering the geographical distribution of classes. The data collection unit can, for example, collect class data based on regional educational curricula. The data collection unit can, for example, prioritize data collection from schools that are geographically close. The data collection unit can, for example, prioritize data collection that includes region-specific class content. By prioritizing data collection while considering the geographical distribution of classes, it is possible to collect data that includes region-specific class content. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical distribution data into a generating AI and have the generating AI perform priority data collection.

[0077] The collection unit can simultaneously collect relevant literature and supplementary materials for the course. For example, the collection unit can collect relevant literature for textbooks used in the course. For example, the collection unit can collect supplementary materials used in the course. For example, the collection unit can collect academic papers related to the course content. By simultaneously collecting relevant literature and supplementary materials for the course, all the information necessary for learning can be collected at once. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input relevant literature data into a generating AI and have the generating AI perform the collection of literature and materials.

[0078] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is showing interest, the analysis unit can perform a detailed analysis. For example, if the user is tired, the analysis unit can perform a simplified analysis. For example, if the user is focused, the analysis unit can perform a highly accurate analysis. By adjusting the accuracy of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using 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, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the lesson content during the analysis. For example, the analysis unit can perform a detailed analysis of important lesson content. For example, the analysis unit can perform a simplified analysis of supplementary lesson content. For example, the analysis unit can perform a highly accurate analysis of content that is likely to appear on an exam. In this way, by adjusting the level of detail of the analysis based on the importance of the lesson content, important content can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson content importance data into a generating AI and have the generating AI perform the analysis with a specified level of detail.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the lesson during analysis. For example, in a science lesson, the analysis unit can apply an experimental data analysis algorithm. For example, in a mathematics lesson, the analysis unit can apply a mathematical formula analysis algorithm. For example, in an English lesson, the analysis unit can apply a speech analysis algorithm. By applying different analysis algorithms depending on the category of the lesson, the analysis unit can provide optimal analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can determine the priority of analysis based on the progress of the lesson during the analysis. For example, the analysis unit can perform analysis in real time if the lesson is in progress. For example, the analysis unit can perform analysis after the lesson has finished. For example, the analysis unit can prioritize the analysis of necessary data according to the progress of the lesson. This allows for real-time analysis of necessary data by determining the priority of analysis based on the progress of the lesson. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson progress data into a generating AI and have the generating AI execute the analysis prioritization.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the lessons during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant lesson content. For example, the analysis unit can postpone the analysis of less relevant lesson content. The analysis unit can adjust the order of analysis based on the relevance of the lessons. By adjusting the order of analysis based on the relevance of the lessons, highly relevant content can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lesson relevance data into a generating AI and have the generating AI execute the analysis order.

[0084] The generation unit can estimate the user's emotions and adjust the presentation of the generated video, audio, and materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate video and audio that proceeds at a relaxed pace. For example, if the user is in a hurry, the generation unit can generate video and audio that gets straight to the point. For example, if the user is excited, the generation unit can generate video and audio with visually stimulating effects. In this way, by adjusting the presentation of video, audio, and materials based on the user's emotions, the optimal learning content can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0085] The generation unit can adjust the level of detail of the generated content based on the importance of the lesson content during generation. For example, the generation unit can generate detailed video and audio for important lesson content. For example, the generation unit can generate simplified video and audio for supplementary lesson content. For example, the generation unit can generate highly accurate video and audio for content that is likely to appear on an exam. In this way, by adjusting the level of detail of the generated content based on the importance of the lesson content, important content can be generated in detail. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson content importance data into a generation AI and have the generation AI perform the level of detail of the generated content.

[0086] The generation unit can apply different generation algorithms depending on the category of the lesson during generation. For example, in a science lesson, the generation unit can apply an algorithm that generates experimental videos. For example, in a mathematics lesson, the generation unit can apply an algorithm that generates videos explaining mathematical formulas. For example, in an English lesson, the generation unit can apply an algorithm that generates audio for pronunciation practice. By applying different generation algorithms depending on the category of the lesson, the optimal learning content can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0087] The generation unit can estimate the user's emotions and adjust the length of the generated video, audio, and materials based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise video and audio. For example, if the user is relaxed, the generation unit can generate longer video and audio with detailed explanations. For example, if the user is excited, the generation unit can generate video and audio with visually stimulating effects. By adjusting the length of the video, audio, and materials based on the user's emotions, the system can provide the user with optimal learning content. 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, 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 user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit can determine the generation priority based on the progress of the lesson during generation. For example, the generation unit can generate video and audio in real time if the lesson is in progress. For example, the generation unit can generate video and audio after the lesson has finished. For example, the generation unit can prioritize the generation of necessary data according to the progress of the lesson. This allows for the generation of necessary data in real time by determining the generation priority based on the progress of the lesson. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson progress data into a generation AI and have the generation AI execute the generation priority.

