system

Abstract

We provide the system. [Solution] Means of collecting educational information from around the world, A means of converting collected educational information into a standard format and integrating it into a database, A means of generating a learning plan based on an individual's learning history and goals, A means of providing the generated learning plan to an individual, A system that includes means for monitoring individual learning progress and providing feedback.

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, 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 Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the education industry, the uneven distribution of educational resources, the decrease in the number of teachers, and the spread of unreliable information are issues. In particular, it is difficult to provide education suitable for regions and cultures, and optimal education according to individual learning needs has not been realized. Therefore, there is a need for a system that can provide educational information efficiently and accurately and offer a customizable learning experience tailored to learners. 【Means for Solving the Problems】 【0005】 This invention provides a means for collecting educational information from around the world, converting it to a standard format, and integrating it into a database. Furthermore, it generates and provides personalized learning plans based on an individual's learning history and goals. It also monitors individual learning progress in real time and provides timely feedback to create an optimized learning experience for each learner. Moreover, by providing learning plans that are tailored to the region and culture, and by incorporating a reward system that enhances learning motivation, it provides a fair and creative learning environment for all learners. 【0006】 "Educational information" refers to all information related to education, including teaching materials, lesson plans, academic papers, and audiovisual content used in instruction. 【0007】 A "standard format" is a unified data format that converts data from different formats into a consistent format, facilitating processing and analysis. 【0008】 A "database" is a collection of data that is organized, stored, and managed in a way that allows for searching and retrieval of collected information. 【0009】 "Learning history" refers to data that records what each learner has learned so far, the materials they have used, the skills they have acquired, and their progress in those skills. 【0010】 A "learning plan" is a systematic arrangement of specific learning activities and materials designed based on the individual learner's goals and needs. 【0011】 "Real-time" refers to a state where processing and responses occur in real time, and information processing and feedback are performed without delay. 【0012】 "Feedback" refers to information about the results and areas for improvement that learners receive after completing a learning activity, and it is useful for their next learning actions. 【0013】 A "reward system" is a mechanism that provides some form of reward or incentive to learners in accordance with the goals they achieve in the course of their learning activities. 【0014】 "Customizable" means that the specifications of a program or system can be changed according to the individual needs and preferences of the user. [Brief explanation of the drawing] 【0015】 [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 the data processing device and 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]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Embodiments for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described according to the accompanying drawings. 【0017】 First, the language used in the following description will be explained. 【0018】 In the following embodiments, the 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. 【0019】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, the 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, and the like. 【0021】 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). 【0022】 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." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 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. 【0026】 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). 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 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. 【0033】 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. 【0034】 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. 【0035】 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". 【0036】 This invention is a system aimed at aggregating global educational information and providing this information in a format suitable for individual learners. The method of implementation is described below. 【0037】 First, the server automatically collects information from various educational resources around the world using web crawling technology. This information includes text, video, and audio data, and is categorized according to academic discipline and educational purpose. The aggregated data is then converted to a standard format and integrated into a database. This standardization ensures consistency across all data formats, facilitating subsequent searching and filtering. 【0038】 The device receives information entered by the user and forms a user profile tailored to the individual's learning objectives and interests. This profile includes learning history, areas of interest, and specific learning goals. This profile information later serves as the foundation for creating a detailed learning plan. 【0039】 The server then uses a generative AI model to match educational content in the database with the user profile to generate a personalized learning plan. This allows users to obtain a learning path tailored specifically to them. This plan takes into account the learner's cultural background and language, and is delivered in a way that is optimized for each individual. 【0040】 Users can learn at their own pace, and their learning progress is recorded on their device in real time. The device continuously sends learning progress to the server and receives feedback as needed, thereby enhancing learning effectiveness. A reward system is also in place, providing incentives based on the user's learning progress and achieved goals. 【0041】 As a concrete example, consider a 10-year-old student who is interested in art and mathematics. When this student uses the platform, the server collects content such as art history and math quizzes and creates a suitable learning plan. Furthermore, as the student progresses, new content is provided as further challenges. In this way, the student can receive education tailored to their individual learning needs. 【0042】 This will create an environment where everyone can receive a high-quality education, without being constrained by region or culture. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The server accesses educational websites on the internet and uses crawling technology to collect publicly available educational content. The data obtained through this process is acquired as text, video, and audio data. 【0046】 Step 2: 【0047】 The server converts the collected raw data into a standard format. Video and audio metadata is automatically extracted, and text data is processed using text analysis techniques. The converted data is then organized and integrated into a database. 【0048】 Step 3: 【0049】 The device receives basic learning-related information, areas of interest, and specific goals from the user as input. This information includes details about the user's age and the topics they wish to learn about. 【0050】 Step 4: 【0051】 The server generates a personalized user profile based on information provided by the user. This profile records individual learning goals and past learning history, which helps in generating subsequent learning plans. 【0052】 Step 5: 【0053】 The server matches user profiles with educational content in the database and uses a generative AI model to create personalized learning plans for each user. These learning plans are optimized to take into account difficulty levels and cultural backgrounds. 【0054】 Step 6: 【0055】 The server sends the completed learning plan to the terminal. The terminal displays this plan to the user and provides an interface for starting the learning process. 【0056】 Step 7: 【0057】 Users learn based on the learning plan displayed on their device. They engage in learning activities such as viewing learning materials and answering practice questions. 【0058】 Step 8: 【0059】 The device records data on the user's learning progress and periodically sends it to the server. This data includes completed learning tasks and accuracy rates. 【0060】 Step 9: 【0061】 The server analyzes progress data and determines whether adjustments to the learning plan are necessary. If needed, it generates feedback and provides learning materials and supplementary resources for the next steps. 【0062】 Step 10: 【0063】 The server calculates incentives based on the user's learning progress and according to the reward system, and notifies the user. This encourages continued learning. 【0064】 (Example 1) 【0065】 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." 【0066】 In recent years, the amount of educational information has increased rapidly, but the provision of information tailored to the individual needs of learners remains insufficient. Furthermore, there is a lack of optimization of educational content according to region and culture, which presents challenges in motivating learning. 【0067】 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. 【0068】 In this invention, the server includes means for collecting educational information, means for converting the collected information into a standard format and integrating it into a recording device, means for generating instructional plans based on an individual's learning history and goals, and means for comparing the instructional plans with the information using a generated AI model to provide personalized instructional plans. This makes it possible to provide high-quality education that meets the individual needs of learners, regardless of region or culture. Furthermore, by setting incentives according to learning progress, motivation to learn can be effectively improved. 【0069】 "Educational information" refers to materials and data necessary for learners to achieve their educational objectives, and includes a variety of formats such as text, video, and audio data. 【0070】 A "standard format" refers to a common data format used to streamline data management and processing by unifying different formats. 【0071】 A "recording device" refers to a database or storage system for permanently storing and managing information. 【0072】 "Learning history" refers to a record of educational activities that a learner has undertaken in the past, and is data used to track the progress of their learning. 【0073】 A "teaching plan" refers to an educational program that includes learning content and methods, formulated based on the individual needs and goals of each learner. 【0074】 A "generative AI model" refers to an artificial intelligence system that uses machine learning techniques to analyze data and generate results for a specific task. 【0075】 "Incentives" refer to rewards or benefits set up to promote learning or improve motivation. 【0076】 This invention is a system that collects educational information and provides a learning experience optimized according to the individual needs of each learner. A specific example of this system is shown below. 【0077】 The server uses web crawling technology to collect education-related data from around the world. Specifically, it uses software such as Selenium and Beautiful Soup to acquire a wide range of online content in various formats (text, video, audio data, etc.). The collected data is then converted to a standard format using Python's Pandas library and SQL, and integrated into a storage device. This conversion enables integrated data management and efficient retrieval. 【0078】 Next, the device forms a profile based on input from the user. Specifically, it creates an individual profile based on information such as the user's past learning history, areas of interest, and set learning goals. 【0079】 The server then uses a generative AI model, such as a popular natural language processing model, to compare the educational content stored in the database with the user profile. Through this process, it generates a lesson plan adapted to the user. Because this generated plan takes into account the user's culture, background, and language, it provides a specific and relevant learning path. 【0080】 Furthermore, users can learn at their own pace, following the provided instructional plan. The device records learning progress in real time and sends it to the server. Based on this information, the server provides feedback tailored to the user's progress and recommends new learning content. This process continuously encourages user growth. 【0081】 For example, if a 10-year-old student is interested in art and mathematics, the server will provide the student with a learning plan that combines art history with math quizzes. New learning themes and content will be added as the student progresses, allowing them to constantly face new challenges. 【0082】 An example of a prompt might be, "A 10-year-old student is interested in both art and mathematics. Please provide the best learning plan for this student." This ensures that high-value education is tailored to individual learners, regardless of region or culture. 【0083】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0084】 Step 1: 【0085】 The server collects educational information using web crawling technology. Specifically, it uses Selenium or Beautiful Soup to retrieve text, video, and audio data from web pages based on URLs and keywords specified as input. As output, it stores the collected data in temporary storage. This process includes classifying the data according to specific academic disciplines and educational purposes. 【0086】 Step 2: 【0087】 The server converts the collected data into a standard format. It receives data in various formats stored in temporary storage as input. Using Python's Pandas library and SQL for data processing, it unifies this data into JSON or XML format. The output is data in a unified standard format, which is then integrated and stored in a database. 【0088】 Step 3: 【0089】 The terminal receives input from the user and forms a user profile. This input includes information about the user's areas of interest, set learning goals, and past learning history. The system analyzes this information as data computation to create an individual profile. The output is a detailed user profile used later to generate a learning plan. 