system

Abstract

We provide the system. [Solution] An information processing device includes means for receiving user information and generating educational materials based on said user information, A means of distributing generated educational materials to user devices via a digital communication network, A means for receiving inquiries from users related to the educational material and generating appropriate answers using an automated response system, A means for recording and managing user learning progress data, A means for generating feedback to provide improved educational materials based on the learning progress data, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In recent years, due to the evolution of communication technology and the spread of information terminals, the demand for education and information provision services for individuals has increased rapidly. However, with the current education provision methods, it is difficult to quickly respond to individual needs, and maintaining a uniform quality of education has become an issue. For example, there is a shortage of educators with specialized knowledge and it is difficult to optimize educational content individually. As a result, it is difficult to improve the satisfaction of learners, and the educational effect is not fully exerted. 【Means for Solving the Problems】 【0005】 To solve the above problems, this invention provides a system that automatically generates educational materials based on user information and distributes these materials via a digital communication network. Furthermore, it enables immediate problem resolution by generating appropriate responses to user inquiries using an automated response system. In addition, by managing user learning progress data, it realizes a feedback function that continuously optimizes educational content, thereby improving the quality of education. This makes it possible to efficiently utilize limited educational resources while providing high-quality educational services tailored to individual users. 【0006】 An "information processing device" is a device that inputs, processes, stores, and outputs data, and includes, but is not limited to, calculating machines and computers. 【0007】 "User information" refers to all data about the system's users, including information such as name, contact information, learning level, and areas of interest. 【0008】 "Educational materials" refer to educational content, including texts, videos, quizzes, and interactive content, created for specific learning purposes. 【0009】 A "digital communication network" is a network that transmits digital data, such as the internet, and enables communication such as email, web access, and data streaming. 【0010】 A "user device" is an electronic device that a user directly operates to receive and transmit information, and includes smartphones, tablets, and personal computers. 【0011】 An "automated response system" is a system or program that mechanically generates responses to address user inquiries. 【0012】 "Learning progress data" refers to data that shows how well a user has understood and progressed through specific educational materials, and includes information such as course history and grades. 【0013】 "Feedback" refers to opinions, comments, and evaluations from users that are collected and analyzed to improve educational materials and systems. [Brief explanation of the drawing] 【0014】 [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined. 【Mode for Carrying Out the Invention】 【0015】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 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. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 The 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. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 This invention describes a method for constructing a system that utilizes an information processing device to provide educational materials tailored to the user. Specific embodiments thereof are described below. 【0036】 First, users access the platform through a dedicated web portal or mobile app and create an account. During this process, users enter their name, email address, smartphone usage level, and educational topics of interest. This information is received by the server and stored in a database. 【0037】 Next, the server analyzes each user's user information and automatically generates optimal educational materials using AI technology tailored to their individual needs. These educational materials consist of various formats, including text, videos, and interactive quizzes, and are customized to the user's skills and interests. This content is securely stored on the server in cloud storage. 【0038】 The server then notifies the user that the generated educational materials are ready. The notification includes a link to access the educational materials, allowing the user to access the content at any time via their device. As long as the user has an internet connection, they can continue their learning at any time and from anywhere. 【0039】 During or after a course, if users have questions, they can enter them in a dedicated Q&A section. The server receives these questions immediately and uses an AI response system to generate the most appropriate and timely answers, which are then sent back to the user. This enables real-time problem solving. 【0040】 Furthermore, the server continuously records each user's learning progress. This includes the history of courses taken, quiz scores, and viewing time. The progress data is used to improve the quality of educational content and as feedback for future content provision. 【0041】 As a concrete example, suppose user A selects an online course on "photo editing techniques." The server considers user A's requests and skill level and generates a video tutorial that includes specific steps that can be easily practiced on a smartphone. By watching this content, user A can learn photo editing techniques using their smartphone, and if they have any further questions, the server will provide immediate answers. 【0042】 In this way, this system can enhance the efficiency and effectiveness of educational delivery and provide personalized learning experiences for various users. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The user accesses a dedicated website or mobile application and opens the new account creation screen. Here, they enter information such as their name, email address, learning level, and areas of interest. Once registration is complete, a success message is displayed on the screen. 【0046】 Step 2: 【0047】 The server receives registration information submitted by the user and securely stores it in the database. After verifying the registration details and confirming there are no problems, it sends an account activation email to the user. 【0048】 Step 3: 【0049】 The user checks the activation email and clicks the link provided to activate their account. Once activation is complete, the user is redirected to the login screen and can access the learning dashboard. 【0050】 Step 4: 【0051】 After the server authenticates the user's login and establishes a secure connection, it provides a user dashboard. This dashboard displays a list of available courses and recommended educational content. 【0052】 Step 5: 【0053】 Users select courses that interest them and view detailed information about the learning content. Once they register for a selected course, they gain access to its content. 【0054】 Step 6: 【0055】 The server automatically generates customized educational materials using AI technology based on the selected course. The generated content is stored in cloud storage and prepared for download or streaming. 【0056】 Step 7: 【0057】 The device provides generated educational materials in streaming or download format upon user request. Users can then begin learning and progress through the educational materials at their own pace. 【0058】 Step 8: 【0059】 If a user has a question during the lesson, they can post it directly to the server via the chat function on their device or the Q&A section. 【0060】 Step 9: 【0061】 The server receives questions from users and uses AI to instantly generate and respond with appropriate answers. This allows users to resolve their questions in real time. 【0062】 Step 10: 【0063】 The server records the user's learning progress and tracks course completion and grades. This information is analyzed to inform future content delivery. 【0064】 Step 11: 【0065】 The server sends a feedback form to the user after the course ends. The collected feedback will be used to improve the educational content and optimize it for future courses. 【0066】 (Example 1) 【0067】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0068】 There is a growing need to efficiently generate and quickly deliver personalized educational materials that meet the diverse learning needs of today's world, while also improving the learning experience for users. However, conventional systems struggle to customize materials for each user and provide prompt responses, and they have difficulty making full use of feedback on learning progress. 【0069】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0070】 In this invention, the server includes means for automatically generating educational materials using a generative model based on user information, means for securely storing the generated educational materials in a cloud environment and making them accessible to user devices via a digital communication network, and means for receiving inquiries from users, generating automated responses using natural language processing, and providing answers in real time. This enables the realization of a personalized learning experience for each user and improves learning efficiency through immediate responses. 【0071】 "User information" refers to individual data such as user attributes, interests, and skill levels, and is input data used to optimize the generation of educational materials. 【0072】 A "generative model" is a processing model that automatically generates optimal educational materials using algorithms and artificial intelligence technology based on user information. 【0073】 "Educational materials" refer to learning content in various forms, including text, videos, and quizzes, generated to support users' learning. 【0074】 A "cloud environment" is a remote data storage service that stores educational materials and related data via the internet, allowing users to access them as needed. 【0075】 A "digital communication network" is a network infrastructure that enables the exchange of information, and is a means of sending and receiving data via the internet or dedicated lines. 【0076】 "Natural language processing" is a field of artificial intelligence technology that enables computers to understand human language, process and analyze information, and respond appropriately. 【0077】 "Feedback" is the process of providing valuable information and suggestions to improve the quality of content and learning materials based on users' learning activities and progress. 【0078】 This invention demonstrates a method for providing users with customized educational content using an information processing system. First, the user accesses the platform using a web portal or mobile application. When creating an account, the user enters their basic information and learning preferences. This information is received by the server and stored in a database. 【0079】 The server uses a generative model to analyze stored user information and automatically generate optimal educational materials tailored to the user's learning needs. This process utilizes programming languages ​​such as Python and AI libraries for data analysis and content generation. The generated educational materials take the form of text, videos, and interactive quizzes and are securely stored in cloud storage services (e.g., AWS® S3 or Google® Cloud Storage). 【0080】 After generation, the server notifies the user that the educational materials are ready and provides an access link via the digital communication network. Users can access this link using their device and proceed with their learning. HTML5 and JavaScript (registered trademark) are used, and the system is designed to display content smoothly. 【0081】 During the learning process, users can submit questions through a dedicated Q&A section. The server utilizes natural language processing technology to receive user inquiries and quickly generate and provide appropriate answers. This allows users to enjoy real-time problem-solving. 【0082】 Furthermore, the server regularly records and analyzes users' learning progress. This establishes a function to optimize the content of educational materials based on progress data and provide continuous feedback. The accumulation of user-specific historical information will improve the quality of future content delivery and learning experiences. 【0083】 For example, if a user expresses interest in "photo editing techniques for beginners," the server will generate a video tutorial tailored to that need. The user can then receive clear, step-by-step instructions on photo editing using their smartphone. An example of a prompt would be, "Please teach me photo editing techniques for beginners using my smartphone." In this way, the system provides a personalized learning experience and delivers content that meets the user's educational needs. 【0084】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0085】 Step 1: 【0086】 Users access the system through a web portal or mobile application and create an account. Here, users enter their name, email address, smartphone usage level, and educational topics of interest. The entered information is sent to the server and stored in a database. This data is then used as foundational data for future material generation processes. 【0087】 Step 2: 【0088】 The server analyzes user information stored in the database. Using a generative AI model, it automatically generates optimal educational materials tailored to the user's needs and skill level. In this analysis, the algorithm constructs educational content in text, video, and quiz formats based on the input user information. The output is personalized educational materials. 【0089】 Step 3: 【0090】 The server securely stores the generated educational materials in cloud storage. Services such as AWS S3 and Google Cloud Storage are used to ensure data security and availability. The output is accessible educational materials stored in the cloud. 【0091】 Step 4: 【0092】 The server notifies the user that the educational materials are ready. This notification includes a direct link to the educational content and is sent to the user via the digital communication network. The output of this notification is an access link sent to the user's device. 【0093】 Step 5: 【0094】 Users access educational materials via a notified link using their device. The content is displayed using HTML5 and JavaScript, allowing users to smoothly browse the materials and progress through their learning. The input is a link, and the output is a display of educational content. 【0095】 Step 6: 【0096】 During the learning process, if a user has a question, they can enter it in a dedicated Q&A section. Upon receiving this input, the server starts a process using natural language processing techniques to generate an appropriate response to the question. The output is an immediate answer provided to the user. 【0097】 Step 7: 【0098】 The server continuously records users' learning progress and analyzes the progress data. This includes detailed data such as course history, quiz scores, and viewing time. This data is used as feedback for future content optimization and quality improvement. Learning activity data is the input, and improvement information based on feedback is obtained as the output. 【0099】 (Application Example 1) 【0100】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0101】 In today's information society, providing educational materials tailored to users' individual interests and skill levels is crucial. However, existing systems fail to adequately address diverse user needs through automated personalized content generation and real-time inquiry support. Furthermore, effectively managing users' learning progress and generating feedback based on that progress is also difficult. This hinders the maximization of educational effectiveness. 【0102】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0103】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information; means for distributing the generated educational materials to user devices via a digital communication network; and means for receiving inquiries from users related to said educational materials and generating appropriate answers using an automated response device. This makes it possible to provide educational materials that are suitable for the user's interests and skills, and to realize an efficient and immediate learning environment. 【0104】 An "information processing device" is a device that receives input from a user and analyzes or generates data based on that information. 【0105】 "User information" refers to data about users receiving education or other services, including personal interests, skills, and other relevant information. 【0106】 "Educational materials" refer to digital content provided for the purpose of user learning and training, including text, videos, quizzes, and other similar materials. 【0107】 A "digital communication network" is a network used to send and receive digital data, including the internet. 【0108】 A "user device" is a device that an individual user can use on their own, such as a smartphone or tablet. 【0109】 An "automated response system" is a system that receives inquiries from users and automatically generates answers based on programmed logic. 【0110】 "Learning progress data" refers to information about the user's learning activity history and results, including, for example, the number of courses taken and quiz scores. 【0111】 "Feedback" refers to information obtained by analyzing user reactions and data during learning and use, which is used to improve future material provision. 【0112】 An "artificial intelligence model" is an algorithm or framework that allows computers to simulate human intellectual behavior and generate content that meets user needs. 【0113】 "Areas of interest" refers to the range of themes or topics that a user is particularly interested in. 【0114】 "Skill level" is an indicator that shows the current level of a user's skills and abilities. 【0115】 To implement this invention, a server as an information processing device, a user's digital terminal, and AI technology are used. The server receives user information provided by the user and stores it in a database. This information includes the user's name, contact information, topics of interest, and skill level. 【0116】 The server uses an AI model to automatically generate personalized educational materials based on user information. This AI model, for example, a generative AI model using natural language processing technology (e.g., GPT-4®), generates content in the form of text, video, and interactive quizzes. These educational materials are securely stored in cloud storage, and users are notified when they are ready. 【0117】 Users can access these educational materials via a digital communication network through their devices (e.g., smartphones and tablets). If users have questions during their studies, they can use a dedicated inquiry function. An automated response system built into the server uses AI technology to instantly generate and provide appropriate answers to received questions to the user. 【0118】 Furthermore, the server tracks and records users' learning progress data, including courses taken, grades, and viewing time. The server analyzes this data to generate feedback for improving the quality of educational content. 【0119】 As a concrete example, suppose a user wants to improve their "digital photo editing" skills. The server uses AI to generate tutorial videos on photo editing techniques suitable for beginners. The user can then access these materials to acquire the skills. If the user has questions along the way, they can get immediate answers using prompts such as, "Please explain the technical details regarding digital photo editing." This makes it possible to improve the quality and efficiency of learning. 【0120】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0121】 Step 1: 【0122】 Users access a web portal or mobile app using their device and create an account. During this process, they enter user information such as their name, email address, areas of interest, and skill level. This information is then transmitted from the device to the server. 