[0089] The generation unit can adjust the generation order based on the relevance of the lessons during generation. For example, the generation unit can prioritize the generation of highly relevant lesson content. For example, the generation unit can postpone the generation of less relevant lesson content. The generation unit can adjust the generation order based on the relevance of the lessons. By adjusting the generation order based on the relevance of the lessons, it is possible to prioritize the generation of highly relevant content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input lesson relevance data into a generation AI and have the generation AI execute the generation order.

[0090] The service provider can estimate the user's emotions and adjust the display method of the video, audio, and materials based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the display method of the video, audio, and materials based on the user's emotions, the service provider can provide the optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0091] The service provider can select the optimal service delivery method by referring to the user's past learning history at the time of delivery. For example, the service provider can provide relevant video, audio, and materials based on what the user has previously learned. For example, the service provider can select the optimal display method from the user's learning history. For example, the service provider can analyze the user's learning history and select the most efficient service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning history data into a generating AI and have the generating AI select the optimal service delivery method.

[0092] The delivery unit can customize the means of delivery based on the user's current learning status at the time of delivery. For example, the delivery unit can provide relevant video, audio, and materials based on the content the user is currently learning. For example, the delivery unit can select the optimal delivery method according to the user's learning progress. For example, the delivery unit can analyze the user's learning status and select the most efficient delivery method. This allows the delivery unit to provide optimal learning content by customizing the means of delivery based on the user's current learning status. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's learning status data into a generating AI and have the generating AI execute the means of delivery.

[0093] The service provider can estimate the user's emotions and determine the priority of the videos, audio, and materials to be provided based on the estimated emotions. For example, the service provider can prioritize providing content that the user is interested in. For example, the service provider can prioritize providing content that the user finds difficult. For example, if the user is relaxed, the service provider can prioritize providing content that allows for learning in a relaxed state. In this way, by determining the priority of videos, audio, and materials based on the user's emotions, the service provider can provide the user with the most suitable learning content. Emotion estimation is achieved using an emotion estimation function, for example, using 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the delivery unit can provide a method that is easy to learn at home. For example, if the user is at school, the delivery unit can provide a method that is easy to learn at school. For example, if the user is on the move, the delivery unit can provide a method that is easy to learn on a mobile device. In this way, the optimal delivery method can be selected by considering the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0095] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide relevant videos, audio, and materials based on learning content shared by the user on social media. For example, the service provider can analyze the user's interests from their social media activity and propose the optimal delivery method. For example, the service provider can provide relevant videos, audio, and materials based on information from educational accounts that the user follows on social media. In this way, the service provider can propose the optimal means of delivery by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute a proposal for a means of delivery.

[0096] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to process the request quickly. This allows the reception desk to provide the optimal processing method for the user by adjusting the request processing method based on the user's emotions. 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 processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The reception unit can select the optimal reception method by referring to the user's past request history when a request is received. For example, the reception unit can automatically display as suggestions the content of requests the user has frequently made in the past. For example, the reception unit can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest the content of requests to be used during a specific time period based on the user's past request history. This allows the reception unit to select the optimal reception method by referring to the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's request history data into a generating AI and have the generating AI select the optimal reception method.

[0098] The reception unit can automatically acquire the user's current location information and simplify the request upon receiving it. For example, when a user opens the app, the reception unit can automatically acquire the current location and reflect it in the request. For example, when a user enters a request, the reception unit can suggest the best option considering the distance from the current location. For example, if a user uses the app while on the move, the reception unit can update the current location in real time and reflect it in the request. This simplifies the request by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI perform the request simplification.

[0099] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process more enjoyable. For example, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the design of the input interface based on the user's emotions, the optimal input environment can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using 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 reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The reception unit can suggest the most suitable reception method by referring to the user's past request history when a request is received. For example, the reception unit can automatically display as suggestions the content of requests the user has frequently made in the past. For example, the reception unit can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest the content of requests to be used during a specific time period based on the user's past request history. In this way, the reception unit can suggest the most suitable reception method by referring to the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's request history data into a generating AI and have the generating AI suggest the most suitable reception method.