【0090】 Step 4: 【0091】 The server uses a generative AI model to match educational content in the database with user profiles. The input consists of standardized educational content and user profiles retrieved from the database. As a data calculation, the generative AI model generates an appropriate lesson plan based on the prompt statements. The output is a personalized lesson plan, which is provided to the user. 【0092】 Step 5: 【0093】 The user progresses through the learning process based on the provided instructional plan. The input is the instructional plan provided by the server. Specifically, the user learns according to the plan and inputs their progress into the terminal. The output is learning progress information, which is recorded on the terminal. 【0094】 Step 6: 【0095】 The terminal sends learning progress data to the server. It receives learning progress entered by the user as input and sends it to the server in real time. The output is progress information available for the server to monitor the progress. 【0096】 Step 7: 【0097】 The server provides feedback based on learning progress and recommends new learning content as needed. The input is learning progress information sent from the device. As a data computation, the progress information is analyzed, and a generative AI model generates improvements and new content. The output is the feedback and additional learning content provided to the user. 【0098】 (Application Example 1) 【0099】 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." 【0100】 In modern society, there is a need to appropriately provide educational content that caters to diverse cultural backgrounds and individual interests. However, traditional education systems fail to adequately address individual learning needs, and challenges remain, particularly in terms of progress management and motivation. Furthermore, the lack of support for learners to effectively progress at their own pace hinders the improvement of individual learning skills. 【0101】 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. 【0102】 In this invention, the server includes means for collecting educational information from around the world, means for converting the collected educational information into a standard format and integrating it into a database, means for generating a learning plan based on an individual's learning history and goals, means for analyzing the user's interests using a generative AI model and recommending optimal educational content, and means for delivering educational content on smart devices and managing learning interactions. This makes it possible to provide a personalized learning experience tailored to each learner's interests and progress. 【0103】 "Means of collecting educational information from around the world" refers to technologies that acquire diverse education-related data from various sources on the internet. 【0104】 "Means of converting collected educational information into a standard format and integrating it into a database" refers to the process of converting acquired data into a consistent format and aggregating it into an easily manageable database. 【0105】 "Means for generating learning plans based on an individual's learning history and goals" refers to a method for formulating individual learning plans based on a user's past learning information and set goals. 【0106】 "A means of analyzing user interests using generative AI models and recommending optimal educational content" refers to a technology that utilizes artificial intelligence to evaluate user interests and suggest the most suitable learning materials. 【0107】 "Means for delivering educational content and managing learning interactions on smart devices" refers to methods of providing learning materials via digital devices such as smartphones and tablets, and recording and managing learners' behavior. 【0108】 To implement this invention, a coordinated system involving a server, a terminal, and a user is used. The server first accesses educational information sources on the internet and collects educational information in various forms using web crawling technology. This information includes a wide range of data formats, including text, video, and audio. The collected information is converted to a standard format using appropriate software such as Scrapy and integrated into a centralized database. 【0109】 The device creates a profile based on learning history and interest information provided by the user. This profile data is analyzed by a server-generated AI model to create an optimized learning plan for each user. By using AI technologies such as OpenAI® GPT, the system gains a deep understanding of the user's interests and provides recommendations accordingly. 【0110】 Users learn at their own pace using learning content provided through smart devices. Learning progress is monitored in real time, recorded on the device, and periodically sent to the server as feedback. This allows for content updates and plan adjustments as needed. 【0111】 For example, if a middle school student user is interested in science and history, this system will provide relevant educational materials such as archaeology documentaries and science experiment tutorial videos. Furthermore, a prompt could be used to instruct the AI ​​in the following format: "Middle school student user B is interested in science and history. They have previously studied introductory science and basic history. Please suggest content they should learn next." 【0112】 This allows individual learners to receive an educational experience optimized for their own learning needs. 【0113】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0114】 Step 1: 【0115】 The server uses web crawling technology to collect text, video, and audio data from educational resources on the internet. In this process, the server analyzes the collected data using software such as Scrapy, converts it to a standard format, and stores it in a centralized database. The input is raw data obtained from various websites and educational platforms, while the output is data formatted to a standard format. 【0116】 Step 2: 【0117】 The device receives learning history and interest information based on user input and creates a profile. This profile uses the user's personal information and areas of interest as input, and the output is profile data that integrates each learner's learning objectives and past learning activities. 【0118】 Step 3: 【0119】 The server inputs collected educational data and profile data received from the device into a generating AI model to analyze user interests. At this stage, OpenAI GPT or similar AI technology is used to generate data that recommends optimal educational content. The input is standardized educational data and profile data, and the output is a personalized learning plan. 【0120】 Step 4: 【0121】 The server sends the generated learning plan to the device, which then uses it to manage and provide learning content to the user on its smart device. The input is the learning plan received from the server, and the output is the learning content managed on the user's device. This process also records the user's interactions with the selected content. 【0122】 Step 5: 【0123】 Users progress through their learning using the provided learning content. The user's progress is recorded in real time on their device and sent to the server as feedback. Input is progress data obtained from the user's learning activities, and output is feedback information and suggestions for the next learning steps. 【0124】 By combining these steps, an educational experience optimized for each individual learner can be achieved. 【0125】 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. 【0126】 This invention provides a system that recognizes user emotions and uses that information to optimize the learning experience. Specific embodiments are shown below. 【0127】 First, the server continuously collects a variety of education-related content from the internet. This data is converted into a standard format and integrated into a database. This data is then cross-referenced with user profile information and used to generate learning plans. 【0128】 The device incorporates an emotion engine that analyzes the user's facial expressions, voice, and input data. When the user uses the learning materials, this emotion engine analyzes the user's emotional state in real time, detecting their level of concentration, satisfaction, and stress. This allows the system to continuously understand the user's learning state. 【0129】 During learning, the server updates the user profile based on collected emotional data and dynamically adjusts the learning plan. For example, if the user is feeling stressed, the system provides easier learning materials or relaxing content. Conversely, when the user is highly focused, challenging problems are added to enhance the learning effect. 【0130】 As learning progresses, the device constantly records changes in the user's emotions and sends this data to a server. The server analyzes this information and provides feedback on the user's learning experience. Furthermore, mechanisms are in place to support user motivation through rewards for meeting certain achievement criteria and gamification elements. 【0131】 As a concrete example, consider a 12-year-old student doing math exercises. If the student shows a confused expression, the emotion engine detects this and sends the data to the server. Based on this, the server immediately adjusts the learning plan and presents content, including visual explanations, to support the student's understanding. This allows students to receive optimal education tailored to their individual needs, creating an effective learning environment. 【0132】 Thus, the present invention aims to realize flexible and effective learning tailored to individual needs by having AI recognize the user's emotions and optimizing the learning content using that information. 【0133】 The following describes the processing flow. 【0134】 Step 1: 【0135】 The server collects educational information from educational websites on the internet. Using crawling technology, it automatically extracts content in various formats. All obtained data is converted to a standard format to facilitate subsequent processing. 【0136】 Step 2: 【0137】 The device collects basic information from the user, including the user's age, areas of learning interest, and specific learning goals. This information is important for creating a user profile. 【0138】 Step 3: 【0139】 The emotion engine built into the device analyzes the user's facial expressions and voice to detect their emotional state in real time. The analysis results include the user's level of concentration, satisfaction, and stress level. 【0140】 Step 4: 【0141】 The server receives user sentiment data sent from the sentiment engine and registers it in the user profile. This profile, along with the learning history, is used to optimize the learning plan. 【0142】 Step 5: 【0143】 The server generates a learning plan using a generative AI model based on user profiles and sentiment data. It adjusts the difficulty level of the learning materials and selects new content as needed. 【0144】 Step 6: 【0145】 The server sends the generated learning plan to the device. The device displays this plan to the user and provides the necessary interface to begin learning. 【0146】 Step 7: 【0147】 Users take courses and solve practice problems based on a learning plan provided on their device. An emotion engine continuously monitors the user's emotions during the learning process. 【0148】 Step 8: 【0149】 The server evaluates the effectiveness of the learning plan based on real-time updated sentiment data and generates feedback as needed. For example, it might suggest taking breaks to alleviate stress. 【0150】 Step 9: 【0151】 The device applies a reward system based on the user's progress and emotional data. When a user meets achievement criteria, they are provided with incentives, which strengthens their motivation to learn. 【0152】 (Example 2) 【0153】 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 as the "terminal". 【0154】 Traditional education systems have struggled to analyze individual learners' emotional states in real time and dynamically adjust learning plans accordingly. Furthermore, they lacked mechanisms to provide appropriate feedback based on individual learning progress, preventing them from maximizing learning effectiveness. Therefore, it is necessary to provide flexible learning methods tailored to learners' concentration levels and stress levels to enhance motivation. 【0155】 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. 【0156】 In this invention, the server includes means for collecting educational data from around the world, means for converting the collected educational data into a standard format and storing it, means for monitoring an individual's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning plan based on the individual's emotional state. This makes it possible to provide education adapted to individual emotional states and to build a flexible and effective learning environment to enhance learning effectiveness. 【0157】 "Educational data" refers to information and materials related to learners' education, and includes a variety of formats such as text, videos, images, and audio. 【0158】 A "standard format" refers to a conversion format used to unify data of different formats into a common structure, thereby facilitating data access and analysis. 【0159】 "Storage" refers to the process of collecting information and saving it in a way that allows it to be used for later processing and analysis. 【0160】 An "emotion analysis engine" refers to a technology that analyzes a user's facial expressions, voice, and behavioral data in real time and evaluates their emotional state. 【0161】 "Real-time monitoring" refers to a process that instantly detects a user's emotional state and immediately tracks any changes in it. 【0162】 A "learning plan" refers to a plan of learning activities that is optimized according to the individual characteristics of each learner. 【0163】 "Dynamic adjustment" refers to flexibly changing plans and system configurations in response to changes in the environment and conditions. 【0164】 "Feedback" refers to the evaluation and guidance provided to learners regarding the progress and outcomes of educational activities, and to reflect these findings in subsequent learning activities. 【0165】 This invention is an educational system that optimizes the user's learning experience by utilizing emotion analysis technology. This system consists of three components: a server, a terminal, and a user. 【0166】 The server is responsible for automatically collecting educational data from the internet, converting the collected data into a standard format, and integrating it. This process utilizes a high-speed database system and data analysis software. As a result, it can generate appropriate learning plans based on the user's learning history and profile, and provide optimal educational content to each individual user. 