【0123】 Step 2: 【0124】 The server stores the received user information in a database. Basic information submitted by the user is used as input, and this information is registered in the database system for organization and storage. As output, user data is stored so that it can be easily searched or used later as needed. 【0125】 Step 3: 【0126】 The server utilizes a generative AI model based on stored user information to create educational materials tailored to each user. The input consists of user interest and skill level data retrieved from a database. The generative AI model processes this data to generate user-specific content. As output, customized educational materials are generated and stored in cloud storage. 【0127】 Step 4: 【0128】 The server notifies the user that the generated educational materials are ready. The system uses the on-device notification system to inform the user that materials created by the generation AI model are available. The prepared content information is used as input, and notification information is generated as output. 【0129】 Step 5: 【0130】 Users access generated educational materials through their devices using links provided by the server. Input consists of notifications and link information from the server, and the content is delivered to the user via the device's display function. Output is the ability for the user to view the educational materials. 【0131】 Step 6: 【0132】 If a user encounters difficulties while studying educational materials, they can send a question to the server using a dedicated inquiry function on their device. The user's query is used as input, and the query content is sent to the server as output. 【0133】 Step 7: 【0134】 The server uses a generative AI model to generate appropriate answers to incoming questions and immediately sends them back to the user. The input is the user's question, and the generative AI model analyzes this question to generate the optimal answer. The output is the generated answer information, which is then provided to the user via their device. 【0135】 Step 8: 【0136】 The server constantly monitors the user's learning progress and records learning progress data such as course data and quiz scores. User learning activity information is continuously used as input, and a dataset detailing that activity is generated as output. 【0137】 Step 9: 【0138】 The server analyzes recorded learning progress data and generates feedback to create improved educational materials. Progress data is used as input, and insights for improving educational materials are gained through data analysis. Feedback information for improvement is generated as output. 【0139】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0140】 This invention describes a method for constructing a system that provides an optimized educational experience for users using an information processing device and emotion recognition technology. Specific embodiments thereof are described below. 【0141】 First, users access the system via a dedicated web portal or mobile application and create an account. They enter their basic information and grant access to their device's camera and microphone to utilize the emotion recognition feature. This information is received by the server and stored in a database. 【0142】 Next, the server analyzes the user's information and automatically generates optimal educational materials tailored to their learning needs using AI technology. The generated materials include text, videos, and interactive quizzes, and are designed to dynamically adjust their content according to the user's emotional state. The emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to detect the user's emotional state in real time. 【0143】 Once the educational materials are ready, the server sends a notification to the user, providing an opportunity to access the materials via a link. The user can then use this to begin learning and progress at their own pace. 【0144】 During learning, the emotion engine is activated, and the device periodically monitors the user's emotions. For example, if the emotion engine determines that the user is stressed or has lost interest, the server modifies the learning content accordingly. This might involve switching to simpler explanations or providing additional encouraging messages. This ensures that the learning experience is seamlessly tailored and personalized. 【0145】 For example, if user A is taking a course called "Introduction to Programming" and is stuck on a difficult assignment, the emotion engine will detect frustration from user A's facial expressions. Based on this information, the server will adjust the content and help user A overcome the challenge by presenting videos that include hints and success stories. 【0146】 Furthermore, after the learning session is complete, the server analyzes emotional data along with learning progress data to improve future content. This feedback loop enhances user satisfaction and promotes a more effective learning experience. With the addition of emotional recognition capabilities, this system provides users with innovative and personalized education. 【0147】 The following describes the processing flow. 【0148】 Step 1: 【0149】 Users access a dedicated web portal or mobile application, enter information such as their name, email address, and smartphone usage level on the account creation screen, and grant access to their camera and microphone. This makes the emotion engine available. 【0150】 Step 2: 【0151】 The server receives information sent by the user and stores it in the database. Then, it sends a confirmation email to the user to activate their account and release them from using the system. 【0152】 Step 3: 【0153】 Users check their activation email and click the link to activate their account. After activation is complete, users can access the learning dashboard and view available courses. 【0154】 Step 4: 【0155】 The server authenticates the user's login and displays a list of available educational content on the dashboard. The user selects courses of interest and registers. 【0156】 Step 5: 【0157】 The server analyzes user information and generates customized educational materials using AI technology. These materials include text, videos, and quizzes, and can be adjusted in real time by an emotion engine. The materials are stored in cloud storage. 【0158】 Step 6: 【0159】 The device provides generated educational materials in streaming or downloadable format upon user request. Users can then use these materials to learn at their own pace. 【0160】 Step 7: 【0161】 During learning, the device periodically provides the user's facial expressions and voice to the emotion engine via the camera and microphone, monitoring the user's emotions. 【0162】 Step 8: 【0163】 The server receives the user's emotional state detected by the emotion engine, and if frustration or loss of interest is recognized, it dynamically changes the content of the educational materials. 【0164】 Step 9: 【0165】 If a user has questions during the lesson, they enter them via the chat function on their device. The server receives the question, uses AI to quickly generate an answer, and responds in real time. 【0166】 Step 10: 【0167】 The server records user learning progress and sentiment data, and analyzes learning completion and problem areas. This data is used to optimize future content and improve user satisfaction. 【0168】 Step 11: 【0169】 After the course ends, the server sends the user a feedback form about their learning experience. The collected feedback will be used to improve future content. 【0170】 (Example 2) 【0171】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0172】 In educational systems, there is a need to accurately understand each user's learning needs and emotional changes, and to provide individualized learning experiences. However, conventional systems have struggled to detect users' emotional states in real time and dynamically adjust content based on them. This could potentially lead to decreased learning efficiency and user satisfaction. 【0173】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0174】 In this invention, the server includes means for generating educational materials based on user information, means for detecting the user's emotional state in real time using emotion recognition technology and dynamically adjusting the educational materials based on the emotional state, and means for generating feedback based on learning progress information. This makes it possible to immediately adjust learning materials to match the individual emotional state of the user, thereby providing a more effective and satisfying learning experience. 【0175】 "Information processing equipment" refers to devices that input, process, store, and output data, and plays a role in coordinating the entire system. 【0176】 "User information" refers to data related to an individual using the system, including name, contact information, and learning history. 【0177】 "Educational materials" refer to learning materials provided to learners, and can take the form of textbooks, videos, quizzes, and other similar formats. 【0178】 A "digital communication network" is a network that transmits information using digital signals, and includes the Internet. 【0179】 "User equipment" refers to devices that users directly operate, and includes computers, smartphones, tablets, and other similar devices. 【0180】 "Emotion recognition technology" refers to technology that analyzes a user's facial expressions and voice to detect their emotional state. 【0181】 "Real-time" refers to a state where processing or operations are executed immediately, with virtually no time delay. 【0182】 "Dynamic adjustment" refers to changing the content and structure on the spot according to the user's situation and needs. 【0183】 "Learning progress information" refers to data on the extent to which users have absorbed educational materials and their progress in that regard. 【0184】 "Feedback" refers to providing information to improve the system and learning materials based on learning progress data and user reactions. 【0185】 This system utilizes information processing equipment and emotion recognition technology to provide user-optimized educational materials. First, users access the system and create an account using a dedicated web portal or mobile application. Here, users enter the necessary basic information and allow the use of their device's camera and microphone to enhance the educational experience. 【0186】 The server uses the received user information to generate optimal educational materials using a generative AI model. This generation process utilizes programming languages ​​such as Python and a database management system. The generated materials are diverse and include text, video, and interactive quiz formats. These materials are dynamically adjusted in real time according to the user's emotional state. Sentiment recognition technology using OpenCV and a speech recognition engine is applied to this adjustment. 【0187】 Once the educational materials have been generated, the server sends a notification to the user and makes them accessible via a link. The user can then use this link to learn at their own pace. 【0188】 For example, suppose user A is taking an "Introduction to Programming" course and is struggling with a difficult assignment. If emotion recognition technology detects user A's frustration, the server will adjust the content accordingly, providing clearer hints and videos demonstrating success stories to support smoother learning. 【0189】 After the learning session is complete, the server analyzes learning progress data and sentiment data to generate feedback that can be used to improve future educational content. This makes it possible to increase user satisfaction and provide a more effective learning environment. 【0190】 An example of a prompt to input into a generative AI model is, "Generate educational materials best suited to the user's learning needs. Dynamic adjustments based on the user's emotional state are required." This prompt provides specific instructions to the generative AI model, helping to create a customized educational experience for each user. 【0191】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0192】 Step 1: 【0193】 Users access the system via a dedicated web portal or mobile application and create an account. During this process, users enter basic information and grant access to their device's camera and microphone. Inputs include personal information such as name and email address, and output is transmitted to the server. This prepares the system to provide an experience optimized for each user. 【0194】 Step 2: 【0195】 The server receives user information and stores it in a database. The input received is the user's personal information, and the output is an update to the database. Specifically, it processes HTTP requests and saves the information to an SQL database. This procedure enables the system to perform analysis based on individual user data. 【0196】 Step 3: 【0197】 The server uses a generative AI model to create appropriate educational materials based on the user's learning needs. The input is information about the user's learning history and areas of interest, and the output is educational materials such as text, videos, and interactive quizzes. Specifically, user data is processed by an algorithm, and the generated prompts are input into the model to produce materials. These materials assist in creating an appropriate learning experience. 【0198】 Step 4: 【0199】 The device uses its camera and microphone to analyze the user's facial expressions and voice, detecting their emotional state in real time. Input is live camera footage and audio data, while output is user emotional state data. Specifically, it uses OpenCV and a speech recognition engine to analyze the data and execute code to determine emotions. 【0200】 Step 5: 【0201】 The server considers the user's emotional state and dynamically adjusts the educational materials. The input is emotional state data, and the output is the adjusted educational materials. For example, if user frustration is detected, the server provides easier content or encouraging videos. This feature allows the learning experience to be customized to individual needs. 【0202】 Step 6: 【0203】 The server sends a notification to provide educational materials to the user. The input is the prepared educational materials, and the output is the sending of the notification. Specifically, a link is sent to the user's application via the notification protocol, allowing the user to access and begin learning. 【0204】 Step 7: 【0205】 After the learning process is complete, the server collects and analyzes learning progress data and sentiment data. The input is the user's learning data and sentiment data, and the output is a feedback report and suggestions for future content improvements. Specifically, data analysis tools are used to visualize progress, and the generative AI model is also improved. This step aims to further enhance the learning experience. 【0206】 (Application Example 2) 【0207】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0208】 In modern learning methods, providing personalized educational experiences is essential to meet the diverse needs of learners. However, systems that can dynamically adjust learning content in real time, taking into account the user's emotional state, are not yet readily available. In particular, there is a growing need for systems that can automatically detect when learners lose interest or experience stress and respond appropriately. 【0209】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0210】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information, means for distributing the generated educational materials to the user's device via electronic communication means, and means for processing data from sensors for analyzing the user's emotional state. This makes it possible to monitor the learner's emotional state in real time and dynamically adjust the content to provide a flexible educational experience tailored to individual needs. 【0211】 "User information" refers to basic data about individual learners that the system uses to acquire data about them. 【0212】 "Educational materials" are content generated to support learning, including text, videos, and interactive quizzes. 【0213】 "Electronic communication means" refers to network infrastructure and protocols used to transmit digital information to user devices. 【0214】 "User devices" refer to terminals used by learners to access educational materials, and include smartphones, tablets, and personal computers. 【0215】 "Emotional state" refers to information that indicates the psychological and emotional responses expressed by the user during the learning process. 【0216】 "Data from sensors" refers to data about the user's facial expressions and voice collected through input devices such as cameras and microphones. 【0217】 "Dynamic adjustment" refers to the process of changing the content of educational materials in real time according to the emotional state of the user. 【0218】 This system begins with the user creating an account through a dedicated application and entering their user information. The user then grants access to the device's camera and microphone for emotion recognition. This information is received by the server and stored in a database. 【0219】 The server uses AI technology based on user information to generate educational materials. These materials include a variety of content, such as device text, videos, and interactive quizzes. These educational materials are delivered to user devices via electronic communication. 【0220】 The device uses a camera and microphone to monitor the user's emotional state in real time. Utilizing OpenCV, PyDub, and TENSORFLOW®, it analyzes emotions from facial expressions and voice to detect the user's psychological state. Based on the emotional state, the server dynamically adjusts the content of educational materials, lowering the difficulty level or adding encouraging messages as needed. 【0221】 For example, if a user is taking a "basic mathematics" class and encounters a difficult problem that causes them stress, the device will detect this emotional state. The server will then provide additional content, including hints, to help the user overcome the problem. 【0222】 An example of a prompt for a generative AI model is, "When the user shows frustration, suggest supportive messages to improve the learning process." This prompt serves as an important guide for the AI ​​to provide the best possible educational experience for the user. 【0223】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0224】 Step 1: 【0225】 The user launches a dedicated application and enters their user information on the account creation screen. This information includes user ID, name, and learning objectives. The server receives this user information and stores it in a database. This process prepares the user for a personalized educational experience. 【0226】 Step 2: 【0227】 The server generates educational materials based on stored user information. It utilizes a generation AI model to create educational content including text, videos, and interactive quizzes. The AI ​​model selects the most suitable content based on the user's learning goals and level. As output, educational materials tailored to the user are prepared. 【0228】 Step 3: 【0229】 The generated educational materials are delivered from the server to the user's device via electronic communication. The device receives the relevant educational materials and prepares them for the user to access at any time. This allows the user to begin learning regardless of time or location. 【0230】 Step 4: 【0231】 The device monitors the user's emotional state in real time during training. It captures facial expressions with a camera and records audio with a microphone. Using OpenCV, PyDub, and TensorFlow, it recognizes emotions from this data and sends the state to the server. The current training progress can be understood by inputting the emotional state. 【0232】 Step 5: 【0233】 The server receives emotional state data sent from the terminal and dynamically adjusts the content of the educational materials. For example, if a user shows frustration, the server provides easier content or encouraging messages. This adjustment optimizes the user's learning experience individually, improving learning effectiveness. 【0234】 Step 6: 【0235】 After a user completes a lesson, the server analyzes their learning progress and emotional state, generating feedback to improve future educational materials. This feedback is used to customize subsequent learning content, continuously improving the educational experience. 【0236】 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. 