[0101] The reception unit can select the optimal reception method at the time of reception, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that is adapted to the screen size. For example, if the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. For example, if the user is using a smartwatch, the reception unit can provide a simple and highly visible reception method. This allows the reception unit to select the optimal reception method by taking into account the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user device information data into a generating AI and have the generating AI select the optimal reception method.

[0102] The reception desk can make suggestions based on the user's schedule by referring to the user's calendar information at the time of reception. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the request content. For example, the reception desk can suggest a request content related to a specific event from the user's calendar information. For example, the reception desk can suggest the most suitable request content based on the user's schedule based on the user's calendar information. In this way, by referring to the user's calendar information, the reception desk can make the best suggestions based on the schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information data into a generating AI and have the generating AI execute suggestions based on the schedule.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The lesson generation system can also include a learning style analysis unit that analyzes the user's learning style. This unit analyzes the user's past learning data and learning history to identify their preferred learning style. For example, for users who prefer visual information, the system can generate materials that heavily utilize videos and diagrams. For users who prefer auditory information, it can generate video and audio content that emphasizes audio explanations. Furthermore, for users who prefer practical learning, it can provide interactive learning materials and experimental videos. This allows the system to provide optimal learning content tailored to the user's learning style.

[0105] The lesson generation system can also include a progress monitoring unit that monitors the user's learning progress. The progress monitoring unit can grasp the user's learning progress in real time and adjust the learning plan as needed. For example, if a user is behind schedule, the progress monitoring unit can simplify the learning content or provide advice to increase the learning pace. Conversely, if a user is ahead of schedule, the progress monitoring unit can provide additional learning materials or challenge problems. This allows for flexible support tailored to the user's learning progress.

[0106] The lesson generation system can also be equipped with a motivation enhancement unit to further improve user learning motivation. Based on the user's learning history and emotional data, the motivation enhancement unit can provide rewards and encouraging messages according to learning progress. For example, if a user achieves a goal, the motivation enhancement unit can award badges or points. Furthermore, if a user has negative feelings towards learning, the motivation enhancement unit can provide encouraging messages and relaxing content. This helps maintain user motivation and supports effective learning.

[0107] The lesson generation system may also include an environment optimization unit to further optimize the user's learning environment. This unit collects data about the user's learning environment and can provide advice to ensure an optimal learning environment. For example, if a user is studying in a noisy environment, the environment optimization unit can recommend the use of noise-canceling headphones. It can also suggest appropriate break times if the user is studying for extended periods. Furthermore, if the user's learning environment is dark, it can advise the use of appropriate lighting. This allows the user to study in an optimal environment.

[0108] The lesson generation system can also include an evaluation unit to assess the user's learning outcomes. This evaluation unit can evaluate the user's learning content through tests and quizzes and provide feedback on the results. For example, it can periodically issue quizzes based on the user's learning content and evaluate their accuracy. It can also identify areas where the user struggles and provide additional learning materials for those areas. Furthermore, it can visually display the user's learning outcomes using graphs and charts, allowing users to grasp their progress at a glance. This enables users to objectively evaluate their learning outcomes and create effective learning plans.

[0109] The lesson generation system can further estimate the user's emotions and adjust the difficulty level of the learning content based on those emotions. For example, if the user is stressed, the system can provide easy content, and if the user is relaxed, it can provide more difficult content. It can also provide challenging problems if the user is excited, and easy review problems if the user is tired. This allows the system to provide optimal learning content tailored to the user's emotional state.

[0110] The lesson generation system can also include a planning suggestion unit that proposes future learning plans based on the user's learning history. This unit analyzes the user's past learning content and progress to propose an optimal learning plan. For example, if a user is falling behind in a particular subject, it can propose a learning plan that focuses on that subject. Furthermore, if a user has set a specific goal, it can provide a step-by-step plan to achieve that goal. It can also propose flexible plans tailored to the user's learning pace and style. This allows users to create effective learning plans.

[0111] The lesson generation system can further estimate the user's emotions and adjust the learning timing based on those emotions. For example, if the user is focused, the system can encourage them to continue learning. Conversely, if the user is tired, the system can suggest taking a break. Also, if the user is relaxed, it can provide content that allows them to learn in a relaxed state. This allows the system to provide the optimal learning timing according to the user's emotional state.