【0167】 The device incorporates an emotion analysis engine that collects the user's facial expressions and voice in real time. This allows for immediate analysis of the user's emotional state and evaluation of stress levels and concentration. Specifically, it collects emotional data using the camera and microphone, and uses an analysis algorithm to understand the emotional state. Based on the user's emotions, the server can dynamically adjust the learning plan and provide the user with the most suitable content. 【0168】 As a concrete example, consider a case where a student shows a confused expression while working on a math problem. The device's emotion analysis engine recognizes this expression and sends the data to the server. Based on this information, the server adjusts the learning plan to immediately provide clearer learning materials and visual guides, thereby helping to deepen the student's understanding. 【0169】 This invention enables the provision of a flexible and effective learning environment based on real-time user emotion data. By utilizing a generative AI model, the accuracy of learning plans and user feedback can be improved. An example of a prompt to the generative AI model might be, "Please tell me a specific way to adjust the learning plan based on the user's emotional state." 【0170】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0171】 Step 1: 【0172】 The server collects educational data from the internet. This process uses APIs to retrieve data such as text, videos, and images from online educational resources. It accepts data in various formats as input and integrates it into a database by converting it into a common standard format (e.g., JSON). The output is a database of educational content unified in a standard format. 【0173】 Step 2: 【0174】 The device collects the user's facial expressions and voice in real time and inputs them into an emotion analysis engine. Using the camera and microphone, it acquires video and audio data from the user, and by applying this data to an emotion analysis algorithm, it analyzes the user's emotional state, such as stress level and concentration level. Using the collected emotion data as input, it generates the user's emotion parameters as output. 【0175】 Step 3: 【0176】 The server receives emotion parameters sent from the terminal and evaluates the user's current learning plan. Using the user's learning history data and emotion parameters as input, it generates an optimal learning plan tailored to the user's emotional state. As output, the adjusted learning plan is created and provided to the terminal. 【0177】 Step 4: 【0178】 The device displays appropriate content to the user based on a pre-configured learning plan provided by the server. It receives a new learning plan as input and presents the user with learning materials and exercises aligned with that plan. The output displays the specific learning materials and exercises the user will use for their studies. 【0179】 Step 5: 【0180】 Users learn using the provided learning materials and exercises. Emotional data may be collected again during the learning process; the device analyzes the data each time and sends it to the server as needed. This ensures that changes in the user's emotional state during learning are constantly reflected. 【0181】 (Application Example 2) 【0182】 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". 【0183】 In today's information-saturated society, appropriately acquiring the information each user needs and providing an optimal experience based on that information is an extremely difficult challenge. Furthermore, understanding user emotions and adjusting services accordingly is a crucial element in enabling the provision of more personalized experiences. This invention aims to optimize purchasing and learning experiences by providing appropriate information based on user emotions. 【0184】 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. 【0185】 In this invention, the server includes means for collecting information from around the world, means for converting the collected information into a standard format and integrating it into memory, means for generating a plan based on an individual's history and goals, means for analyzing an individual's emotional state and providing information based on the analysis results, and means for generating an appropriate experience based on the analyzed emotions and behaviors. This makes it possible to provide a personalized experience for each user and improve the efficiency of information acquisition and utilization. 【0186】 "Information" refers to data and knowledge that are collected, processed, and analyzed for a specific purpose. 【0187】 "Means" refers to the methods or techniques used to achieve a certain objective. 【0188】 "Memory" refers to a mechanism for storing information and retrieving and using it as needed. 【0189】 "History" refers to records and data about past actions, achievements, and events. 【0190】 A "goal" refers to a destination that represents a specific state or result that should be achieved. 【0191】 A "plan" refers to a set of plans or concepts developed to achieve a specific goal. 【0192】 "Emotional state" refers to the psychological reactions and emotional conditions an individual exhibits in a particular situation. 【0193】 "Analysis results" refer to conclusions and information derived from analyzing data and circumstances. 【0194】 "Information provision" refers to the act of transmitting data or knowledge to others and making it available for their use. 【0195】 "Experience generation" refers to the process of designing and providing events and experiences that individuals will experience in specific situations or environments. 【0196】 "Experience" refers to a series of events or activities that an individual directly experiences in a particular situation or environment. 【0197】 The server continuously collects information from around the world via the internet, converts this information into a standard format, and integrates it into a database. It primarily uses high-performance server equipment for data processing and analysis, possessing the ability to quickly organize and store large amounts of data. Software used in this process includes database management systems and APIs for information collection. 【0198】 The device uses cameras and sensors to collect user facial expressions, voice, and input data in real time. Based on this, an emotion engine analyzes the user's emotional state and determines their level of concentration, satisfaction, and stress. The device uses OpenCV as its image processing library and employs dedicated speech recognition technology for voice analysis. 【0199】 When a user interacts with the system, analyzed emotional data is sent to the server. The server uses this information to update the user profile and provide optimized information and services. For example, if a user is feeling stressed, the system will present relaxing content, and if the user is in a good mood, it will provide promotional information. In this way, the user experience is personalized, and satisfaction is improved. 【0200】 As a concrete example, consider a scenario where a user is online shopping. In this situation, a camera is used to read the user's facial expressions, and based on the analysis results, special offers such as a "10% discount coupon usable now" are displayed. This can increase the user's desire to purchase and provide a satisfying experience. 【0201】 An example of a prompt message to send to a generative AI model is, "Analyze the user's emotions in real time and suggest ways to provide a suitable experience." 【0202】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0203】 Step 1: 【0204】 The server collects information from around the world via the internet. Inputs are various data sources on the internet, and output is information converted into a standard format. The server uses data collection APIs to systematically collect information and prepare it for storage in a database. 【0205】 Step 2: 【0206】 The server converts the collected information into a standard format and integrates it into the database. The input is the raw data collected in step 1, and the output is structured data in a unified format. Through the data conversion process, data in different formats is organized into a consistent format and stored in the database. 【0207】 Step 3: 【0208】 The device uses cameras and sensors to capture the user's facial expressions and voice in real time. The input is perceptual data from the user's physical presence, and the output is analyzable digital data. The device processes the data using image processing libraries such as OpenCV and sends it to the emotion engine. 【0209】 Step 4: 【0210】 The emotion engine analyzes the user's emotional state to determine their level of concentration, satisfaction, and stress. The input is the digital data acquired in step 3, and the output is metadata indicating the user's emotional state. The emotion analysis algorithm is used to evaluate the emotional data in real time. 【0211】 Step 5: 【0212】 The server receives data sent from the emotion engine and updates the user profile. The input is information about the emotional state, and the output is the updated user profile. The server uses a machine learning model to refine the profile and prepare for the next interaction. 【0213】 Step 6: 【0214】 Users receive optimized information and services based on the analyzed data. The input is the updated information determined in step 5, and the output is a customized experience and information. Promotional and support information is presented to the user on the device, providing a personalized experience. 【0215】 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. 【0216】 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. 【0217】 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. 【0218】 [Second Embodiment] 【0219】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0220】 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. 【0221】 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). 【0222】 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. 【0223】 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. 【0224】 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). 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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. 【0229】 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. 【0230】 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". 【0231】 This invention is a system aimed at aggregating global educational information and providing this information in a format suitable for individual learners. The method of implementation is described below. 【0232】 First, the server automatically collects information from various educational resources around the world using web crawling technology. This information includes text, video, and audio data, and is categorized according to academic discipline and educational purpose. The aggregated data is then converted to a standard format and integrated into a database. This standardization ensures consistency across all data formats, facilitating subsequent searching and filtering. 【0233】 The device receives information entered by the user and forms a user profile tailored to the individual's learning objectives and interests. This profile includes learning history, areas of interest, and specific learning goals. This profile information later serves as the foundation for creating a detailed learning plan. 【0234】 The server then uses a generative AI model to match educational content in the database with the user profile to generate a personalized learning plan. This allows users to obtain a learning path tailored specifically to them. This plan takes into account the learner's cultural background and language, and is delivered in a way that is optimized for each individual. 【0235】 Users can learn at their own pace, and their learning progress is recorded on their device in real time. The device continuously sends learning progress to the server and receives feedback as needed, thereby enhancing learning effectiveness. A reward system is also in place, providing incentives based on the user's learning progress and achieved goals. 【0236】 As a concrete example, consider a 10-year-old student who is interested in art and mathematics. When this student uses the platform, the server collects content such as art history and math quizzes and creates a suitable learning plan. Furthermore, as the student progresses, new content is provided as further challenges. In this way, the student can receive education tailored to their individual learning needs. 【0237】 This will create an environment where everyone can receive a high-quality education, without being constrained by region or culture. 【0238】 The following describes the processing flow. 【0239】 Step 1: 【0240】 The server accesses educational websites on the internet and uses crawling technology to collect publicly available educational content. The data obtained through this process is acquired as text, video, and audio data. 【0241】 Step 2: 【0242】 The server converts the collected raw data into a standard format. Video and audio metadata is automatically extracted, and text data is processed using text analysis techniques. The converted data is then organized and integrated into a database. 【0243】 Step 3: 【0244】 The device receives basic learning-related information, areas of interest, and specific goals from the user as input. This information includes details about the user's age and the topics they wish to learn about. 【0245】 Step 4: 【0246】 The server generates a personalized user profile based on information provided by the user. This profile records individual learning goals and past learning history, which helps in generating subsequent learning plans. 【0247】 Step 5: 【0248】 The server matches user profiles with educational content in the database and uses a generative AI model to create personalized learning plans for each user. These learning plans are optimized to take into account difficulty levels and cultural backgrounds. 【0249】 Step 6: 【0250】 The server sends the completed learning plan to the terminal. The terminal displays this plan to the user and provides an interface for starting the learning process. 【0251】 Step 7: 【0252】 Users learn based on the learning plan displayed on their device. They engage in learning activities such as viewing learning materials and answering practice questions. 【0253】 Step 8: 【0254】 The device records data on the user's learning progress and periodically sends it to the server. This data includes completed learning tasks and accuracy rates. 【0255】 Step 9: 【0256】 The server analyzes progress data and determines whether adjustments to the learning plan are necessary. If needed, it generates feedback and provides learning materials and supplementary resources for the next steps. 【0257】 Step 10: 【0258】 The server calculates incentives based on the user's learning progress and according to the reward system, and notifies the user. This encourages continued learning. 