【0237】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0238】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0239】 [Second Embodiment] 【0240】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0241】 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. 【0242】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0243】 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. 【0244】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0245】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0246】 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. 【0247】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0248】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0249】 The 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. 【0250】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0251】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0252】 This invention describes a method for constructing a system that utilizes an information processing device to provide educational materials tailored to the user. Specific embodiments thereof are described below. 【0253】 First, users access the platform through a dedicated web portal or mobile app and create an account. During this process, users enter their name, email address, smartphone usage level, and educational topics of interest. This information is received by the server and stored in a database. 【0254】 Next, the server analyzes each user's user information and automatically generates optimal educational materials using AI technology tailored to their individual needs. These educational materials consist of various formats, including text, videos, and interactive quizzes, and are customized to the user's skills and interests. This content is securely stored on the server in cloud storage. 【0255】 The server then notifies the user that the generated educational materials are ready. The notification includes a link to access the educational materials, allowing the user to access the content at any time via their device. As long as the user has an internet connection, they can continue their learning at any time and from anywhere. 【0256】 During or after a course, if users have questions, they can enter them in a dedicated Q&A section. The server receives these questions immediately and uses an AI response system to generate the most appropriate and timely answers, which are then sent back to the user. This enables real-time problem solving. 【0257】 Furthermore, the server continuously records each user's learning progress. This includes the history of courses taken, quiz scores, and viewing time. The progress data is used to improve the quality of educational content and as feedback for future content provision. 【0258】 As a concrete example, suppose user A selects an online course on "photo editing techniques." The server considers user A's requests and skill level and generates a video tutorial that includes specific steps that can be easily practiced on a smartphone. By watching this content, user A can learn photo editing techniques using their smartphone, and if they have any further questions, the server will provide immediate answers. 【0259】 In this way, this system can enhance the efficiency and effectiveness of educational delivery and provide personalized learning experiences for various users. 【0260】 The following describes the processing flow. 【0261】 Step 1: 【0262】 The user accesses a dedicated website or mobile application and opens the new account creation screen. Here, they enter information such as their name, email address, learning level, and areas of interest. Once registration is complete, a success message is displayed on the screen. 【0263】 Step 2: 【0264】 The server receives registration information submitted by the user and securely stores it in the database. After verifying the registration details and confirming there are no problems, it sends an account activation email to the user. 【0265】 Step 3: 【0266】 The user checks the activation email and clicks the link provided to activate their account. Once activation is complete, the user is redirected to the login screen and can access the learning dashboard. 【0267】 Step 4: 【0268】 After the server authenticates the user's login and establishes a secure connection, it provides a user dashboard. This dashboard displays a list of available courses and recommended educational content. 【0269】 Step 5: 【0270】 Users select courses that interest them and view detailed information about the learning content. Once they register for a selected course, they gain access to its content. 【0271】 Step 6: 【0272】 The server automatically generates customized educational materials using AI technology based on the selected course. The generated content is stored in cloud storage and prepared for download or streaming. 【0273】 Step 7: 【0274】 The device provides generated educational materials in streaming or download format upon user request. Users can then begin learning and progress through the educational materials at their own pace. 【0275】 Step 8: 【0276】 If a user has a question during the lesson, they can post it directly to the server via the chat function on their device or the Q&A section. 【0277】 Step 9: 【0278】 The server receives questions from users and uses AI to instantly generate and respond with appropriate answers. This allows users to resolve their questions in real time. 【0279】 Step 10: 【0280】 The server records the user's learning progress and tracks course completion and grades. This information is analyzed to inform future content delivery. 【0281】 Step 11: 【0282】 The server sends a feedback form to the user after the course ends. The collected feedback will be used to improve the educational content and optimize it for future courses. 【0283】 (Example 1) 【0284】 Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal". 【0285】 There is a need to efficiently generate and quickly provide individualized educational materials according to various modern learning needs, and to improve the learning experience of users. However, in conventional systems, it is difficult to customize materials and respond quickly for each user, and there is a problem that the feedback on learning progress cannot be fully utilized. 【0286】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0287】 In this invention, the server includes means for automatically generating educational materials using a generation model based on user information, means for securely storing the generated educational materials in a cloud environment and making them accessible to user devices via a digital communication network, and means for receiving inquiries from users, generating an automatic response by utilizing natural language processing, and answering in real time. Thereby, it becomes possible to realize an individualized learning experience for each user and to improve learning efficiency by immediate response. 【0288】 "User information" is individual data such as a user's attributes, interests, and skill levels, and is input data used to optimize the generation of educational materials. 【0289】 A "generation model" is a processing model that automatically generates optimal educational materials using algorithms and artificial intelligence technologies based on user information. 【0290】 "Educational materials" are various forms of learning content including text, video, quizzes, etc. generated to support users' learning. [[ID=We]] 【0291】 A "cloud environment" is a remote data storage service that stores educational materials and related data via the Internet and enables users to access them as needed. 【0292】 A "digital communication network" is a network infrastructure that enables the exchange of information, and is a means of sending and receiving data via the internet or dedicated lines. 【0293】 "Natural language processing" is a field of artificial intelligence technology that enables computers to understand human language, process and analyze information, and respond appropriately. 【0294】 "Feedback" is the process of providing valuable information and suggestions to improve the quality of content and learning materials based on users' learning activities and progress. 【0295】 This invention demonstrates a method for providing users with customized educational content using an information processing system. First, the user accesses the platform using a web portal or mobile application. When creating an account, the user enters their basic information and learning preferences. This information is received by the server and stored in a database. 【0296】 The server uses a generative model to analyze stored user information and automatically generate optimal educational materials tailored to the user's learning needs. This process utilizes programming languages ​​such as Python and AI libraries for data analysis and content generation. The generated educational materials take the form of text, videos, and interactive quizzes and are securely stored in cloud storage services (e.g., AWS S3 or Google Cloud Storage). 【0297】 After generation, the server notifies the user that the educational materials are ready and provides an access link via the digital network. Users can access this link using their device and proceed with their learning. HTML5 and JavaScript are used, and the system is designed to display content smoothly. 【0298】 During the learning process, users can submit questions through a dedicated Q&A section. The server utilizes natural language processing technology to receive user inquiries and quickly generate and provide appropriate answers. This allows users to enjoy real-time problem-solving. 【0299】 Furthermore, the server regularly records and analyzes users' learning progress. This establishes a function to optimize the content of educational materials based on progress data and provide continuous feedback. The accumulation of user-specific historical information will improve the quality of future content delivery and learning experiences. 【0300】 For example, if a user expresses interest in "photo editing techniques for beginners," the server will generate a video tutorial tailored to that need. The user can then receive clear, step-by-step instructions on photo editing using their smartphone. An example of a prompt would be, "Please teach me photo editing techniques for beginners using my smartphone." In this way, the system provides a personalized learning experience and delivers content that meets the user's educational needs. 【0301】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0302】 Step 1: 【0303】 Users access the system through a web portal or mobile application and create an account. Here, users enter their name, email address, smartphone usage level, and educational topics of interest. The entered information is sent to the server and stored in a database. This data is then used as foundational data for future material generation processes. 【0304】 Step 2: 【0305】 The server analyzes the user information stored in the database. By using a generative AI model, it automatically generates optimal educational materials according to the needs and skill levels of the users. In this analysis, based on the input user information, an algorithm composes educational content in the form of text, video, and quizzes. As output, individualized educational materials are generated. 【0306】 Step 3: 【0307】 The server securely stores the generated educational materials in cloud storage. At this time, services such as AWS S3 and Google Cloud Storage are used to ensure the security and availability of the data. As output, there are educational materials stored on the cloud in an accessible state. 【0308】 Step 4: 【0309】 The server notifies the user that the educational materials are ready. This notification includes a direct link to the educational content and is sent to the user via a digital communication network. Here, as output, there is an access link sent to the user's device. 【0310】 Step 5: 【0311】 The user accesses the educational materials from the link notified using the device. Since the content is displayed using HTML5 and JavaScript, the user can smoothly view the teaching materials and proceed with learning. The input is the link, and the output is the display of the educational content. 【0312】 Step 6: 【0313】 During learning, if the user has questions, they can enter the questions in a dedicated QA section. Upon receiving this input, the server starts a process of generating an appropriate response to the questions using natural language processing technology. As output, there is an immediate answer provided to the user. 【0314】 Step 7: 【0315】 The server continuously records users' learning progress and analyzes the progress data. This includes detailed data such as course history, quiz scores, and viewing time. This data is used as feedback for future content optimization and quality improvement. Learning activity data is the input, and improvement information based on feedback is obtained as the output. 【0316】 (Application Example 1) 【0317】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0318】 In today's information society, providing educational materials tailored to users' individual interests and skill levels is crucial. However, existing systems fail to adequately address diverse user needs through automated personalized content generation and real-time inquiry support. Furthermore, effectively managing users' learning progress and generating feedback based on that progress is also difficult. This hinders the maximization of educational effectiveness. 【0319】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0320】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information; means for distributing the generated educational materials to user devices via a digital communication network; and means for receiving inquiries from users related to said educational materials and generating appropriate answers using an automated response device. This makes it possible to provide educational materials that are suitable for the user's interests and skills, and to realize an efficient and immediate learning environment. 【0321】 An "information processing device" is a device that receives input from a user and analyzes or generates data based on that information. 【0322】 "User information" refers to data about users receiving education or other services, including personal interests, skills, and other relevant information. 【0323】 "Educational materials" refer to digital content provided for the purpose of user learning and training, including text, videos, quizzes, and other similar materials. 【0324】 A "digital communication network" is a network used to send and receive digital data, including the internet. 【0325】 A "user device" is a device that an individual user can use on their own, such as a smartphone or tablet. 【0326】 An "automated response system" is a system that receives inquiries from users and automatically generates answers based on programmed logic. 【0327】 "Learning progress data" refers to information about the user's learning activity history and results, including, for example, the number of courses taken and quiz scores. 【0328】 "Feedback" refers to information obtained by analyzing user reactions and data during learning and use, which is used to improve future material provision. 【0329】 An "artificial intelligence model" is an algorithm or framework that allows computers to simulate human intellectual behavior and generate content that meets user needs. 【0330】 "Areas of interest" refers to the range of themes or topics that a user is particularly interested in. 【0331】 "Skill level" is an indicator that shows the current level of a user's skills and abilities. 【0332】 To implement this invention, a server as an information processing device, a user's digital terminal, and AI technology are used. The server receives user information provided by the user and stores it in a database. This information includes the user's name, contact information, topics of interest, and skill level. 【0333】 The server uses an AI model to automatically generate personalized educational materials based on user information. This AI model, for example, a generative AI model using natural language processing technology (e.g., GPT-4), generates content in the form of text, video, and interactive quizzes. These educational materials are securely stored in cloud storage, and users are notified when they are ready. 【0334】 Users can access these educational materials via a digital communication network through their devices (e.g., smartphones and tablets). If users have questions during their studies, they can use a dedicated inquiry function. An automated response system built into the server uses AI technology to instantly generate and provide appropriate answers to received questions to the user. 【0335】 Furthermore, the server tracks and records users' learning progress data, including courses taken, grades, and viewing time. The server analyzes this data to generate feedback for improving the quality of educational content. 【0336】 As a concrete example, suppose a user wants to improve their "digital photo editing" skills. The server uses AI to generate tutorial videos on photo editing techniques suitable for beginners. The user can then access these materials to acquire the skills. If the user has questions along the way, they can get immediate answers using prompts such as, "Please explain the technical details regarding digital photo editing." This makes it possible to improve the quality and efficiency of learning. 【0337】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0338】 Step 1: 【0339】 Users access a web portal or mobile app using their device and create an account. During this process, they enter user information such as their name, email address, areas of interest, and skill level. This information is then transmitted from the device to the server. 【0340】 Step 2: 【0341】 The server stores the received user information in a database. Basic information submitted by the user is used as input, and this information is registered in the database system for organization and storage. As output, user data is stored so that it can be easily searched or used later as needed. 【0342】 Step 3: 【0343】 The server utilizes a generative AI model based on stored user information to create educational materials tailored to each user. The input consists of user interest and skill level data retrieved from a database. The generative AI model processes this data to generate user-specific content. As output, customized educational materials are generated and stored in cloud storage. 【0344】 Step 4: 【0345】 The server notifies the user that the generated educational materials are ready. The system uses the on-device notification system to inform the user that materials created by the generation AI model are available. The prepared content information is used as input, and notification information is generated as output. 【0346】 Step 5: 【0347】 Users access generated educational materials through their devices using links provided by the server. Input consists of notifications and link information from the server, and the content is delivered to the user via the device's display function. Output is the ability for the user to view the educational materials. 【0348】 Step 6: 【0349】 If a user encounters difficulties while studying educational materials, they can send a question to the server using a dedicated inquiry function on their device. The user's query is used as input, and the query content is sent to the server as output. 【0350】 Step 7: 【0351】 The server uses a generative AI model to generate appropriate answers to incoming questions and immediately sends them back to the user. The input is the user's question, and the generative AI model analyzes this question to generate the optimal answer. The output is the generated answer information, which is then provided to the user via their device. 【0352】 Step 8: 【0353】 The server constantly monitors the user's learning progress and records learning progress data such as course data and quiz scores. User learning activity information is continuously used as input, and a dataset detailing that activity is generated as output. 【0354】 Step 9: 【0355】 The server analyzes recorded learning progress data and generates feedback to create improved educational materials. Progress data is used as input, and insights for improving educational materials are gained through data analysis. Feedback information for improvement is generated as output. 【0356】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0357】 This invention describes a method for constructing a system that provides an optimized educational experience for users using an information processing device and emotion recognition technology. Specific embodiments thereof are described below. 