[0112] The lesson generation system can also include a community building section that forms user learning communities. This section connects users with similar subjects or interests, facilitating the exchange of learning-related information and discussions. For example, if a user has a question about a particular subject, they can ask other users within the community. Users can also share their learning progress and encourage each other. Furthermore, learning events and challenges can be held within the community to enhance user motivation. This allows users to progress in their learning without feeling isolated.

[0113] The lesson generation system can further estimate the user's emotions and adjust learning feedback based on those emotions. For example, if the user is feeling positive, the system can provide detailed feedback; if the user is feeling negative, it can provide a concise and encouraging message. It can also provide challenging feedback if the user is excited, and relaxing feedback if the user is tired. This allows the system to provide optimal feedback tailored to the user's emotional state.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The collection unit collects video, audio, and textbook data from lessons conducted in schools nationwide. The collection unit can collect data from public schools, private schools, and schools in specific regions across the country, for example. The collection unit can collect video and audio data in various formats, including video format, audio format, and recording quality, as well as textbook data in formats such as PDF, text, and image. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using data classification methods and analysis algorithms. For example, it can use AI to extract information about the content of each lesson and textbooks. Step 3: The generation unit generates video, audio, or materials for the desired portion of the lesson based on the data analyzed by the analysis unit. The generation unit can generate the desired portion of the lesson using video and audio editing methods or material creation methods. For example, AI can be used to generate video, audio, or materials for the desired portion of the lesson. Step 4: The provider unit provides the video, audio, or materials generated by the generator unit. The provider unit can provide the generated video, audio, or materials to the user through methods such as online distribution or download. For example, the provider unit can provide the generated video, audio, or materials using AI.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and reception unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects video and audio of the lesson using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects textbook data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates video and audio of the lesson and materials based on the analyzed data. The provision unit is implemented, for example, in the control unit 46A of the smart device 14, and provides the generated video and audio and materials to the user. The reception unit is implemented, for example, in the control unit 46A of the smart device 14, and receives user requests. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and reception unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects video and audio of the lesson using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects textbook data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates video and audio of the lesson and materials based on the analyzed data. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides the generated video and audio and materials to the user. The reception unit is implemented, for example, in the control unit 46A of the smart glasses 214, and receives user requests. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and reception unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects video and audio of the lesson using the camera 42 and microphone 238 of the headset terminal 314 and collects textbook data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates video and audio of the lesson and materials based on the analyzed data. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the generated video and audio and materials to the user. The reception unit is implemented in the specific processing unit 46A of the headset terminal 314 and receives user requests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and reception 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 video and audio of the lesson using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects textbook data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates video and audio of the lesson and materials based on the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated video and audio and materials to the user. The reception unit is implemented, for example, by the control unit 46A of the robot 414, and receives user requests. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A system characterized by comprising: a collection unit that collects video, audio, and textbook data of lessons conducted at schools nationwide; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates video, audio, or materials for the desired portion of a lesson based on the data analyzed by the analysis unit; and a provision unit that provides the video, audio, or materials generated by the generation unit. (Note 2) It has a reception area to receive user requests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system according to Appendix 1, characterized in that the collection unit estimates the user's emotions and determines the order of the lesson data to be collected based on the estimated user's emotions. (Note 4) The system described in Appendix 1 is characterized in that the collection unit immediately reflects the progress of the lesson and updated textbook information at the time of collection. (Note 5) The system according to Appendix 1, characterized in that the collection unit applies different collection algorithms depending on the content of the lesson during collection. (Note 6) The system according to Appendix 1, characterized in that the collection unit estimates the user's emotions and adjusts the type of data to be collected based on the estimated user's emotions. (Note 7) The system described in Appendix 1 is characterized in that the collection unit prioritizes the collection of data while taking into consideration the geographical distribution of classes. (Note 8) The system described in Appendix 1 is characterized in that the collection unit simultaneously collects relevant literature and supplementary materials for the lesson at the time of collection. (Note 9) The system according to Appendix 1, characterized in