【0259】 (Example 1) 【0260】 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 glasses 214 will be referred to as the "terminal." 【0261】 In recent years, the amount of educational information has increased rapidly, but the provision of information tailored to the individual needs of learners remains insufficient. Furthermore, there is a lack of optimization of educational content according to region and culture, which presents challenges in motivating learning. 【0262】 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. 【0263】 In this invention, the server includes means for collecting educational information, means for converting the collected information into a standard format and integrating it into a recording device, means for generating instructional plans based on an individual's learning history and goals, and means for comparing the instructional plans with the information using a generated AI model to provide personalized instructional plans. This makes it possible to provide high-quality education that meets the individual needs of learners, regardless of region or culture. Furthermore, by setting incentives according to learning progress, motivation to learn can be effectively improved. 【0264】 "Educational information" refers to materials and data necessary for learners to achieve their educational objectives, and includes a variety of formats such as text, video, and audio data. 【0265】 A "standard format" refers to a common data format used to streamline data management and processing by unifying different formats. 【0266】 A "recording device" refers to a database or storage system for permanently storing and managing information. 【0267】 "Learning history" refers to a record of educational activities that a learner has undertaken in the past, and is data used to track the progress of their learning. 【0268】 A "teaching plan" refers to an educational program that includes learning content and methods, formulated based on the individual needs and goals of each learner. 【0269】 A "generative AI model" refers to an artificial intelligence system that uses machine learning techniques to analyze data and generate results for a specific task. 【0270】 "Incentives" refer to rewards or benefits set up to promote learning or improve motivation. 【0271】 This invention is a system that collects educational information and provides a learning experience optimized according to the individual needs of each learner. A specific example of this system is shown below. 【0272】 The server uses web crawling technology to collect education-related data from around the world. Specifically, it uses software such as Selenium and Beautiful Soup to acquire a wide range of online content in various formats (text, video, audio data, etc.). The collected data is then converted to a standard format using Python's Pandas library and SQL, and integrated into a storage device. This conversion enables integrated data management and efficient retrieval. 【0273】 Next, the device forms a profile based on input from the user. Specifically, it creates an individual profile based on information such as the user's past learning history, areas of interest, and set learning goals. 【0274】 The server then uses a generative AI model, such as a popular natural language processing model, to compare the educational content stored in the database with the user profile. Through this process, it generates a lesson plan adapted to the user. Because this generated plan takes into account the user's culture, background, and language, it provides a specific and relevant learning path. 【0275】 Furthermore, users can learn at their own pace, following the provided instructional plan. The device records learning progress in real time and sends it to the server. Based on this information, the server provides feedback tailored to the user's progress and recommends new learning content. This process continuously encourages user growth. 【0276】 For example, if a 10-year-old student is interested in art and mathematics, the server will provide the student with a learning plan that combines art history with math quizzes. New learning themes and content will be added as the student progresses, allowing them to constantly face new challenges. 【0277】 Examples of prompt texts may include "A 10-year-old student is interested in both art and mathematics. Please provide the optimal learning plan for this student." By doing so, regardless of region or culture, highly valuable education optimized for individual learners is provided. 【0278】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0279】 Step 1: 【0280】 The server collects educational information using web crawling technology. Specifically, based on the URLs or keywords specified as input, it uses Selenium or Beautiful Soup to obtain text, video, and audio data from web pages. As output, the collected data is saved in a temporary storage device. This process includes the operation of classifying data according to specific academic fields or educational purposes. 【0281】 Step 2: 【0282】 The server converts the collected data into a standard format. It receives data in various formats saved in the temporary storage device as input. By using the Pandas library of Python or SQL for data processing, it unifies this into the format of JSON or XML. The output is data in the unified standard format, which is integrated and saved in the database. 【0283】 Step 3: 【0284】 The terminal receives input from the user and forms a user profile. The input here is information regarding the academic fields the user is interested in, the set learning goals, and past learning histories. As data calculation, it analyzes this information and creates individual profiles. The output is a detailed user profile used for subsequent learning plan generation. 【0285】 Step 4: 【0286】 The server uses the generative AI model to compare the educational content in the database with the user profile. The inputs are the standardized educational content and user profile retrieved from the database. As a data operation, the generative AI model generates an appropriate teaching plan based on the prompt text. The output is an individualized teaching plan, which is provided to the user. 【0287】 Step 5: 【0288】 The user proceeds with learning based on the provided teaching plan. The input is the teaching plan provided by the server. As a specific action, the user conducts learning according to the plan and inputs the progress thereof into the terminal. The output is the learning progress information, which is recorded on the terminal. 【0289】 Step 6: 【0290】 The terminal sends the learning progress data to the server. Receiving the learning progress input by the user as an input, it sends the data to the server in real time. The output is progress information available for the server to monitor the progress. 【0291】 Step 7: 【0292】 The server provides feedback based on the learning progress and recommends new learning content if necessary. The input is the learning progress information sent from the terminal. As a data operation, it analyzes the progress information, and the generative AI model generates improvement points and new content. The output is the feedback provided to the user and additional learning content. 【0293】 (Application Example 1) 【0294】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0295】 In modern society, there is a need to appropriately provide educational content that caters to diverse cultural backgrounds and individual interests. However, traditional education systems fail to adequately address individual learning needs, and challenges remain, particularly in terms of progress management and motivation. Furthermore, the lack of support for learners to effectively progress at their own pace hinders the improvement of individual learning skills. 【0296】 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. 【0297】 In this invention, the server includes means for collecting educational information from around the world, means for converting the collected educational information into a standard format and integrating it into a database, means for generating a learning plan based on an individual's learning history and goals, means for analyzing the user's interests using a generative AI model and recommending optimal educational content, and means for delivering educational content on smart devices and managing learning interactions. This makes it possible to provide a personalized learning experience tailored to each learner's interests and progress. 【0298】 "Means of collecting educational information from around the world" refers to technologies that acquire diverse education-related data from various sources on the internet. 【0299】 "Means of converting collected educational information into a standard format and integrating it into a database" refers to the process of converting acquired data into a consistent format and aggregating it into an easily manageable database. 【0300】 "Means for generating learning plans based on an individual's learning history and goals" refers to a method for formulating individual learning plans based on a user's past learning information and set goals. 【0301】 "A means of analyzing user interests using generative AI models and recommending optimal educational content" refers to a technology that utilizes artificial intelligence to evaluate user interests and suggest the most suitable learning materials. 【0302】 "Means for delivering educational content and managing learning interactions on smart devices" refers to methods of providing learning materials via digital devices such as smartphones and tablets, and recording and managing learners' behavior. 【0303】 To implement this invention, a coordinated system involving a server, a terminal, and a user is used. The server first accesses educational information sources on the internet and collects educational information in various forms using web crawling technology. This information includes a wide range of data formats, including text, video, and audio. The collected information is converted to a standard format using appropriate software such as Scrapy and integrated into a centralized database. 【0304】 The device creates a profile based on the user's learning history and interest information. This profile data is analyzed by a server-generated AI model to create an optimized learning plan for each user. By using AI technologies such as OpenAI GPT, the system gains a deep understanding of the user's interests and provides recommendations accordingly. 【0305】 Users learn at their own pace using learning content provided through smart devices. Learning progress is monitored in real time, recorded on the device, and periodically sent to the server as feedback. This allows for content updates and plan adjustments as needed. 【0306】 For example, if a middle school student user is interested in science and history, this system will provide relevant educational materials such as archaeology documentaries and science experiment tutorial videos. Furthermore, a prompt could be used to instruct the AI ​​in the following format: "Middle school student user B is interested in science and history. They have previously studied introductory science and basic history. Please suggest content they should learn next." 【0307】 This enables each individual learner to obtain an educational experience optimized for their respective learning. 【0308】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0309】 Step 1: 【0310】 The server collects text, video, and audio data from educational information sources on the Internet using web crawling technology. In this process, the data collected by the server is analyzed using software such as Scrapy, converted into a standard format, and then stored in a unified database. The input is raw data obtained from various websites and educational platforms, and the output is data formatted into a standard format. 【0311】 Step 2: 【0312】 The terminal receives learning history and interest information based on user input and creates a profile. This profile has as input the personal information and areas of interest provided by the user, and as output, profile data in which the learning objectives and past learning activities of individual learners are integrated. 【0313】 Step 3: 【0314】 The server inputs the collected educational data and the profile data received from the terminal into a generated AI model to analyze the user's interests. At this stage, data for recommending optimal educational content is generated using OpenAI GPT or similar AI technologies. The input is standardized educational data and profile data, and the output is an individualized learning plan. 【0315】 Step 4: 【0316】 The server sends the generated learning plan to the device, which then uses it to manage and provide learning content to the user on its smart device. The input is the learning plan received from the server, and the output is the learning content managed on the user's device. This process also records the user's interactions with the selected content. 【0317】 Step 5: 【0318】 Users progress through their learning using the provided learning content. The user's progress is recorded in real time on their device and sent to the server as feedback. Input is progress data obtained from the user's learning activities, and output is feedback information and suggestions for the next learning steps. 【0319】 By combining these steps, an educational experience optimized for each individual learner can be achieved. 【0320】 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. 【0321】 This invention provides a system that recognizes user emotions and uses that information to optimize the learning experience. Specific embodiments are shown below. 【0322】 First, the server continuously collects a variety of education-related content from the internet. This data is converted into a standard format and integrated into a database. This data is then cross-referenced with user profile information and used to generate learning plans. 【0323】 The device incorporates an emotion engine that analyzes the user's facial expressions, voice, and input data. When the user uses the learning materials, this emotion engine analyzes the user's emotional state in real time, detecting their level of concentration, satisfaction, and stress. This allows the system to continuously understand the user's learning state. 【0324】 During learning, the server updates the user profile based on collected emotional data and dynamically adjusts the learning plan. For example, if the user is feeling stressed, the system provides easier learning materials or relaxing content. Conversely, when the user is highly focused, challenging problems are added to enhance the learning effect. 【0325】 As learning progresses, the device constantly records changes in the user's emotions and sends this data to a server. The server analyzes this information and provides feedback on the user's learning experience. Furthermore, mechanisms are in place to support user motivation through rewards for meeting certain achievement criteria and gamification elements. 