【0358】 First, users access the system via a dedicated web portal or mobile application and create an account. They enter their basic information and grant access to their device's camera and microphone to utilize the emotion recognition feature. This information is received by the server and stored in a database. 【0359】 Next, the server analyzes the user's information and automatically generates optimal educational materials tailored to their learning needs using AI technology. The generated materials include text, videos, and interactive quizzes, and are designed to dynamically adjust their content according to the user's emotional state. The emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to detect the user's emotional state in real time. 【0360】 Once the educational materials are ready, the server sends a notification to the user, providing an opportunity to access the materials via a link. The user can then use this to begin learning and progress at their own pace. 【0361】 During learning, the emotion engine is activated, and the device periodically monitors the user's emotions. For example, if the emotion engine determines that the user is stressed or has lost interest, the server modifies the learning content accordingly. This might involve switching to simpler explanations or providing additional encouraging messages. This ensures that the learning experience is seamlessly tailored and personalized. 【0362】 For example, if user A is taking a course called "Introduction to Programming" and is stuck on a difficult assignment, the emotion engine will detect frustration from user A's facial expressions. Based on this information, the server will adjust the content and help user A overcome the challenge by presenting videos that include hints and success stories. 【0363】 Furthermore, after the learning session is complete, the server analyzes emotional data along with learning progress data to improve future content. This feedback loop enhances user satisfaction and promotes a more effective learning experience. With the addition of emotional recognition capabilities, this system provides users with innovative and personalized education. 【0364】 The following describes the processing flow. 【0365】 Step 1: 【0366】 Users access a dedicated web portal or mobile application, enter information such as their name, email address, and smartphone usage level on the account creation screen, and grant access to their camera and microphone. This makes the emotion engine available. 【0367】 Step 2: 【0368】 The server receives information sent by the user and stores it in the database. Then, it sends a confirmation email to the user to activate their account and release them from using the system. 【0369】 Step 3: 【0370】 Users check their activation email and click the link to activate their account. After activation is complete, users can access the learning dashboard and view available courses. 【0371】 Step 4: 【0372】 The server authenticates the user's login and displays a list of available educational content on the dashboard. The user selects courses of interest and registers. 【0373】 Step 5: 【0374】 The server analyzes user information and generates customized educational materials using AI technology. These materials include text, videos, and quizzes, and can be adjusted in real time by an emotion engine. The materials are stored in cloud storage. 【0375】 Step 6: 【0376】 The device provides generated educational materials in streaming or downloadable format upon user request. Users can then use these materials to learn at their own pace. 【0377】 Step 7: 【0378】 During learning, the device periodically provides the user's facial expressions and voice to the emotion engine via the camera and microphone, monitoring the user's emotions. 【0379】 Step 8: 【0380】 The server receives the user's emotional state detected by the emotion engine, and if frustration or loss of interest is recognized, it dynamically changes the content of the educational materials. 【0381】 Step 9: 【0382】 If a user has questions during the lesson, they enter them via the chat function on their device. The server receives the question, uses AI to quickly generate an answer, and responds in real time. 【0383】 Step 10: 【0384】 The server records user learning progress and sentiment data, and analyzes learning completion and problem areas. This data is used to optimize future content and improve user satisfaction. 【0385】 Step 11: 【0386】 After the course ends, the server sends the user a feedback form about their learning experience. The collected feedback will be used to improve future content. 【0387】 (Example 2) 【0388】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0389】 In educational systems, there is a need to accurately understand each user's learning needs and emotional changes, and to provide individualized learning experiences. However, conventional systems have struggled to detect users' emotional states in real time and dynamically adjust content based on them. This could potentially lead to decreased learning efficiency and user satisfaction. 【0390】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0391】 In this invention, the server includes means for generating educational materials based on user information, means for detecting the user's emotional state in real time using emotion recognition technology and dynamically adjusting the educational materials based on the emotional state, and means for generating feedback based on learning progress information. This makes it possible to immediately adjust learning materials to match the individual emotional state of the user, thereby providing a more effective and satisfying learning experience. 【0392】 "Information processing equipment" refers to devices that input, process, store, and output data, and plays a role in coordinating the entire system. 【0393】 "User information" refers to data related to an individual using the system, including name, contact information, and learning history. 【0394】 "Educational materials" refer to learning materials provided to learners, and can take the form of textbooks, videos, quizzes, and other similar formats. 【0395】 A "digital communication network" is a network that transmits information using digital signals, and includes the Internet. 【0396】 "User equipment" refers to devices that users directly operate, and includes computers, smartphones, tablets, and other similar devices. 【0397】 "Emotion recognition technology" refers to technology that analyzes a user's facial expressions and voice to detect their emotional state. 【0398】 "Real-time" refers to a state where processing or operations are executed immediately, with virtually no time delay. 【0399】 "Dynamic adjustment" refers to changing the content and structure on the spot according to the user's situation and needs. 【0400】 "Learning progress information" refers to data on the extent to which users have absorbed educational materials and their progress in that regard. 【0401】 "Feedback" refers to providing information to improve the system and learning materials based on learning progress data and user reactions. 【0402】 This system utilizes information processing equipment and emotion recognition technology to provide user-optimized educational materials. First, users access the system and create an account using a dedicated web portal or mobile application. Here, users enter the necessary basic information and allow the use of their device's camera and microphone to enhance the educational experience. 【0403】 The server uses the received user information to generate optimal educational materials using a generative AI model. This generation process utilizes programming languages ​​such as Python and a database management system. The generated materials are diverse and include text, video, and interactive quiz formats. These materials are dynamically adjusted in real time according to the user's emotional state. Sentiment recognition technology using OpenCV and a speech recognition engine is applied to this adjustment. 【0404】 Once the educational materials have been generated, the server sends a notification to the user and makes them accessible via a link. The user can then use this link to learn at their own pace. 【0405】 For example, suppose user A is taking an "Introduction to Programming" course and is struggling with a difficult assignment. If emotion recognition technology detects user A's frustration, the server will adjust the content accordingly, providing clearer hints and videos demonstrating success stories to support smoother learning. 【0406】 After the learning session is complete, the server analyzes learning progress data and sentiment data to generate feedback that can be used to improve future educational content. This makes it possible to increase user satisfaction and provide a more effective learning environment. 【0407】 An example of a prompt to input into a generative AI model is, "Generate educational materials best suited to the user's learning needs. Dynamic adjustments based on the user's emotional state are required." This prompt provides specific instructions to the generative AI model, helping to create a customized educational experience for each user. 【0408】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0409】 Step 1: 【0410】 Users access the system via a dedicated web portal or mobile application and create an account. During this process, users enter basic information and grant access to their device's camera and microphone. Inputs include personal information such as name and email address, and output is transmitted to the server. This prepares the system to provide an experience optimized for each user. 【0411】 Step 2: 【0412】 The server receives user information and stores it in a database. The input received is the user's personal information, and the output is an update to the database. Specifically, it processes HTTP requests and saves the information to an SQL database. This procedure enables the system to perform analysis based on individual user data. 【0413】 Step 3: 【0414】 The server uses a generative AI model to create appropriate educational materials based on the user's learning needs. The input is information about the user's learning history and areas of interest, and the output is educational materials such as text, videos, and interactive quizzes. Specifically, user data is processed by an algorithm, and the generated prompts are input into the model to produce materials. These materials assist in creating an appropriate learning experience. 【0415】 Step 4: 【0416】 The device uses its camera and microphone to analyze the user's facial expressions and voice, detecting their emotional state in real time. Input is live camera footage and audio data, while output is user emotional state data. Specifically, it uses OpenCV and a speech recognition engine to analyze the data and execute code to determine emotions. 【0417】 Step 5: 【0418】 The server considers the user's emotional state and dynamically adjusts the educational materials. The input is emotional state data, and the output is the adjusted educational materials. For example, if user frustration is detected, the server provides easier content or encouraging videos. This feature allows the learning experience to be customized to individual needs. 【0419】 Step 6: 【0420】 The server sends a notification to provide educational materials to the user. The input is the prepared educational materials, and the output is the sending of the notification. Specifically, a link is sent to the user's application via the notification protocol, allowing the user to access and begin learning. 【0421】 Step 7: 【0422】 After the learning process is complete, the server collects and analyzes learning progress data and sentiment data. The input is the user's learning data and sentiment data, and the output is a feedback report and suggestions for future content improvements. Specifically, data analysis tools are used to visualize progress, and the generative AI model is also improved. This step aims to further enhance the learning experience. 【0423】 (Application Example 2) 【0424】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0425】 In modern learning methods, providing personalized educational experiences is essential to meet the diverse needs of learners. However, systems that can dynamically adjust learning content in real time, taking into account the user's emotional state, are not yet readily available. In particular, there is a growing need for systems that can automatically detect when learners lose interest or experience stress and respond appropriately. 【0426】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0427】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information, means for distributing the generated educational materials to the user's device via electronic communication means, and means for processing data from sensors for analyzing the user's emotional state. This makes it possible to monitor the learner's emotional state in real time and dynamically adjust the content to provide a flexible educational experience tailored to individual needs. 【0428】 "User information" refers to basic data about individual learners that the system uses to acquire data about them. 【0429】 "Educational materials" are content generated to support learning, including text, videos, and interactive quizzes. 【0430】 "Electronic communication means" refers to network infrastructure and protocols used to transmit digital information to user devices. 【0431】 "User devices" refer to terminals used by learners to access educational materials, and include smartphones, tablets, and personal computers. 【0432】 "Emotional state" refers to information that indicates the psychological and emotional responses expressed by the user during the learning process. 【0433】 "Data from sensors" refers to data about the user's facial expressions and voice collected through input devices such as cameras and microphones. 【0434】 "Dynamic adjustment" refers to the process of changing the content of educational materials in real time according to the emotional state of the user. 【0435】 This system begins with the user creating an account through a dedicated application and entering their user information. The user then grants access to the device's camera and microphone for emotion recognition. This information is received by the server and stored in a database. 【0436】 The server uses AI technology based on user information to generate educational materials. These materials include a variety of content, such as device text, videos, and interactive quizzes. These educational materials are delivered to user devices via electronic communication. 【0437】 The device uses a camera and microphone to monitor the user's emotional state in real time. Utilizing OpenCV, PyDub, and TensorFlow, it analyzes emotions from facial expressions and voice to detect the user's psychological state. Based on the emotional state, the server dynamically adjusts the content of educational materials, lowering the difficulty level or adding encouraging messages as needed. 【0438】 For example, if a user is taking a "basic mathematics" class and encounters a difficult problem that causes them stress, the device will detect this emotional state. The server will then provide additional content, including hints, to help the user overcome the problem. 【0439】 An example of a prompt for a generative AI model is, "When the user shows frustration, suggest supportive messages to improve the learning process." This prompt serves as an important guide for the AI ​​to provide the best possible educational experience for the user. 【0440】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0441】 Step 1: 【0442】 The user launches a dedicated application and enters their user information on the account creation screen. This information includes user ID, name, and learning objectives. The server receives this user information and stores it in a database. This process prepares the user for a personalized educational experience. 【0443】 Step 2: 【0444】 The server generates educational materials based on stored user information. It utilizes a generation AI model to create educational content including text, videos, and interactive quizzes. The AI ​​model selects the most suitable content based on the user's learning goals and level. As output, educational materials tailored to the user are prepared. 【0445】 Step 3: 【0446】 The generated educational materials are delivered from the server to the user's device via electronic communication. The device receives the relevant educational materials and prepares them for the user to access at any time. This allows the user to begin learning regardless of time or location. 【0447】 Step 4: 【0448】 The device monitors the user's emotional state in real time during training. It captures facial expressions with a camera and records audio with a microphone. Using OpenCV, PyDub, and TensorFlow, it recognizes emotions from this data and sends the state to the server. The current training progress can be understood by inputting the emotional state. 【0449】 Step 5: 【0450】 The server receives emotional state data sent from the terminal and dynamically adjusts the content of the educational materials. For example, if a user shows frustration, the server provides easier content or encouraging messages. This adjustment optimizes the user's learning experience individually, improving learning effectiveness. 【0451】 Step 6: 【0452】 After a user completes a lesson, the server analyzes their learning progress and emotional state, generating feedback to improve future educational materials. This feedback is used to customize subsequent learning content, continuously improving the educational experience. 【0453】 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. 【0454】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0455】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0456】 [Third Embodiment] 【0457】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0458】 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. 【0459】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0460】 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. 【0461】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0462】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0463】 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. 【0464】 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. 【0465】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0466】 The 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. 【0467】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0468】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0469】 This invention describes a method for constructing a system that utilizes an information processing device to provide educational materials tailored to the user. Specific embodiments thereof are described below. 【0470】 First, users access the platform through a dedicated web portal or mobile app and create an account. During this process, users enter their name, email address, smartphone usage level, and educational topics of interest. This information is received by the server and stored in a database. 【0471】 Next, the server analyzes each user's user information and automatically generates optimal educational materials using AI technology tailored to their individual needs. These educational materials consist of various formats, including text, videos, and interactive quizzes, and are customized to the user's skills and interests. This content is securely stored on the server in cloud storage. 【0472】 The server then notifies the user that the generated educational materials are ready. The notification includes a link to access the educational materials, allowing the user to access the content at any time via their device. As long as the user has an internet connection, they can continue their learning at any time and from anywhere. 【0473】 During or after a course, if users have questions, they can enter them in a dedicated Q&A section. The server receives these questions immediately and uses an AI response system to generate the most appropriate and timely answers, which are then sent back to the user. This enables real-time problem solving. 【0474】 Furthermore, the server continuously records each user's learning progress. This includes the history of courses taken, quiz scores, and viewing time. The progress data is used to improve the quality of educational content and as feedback for future content provision. 