that the analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user's emotions. (Note 10) The system described in Appendix 1 is characterized in that the analysis unit adjusts the level of detail of the analysis based on the importance of the content of the lesson during the analysis. (Note 11) The system described in Appendix 1 is characterized in that the analysis unit applies different analysis algorithms depending on the category of the course during the analysis. (Note 12) The system according to Appendix 1, characterized in that the analysis unit estimates the user's emotions and adjusts the display format of the analysis results based on the estimated user emotions. (Note 13) The system described in Appendix 1, characterized in that the analysis unit determines the order of analysis based on the progress of the lesson during the analysis. (Note 14) The system described in Appendix 1 is characterized in that the analysis unit adjusts the order of analysis based on the relevance of the lessons during the analysis. (Note 15) The system according to Appendix 1, characterized in that the generation unit estimates the user's emotions and adjusts the method of expression of the generated video, audio, and materials based on the estimated user's emotions. (Note 16) The system according to Appendix 1, characterized in that the generation unit adjusts the level of detail of the generation based on the importance of the lesson content during generation. (Note 17) The system according to Appendix 1, characterized in that the generation unit applies different generation algorithms depending on the category of the course during generation. (Note 18) The system according to Appendix 1, characterized in that the generation unit estimates the user's emotions and adjusts the length of the generated video, audio, and materials based on the estimated user's emotions. (Note 19) The system according to Appendix 1, characterized in that the generation unit determines the order of generation based on the progress of the lesson during generation. (Note 20) The system according to Appendix 1, characterized in that the generation unit adjusts the generation order based on the relevance of the lessons during generation. (Note 21) The system described in Appendix 1 is characterized in that the providing unit estimates the user's emotions and adjusts the display method of the video, audio, and materials provided based on the estimated user's emotions. (Note 22) The system described in Appendix 1 is characterized in that the providing unit selects the optimal providing method by referring to the user's past learning history at the time of provision. (Note 23) The system described in Appendix 1, characterized in that the provisioning unit customizes the means of provision based on the user's current learning status at the time of provision. (Note 24) The system according to Appendix 1, characterized in that the providing unit estimates the user's emotions and determines the order of video, audio, and materials to be provided based on the estimated user's emotions. (Note 25) The system described in Appendix 1, characterized in that the provisioning unit selects the optimal provisioning method considering the user's geographical location information at the time of provision. (Note 26) The system described in Appendix 1, wherein the provisioning unit analyzes the user's social media activity at the time of provision and proposes a means of provision. (Note 27) The system according to Appendix 2, characterized in that the reception unit estimates the user's emotions and adjusts the method of receiving requests based on the estimated user's emotions. (Note 28) The system described in Appendix 2 is characterized in that the reception unit selects the optimal reception method by referring to the user's past request history when receiving a request. (Note 29) The reception unit is characterized by automatically acquiring the user's current location information at the time of reception to simplify the request, as described in Appendix 2. (Note 30) The system according to Appendix 2, characterized in that the reception unit estimates the user's emotions and adjusts the design of the input interface based on the estimated user emotions. (Note 31) The system described in Appendix 2 is characterized in that the reception unit proposes the optimal reception method when receiving a request, by referring to the user's past request history. (Note 32) The system as described in Appendix 2, characterized in that the reception unit selects the optimal reception method considering the user's device information at the time of reception. (Note 33) The system described in Appendix 2 is characterized in that the reception unit, upon receiving a request, refers to the user's calendar information and makes a proposal based on the schedule. [Explanation of Symbols]

[0188] 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. A system characterized by comprising: a collection unit that collects video, audio, and textbook data of lessons conducted at schools nationwide; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates video, audio, or materials for the desired portion of a lesson based on the data analyzed by the analysis unit; and a provision unit that provides the video, audio, or materials generated by the generation unit.

2. It has a reception area to receive user requests. The system according to feature 1.

3. The system according to claim 1, characterized in that the collection unit estimates the user's emotions and determines the order of the lesson data to be collected based on the estimated user's emotions.

4. The system according to claim 1, characterized in that the collection unit immediately reflects the progress of the lesson and updated textbook information at the time of collection.

5. The system according to claim 1, characterized in that the collection unit applies different collection algorithms depending on the content of the lesson during collection.

6. The system according to claim 1, characterized in that the collection unit estimates the user's emotions and adjusts the type of data to be collected based on the estimated user's emotions.

7. The system according to claim 1, characterized in that the collection unit prioritizes the collection of data considering the geographical distribution of classes during collection.

8. The system according to claim 1, characterized in that the collection unit simultaneously collects relevant literature and supplementary materials for the lesson at the time of collection.

9. The system according to claim 1, characterized in that the analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user's emotions.

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