【0326】 As a concrete example, consider a 12-year-old student doing math exercises. If the student shows a confused expression, the emotion engine detects this and sends the data to the server. Based on this, the server immediately adjusts the learning plan and presents content, including visual explanations, to support the student's understanding. This allows students to receive optimal education tailored to their individual needs, creating an effective learning environment. 【0327】 Thus, the present invention aims to realize flexible and effective learning tailored to individual needs by having AI recognize the user's emotions and optimizing the learning content using that information. 【0328】 The following describes the processing flow. 【0329】 Step 1: 【0330】 The server collects educational information from educational websites on the internet. Using crawling technology, it automatically extracts content in various formats. All obtained data is converted to a standard format to facilitate subsequent processing. 【0331】 Step 2: 【0332】 The device collects basic information from the user, including the user's age, areas of learning interest, and specific learning goals. This information is important for creating a user profile. 【0333】 Step 3: 【0334】 The emotion engine built into the device analyzes the user's facial expressions and voice to detect their emotional state in real time. The analysis results include the user's level of concentration, satisfaction, and stress level. 【0335】 Step 4: 【0336】 The server receives user sentiment data sent from the sentiment engine and registers it in the user profile. This profile, along with the learning history, is used to optimize the learning plan. 【0337】 Step 5: 【0338】 The server generates a learning plan using a generative AI model based on user profiles and sentiment data. It adjusts the difficulty level of the learning materials and selects new content as needed. 【0339】 Step 6: 【0340】 The server sends the generated learning plan to the device. The device displays this plan to the user and provides the necessary interface to begin learning. 【0341】 Step 7: 【0342】 Users take courses and solve practice problems based on a learning plan provided on their device. An emotion engine continuously monitors the user's emotions during the learning process. 【0343】 Step 8: 【0344】 The server evaluates the effectiveness of the learning plan based on real-time updated sentiment data and generates feedback as needed. For example, it might suggest taking breaks to alleviate stress. 【0345】 Step 9: 【0346】 The device applies a reward system based on the user's progress and emotional data. When a user meets achievement criteria, they are provided with incentives, which strengthens their motivation to learn. 【0347】 (Example 2) 【0348】 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". 【0349】 Traditional education systems have struggled to analyze individual learners' emotional states in real time and dynamically adjust learning plans accordingly. Furthermore, they lacked mechanisms to provide appropriate feedback based on individual learning progress, preventing them from maximizing learning effectiveness. Therefore, it is necessary to provide flexible learning methods tailored to learners' concentration levels and stress levels to enhance motivation. 【0350】 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. 【0351】 In this invention, the server includes means for collecting educational data from around the world, means for converting the collected educational data into a standard format and storing it, means for monitoring an individual's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning plan based on the individual's emotional state. This makes it possible to provide education adapted to individual emotional states and to build a flexible and effective learning environment to enhance learning effectiveness. 【0352】 "Educational data" refers to information and materials related to learners' education, and includes a variety of formats such as text, videos, images, and audio. 【0353】 A "standard format" refers to a conversion format used to unify data of different formats into a common structure, thereby facilitating data access and analysis. 【0354】 "Storage" refers to the process of collecting information and saving it in a way that allows it to be used for later processing and analysis. 【0355】 An "emotion analysis engine" refers to a technology that analyzes a user's facial expressions, voice, and behavioral data in real time and evaluates their emotional state. 【0356】 "Real-time monitoring" refers to a process that instantly detects a user's emotional state and immediately tracks any changes in it. 【0357】 A "learning plan" refers to a plan of learning activities that is optimized according to the individual characteristics of each learner. 【0358】 "Dynamic adjustment" refers to flexibly changing plans and system configurations in response to changes in the environment and conditions. 【0359】 "Feedback" refers to the evaluation and guidance provided to learners regarding the progress and outcomes of educational activities, and to reflect these findings in subsequent learning activities. 【0360】 This invention is an educational system that optimizes the user's learning experience by utilizing emotion analysis technology. This system consists of three components: a server, a terminal, and a user. 【0361】 The server is responsible for automatically collecting educational data from the internet, converting the collected data into a standard format, and integrating it. This process utilizes a high-speed database system and data analysis software. As a result, it can generate appropriate learning plans based on the user's learning history and profile, and provide optimal educational content to each individual user. 【0362】 The device incorporates an emotion analysis engine that collects the user's facial expressions and voice in real time. This allows for immediate analysis of the user's emotional state and evaluation of stress levels and concentration. Specifically, it collects emotional data using the camera and microphone, and uses an analysis algorithm to understand the emotional state. Based on the user's emotions, the server can dynamically adjust the learning plan and provide the user with the most suitable content. 【0363】 As a concrete example, consider a case where a student shows a confused expression while working on a math problem. The device's emotion analysis engine recognizes this expression and sends the data to the server. Based on this information, the server adjusts the learning plan to immediately provide clearer learning materials and visual guides, thereby helping to deepen the student's understanding. 【0364】 This invention enables the provision of a flexible and effective learning environment based on real-time user emotion data. By utilizing a generative AI model, the accuracy of learning plans and user feedback can be improved. An example of a prompt to the generative AI model might be, "Please tell me a specific way to adjust the learning plan based on the user's emotional state." 【0365】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0366】 Step 1: 【0367】 The server collects educational data from the internet. This process uses APIs to retrieve data such as text, videos, and images from online educational resources. It accepts data in various formats as input and integrates it into a database by converting it into a common standard format (e.g., JSON). The output is a database of educational content unified in a standard format. 【0368】 Step 2: 【0369】 The device collects the user's facial expressions and voice in real time and inputs them into an emotion analysis engine. Using the camera and microphone, it acquires video and audio data from the user, and by applying this data to an emotion analysis algorithm, it analyzes the user's emotional state, such as stress level and concentration level. Using the collected emotion data as input, it generates the user's emotion parameters as output. 【0370】 Step 3: 【0371】 The server receives emotion parameters sent from the terminal and evaluates the user's current learning plan. Using the user's learning history data and emotion parameters as input, it generates an optimal learning plan tailored to the user's emotional state. As output, the adjusted learning plan is created and provided to the terminal. 【0372】 Step 4: 【0373】 The device displays appropriate content to the user based on a pre-configured learning plan provided by the server. It receives a new learning plan as input and presents the user with learning materials and exercises aligned with that plan. The output displays the specific learning materials and exercises the user will use for their studies. 【0374】 Step 5: 【0375】 Users learn using the provided learning materials and exercises. Emotional data may be collected again during the learning process; the device analyzes the data each time and sends it to the server as needed. This ensures that changes in the user's emotional state during learning are constantly reflected. 【0376】 (Application Example 2) 【0377】 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 as the "terminal". 【0378】 In today's information-saturated society, appropriately acquiring the information each user needs and providing an optimal experience based on that information is an extremely difficult challenge. Furthermore, understanding user emotions and adjusting services accordingly is a crucial element in enabling the provision of more personalized experiences. This invention aims to optimize purchasing and learning experiences by providing appropriate information based on user emotions. 【0379】 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. 【0380】 In this invention, the server includes means for collecting information from around the world, means for converting the collected information into a standard format and integrating it into memory, means for generating a plan based on an individual's history and goals, means for analyzing an individual's emotional state and providing information based on the analysis results, and means for generating an appropriate experience based on the analyzed emotions and behaviors. This makes it possible to provide a personalized experience for each user and improve the efficiency of information acquisition and utilization. 【0381】 "Information" refers to data and knowledge that are collected, processed, and analyzed for a specific purpose. 【0382】 "Means" refers to the methods or techniques used to achieve a certain objective. 【0383】 "Memory" refers to a mechanism for storing information and retrieving and using it as needed. 【0384】 "History" refers to records and data about past actions, achievements, and events. 【0385】 A "goal" refers to a destination that represents a specific state or result that should be achieved. 【0386】 A "plan" refers to a set of plans or concepts developed to achieve a specific goal. 【0387】 "Emotional state" refers to the psychological reactions and emotional conditions an individual exhibits in a particular situation. 【0388】 "Analysis results" refer to conclusions and information derived from analyzing data and circumstances. 【0389】 "Information provision" refers to the act of transmitting data or knowledge to others and making it available for their use. 【0390】 "Experience generation" refers to the process of designing and providing events and experiences that individuals will experience in specific situations or environments. 【0391】 "Experience" refers to a series of events or activities that an individual directly experiences in a particular situation or environment. 【0392】 The server continuously collects information from around the world via the internet, converts this information into a standard format, and integrates it into a database. It primarily uses high-performance server equipment for data processing and analysis, possessing the ability to quickly organize and store large amounts of data. Software used in this process includes database management systems and APIs for information collection. 【0393】 The device uses cameras and sensors to collect user facial expressions, voice, and input data in real time. Based on this, an emotion engine analyzes the user's emotional state and determines their level of concentration, satisfaction, and stress. The device uses OpenCV as its image processing library and employs dedicated speech recognition technology for voice analysis. 【0394】 When a user interacts with the system, analyzed emotional data is sent to the server. The server uses this information to update the user profile and provide optimized information and services. For example, if a user is feeling stressed, the system will present relaxing content, and if the user is in a good mood, it will provide promotional information. In this way, the user experience is personalized, and satisfaction is improved. 【0395】 As a concrete example, consider a scenario where a user is online shopping. In this situation, a camera is used to read the user's facial expressions, and based on the analysis results, special offers such as a "10% discount coupon usable now" are displayed. This can increase the user's desire to purchase and provide a satisfying experience. 【0396】 An example of a prompt message to send to a generative AI model is, "Analyze the user's emotions in real time and suggest ways to provide a suitable experience." 【0397】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0398】 Step 1: 【0399】 The server collects information from around the world via the internet. Inputs are various data sources on the internet, and output is information converted into a standard format. The server uses data collection APIs to systematically collect information and prepare it for storage in a database. 【0400】 Step 2: 【0401】 The server converts the collected information into a standard format and integrates it into the database. The input is the raw data collected in step 1, and the output is structured data in a unified format. Through the data conversion process, data in different formats is organized into a consistent format and stored in the database. 【0402】 Step 3: 【0403】 The device uses cameras and sensors to capture the user's facial expressions and voice in real time. The input is perceptual data from the user's physical presence, and the output is analyzable digital data. The device processes the data using image processing libraries such as OpenCV and sends it to the emotion engine. 【0404】 Step 4: 【0405】 The emotion engine analyzes the user's emotional state to determine their level of concentration, satisfaction, and stress. The input is the digital data acquired in step 3, and the output is metadata indicating the user's emotional state. The emotion analysis algorithm is used to evaluate the emotional data in real time. 