【0475】 As a concrete example, suppose user A selects an online course on "photo editing techniques." The server considers user A's requests and skill level and generates a video tutorial that includes specific steps that can be easily practiced on a smartphone. By watching this content, user A can learn photo editing techniques using their smartphone, and if they have any further questions, the server will provide immediate answers. 【0476】 In this way, this system can enhance the efficiency and effectiveness of educational delivery and provide personalized learning experiences for various users. 【0477】 The following describes the processing flow. 【0478】 Step 1: 【0479】 The user accesses a dedicated website or mobile application and opens the new account creation screen. Here, they enter information such as their name, email address, learning level, and areas of interest. Once registration is complete, a success message is displayed on the screen. 【0480】 Step 2: 【0481】 The server receives registration information submitted by the user and securely stores it in the database. After verifying the registration details and confirming there are no problems, it sends an account activation email to the user. 【0482】 Step 3: 【0483】 The user checks the activation email and clicks the link provided to activate their account. Once activation is complete, the user is redirected to the login screen and can access the learning dashboard. 【0484】 Step 4: 【0485】 After the server authenticates the user's login and establishes a secure connection, it provides a user dashboard. This dashboard displays a list of available courses and recommended educational content. 【0486】 Step 5: 【0487】 Users select courses that interest them and view detailed information about the learning content. Once they register for a selected course, they gain access to its content. 【0488】 Step 6: 【0489】 The server automatically generates customized educational materials using AI technology based on the selected course. The generated content is stored in cloud storage and prepared for download or streaming. 【0490】 Step 7: 【0491】 The device provides generated educational materials in streaming or download format upon user request. Users can then begin learning and progress through the educational materials at their own pace. 【0492】 Step 8: 【0493】 If a user has a question during the lesson, they can post it directly to the server via the chat function on their device or the Q&A section. 【0494】 Step 9: 【0495】 The server receives questions from users and uses AI to instantly generate and respond with appropriate answers. This allows users to resolve their questions in real time. 【0496】 Step 10: 【0497】 The server records the user's learning progress and tracks course completion and grades. This information is analyzed to inform future content delivery. 【0498】 Step 11: 【0499】 The server sends a feedback form to the user after the course ends. The collected feedback will be used to improve the educational content and optimize it for future courses. 【0500】 (Example 1) 【0501】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0502】 There is a growing need to efficiently generate and quickly deliver personalized educational materials that meet the diverse learning needs of today's world, while also improving the learning experience for users. However, conventional systems struggle to customize materials for each user and provide prompt responses, and they have difficulty making full use of feedback on learning progress. 【0503】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0504】 In this invention, the server includes means for automatically generating educational materials using a generative model based on user information, means for securely storing the generated educational materials in a cloud environment and making them accessible to user devices via a digital communication network, and means for receiving inquiries from users, generating automated responses using natural language processing, and providing answers in real time. This enables the realization of a personalized learning experience for each user and improves learning efficiency through immediate responses. 【0505】 "User information" refers to individual data such as user attributes, interests, and skill levels, and is input data used to optimize the generation of educational materials. 【0506】 A "generative model" is a processing model that automatically generates optimal educational materials using algorithms and artificial intelligence technology based on user information. 【0507】 "Educational materials" refer to learning content in various forms, including text, videos, and quizzes, generated to support users' learning. 【0508】 A "cloud environment" is a remote data storage service that stores educational materials and related data via the internet, allowing users to access them as needed. 【0509】 A "digital communication network" is a network infrastructure that enables the exchange of information, and is a means of sending and receiving data via the internet or dedicated lines. 【0510】 "Natural language processing" is a field of artificial intelligence technology that enables computers to understand human language, process and analyze information, and respond appropriately. 【0511】 "Feedback" is the process of providing valuable information and suggestions to improve the quality of content and learning materials based on users' learning activities and progress. 【0512】 This invention demonstrates a method for providing users with customized educational content using an information processing system. First, the user accesses the platform using a web portal or mobile application. When creating an account, the user enters their basic information and learning preferences. This information is received by the server and stored in a database. 【0513】 The server uses a generative model to analyze stored user information and automatically generate optimal educational materials tailored to the user's learning needs. This process utilizes programming languages ​​such as Python and AI libraries for data analysis and content generation. The generated educational materials take the form of text, videos, and interactive quizzes and are securely stored in cloud storage services (e.g., AWS S3 or Google Cloud Storage). 【0514】 After generation, the server notifies the user that the educational materials are ready and provides an access link via the digital network. Users can access this link using their device and proceed with their learning. HTML5 and JavaScript are used, and the system is designed to display content smoothly. 【0515】 During the learning process, users can submit questions through a dedicated Q&A section. The server utilizes natural language processing technology to receive user inquiries and quickly generate and provide appropriate answers. This allows users to enjoy real-time problem-solving. 【0516】 Furthermore, the server regularly records and analyzes users' learning progress. This establishes a function to optimize the content of educational materials based on progress data and provide continuous feedback. The accumulation of user-specific historical information will improve the quality of future content delivery and learning experiences. 【0517】 For example, if a user expresses interest in "photo editing techniques for beginners," the server will generate a video tutorial tailored to that need. The user can then receive clear, step-by-step instructions on photo editing using their smartphone. An example of a prompt would be, "Please teach me photo editing techniques for beginners using my smartphone." In this way, the system provides a personalized learning experience and delivers content that meets the user's educational needs. 【0518】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0519】 Step 1: 【0520】 Users access the system through a web portal or mobile application and create an account. Here, users enter their name, email address, smartphone usage level, and educational topics of interest. The entered information is sent to the server and stored in a database. This data is then used as foundational data for future material generation processes. 【0521】 Step 2: 【0522】 The server analyzes user information stored in the database. Using a generative AI model, it automatically generates optimal educational materials tailored to the user's needs and skill level. In this analysis, the algorithm constructs educational content in text, video, and quiz formats based on the input user information. The output is personalized educational materials. 【0523】 Step 3: 【0524】 The server securely stores the generated educational materials in cloud storage. Services such as AWS S3 and Google Cloud Storage are used to ensure data security and availability. The output is accessible educational materials stored in the cloud. 【0525】 Step 4: 【0526】 The server notifies the user that the educational materials are ready. This notification includes a direct link to the educational content and is sent to the user via the digital communication network. The output of this notification is an access link sent to the user's device. 【0527】 Step 5: 【0528】 Users access educational materials via a notified link using their device. The content is displayed using HTML5 and JavaScript, allowing users to smoothly browse the materials and progress through their learning. The input is a link, and the output is a display of educational content. 【0529】 Step 6: 【0530】 During the learning process, if a user has a question, they can enter it in a dedicated Q&A section. Upon receiving this input, the server starts a process using natural language processing techniques to generate an appropriate response to the question. The output is an immediate answer provided to the user. 【0531】 Step 7: 【0532】 The server continuously records users' learning progress and analyzes the progress data. This includes detailed data such as course history, quiz scores, and viewing time. This data is used as feedback for future content optimization and quality improvement. Learning activity data is the input, and improvement information based on feedback is obtained as the output. 【0533】 (Application Example 1) 【0534】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0535】 In today's information society, providing educational materials tailored to users' individual interests and skill levels is crucial. However, existing systems fail to adequately address diverse user needs through automated personalized content generation and real-time inquiry support. Furthermore, effectively managing users' learning progress and generating feedback based on that progress is also difficult. This hinders the maximization of educational effectiveness. 【0536】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0537】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information; means for distributing the generated educational materials to user devices via a digital communication network; and means for receiving inquiries from users related to said educational materials and generating appropriate answers using an automated response device. This makes it possible to provide educational materials that are suitable for the user's interests and skills, and to realize an efficient and immediate learning environment. 【0538】 An "information processing device" is a device that receives input from a user and analyzes or generates data based on that information. 【0539】 "User information" refers to data about users receiving education or other services, including personal interests, skills, and other relevant information. 【0540】 "Educational materials" refer to digital content provided for the purpose of user learning and training, including text, videos, quizzes, and other similar materials. 【0541】 A "digital communication network" is a network used to send and receive digital data, including the internet. 【0542】 A "user device" is a device that an individual user can use on their own, such as a smartphone or tablet. 【0543】 An "automated response system" is a system that receives inquiries from users and automatically generates answers based on programmed logic. 【0544】 "Learning progress data" refers to information about the user's learning activity history and results, including, for example, the number of courses taken and quiz scores. 【0545】 "Feedback" refers to information obtained by analyzing user reactions and data during learning and use, which is used to improve future material provision. 【0546】 An "artificial intelligence model" is an algorithm or framework that allows computers to simulate human intellectual behavior and generate content that meets user needs. 【0547】 "Areas of interest" refers to the range of themes or topics that a user is particularly interested in. 【0548】 "Skill level" is an indicator that shows the current level of a user's skills and abilities. 【0549】 To implement this invention, a server as an information processing device, a user's digital terminal, and AI technology are used. The server receives user information provided by the user and stores it in a database. This information includes the user's name, contact information, topics of interest, and skill level. 【0550】 The server uses an AI model to automatically generate personalized educational materials based on user information. This AI model, for example, a generative AI model using natural language processing technology (e.g., GPT-4), generates content in the form of text, video, and interactive quizzes. These educational materials are securely stored in cloud storage, and users are notified when they are ready. 【0551】 Users can access these educational materials via a digital communication network through their devices (e.g., smartphones and tablets). If users have questions during their studies, they can use a dedicated inquiry function. An automated response system built into the server uses AI technology to instantly generate and provide appropriate answers to received questions to the user. 【0552】 Furthermore, the server tracks and records users' learning progress data, including courses taken, grades, and viewing time. The server analyzes this data to generate feedback for improving the quality of educational content. 【0553】 As a concrete example, suppose a user wants to improve their "digital photo editing" skills. The server uses AI to generate tutorial videos on photo editing techniques suitable for beginners. The user can then access these materials to acquire the skills. If the user has questions along the way, they can get immediate answers using prompts such as, "Please explain the technical details regarding digital photo editing." This makes it possible to improve the quality and efficiency of learning. 【0554】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0555】 Step 1: 【0556】 Users access a web portal or mobile app using their device and create an account. During this process, they enter user information such as their name, email address, areas of interest, and skill level. This information is then transmitted from the device to the server. 【0557】 Step 2: 【0558】 The server stores the received user information in a database. Basic information submitted by the user is used as input, and this information is registered in the database system for organization and storage. As output, user data is stored so that it can be easily searched or used later as needed. 【0559】 Step 3: 【0560】 The server utilizes a generative AI model based on stored user information to create educational materials tailored to each user. The input consists of user interest and skill level data retrieved from a database. The generative AI model processes this data to generate user-specific content. As output, customized educational materials are generated and stored in cloud storage. 【0561】 Step 4: 【0562】 The server notifies the user that the generated educational materials are ready. The system uses the on-device notification system to inform the user that materials created by the generation AI model are available. The prepared content information is used as input, and notification information is generated as output. 【0563】 Step 5: 【0564】 Users access generated educational materials through their devices using links provided by the server. Input consists of notifications and link information from the server, and the content is delivered to the user via the device's display function. Output is the ability for the user to view the educational materials. 【0565】 Step 6: 【0566】 If a user encounters difficulties while studying educational materials, they can send a question to the server using a dedicated inquiry function on their device. The user's query is used as input, and the query content is sent to the server as output. 【0567】 Step 7: 【0568】 The server uses a generative AI model to generate appropriate answers to incoming questions and immediately sends them back to the user. The input is the user's question, and the generative AI model analyzes this question to generate the optimal answer. The output is the generated answer information, which is then provided to the user via their device. 【0569】 Step 8: 【0570】 The server constantly monitors the user's learning progress and records learning progress data such as course data and quiz scores. User learning activity information is continuously used as input, and a dataset detailing that activity is generated as output. 【0571】 Step 9: 【0572】 The server analyzes recorded learning progress data and generates feedback to create improved educational materials. Progress data is used as input, and insights for improving educational materials are gained through data analysis. Feedback information for improvement is generated as output. 【0573】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0574】 This invention describes a method for constructing a system that provides an optimized educational experience for users using an information processing device and emotion recognition technology. Specific embodiments thereof are described below. 【0575】 First, users access the system via a dedicated web portal or mobile application and create an account. They enter their basic information and grant access to their device's camera and microphone to utilize the emotion recognition feature. This information is received by the server and stored in a database. 【0576】 Next, the server analyzes the user's information and automatically generates optimal educational materials tailored to their learning needs using AI technology. The generated materials include text, videos, and interactive quizzes, and are designed to dynamically adjust their content according to the user's emotional state. The emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to detect the user's emotional state in real time. 【0577】 Once the educational materials are ready, the server sends a notification to the user, providing an opportunity to access the materials via a link. The user can then use this to begin learning and progress at their own pace. 【0578】 During learning, the emotion engine is activated, and the device periodically monitors the user's emotions. For example, if the emotion engine determines that the user is stressed or has lost interest, the server modifies the learning content accordingly. This might involve switching to simpler explanations or providing additional encouraging messages. This ensures that the learning experience is seamlessly tailored and personalized. 【0579】 For example, if user A is taking a course called "Introduction to Programming" and is stuck on a difficult assignment, the emotion engine will detect frustration from user A's facial expressions. Based on this information, the server will adjust the content and help user A overcome the challenge by presenting videos that include hints and success stories. 【0580】 Furthermore, after the learning session is complete, the server analyzes emotional data along with learning progress data to improve future content. This feedback loop enhances user satisfaction and promotes a more effective learning experience. With the addition of emotional recognition capabilities, this system provides users with innovative and personalized education. 【0581】 The following describes the processing flow. 【0582】 Step 1: 【0583】 Users access a dedicated web portal or mobile application, enter information such as their name, email address, and smartphone usage level on the account creation screen, and grant access to their camera and microphone. This makes the emotion engine available. 【0584】 Step 2: 【0585】 The server receives information sent by the user and stores it in the database. Then, it sends a confirmation email to the user to activate their account and release them from using the system. 【0586】 Step 3: 【0587】 Users check their activation email and click the link to activate their account. After activation is complete, users can access the learning dashboard and view available courses. 【0588】 Step 4: 【0589】 The server authenticates the user's login and displays a list of available educational content on the dashboard. The user selects courses of interest and registers. 【0590】 Step 5: 【0591】 The server analyzes user information and generates customized educational materials using AI technology. These materials include text, videos, and quizzes, and can be adjusted in real time by an emotion engine. The materials are stored in cloud storage. 【0592】 Step 6: 【0593】 The device provides generated educational materials in streaming or downloadable format upon user request. Users can then use these materials to learn at their own pace. 【0594】 Step 7: 【0595】 During learning, the device periodically provides the user's facial expressions and voice to the emotion engine via the camera and microphone, monitoring the user's emotions. 【0596】 Step 8: 【0597】 The server receives the user's emotional state detected by the emotion engine, and if frustration or loss of interest is recognized, it dynamically changes the content of the educational materials. 【0598】 Step 9: 【0599】 If a user has questions during the lesson, they enter them via the chat function on their device. The server receives the question, uses AI to quickly generate an answer, and responds in real time. 【0600】 Step 10: 【0601】 The server records user learning progress and sentiment data, and analyzes learning completion and problem areas. This data is used to optimize future content and improve user satisfaction. 【0602】 Step 11: 【0603】 After the course ends, the server sends the user a feedback form about their learning experience. The collected feedback will be used to improve future content. 【0604】 (Example 2) 【0605】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0606】 In educational systems, there is a need to accurately understand each user's learning needs and emotional changes, and to provide individualized learning experiences. However, conventional systems have struggled to detect users' emotional states in real time and dynamically adjust content based on them. This could potentially lead to decreased learning efficiency and user satisfaction. 【0607】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0608】 In this invention, the server includes means for generating educational materials based on user information, means for detecting the user's emotional state in real time using emotion recognition technology and dynamically adjusting the educational materials based on the emotional state, and means for generating feedback based on learning progress information. This makes it possible to immediately adjust learning materials to match the individual emotional state of the user, thereby providing a more effective and satisfying learning experience. 【0609】 "Information processing equipment" refers to devices that input, process, store, and output data, and plays a role in coordinating the entire system. 【0610】 "User information" refers to data related to an individual using the system, including name, contact information, and learning history. 【0611】 "Educational materials" refer to learning materials provided to learners, and can take the form of textbooks, videos, quizzes, and other similar formats. 【0612】 A "digital communication network" is a network that transmits information using digital signals, and includes the Internet. 【0613】 "User equipment" refers to devices that users directly operate, and includes computers, smartphones, tablets, and other similar devices. 【0614】 "Emotion recognition technology" refers to technology that analyzes a user's facial expressions and voice to detect their emotional state. 【0615】 "Real-time" refers to a state where processing or operations are executed immediately, with virtually no time delay. 【0616】 "Dynamic adjustment" refers to changing the content and structure on the spot according to the user's situation and needs. 【0617】 "Learning progress information" refers to data on the extent to which users have absorbed educational materials and their progress in that regard. 【0618】 "Feedback" refers to providing information to improve the system and learning materials based on learning progress data and user reactions. 【0619】 This system utilizes information processing equipment and emotion recognition technology to provide user-optimized educational materials. First, users access the system and create an account using a dedicated web portal or mobile application. Here, users enter the necessary basic information and allow the use of their device's camera and microphone to enhance the educational experience. 【0620】 The server uses the received user information to generate optimal educational materials using a generative AI model. This generation process utilizes programming languages ​​such as Python and a database management system. The generated materials are diverse and include text, video, and interactive quiz formats. These materials are dynamically adjusted in real time according to the user's emotional state. Sentiment recognition technology using OpenCV and a speech recognition engine is applied to this adjustment. 【0621】 Once the educational materials have been generated, the server sends a notification to the user and makes them accessible via a link. The user can then use this link to learn at their own pace. 【0622】 For example, suppose user A is taking an "Introduction to Programming" course and is struggling with a difficult assignment. If emotion recognition technology detects user A's frustration, the server will adjust the content accordingly, providing clearer hints and videos demonstrating success stories to support smoother learning. 【0623】 After the learning session is complete, the server analyzes learning progress data and sentiment data to generate feedback that can be used to improve future educational content. This makes it possible to increase user satisfaction and provide a more effective learning environment. 【0624】 An example of a prompt to input into a generative AI model is, "Generate educational materials best suited to the user's learning needs. Dynamic adjustments based on the user's emotional state are required." This prompt provides specific instructions to the generative AI model, helping to create a customized educational experience for each user. 【0625】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0626】 Step 1: 【0627】 Users access the system via a dedicated web portal or mobile application and create an account. During this process, users enter basic information and grant access to their device's camera and microphone. Inputs include personal information such as name and email address, and output is transmitted to the server. This prepares the system to provide an experience optimized for each user. 【0628】 Step 2: 【0629】 The server receives user information and stores it in a database. The input received is the user's personal information, and the output is an update to the database. Specifically, it processes HTTP requests and saves the information to an SQL database. This procedure enables the system to perform analysis based on individual user data. 【0630】 Step 3: 【0631】 The server uses a generative AI model to create appropriate educational materials based on the user's learning needs. The input is information about the user's learning history and areas of interest, and the output is educational materials such as text, videos, and interactive quizzes. Specifically, user data is processed by an algorithm, and the generated prompts are input into the model to produce materials. These materials assist in creating an appropriate learning experience. 【0632】 Step 4: 【0633】 The device uses its camera and microphone to analyze the user's facial expressions and voice, detecting their emotional state in real time. Input is live camera footage and audio data, while output is user emotional state data. Specifically, it uses OpenCV and a speech recognition engine to analyze the data and execute code to determine emotions. 【0634】 Step 5: 【0635】 The server considers the user's emotional state and dynamically adjusts the educational materials. The input is emotional state data, and the output is the adjusted educational materials. For example, if user frustration is detected, the server provides easier content or encouraging videos. This feature allows the learning experience to be customized to individual needs. 【0636】 Step 6: 【0637】 The server sends a notification to provide educational materials to the user. The input is the prepared educational materials, and the output is the sending of the notification. Specifically, a link is sent to the user's application via the notification protocol, allowing the user to access and begin learning. 【0638】 Step 7: 【0639】 After the learning process is complete, the server collects and analyzes learning progress data and sentiment data. The input is the user's learning data and sentiment data, and the output is a feedback report and suggestions for future content improvements. Specifically, data analysis tools are used to visualize progress, and the generative AI model is also improved. This step aims to further enhance the learning experience. 【0640】 (Application Example 2) 【0641】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0642】 In modern learning methods, providing personalized educational experiences is essential to meet the diverse needs of learners. However, systems that can dynamically adjust learning content in real time, taking into account the user's emotional state, are not yet readily available. In particular, there is a growing need for systems that can automatically detect when learners lose interest or experience stress and respond appropriately. 【0643】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0644】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information, means for distributing the generated educational materials to the user's device via electronic communication means, and means for processing data from sensors for analyzing the user's emotional state. This makes it possible to monitor the learner's emotional state in real time and dynamically adjust the content to provide a flexible educational experience tailored to individual needs. 【0645】 "User information" refers to basic data about individual learners that the system uses to acquire data about them. 【0646】 "Educational materials" are content generated to support learning, including text, videos, and interactive quizzes. 【0647】 "Electronic communication means" refers to network infrastructure and protocols used to transmit digital information to user devices. 【0648】 "User devices" refer to terminals used by learners to access educational materials, and include smartphones, tablets, and personal computers. 【0649】 "Emotional state" refers to information that indicates the psychological and emotional responses expressed by the user during the learning process. 【0650】 "Data from sensors" refers to data about the user's facial expressions and voice collected through input devices such as cameras and microphones. 【0651】 "Dynamic adjustment" refers to the process of changing the content of educational materials in real time according to the emotional state of the user. 【0652】 This system begins with the user creating an account through a dedicated application and entering their user information. The user then grants access to the device's camera and microphone for emotion recognition. This information is received by the server and stored in a database. 【0653】 The server uses AI technology based on user information to generate educational materials. These materials include a variety of content, such as device text, videos, and interactive quizzes. These educational materials are delivered to user devices via electronic communication. 【0654】 The device uses a camera and microphone to monitor the user's emotional state in real time. Utilizing OpenCV, PyDub, and TensorFlow, it analyzes emotions from facial expressions and voice to detect the user's psychological state. Based on the emotional state, the server dynamically adjusts the content of educational materials, lowering the difficulty level or adding encouraging messages as needed. 【0655】 For example, if a user is taking a "basic mathematics" class and encounters a difficult problem that causes them stress, the device will detect this emotional state. The server will then provide additional content, including hints, to help the user overcome the problem. 【0656】 An example of a prompt for a generative AI model is, "When the user shows frustration, suggest supportive messages to improve the learning process." This prompt serves as an important guide for the AI ​​to provide the best possible educational experience for the user. 【0657】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0658】 Step 1: 【0659】 The user launches a dedicated application and enters their user information on the account creation screen. This information includes user ID, name, and learning objectives. The server receives this user information and stores it in a database. This process prepares the user for a personalized educational experience. 【0660】 Step 2: 【0661】 The server generates educational materials based on stored user information. It utilizes a generation AI model to create educational content including text, videos, and interactive quizzes. The AI ​​model selects the most suitable content based on the user's learning goals and level. As output, educational materials tailored to the user are prepared. 【0662】 Step 3: 【0663】 The generated educational materials are delivered from the server to the user's device via electronic communication. The device receives the relevant educational materials and prepares them for the user to access at any time. This allows the user to begin learning regardless of time or location. 【0664】 Step 4: 【0665】 The device monitors the user's emotional state in real time during training. It captures facial expressions with a camera and records audio with a microphone. Using OpenCV, PyDub, and TensorFlow, it recognizes emotions from this data and sends the state to the server. The current training progress can be understood by inputting the emotional state. 【0666】 Step 5: 【0667】 The server receives emotional state data sent from the terminal and dynamically adjusts the content of the educational materials. For example, if a user shows frustration, the server provides easier content or encouraging messages. This adjustment optimizes the user's learning experience individually, improving learning effectiveness. 【0668】 Step 6: 【0669】 After a user completes a lesson, the server analyzes their learning progress and emotional state, generating feedback to improve future educational materials. This feedback is used to customize subsequent learning content, continuously improving the educational experience. 【0670】 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. 【0671】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0672】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0673】 [Fourth Embodiment] 【0674】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0675】 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. 【0676】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0677】 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. 【0678】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0679】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0680】 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. 【0681】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0682】 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. 【0683】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0684】 The 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. 【0685】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0686】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0687】 This invention describes a method for constructing a system that utilizes an information processing device to provide educational materials tailored to the user. Specific embodiments thereof are described below. 【0688】 First, users access the platform through a dedicated web portal or mobile app and create an account. During this process, users enter their name, email address, smartphone usage level, and educational topics of interest. This information is received by the server and stored in a database. 【0689】 Next, the server analyzes each user's user information and automatically generates optimal educational materials using AI technology tailored to their individual needs. These educational materials consist of various formats, including text, videos, and interactive quizzes, and are customized to the user's skills and interests. This content is securely stored on the server in cloud storage. 【0690】 The server then notifies the user that the generated educational materials are ready. The notification includes a link to access the educational materials, allowing the user to access the content at any time via their device. As long as the user has an internet connection, they can continue their learning at any time and from anywhere. 【0691】 During or after a course, if users have questions, they can enter them in a dedicated Q&A section. The server receives these questions immediately and uses an AI response system to generate the most appropriate and timely answers, which are then sent back to the user. This enables real-time problem solving. 【0692】 Furthermore, the server continuously records each user's learning progress. This includes the history of courses taken, quiz scores, and viewing time. The progress data is used to improve the quality of educational content and as feedback for future content provision. 【0693】 As a concrete example, suppose user A selects an online course on "photo editing techniques." The server considers user A's requests and skill level and generates a video tutorial that includes specific steps that can be easily practiced on a smartphone. By watching this content, user A can learn photo editing techniques using their smartphone, and if they have any further questions, the server will provide immediate answers. 【0694】 In this way, this system can enhance the efficiency and effectiveness of educational delivery and provide personalized learning experiences for various users. 【0695】 The following describes the processing flow. 【0696】 Step 1: 【0697】 The user accesses a dedicated website or mobile application and opens the new account creation screen. Here, they enter information such as their name, email address, learning level, and areas of interest. Once registration is complete, a success message is displayed on the screen. 【0698】 Step 2: 【0699】 The server receives registration information submitted by the user and securely stores it in the database. After verifying the registration details and confirming there are no problems, it sends an account activation email to the user. 【0700】 Step 3: 【0701】 The user checks the activation email and clicks the link provided to activate their account. Once activation is complete, the user is redirected to the login screen and can access the learning dashboard. 【0702】 Step 4: 【0703】 After the server authenticates the user's login and establishes a secure connection, it provides a user dashboard. This dashboard displays a list of available courses and recommended educational content. 【0704】 Step 5: 【0705】 Users select courses that interest them and view detailed information about the learning content. Once they register for a selected course, they gain access to its content. 【0706】 Step 6: 【0707】 The server automatically generates customized educational materials using AI technology based on the selected course. The generated content is stored in cloud storage and prepared for download or streaming. 【0708】 Step 7: 【0709】 The device provides generated educational materials in streaming or download format upon user request. Users can then begin learning and progress through the educational materials at their own pace. 【0710】 Step 8: 【0711】 If a user has a question during the lesson, they can post it directly to the server via the chat function on their device or the Q&A section. 【0712】 Step 9: 【0713】 The server receives questions from users and uses AI to instantly generate and respond with appropriate answers. This allows users to resolve their questions in real time. 【0714】 Step 10: 【0715】 The server records the user's learning progress and tracks course completion and grades. This information is analyzed to inform future content delivery. 【0716】 Step 11: 【0717】 The server sends a feedback form to the user after the course ends. The collected feedback will be used to improve the educational content and optimize it for future courses. 