【0406】 Step 5: 【0407】 The server receives data sent from the emotion engine and updates the user profile. The input is information about the emotional state, and the output is the updated user profile. The server uses a machine learning model to refine the profile and prepare for the next interaction. 【0408】 Step 6: 【0409】 Users receive optimized information and services based on the analyzed data. The input is the updated information determined in step 5, and the output is a customized experience and information. Promotional and support information is presented to the user on the device, providing a personalized experience. 【0410】 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. 【0411】 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. 【0412】 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. 【0413】 [Third Embodiment] 【0414】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0415】 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. 【0416】 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). 【0417】 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. 【0418】 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. 【0419】 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). 【0420】 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. 【0421】 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. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 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". 【0426】 This invention is a system aimed at aggregating global educational information and providing this information in a format suitable for individual learners. The method of implementation is described below. 【0427】 First, the server automatically collects information from various educational resources around the world using web crawling technology. This information includes text, video, and audio data, and is categorized according to academic discipline and educational purpose. The aggregated data is then converted to a standard format and integrated into a database. This standardization ensures consistency across all data formats, facilitating subsequent searching and filtering. 【0428】 The device receives information entered by the user and forms a user profile tailored to the individual's learning objectives and interests. This profile includes learning history, areas of interest, and specific learning goals. This profile information later serves as the foundation for creating a detailed learning plan. 【0429】 The server then uses a generative AI model to match educational content in the database with the user profile to generate a personalized learning plan. This allows users to obtain a learning path tailored specifically to them. This plan takes into account the learner's cultural background and language, and is delivered in a way that is optimized for each individual. 【0430】 Users can learn at their own pace, and their learning progress is recorded on their device in real time. The device continuously sends learning progress to the server and receives feedback as needed, thereby enhancing learning effectiveness. A reward system is also in place, providing incentives based on the user's learning progress and achieved goals. 【0431】 As a concrete example, consider a 10-year-old student who is interested in art and mathematics. When this student uses the platform, the server collects content such as art history and math quizzes and creates a suitable learning plan. Furthermore, as the student progresses, new content is provided as further challenges. In this way, the student can receive education tailored to their individual learning needs. 【0432】 This will create an environment where everyone can receive a high-quality education, without being constrained by region or culture. 【0433】 The following describes the processing flow. 【0434】 Step 1: 【0435】 The server accesses educational websites on the internet and uses crawling technology to collect publicly available educational content. The data obtained through this process is acquired as text, video, and audio data. 【0436】 Step 2: 【0437】 The server converts the collected raw data into a standard format. Video and audio metadata is automatically extracted, and text data is processed using text analysis techniques. The converted data is then organized and integrated into a database. 【0438】 Step 3: 【0439】 The device receives basic learning-related information, areas of interest, and specific goals from the user as input. This information includes details about the user's age and the topics they wish to learn about. 【0440】 Step 4: 【0441】 The server generates a personalized user profile based on information provided by the user. This profile records individual learning goals and past learning history, which helps in generating subsequent learning plans. 【0442】 Step 5: 【0443】 The server matches user profiles with educational content in the database and uses a generative AI model to create personalized learning plans for each user. These learning plans are optimized to take into account difficulty levels and cultural backgrounds. 【0444】 Step 6: 【0445】 The server sends the completed learning plan to the terminal. The terminal displays this plan to the user and provides an interface for starting the learning process. 【0446】 Step 7: 【0447】 Users learn based on the learning plan displayed on their device. They engage in learning activities such as viewing learning materials and answering practice questions. 【0448】 Step 8: 【0449】 The device records data on the user's learning progress and periodically sends it to the server. This data includes completed learning tasks and accuracy rates. 【0450】 Step 9: 【0451】 The server analyzes progress data and determines whether adjustments to the learning plan are necessary. If needed, it generates feedback and provides learning materials and supplementary resources for the next steps. 【0452】 Step 10: 【0453】 The server calculates incentives based on the user's learning progress and according to the reward system, and notifies the user. This encourages continued learning. 【0454】 (Example 1) 【0455】 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." 【0456】 In recent years, the amount of educational information has increased rapidly, but the provision of information tailored to the individual needs of learners remains insufficient. Furthermore, there is a lack of optimization of educational content according to region and culture, which presents challenges in motivating learning. 【0457】 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. 【0458】 In this invention, the server includes means for collecting educational information, means for converting the collected information into a standard format and integrating it into a recording device, means for generating instructional plans based on an individual's learning history and goals, and means for comparing the instructional plans with the information using a generated AI model to provide personalized instructional plans. This makes it possible to provide high-quality education that meets the individual needs of learners, regardless of region or culture. Furthermore, by setting incentives according to learning progress, motivation to learn can be effectively improved. 【0459】 "Educational information" refers to materials and data necessary for learners to achieve their educational objectives, and includes a variety of formats such as text, video, and audio data. 【0460】 A "standard format" refers to a common data format used to streamline data management and processing by unifying different formats. 【0461】 A "recording device" refers to a database or storage system for permanently storing and managing information. 【0462】 "Learning history" refers to a record of educational activities that a learner has undertaken in the past, and is data used to track the progress of their learning. 【0463】 A "teaching plan" refers to an educational program that includes learning content and methods, formulated based on the individual needs and goals of each learner. 【0464】 A "generative AI model" refers to an artificial intelligence system that uses machine learning techniques to analyze data and generate results for a specific task. 【0465】 "Incentives" refer to rewards or benefits set up to promote learning or improve motivation. 【0466】 This invention is a system that collects educational information and provides a learning experience optimized according to the individual needs of each learner. A specific example of this system is shown below. 【0467】 The server uses web crawling technology to collect education-related data from around the world. Specifically, it uses software such as Selenium and Beautiful Soup to acquire a wide range of online content in various formats (text, video, audio data, etc.). The collected data is then converted to a standard format using Python's Pandas library and SQL, and integrated into a storage device. This conversion enables integrated data management and efficient retrieval. 【0468】 Next, the device forms a profile based on input from the user. Specifically, it creates an individual profile based on information such as the user's past learning history, areas of interest, and set learning goals. 【0469】 The server then uses a generative AI model, such as a popular natural language processing model, to compare the educational content stored in the database with the user profile. Through this process, it generates a lesson plan adapted to the user. Because this generated plan takes into account the user's culture, background, and language, it provides a specific and relevant learning path. 【0470】 Furthermore, users can learn at their own pace, following the provided instructional plan. The device records learning progress in real time and sends it to the server. Based on this information, the server provides feedback tailored to the user's progress and recommends new learning content. This process continuously encourages user growth. 【0471】 For example, if a 10-year-old student is interested in art and mathematics, the server will provide the student with a learning plan that combines art history with math quizzes. New learning themes and content will be added as the student progresses, allowing them to constantly face new challenges. 【0472】 An example of a prompt might be, "A 10-year-old student is interested in both art and mathematics. Please provide the best learning plan for this student." This ensures that high-value education is tailored to individual learners, regardless of region or culture. 【0473】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0474】 Step 1: 【0475】 The server collects educational information using web crawling technology. Specifically, it uses Selenium or Beautiful Soup to retrieve text, video, and audio data from web pages based on URLs and keywords specified as input. As output, it stores the collected data in temporary storage. This process includes classifying the data according to specific academic disciplines and educational purposes. 【0476】 Step 2: 【0477】 The server converts the collected data into a standard format. It receives data in various formats stored in temporary storage as input. Using Python's Pandas library and SQL for data processing, it unifies this data into JSON or XML format. The output is data in a unified standard format, which is then integrated and stored in a database. 【0478】 Step 3: 【0479】 The terminal receives input from the user and forms a user profile. This input includes information about the user's areas of interest, set learning goals, and past learning history. The system analyzes this information as data computation to create an individual profile. The output is a detailed user profile used later to generate a learning plan. 【0480】 Step 4: 【0481】 The server uses a generative AI model to match educational content in the database with user profiles. The input consists of standardized educational content and user profiles retrieved from the database. As a data calculation, the generative AI model generates an appropriate lesson plan based on the prompt statements. The output is a personalized lesson plan, which is provided to the user. 【0482】 Step 5: 【0483】 The user progresses through the learning process based on the provided instructional plan. The input is the instructional plan provided by the server. Specifically, the user learns according to the plan and inputs their progress into the terminal. The output is learning progress information, which is recorded on the terminal. 【0484】 Step 6: 【0485】 The terminal sends learning progress data to the server. It receives learning progress entered by the user as input and sends it to the server in real time. The output is progress information available for the server to monitor the progress. 【0486】 Step 7: 【0487】 The server provides feedback based on learning progress and recommends new learning content as needed. The input is learning progress information sent from the device. As a data computation, the progress information is analyzed, and a generative AI model generates improvements and new content. The output is the feedback and additional learning content provided to the user. 【0488】 (Application Example 1) 【0489】 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." 【0490】 In modern society, there is a need to appropriately provide educational content that caters to diverse cultural backgrounds and individual interests. However, traditional education systems fail to adequately address individual learning needs, and challenges remain, particularly in terms of progress management and motivation. Furthermore, the lack of support for learners to effectively progress at their own pace hinders the improvement of individual learning skills. 【0491】 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. 【0492】 In this invention, the server includes means for collecting educational information from around the world, means for converting the collected educational information into a standard format and integrating it into a database, means for generating a learning plan based on an individual's learning history and goals, means for analyzing the user's interests using a generative AI model and recommending optimal educational content, and means for delivering educational content on smart devices and managing learning interactions. This makes it possible to provide a personalized learning experience tailored to each learner's interests and progress. 【0493】 "Means of collecting educational information from around the world" refers to technologies that acquire diverse education-related data from various sources on the internet. 【0494】 "Means of converting collected educational information into a standard format and integrating it into a database" refers to the process of converting acquired data into a consistent format and aggregating it into an easily manageable database. 【0495】 "Means for generating learning plans based on an individual's learning history and goals" refers to a method for formulating individual learning plans based on a user's past learning information and set goals. 【0496】 "A means of analyzing user interests using generative AI models and recommending optimal educational content" refers to a technology that utilizes artificial intelligence to evaluate user interests and suggest the most suitable learning materials. 【0497】 "Means for delivering educational content and managing learning interactions on smart devices" refers to methods of providing learning materials via digital devices such as smartphones and tablets, and recording and managing learners' behavior. 【0498】 To implement this invention, a coordinated system involving a server, a terminal, and a user is used. The server first accesses educational information sources on the internet and collects educational information in various forms using web crawling technology. This information includes a wide range of data formats, including text, video, and audio. The collected information is converted to a standard format using appropriate software such as Scrapy and integrated into a centralized database. 【0499】 The device creates a profile based on the user's learning history and interest information. This profile data is analyzed by a server-generated AI model to create an optimized learning plan for each user. By using AI technologies such as OpenAI GPT, the system gains a deep understanding of the user's interests and provides recommendations accordingly. 【0500】 Users learn at their own pace using learning content provided through smart devices. Learning progress is monitored in real time, recorded on the device, and periodically sent to the server as feedback. This allows for content updates and plan adjustments as needed. 【0501】 For example, if a middle school student user is interested in science and history, this system will provide relevant educational materials such as archaeology documentaries and science experiment tutorial videos. Furthermore, a prompt could be used to instruct the AI ​​in the following format: "Middle school student user B is interested in science and history. They have previously studied introductory science and basic history. Please suggest content they should learn next." 【0502】 This allows individual learners to receive an educational experience optimized for their own learning needs. 【0503】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0504】 Step 1: 【0505】 The server uses web crawling technology to collect text, video, and audio data from educational resources on the internet. In this process, the server analyzes the collected data using software such as Scrapy, converts it to a standard format, and stores it in a centralized database. The input is raw data obtained from various websites and educational platforms, while the output is data formatted to a standard format. 【0506】 Step 2: 【0507】 The device receives learning history and interest information based on user input and creates a profile. This profile uses the user's personal information and areas of interest as input, and the output is profile data that integrates each learner's learning objectives and past learning activities. 【0508】 Step 3: 【0509】 The server inputs collected educational data and profile data received from the device into a generating AI model to analyze user interests. At this stage, OpenAI GPT or similar AI technology is used to generate data that recommends optimal educational content. The input is standardized educational data and profile data, and the output is a personalized learning plan. 【0510】 Step 4: 【0511】 The server sends the generated learning plan to the device, which then uses it to manage and provide learning content to the user on its smart device. The input is the learning plan received from the server, and the output is the learning content managed on the user's device. This process also records the user's interactions with the selected content. 【0512】 Step 5: 【0513】 Users progress through their learning using the provided learning content. The user's progress is recorded in real time on their device and sent to the server as feedback. Input is progress data obtained from the user's learning activities, and output is feedback information and suggestions for the next learning steps. 【0514】 By combining these steps, an educational experience optimized for each individual learner can be achieved. 【0515】 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. 【0516】 This invention provides a system that recognizes user emotions and uses that information to optimize the learning experience. Specific embodiments are shown below. 【0517】 First, the server continuously collects a variety of education-related content from the internet. This data is converted into a standard format and integrated into a database. This data is then cross-referenced with user profile information and used to generate learning plans. 【0518】 The device incorporates an emotion engine that analyzes the user's facial expressions, voice, and input data. When the user uses the learning materials, this emotion engine analyzes the user's emotional state in real time, detecting their level of concentration, satisfaction, and stress. This allows the system to continuously understand the user's learning state. 【0519】 During learning, the server updates the user profile based on collected emotional data and dynamically adjusts the learning plan. For example, if the user is feeling stressed, the system provides easier learning materials or relaxing content. Conversely, when the user is highly focused, challenging problems are added to enhance the learning effect. 【0520】 As learning progresses, the device constantly records changes in the user's emotions and sends this data to a server. The server analyzes this information and provides feedback on the user's learning experience. Furthermore, mechanisms are in place to support user motivation through rewards for meeting certain achievement criteria and gamification elements. 【0521】 As a concrete example, consider a 12-year-old student doing math exercises. If the student shows a confused expression, the emotion engine detects this and sends the data to the server. Based on this, the server immediately adjusts the learning plan and presents content, including visual explanations, to support the student's understanding. This allows students to receive optimal education tailored to their individual needs, creating an effective learning environment. 【0522】 Thus, the present invention aims to realize flexible and effective learning tailored to individual needs by having AI recognize the user's emotions and optimizing the learning content using that information. 【0523】 The following describes the processing flow. 【0524】 Step 1: 【0525】 The server collects educational information from educational websites on the internet. Using crawling technology, it automatically extracts content in various formats. All obtained data is converted to a standard format to facilitate subsequent processing. 【0526】 Step 2: 【0527】 The device collects basic information from the user, including the user's age, areas of learning interest, and specific learning goals. This information is important for creating a user profile. 【0528】 Step 3: 【0529】 The emotion engine built into the device analyzes the user's facial expressions and voice to detect their emotional state in real time. The analysis results include the user's level of concentration, satisfaction, and stress level. 【0530】 Step 4: 【0531】 The server receives user sentiment data sent from the sentiment engine and registers it in the user profile. This profile, along with the learning history, is used to optimize the learning plan. 【0532】 Step 5: 【0533】 The server generates a learning plan using a generative AI model based on user profiles and sentiment data. It adjusts the difficulty level of the learning materials and selects new content as needed. 【0534】 Step 6: 【0535】 The server sends the generated learning plan to the device. The device displays this plan to the user and provides the necessary interface to begin learning. 【0536】 Step 7: 【0537】 Users take courses and solve practice problems based on a learning plan provided on their device. An emotion engine continuously monitors the user's emotions during the learning process. 【0538】 Step 8: 【0539】 The server evaluates the effectiveness of the learning plan based on real-time updated sentiment data and generates feedback as needed. For example, it might suggest taking breaks to alleviate stress. 【0540】 Step 9: 【0541】 The device applies a reward system based on the user's progress and emotional data. When a user meets achievement criteria, they are provided with incentives, which strengthens their motivation to learn. 【0542】 (Example 2) 【0543】 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." 【0544】 Traditional education systems have struggled to analyze individual learners' emotional states in real time and dynamically adjust learning plans accordingly. Furthermore, they lacked mechanisms to provide appropriate feedback based on individual learning progress, preventing them from maximizing learning effectiveness. Therefore, it is necessary to provide flexible learning methods tailored to learners' concentration levels and stress levels to enhance motivation. 【0545】 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. 【0546】 In this invention, the server includes means for collecting educational data from around the world, means for converting the collected educational data into a standard format and storing it, means for monitoring an individual's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning plan based on the individual's emotional state. This makes it possible to provide education adapted to individual emotional states and to build a flexible and effective learning environment to enhance learning effectiveness. 【0547】 "Educational data" refers to information and materials related to learners' education, and includes a variety of formats such as text, videos, images, and audio. 【0548】 A "standard format" refers to a conversion format used to unify data of different formats into a common structure, thereby facilitating data access and analysis. 【0549】 "Storage" refers to the process of collecting information and saving it in a way that allows it to be used for later processing and analysis. 【0550】 An "emotion analysis engine" refers to a technology that analyzes a user's facial expressions, voice, and behavioral data in real time and evaluates their emotional state. 【0551】 "Real-time monitoring" refers to a process that instantly detects a user's emotional state and immediately tracks any changes in it. 【0552】 A "learning plan" refers to a plan of learning activities that is optimized according to the individual characteristics of each learner. 【0553】 "Dynamic adjustment" refers to flexibly changing plans and system configurations in response to changes in the environment and conditions. 【0554】 "Feedback" refers to the evaluation and guidance provided to learners regarding the progress and outcomes of educational activities, and to reflect these findings in subsequent learning activities. 【0555】 This invention is an educational system that optimizes the user's learning experience by utilizing emotion analysis technology. This system consists of three components: a server, a terminal, and a user. 【0556】 The server is responsible for automatically collecting educational data from the internet, converting the collected data into a standard format, and integrating it. This process utilizes a high-speed database system and data analysis software. As a result, it can generate appropriate learning plans based on the user's learning history and profile, and provide optimal educational content to each individual user. 【0557】 The device incorporates an emotion analysis engine that collects the user's facial expressions and voice in real time. This allows for immediate analysis of the user's emotional state and evaluation of stress levels and concentration. Specifically, it collects emotional data using the camera and microphone, and uses an analysis algorithm to understand the emotional state. Based on the user's emotions, the server can dynamically adjust the learning plan and provide the user with the most suitable content. 【0558】 As a concrete example, consider a case where a student shows a confused expression while working on a math problem. The device's emotion analysis engine recognizes this expression and sends the data to the server. Based on this information, the server adjusts the learning plan to immediately provide clearer learning materials and visual guides, thereby helping to deepen the student's understanding. 【0559】 This invention enables the provision of a flexible and effective learning environment based on real-time user emotion data. By utilizing a generative AI model, the accuracy of learning plans and user feedback can be improved. An example of a prompt to the generative AI model might be, "Please tell me a specific way to adjust the learning plan based on the user's emotional state." 【0560】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0561】 Step 1: 【0562】 The server collects educational data from the internet. This process uses APIs to retrieve data such as text, videos, and images from online educational resources. It accepts data in various formats as input and integrates it into a database by converting it into a common standard format (e.g., JSON). The output is a database of educational content unified in a standard format. 【0563】 Step 2: 【0564】 The device collects the user's facial expressions and voice in real time and inputs them into an emotion analysis engine. Using the camera and microphone, it acquires video and audio data from the user, and by applying this data to an emotion analysis algorithm, it analyzes the user's emotional state, such as stress level and concentration level. Using the collected emotion data as input, it generates the user's emotion parameters as output. 【0565】 Step 3: 【0566】 The server receives emotion parameters sent from the terminal and evaluates the user's current learning plan. Using the user's learning history data and emotion parameters as input, it generates an optimal learning plan tailored to the user's emotional state. As output, the adjusted learning plan is created and provided to the terminal. 【0567】 Step 4: 【0568】 The device displays appropriate content to the user based on a pre-configured learning plan provided by the server. It receives a new learning plan as input and presents the user with learning materials and exercises aligned with that plan. The output displays the specific learning materials and exercises the user will use for their studies. 【0569】 Step 5: 【0570】 Users learn using the provided learning materials and exercises. Emotional data may be collected again during the learning process; the device analyzes the data each time and sends it to the server as needed. This ensures that changes in the user's emotional state during learning are constantly reflected. 【0571】 (Application Example 2) 【0572】 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." 