【0718】 (Example 1) 【0719】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0720】 There is a growing need to efficiently generate and quickly deliver personalized educational materials that meet the diverse learning needs of today's world, while also improving the learning experience for users. However, conventional systems struggle to customize materials for each user and provide prompt responses, and they have difficulty making full use of feedback on learning progress. 【0721】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0722】 In this invention, the server includes means for automatically generating educational materials using a generative model based on user information, means for securely storing the generated educational materials in a cloud environment and making them accessible to user devices via a digital communication network, and means for receiving inquiries from users, generating automated responses using natural language processing, and providing answers in real time. This enables the realization of a personalized learning experience for each user and improves learning efficiency through immediate responses. 【0723】 "User information" refers to individual data such as user attributes, interests, and skill levels, and is input data used to optimize the generation of educational materials. 【0724】 A "generative model" is a processing model that automatically generates optimal educational materials using algorithms and artificial intelligence technology based on user information. 【0725】 "Educational materials" refer to learning content in various forms, including text, videos, and quizzes, generated to support users' learning. 【0726】 A "cloud environment" is a remote data storage service that stores educational materials and related data via the internet, allowing users to access them as needed. 【0727】 A "digital communication network" is a network infrastructure that enables the exchange of information, and is a means of sending and receiving data via the internet or dedicated lines. 【0728】 "Natural language processing" is a field of artificial intelligence technology that enables computers to understand human language, process and analyze information, and respond appropriately. 【0729】 "Feedback" is the process of providing valuable information and suggestions to improve the quality of content and learning materials based on users' learning activities and progress. 【0730】 This invention demonstrates a method for providing users with customized educational content using an information processing system. First, the user accesses the platform using a web portal or mobile application. When creating an account, the user enters their basic information and learning preferences. This information is received by the server and stored in a database. 【0731】 The server uses a generative model to analyze stored user information and automatically generate optimal educational materials tailored to the user's learning needs. This process utilizes programming languages ​​such as Python and AI libraries for data analysis and content generation. The generated educational materials take the form of text, videos, and interactive quizzes and are securely stored in cloud storage services (e.g., AWS S3 or Google Cloud Storage). 【0732】 After generation, the server notifies the user that the educational materials are ready and provides an access link via the digital network. Users can access this link using their device and proceed with their learning. HTML5 and JavaScript are used, and the system is designed to display content smoothly. 【0733】 During the learning process, users can submit questions through a dedicated Q&A section. The server utilizes natural language processing technology to receive user inquiries and quickly generate and provide appropriate answers. This allows users to enjoy real-time problem-solving. 【0734】 Furthermore, the server regularly records and analyzes users' learning progress. This establishes a function to optimize the content of educational materials based on progress data and provide continuous feedback. The accumulation of user-specific historical information will improve the quality of future content delivery and learning experiences. 【0735】 For example, if a user expresses interest in "photo editing techniques for beginners," the server will generate a video tutorial tailored to that need. The user can then receive clear, step-by-step instructions on photo editing using their smartphone. An example of a prompt would be, "Please teach me photo editing techniques for beginners using my smartphone." In this way, the system provides a personalized learning experience and delivers content that meets the user's educational needs. 【0736】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0737】 Step 1: 【0738】 Users access the system through a web portal or mobile application and create an account. Here, users enter their name, email address, smartphone usage level, and educational topics of interest. The entered information is sent to the server and stored in a database. This data is then used as foundational data for future material generation processes. 【0739】 Step 2: 【0740】 The server analyzes user information stored in the database. Using a generative AI model, it automatically generates optimal educational materials tailored to the user's needs and skill level. In this analysis, the algorithm constructs educational content in text, video, and quiz formats based on the input user information. The output is personalized educational materials. 【0741】 Step 3: 【0742】 The server securely stores the generated educational materials in cloud storage. Services such as AWS S3 and Google Cloud Storage are used to ensure data security and availability. The output is accessible educational materials stored in the cloud. 【0743】 Step 4: 【0744】 The server notifies the user that the educational materials are ready. This notification includes a direct link to the educational content and is sent to the user via the digital communication network. The output of this notification is an access link sent to the user's device. 【0745】 Step 5: 【0746】 Users access educational materials via a notified link using their device. The content is displayed using HTML5 and JavaScript, allowing users to smoothly browse the materials and progress through their learning. The input is a link, and the output is a display of educational content. 【0747】 Step 6: 【0748】 During the learning process, if a user has a question, they can enter it in a dedicated Q&A section. Upon receiving this input, the server starts a process using natural language processing techniques to generate an appropriate response to the question. The output is an immediate answer provided to the user. 【0749】 Step 7: 【0750】 The server continuously records users' learning progress and analyzes the progress data. This includes detailed data such as course history, quiz scores, and viewing time. This data is used as feedback for future content optimization and quality improvement. Learning activity data is the input, and improvement information based on feedback is obtained as the output. 【0751】 (Application Example 1) 【0752】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0753】 In today's information society, providing educational materials tailored to users' individual interests and skill levels is crucial. However, existing systems fail to adequately address diverse user needs through automated personalized content generation and real-time inquiry support. Furthermore, effectively managing users' learning progress and generating feedback based on that progress is also difficult. This hinders the maximization of educational effectiveness. 【0754】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0755】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information; means for distributing the generated educational materials to user devices via a digital communication network; and means for receiving inquiries from users related to said educational materials and generating appropriate answers using an automated response device. This makes it possible to provide educational materials that are suitable for the user's interests and skills, and to realize an efficient and immediate learning environment. 【0756】 An "information processing device" is a device that receives input from a user and analyzes or generates data based on that information. 【0757】 "User information" refers to data about users receiving education or other services, including personal interests, skills, and other relevant information. 【0758】 "Educational materials" refer to digital content provided for the purpose of user learning and training, including text, videos, quizzes, and other similar materials. 【0759】 A "digital communication network" is a network used to send and receive digital data, including the internet. 【0760】 A "user device" is a device that an individual user can use on their own, such as a smartphone or tablet. 【0761】 An "automated response system" is a system that receives inquiries from users and automatically generates answers based on programmed logic. 【0762】 "Learning progress data" refers to information about the user's learning activity history and results, including, for example, the number of courses taken and quiz scores. 【0763】 "Feedback" refers to information obtained by analyzing user reactions and data during learning and use, which is used to improve future material provision. 【0764】 An "artificial intelligence model" is an algorithm or framework that allows computers to simulate human intellectual behavior and generate content that meets user needs. 【0765】 "Areas of interest" refers to the range of themes or topics that a user is particularly interested in. 【0766】 "Skill level" is an indicator that shows the current level of a user's skills and abilities. 【0767】 To implement this invention, a server as an information processing device, a user's digital terminal, and AI technology are used. The server receives user information provided by the user and stores it in a database. This information includes the user's name, contact information, topics of interest, and skill level. 【0768】 The server uses an AI model to automatically generate personalized educational materials based on user information. This AI model, for example, a generative AI model using natural language processing technology (e.g., GPT-4), generates content in the form of text, video, and interactive quizzes. These educational materials are securely stored in cloud storage, and users are notified when they are ready. 【0769】 Users can access these educational materials via a digital communication network through their devices (e.g., smartphones and tablets). If users have questions during their studies, they can use a dedicated inquiry function. An automated response system built into the server uses AI technology to instantly generate and provide appropriate answers to received questions to the user. 【0770】 Furthermore, the server tracks and records users' learning progress data, including courses taken, grades, and viewing time. The server analyzes this data to generate feedback for improving the quality of educational content. 【0771】 As a concrete example, suppose a user wants to improve their "digital photo editing" skills. The server uses AI to generate tutorial videos on photo editing techniques suitable for beginners. The user can then access these materials to acquire the skills. If the user has questions along the way, they can get immediate answers using prompts such as, "Please explain the technical details regarding digital photo editing." This makes it possible to improve the quality and efficiency of learning. 【0772】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0773】 Step 1: 【0774】 Users access a web portal or mobile app using their device and create an account. During this process, they enter user information such as their name, email address, areas of interest, and skill level. This information is then transmitted from the device to the server. 【0775】 Step 2: 【0776】 The server stores the received user information in a database. Basic information submitted by the user is used as input, and this information is registered in the database system for organization and storage. As output, user data is stored so that it can be easily searched or used later as needed. 【0777】 Step 3: 【0778】 The server utilizes a generative AI model based on stored user information to create educational materials tailored to each user. The input consists of user interest and skill level data retrieved from a database. The generative AI model processes this data to generate user-specific content. As output, customized educational materials are generated and stored in cloud storage. 【0779】 Step 4: 【0780】 The server notifies the user that the generated educational materials are ready. The system uses the on-device notification system to inform the user that materials created by the generation AI model are available. The prepared content information is used as input, and notification information is generated as output. 【0781】 Step 5: 【0782】 Users access generated educational materials through their devices using links provided by the server. Input consists of notifications and link information from the server, and the content is delivered to the user via the device's display function. Output is the ability for the user to view the educational materials. 【0783】 Step 6: 【0784】 If a user encounters difficulties while studying educational materials, they can send a question to the server using a dedicated inquiry function on their device. The user's query is used as input, and the query content is sent to the server as output. 【0785】 Step 7: 【0786】 The server uses a generative AI model to generate appropriate answers to incoming questions and immediately sends them back to the user. The input is the user's question, and the generative AI model analyzes this question to generate the optimal answer. The output is the generated answer information, which is then provided to the user via their device. 【0787】 Step 8: 【0788】 The server constantly monitors the user's learning progress and records learning progress data such as course data and quiz scores. User learning activity information is continuously used as input, and a dataset detailing that activity is generated as output. 【0789】 Step 9: 【0790】 The server analyzes recorded learning progress data and generates feedback to create improved educational materials. Progress data is used as input, and insights for improving educational materials are gained through data analysis. Feedback information for improvement is generated as output. 【0791】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0792】 This invention describes a method for constructing a system that provides an optimized educational experience for users using an information processing device and emotion recognition technology. Specific embodiments thereof are described below. 【0793】 First, users access the system via a dedicated web portal or mobile application and create an account. They enter their basic information and grant access to their device's camera and microphone to utilize the emotion recognition feature. This information is received by the server and stored in a database. 【0794】 Next, the server analyzes the user's information and automatically generates optimal educational materials tailored to their learning needs using AI technology. The generated materials include text, videos, and interactive quizzes, and are designed to dynamically adjust their content according to the user's emotional state. The emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to detect the user's emotional state in real time. 【0795】 Once the educational materials are ready, the server sends a notification to the user, providing an opportunity to access the materials via a link. The user can then use this to begin learning and progress at their own pace. 【0796】 During learning, the emotion engine is activated, and the device periodically monitors the user's emotions. For example, if the emotion engine determines that the user is stressed or has lost interest, the server modifies the learning content accordingly. This might involve switching to simpler explanations or providing additional encouraging messages. This ensures that the learning experience is seamlessly tailored and personalized. 【0797】 For example, if user A is taking a course called "Introduction to Programming" and is stuck on a difficult assignment, the emotion engine will detect frustration from user A's facial expressions. Based on this information, the server will adjust the content and help user A overcome the challenge by presenting videos that include hints and success stories. 【0798】 Furthermore, after the learning session is complete, the server analyzes emotional data along with learning progress data to improve future content. This feedback loop enhances user satisfaction and promotes a more effective learning experience. With the addition of emotional recognition capabilities, this system provides users with innovative and personalized education. 【0799】 The following describes the processing flow. 【0800】 Step 1: 【0801】 Users access a dedicated web portal or mobile application, enter information such as their name, email address, and smartphone usage level on the account creation screen, and grant access to their camera and microphone. This makes the emotion engine available. 【0802】 Step 2: 【0803】 The server receives information sent by the user and stores it in the database. Then, it sends a confirmation email to the user to activate their account and release them from using the system. 【0804】 Step 3: 【0805】 Users check their activation email and click the link to activate their account. After activation is complete, users can access the learning dashboard and view available courses. 【0806】 Step 4: 【0807】 The server authenticates the user's login and displays a list of available educational content on the dashboard. The user selects courses of interest and registers. 【0808】 Step 5: 【0809】 The server analyzes user information and generates customized educational materials using AI technology. These materials include text, videos, and quizzes, and can be adjusted in real time by an emotion engine. The materials are stored in cloud storage. 【0810】 Step 6: 【0811】 The device provides generated educational materials in streaming or downloadable format upon user request. Users can then use these materials to learn at their own pace. 【0812】 Step 7: 【0813】 During learning, the device periodically provides the user's facial expressions and voice to the emotion engine via the camera and microphone, monitoring the user's emotions. 【0814】 Step 8: 【0815】 The server receives the user's emotional state detected by the emotion engine, and if frustration or loss of interest is recognized, it dynamically changes the content of the educational materials. 【0816】 Step 9: 【0817】 If a user has questions during the lesson, they enter them via the chat function on their device. The server receives the question, uses AI to quickly generate an answer, and responds in real time. 【0818】 Step 10: 【0819】 The server records user learning progress and sentiment data, and analyzes learning completion and problem areas. This data is used to optimize future content and improve user satisfaction. 【0820】 Step 11: 【0821】 After the course ends, the server sends the user a feedback form about their learning experience. The collected feedback will be used to improve future content. 【0822】 (Example 2) 【0823】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0824】 In educational systems, there is a need to accurately understand each user's learning needs and emotional changes, and to provide individualized learning experiences. However, conventional systems have struggled to detect users' emotional states in real time and dynamically adjust content based on them. This could potentially lead to decreased learning efficiency and user satisfaction. 【0825】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0826】 In this invention, the server includes means for generating educational materials based on user information, means for detecting the user's emotional state in real time using emotion recognition technology and dynamically adjusting the educational materials based on the emotional state, and means for generating feedback based on learning progress information. This makes it possible to immediately adjust learning materials to match the individual emotional state of the user, thereby providing a more effective and satisfying learning experience. 【0827】 "Information processing equipment" refers to devices that input, process, store, and output data, and plays a role in coordinating the entire system. 【0828】 "User information" refers to data related to an individual using the system, including name, contact information, and learning history. 【0829】 "Educational materials" refer to learning materials provided to learners, and can take the form of textbooks, videos, quizzes, and other similar formats. 【0830】 A "digital communication network" is a network that transmits information using digital signals, and includes the Internet. 【0831】 "User equipment" refers to devices that users directly operate, and includes computers, smartphones, tablets, and other similar devices. 【0832】 "Emotion recognition technology" refers to technology that analyzes a user's facial expressions and voice to detect their emotional state. 【0833】 "Real-time" refers to a state where processing or operations are executed immediately, with virtually no time delay. 【0834】 "Dynamic adjustment" refers to changing the content and structure on the spot according to the user's situation and needs. 【0835】 "Learning progress information" refers to data on the extent to which users have absorbed educational materials and their progress in that regard. 【0836】 "Feedback" refers to providing information to improve the system and learning materials based on learning progress data and user reactions. 【0837】 This system utilizes information processing equipment and emotion recognition technology to provide user-optimized educational materials. First, users access the system and create an account using a dedicated web portal or mobile application. Here, users enter the necessary basic information and allow the use of their device's camera and microphone to enhance the educational experience. 【0838】 The server uses the received user information to generate optimal educational materials using a generative AI model. This generation process utilizes programming languages ​​such as Python and a database management system. The generated materials are diverse and include text, video, and interactive quiz formats. These materials are dynamically adjusted in real time according to the user's emotional state. Sentiment recognition technology using OpenCV and a speech recognition engine is applied to this adjustment. 【0839】 Once the educational materials have been generated, the server sends a notification to the user and makes them accessible via a link. The user can then use this link to learn at their own pace. 【0840】 For example, suppose user A is taking an "Introduction to Programming" course and is struggling with a difficult assignment. If emotion recognition technology detects user A's frustration, the server will adjust the content accordingly, providing clearer hints and videos demonstrating success stories to support smoother learning. 【0841】 After the learning session is complete, the server analyzes learning progress data and sentiment data to generate feedback that can be used to improve future educational content. This makes it possible to increase user satisfaction and provide a more effective learning environment. 【0842】 An example of a prompt to input into a generative AI model is, "Generate educational materials best suited to the user's learning needs. Dynamic adjustments based on the user's emotional state are required." This prompt provides specific instructions to the generative AI model, helping to create a customized educational experience for each user. 【0843】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0844】 Step 1: 【0845】 Users access the system via a dedicated web portal or mobile application and create an account. During this process, users enter basic information and grant access to their device's camera and microphone. Inputs include personal information such as name and email address, and output is transmitted to the server. This prepares the system to provide an experience optimized for each user. 【0846】 Step 2: 【0847】 The server receives user information and stores it in a database. The input received is the user's personal information, and the output is an update to the database. Specifically, it processes HTTP requests and saves the information to an SQL database. This procedure enables the system to perform analysis based on individual user data. 【0848】 Step 3: 【0849】 The server uses a generative AI model to create appropriate educational materials based on the user's learning needs. The input is information about the user's learning history and areas of interest, and the output is educational materials such as text, videos, and interactive quizzes. Specifically, user data is processed by an algorithm, and the generated prompts are input into the model to produce materials. These materials assist in creating an appropriate learning experience. 【0850】 Step 4: 【0851】 The device uses its camera and microphone to analyze the user's facial expressions and voice, detecting their emotional state in real time. Input is live camera footage and audio data, while output is user emotional state data. Specifically, it uses OpenCV and a speech recognition engine to analyze the data and execute code to determine emotions. 【0852】 Step 5: 【0853】 The server considers the user's emotional state and dynamically adjusts the educational materials. The input is emotional state data, and the output is the adjusted educational materials. For example, if user frustration is detected, the server provides easier content or encouraging videos. This feature allows the learning experience to be customized to individual needs. 【0854】 Step 6: 【0855】 The server sends a notification to provide educational materials to the user. The input is the prepared educational materials, and the output is the sending of the notification. Specifically, a link is sent to the user's application via the notification protocol, allowing the user to access and begin learning. 【0856】 Step 7: 【0857】 After the learning process is complete, the server collects and analyzes learning progress data and sentiment data. The input is the user's learning data and sentiment data, and the output is a feedback report and suggestions for future content improvements. Specifically, data analysis tools are used to visualize progress, and the generative AI model is also improved. This step aims to further enhance the learning experience. 【0858】 (Application Example 2) 【0859】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0860】 In modern learning methods, providing personalized educational experiences is essential to meet the diverse needs of learners. However, systems that can dynamically adjust learning content in real time, taking into account the user's emotional state, are not yet readily available. In particular, there is a growing need for systems that can automatically detect when learners lose interest or experience stress and respond appropriately. 【0861】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0862】 In this invention, the server includes means for receiving user information and generating educational materials based on said user information, means for distributing the generated educational materials to the user's device via electronic communication means, and means for processing data from sensors for analyzing the user's emotional state. This makes it possible to monitor the learner's emotional state in real time and dynamically adjust the content to provide a flexible educational experience tailored to individual needs. 【0863】 "User information" refers to basic data about individual learners that the system uses to acquire data about them. 【0864】 "Educational materials" are content generated to support learning, including text, videos, and interactive quizzes. 【0865】 "Electronic communication means" refers to network infrastructure and protocols used to transmit digital information to user devices. 【0866】 "User devices" refer to terminals used by learners to access educational materials, and include smartphones, tablets, and personal computers. 【0867】 "Emotional state" refers to information that indicates the psychological and emotional responses expressed by the user during the learning process. 【0868】 "Data from sensors" refers to data about the user's facial expressions and voice collected through input devices such as cameras and microphones. 【0869】 "Dynamic adjustment" refers to the process of changing the content of educational materials in real time according to the emotional state of the user. 【0870】 This system begins with the user creating an account through a dedicated application and entering their user information. The user then grants access to the device's camera and microphone for emotion recognition. This information is received by the server and stored in a database. 【0871】 The server uses AI technology based on user information to generate educational materials. These materials include a variety of content, such as device text, videos, and interactive quizzes. These educational materials are delivered to user devices via electronic communication. 【0872】 The device uses a camera and microphone to monitor the user's emotional state in real time. Utilizing OpenCV, PyDub, and TensorFlow, it analyzes emotions from facial expressions and voice to detect the user's psychological state. Based on the emotional state, the server dynamically adjusts the content of educational materials, lowering the difficulty level or adding encouraging messages as needed. 【0873】 For example, if a user is taking a "basic mathematics" class and encounters a difficult problem that causes them stress, the device will detect this emotional state. The server will then provide additional content, including hints, to help the user overcome the problem. 【0874】 An example of a prompt for a generative AI model is, "When the user shows frustration, suggest supportive messages to improve the learning process." This prompt serves as an important guide for the AI ​​to provide the best possible educational experience for the user. 【0875】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0876】 Step 1: 【0877】 The user launches a dedicated application and enters their user information on the account creation screen. This information includes user ID, name, and learning objectives. The server receives this user information and stores it in a database. This process prepares the user for a personalized educational experience. 【0878】 Step 2: 【0879】 The server generates educational materials based on stored user information. It utilizes a generation AI model to create educational content including text, videos, and interactive quizzes. The AI ​​model selects the most suitable content based on the user's learning goals and level. As output, educational materials tailored to the user are prepared. 【0880】 Step 3: 【0881】 The generated educational materials are delivered from the server to the user's device via electronic communication. The device receives the relevant educational materials and prepares them for the user to access at any time. This allows the user to begin learning regardless of time or location. 【0882】 Step 4: 【0883】 The device monitors the user's emotional state in real time during training. It captures facial expressions with a camera and records audio with a microphone. Using OpenCV, PyDub, and TensorFlow, it recognizes emotions from this data and sends the state to the server. The current training progress can be understood by inputting the emotional state. 【0884】 Step 5: 【0885】 The server receives emotional state data sent from the terminal and dynamically adjusts the content of the educational materials. For example, if a user shows frustration, the server provides easier content or encouraging messages. This adjustment optimizes the user's learning experience individually, improving learning effectiveness. 【0886】 Step 6: 【0887】 After a user completes a lesson, the server analyzes their learning progress and emotional state, generating feedback to improve future educational materials. This feedback is used to customize subsequent learning content, continuously improving the educational experience. 【0888】 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. 【0889】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0890】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0891】 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. 【0892】 Figure 9 shows an 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. 【0893】 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. 【0894】 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. 【0895】 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, motorcycles, etc., 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, for example, based 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. 【0896】 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." 【0897】 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. 【0898】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0899】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0900】 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. 【0901】 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. 【0902】 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. 【0903】 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. 【0904】 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. 【0905】 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. 【0906】 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. 【0907】 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 the like 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. 【0908】 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 as being incorporated by reference. 【0909】 The following is further disclosed regarding the embodiments described above. 【0910】 (Claim 1) 【0911】 An information processing device includes means for receiving user information and generating educational materials based on said user information, 【0912】 A means of distributing generated educational materials to user devices via a digital communication network, 【0913】 A means for receiving inquiries from users related to the educational material and generating appropriate answers using an automated response system, 【0914】 A means for recording and managing user learning progress data, 【0915】 A means for generating feedback to provide improved educational materials based on the learning progress data, 【0916】 A system that includes this. 【0917】 (Claim 2) 【0918】 The system according to claim 1, further comprising means for collecting user evaluation data on generated educational materials and optimizing the content of the educational materials based on said evaluation data. 【0919】 (Claim 3) 【0920】 The system according to claim 1, comprising a data analysis module for customizing educational materials based on the individual needs of users. 【0921】 "Example 1" 【0922】 (Claim 1) 【0923】 A means for receiving user information and automatically generating educational materials using a generative model based on said user information, 【0924】 A means of securely storing the generated educational materials in a cloud environment and making them accessible to user devices via a digital communication network, 【0925】 A means of receiving inquiries from users, generating automated responses using natural language processing, and providing answers in real time. 【0926】 A means for collecting and recording user learning progress data and providing feedback to optimize the learning process based on that historical information, 【0927】 A system that includes this. 【0928】 (Claim 2) 【0929】 The system according to claim 1, comprising means for collecting evaluation data, optimizing educational materials based on said data, and enabling further customization. 【0930】 (Claim 3) 【0931】 The system according to claim 1, which includes a data analysis function for adjusting educational materials according to the specific needs of users and providing an individualized learning experience through dynamic analysis. 【0932】 "Application Example 1" 【0933】 (Claim 1) 【0934】 An information processing device includes means for receiving user information and generating educational materials based on said user information, 【0935】 A means of distributing generated educational materials to user devices via a digital communication network, 【0936】 A means for receiving inquiries from users related to the educational material and generating appropriate answers using an automated response system, 【0937】 A means for recording and managing user learning progress data, 【0938】 A means for generating feedback to provide improved educational materials based on the learning progress data, 【0939】 A means of using an artificial intelligence model to automatically generate educational materials according to the user's areas of interest and skill level, 【0940】 A system that includes this. 【0941】 (Claim 2) 【0942】 The system according to claim 1, further comprising means for collecting user evaluation data on generated educational materials and optimizing the content of the educational materials based on said evaluation data. 【0943】 (Claim 3) 【0944】 The system according to claim 1, comprising a data analysis device for customizing educational materials based on the individual needs of users. 【0945】 "Example 2 of combining an emotion engine" 【0946】 (Claim 1) 【0947】 An information processing device includes means for receiving user information and generating educational materials based on said user information, 【0948】 A means for distributing the generated educational materials to user devices via a digital communication network, 【0949】 A means for detecting a user's emotional state in real time using emotion recognition technology and dynamically adjusting educational materials based on that emotional state, 【0950】 A means for recording and managing user learning progress information, 【0951】 A means for generating feedback to provide improved educational materials based on the learning progress information, 【0952】 A system that includes this. 【0953】 (Claim 2) 【0954】 The system according to claim 1, further comprising means for collecting user evaluation information on generated educational materials and optimizing the content of the educational materials based on said evaluation information. 【0955】 (Claim 3) 【0956】 The system according to claim 1, comprising a data analysis module for customizing educational materials based on the individual needs of users. 【0957】 "Application example 2 when combining with an emotional engine" 【0958】 (Claim 1) 【0959】 A means for receiving user information and generating educational materials based on said user information, 【0960】 A means of distributing generated educational materials to user devices via electronic communication means, 【0961】 A means for receiving inquiries from users related to educational materials and generating appropriate answers using a response device, 【0962】 A means for recording and managing user learning progress information, 【0963】 A means for processing data from sensors to analyze the emotional state of users, 【0964】 A means for dynamically adjusting the content of educational materials based on the emotional state, 【0965】 A means for generating feedback to provide improved educational materials based on the learning progress information and emotional state, 【0966】 A system that includes this. 【0967】 (Claim 2) 【0968】 The system according to claim 1, comprising means for collecting user evaluation information on generated educational materials and optimizing the content of the educational materials based on the evaluation information and the user's emotional state. 【0969】 (Claim 3) 【0970】 The system according to claim 1, comprising an information processing module for customizing educational materials based on the individual needs of users. [Explanation of Symbols] 【0971】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] An information processing device includes means for receiving user information and generating educational materials based on said user information, A means of distributing generated educational materials to user devices via a digital communication network, A means for receiving inquiries from users related to the educational material and generating appropriate answers using an automated response system, A means for recording and managing user learning progress data, A means for generating feedback to provide improved educational materials based on the learning progress data, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for collecting user evaluation data on generated educational materials and optimizing the content of the educational materials based on said evaluation data. [Claim 3] The system according to claim 1, comprising a data analysis module for customizing educational materials based on the individual needs of users.

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