【0573】 In today's information-saturated society, appropriately acquiring the information each user needs and providing an optimal experience based on that information is an extremely difficult challenge. Furthermore, understanding user emotions and adjusting services accordingly is a crucial element in enabling the provision of more personalized experiences. This invention aims to optimize purchasing and learning experiences by providing appropriate information based on user emotions. 【0574】 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. 【0575】 In this invention, the server includes means for collecting information from around the world, means for converting the collected information into a standard format and integrating it into memory, means for generating a plan based on an individual's history and goals, means for analyzing an individual's emotional state and providing information based on the analysis results, and means for generating an appropriate experience based on the analyzed emotions and behaviors. This makes it possible to provide a personalized experience for each user and improve the efficiency of information acquisition and utilization. 【0576】 "Information" refers to data and knowledge that are collected, processed, and analyzed for a specific purpose. 【0577】 "Means" refers to the methods or techniques used to achieve a certain objective. 【0578】 "Memory" refers to a mechanism for storing information and retrieving and using it as needed. 【0579】 "History" refers to records and data about past actions, achievements, and events. 【0580】 A "goal" refers to a destination that represents a specific state or result that should be achieved. 【0581】 A "plan" refers to a set of plans or concepts developed to achieve a specific goal. 【0582】 "Emotional state" refers to the psychological reactions and emotional conditions an individual exhibits in a particular situation. 【0583】 "Analysis results" refer to conclusions and information derived from analyzing data and circumstances. 【0584】 "Information provision" refers to the act of transmitting data or knowledge to others and making it available for their use. 【0585】 "Experience generation" refers to the process of designing and providing events and experiences that individuals will experience in specific situations or environments. 【0586】 "Experience" refers to a series of events or activities that an individual directly experiences in a particular situation or environment. 【0587】 The server continuously collects information from around the world via the internet, converts this information into a standard format, and integrates it into a database. It primarily uses high-performance server equipment for data processing and analysis, possessing the ability to quickly organize and store large amounts of data. Software used in this process includes database management systems and APIs for information collection. 【0588】 The device uses cameras and sensors to collect user facial expressions, voice, and input data in real time. Based on this, an emotion engine analyzes the user's emotional state and determines their level of concentration, satisfaction, and stress. The device uses OpenCV as its image processing library and employs dedicated speech recognition technology for voice analysis. 【0589】 When a user interacts with the system, analyzed emotional data is sent to the server. The server uses this information to update the user profile and provide optimized information and services. For example, if a user is feeling stressed, the system will present relaxing content, and if the user is in a good mood, it will provide promotional information. In this way, the user experience is personalized, and satisfaction is improved. 【0590】 As a concrete example, consider a scenario where a user is online shopping. In this situation, a camera is used to read the user's facial expressions, and based on the analysis results, special offers such as a "10% discount coupon usable now" are displayed. This can increase the user's desire to purchase and provide a satisfying experience. 【0591】 An example of a prompt message to send to a generative AI model is, "Analyze the user's emotions in real time and suggest ways to provide a suitable experience." 【0592】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0593】 Step 1: 【0594】 The server collects information from around the world via the internet. Inputs are various data sources on the internet, and output is information converted into a standard format. The server uses data collection APIs to systematically collect information and prepare it for storage in a database. 【0595】 Step 2: 【0596】 The server converts the collected information into a standard format and integrates it into the database. The input is the raw data collected in step 1, and the output is structured data in a unified format. Through the data conversion process, data in different formats is organized into a consistent format and stored in the database. 【0597】 Step 3: 【0598】 The device uses cameras and sensors to capture the user's facial expressions and voice in real time. The input is perceptual data from the user's physical presence, and the output is analyzable digital data. The device processes the data using image processing libraries such as OpenCV and sends it to the emotion engine. 【0599】 Step 4: 【0600】 The emotion engine analyzes the user's emotional state to determine their level of concentration, satisfaction, and stress. The input is the digital data acquired in step 3, and the output is metadata indicating the user's emotional state. The emotion analysis algorithm is used to evaluate the emotional data in real time. 【0601】 Step 5: 【0602】 The server receives data sent from the emotion engine and updates the user profile. The input is information about the emotional state, and the output is the updated user profile. The server uses a machine learning model to refine the profile and prepare for the next interaction. 【0603】 Step 6: 【0604】 Users receive optimized information and services based on the analyzed data. The input is the updated information determined in step 5, and the output is a customized experience and information. Promotional and support information is presented to the user on the device, providing a personalized experience. 【0605】 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. 【0606】 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. 【0607】 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. 【0608】 [Fourth Embodiment] 【0609】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0610】 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. 【0611】 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). 【0612】 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. 【0613】 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. 【0614】 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). 【0615】 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. 【0616】 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. 【0617】 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. 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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". 【0622】 This invention is a system aimed at aggregating global educational information and providing this information in a format suitable for individual learners. The method of implementation is described below. 【0623】 First, the server automatically collects information from various educational resources around the world using web crawling technology. This information includes text, video, and audio data, and is categorized according to academic discipline and educational purpose. The aggregated data is then converted to a standard format and integrated into a database. This standardization ensures consistency across all data formats, facilitating subsequent searching and filtering. 【0624】 The device receives information entered by the user and forms a user profile tailored to the individual's learning objectives and interests. This profile includes learning history, areas of interest, and specific learning goals. This profile information later serves as the foundation for creating a detailed learning plan. 【0625】 The server then uses a generative AI model to match educational content in the database with the user profile to generate a personalized learning plan. This allows users to obtain a learning path tailored specifically to them. This plan takes into account the learner's cultural background and language, and is delivered in a way that is optimized for each individual. 【0626】 Users can learn at their own pace, and their learning progress is recorded on their device in real time. The device continuously sends learning progress to the server and receives feedback as needed, thereby enhancing learning effectiveness. A reward system is also in place, providing incentives based on the user's learning progress and achieved goals. 【0627】 As a concrete example, consider a 10-year-old student who is interested in art and mathematics. When this student uses the platform, the server collects content such as art history and math quizzes and creates a suitable learning plan. Furthermore, as the student progresses, new content is provided as further challenges. In this way, the student can receive education tailored to their individual learning needs. 【0628】 This will create an environment where everyone can receive a high-quality education, without being constrained by region or culture. 【0629】 The following describes the processing flow. 【0630】 Step 1: 【0631】 The server accesses educational websites on the internet and uses crawling technology to collect publicly available educational content. The data obtained through this process is acquired as text, video, and audio data. 【0632】 Step 2: 【0633】 The server converts the collected raw data into a standard format. Video and audio metadata is automatically extracted, and text data is processed using text analysis techniques. The converted data is then organized and integrated into a database. 【0634】 Step 3: 【0635】 The device receives basic learning-related information, areas of interest, and specific goals from the user as input. This information includes details about the user's age and the topics they wish to learn about. 【0636】 Step 4: 【0637】 The server generates a personalized user profile based on information provided by the user. This profile records individual learning goals and past learning history, which helps in generating subsequent learning plans. 【0638】 Step 5: 【0639】 The server matches user profiles with educational content in the database and uses a generative AI model to create personalized learning plans for each user. These learning plans are optimized to take into account difficulty levels and cultural backgrounds. 【0640】 Step 6: 【0641】 The server sends the completed learning plan to the terminal. The terminal displays this plan to the user and provides an interface for starting the learning process. 【0642】 Step 7: 【0643】 Users learn based on the learning plan displayed on their device. They engage in learning activities such as viewing learning materials and answering practice questions. 【0644】 Step 8: 【0645】 The device records data on the user's learning progress and periodically sends it to the server. This data includes completed learning tasks and accuracy rates. 【0646】 Step 9: 【0647】 The server analyzes progress data and determines whether adjustments to the learning plan are necessary. If needed, it generates feedback and provides learning materials and supplementary resources for the next steps. 【0648】 Step 10: 【0649】 The server calculates incentives based on the user's learning progress and according to the reward system, and notifies the user. This encourages continued learning. 【0650】 (Example 1) 【0651】 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". 【0652】 In recent years, the amount of educational information has increased rapidly, but the provision of information tailored to the individual needs of learners remains insufficient. Furthermore, there is a lack of optimization of educational content according to region and culture, which presents challenges in motivating learning. 【0653】 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. 【0654】 In this invention, the server includes means for collecting educational information, means for converting the collected information into a standard format and integrating it into a recording device, means for generating instructional plans based on an individual's learning history and goals, and means for comparing the instructional plans with the information using a generated AI model to provide personalized instructional plans. This makes it possible to provide high-quality education that meets the individual needs of learners, regardless of region or culture. Furthermore, by setting incentives according to learning progress, motivation to learn can be effectively improved. 【0655】 "Educational information" refers to materials and data necessary for learners to achieve their educational objectives, and includes a variety of formats such as text, video, and audio data. 【0656】 A "standard format" refers to a common data format used to streamline data management and processing by unifying different formats. 【0657】 A "recording device" refers to a database or storage system for permanently storing and managing information. 【0658】 "Learning history" refers to a record of educational activities that a learner has undertaken in the past, and is data used to track the progress of their learning. 【0659】 A "teaching plan" refers to an educational program that includes learning content and methods, formulated based on the individual needs and goals of each learner. 【0660】 A "generative AI model" refers to an artificial intelligence system that uses machine learning techniques to analyze data and generate results for a specific task. 【0661】 "Incentives" refer to rewards or benefits set up to promote learning or improve motivation. 【0662】 This invention is a system that collects educational information and provides a learning experience optimized according to the individual needs of each learner. A specific example of this system is shown below. 【0663】 The server uses web crawling technology to collect education-related data from around the world. Specifically, it uses software such as Selenium and Beautiful Soup to acquire a wide range of online content in various formats (text, video, audio data, etc.). The collected data is then converted to a standard format using Python's Pandas library and SQL, and integrated into a storage device. This conversion enables integrated data management and efficient retrieval. 【0664】 Next, the device forms a profile based on input from the user. Specifically, it creates an individual profile based on information such as the user's past learning history, areas of interest, and set learning goals. 【0665】 The server then uses a generative AI model, such as a popular natural language processing model, to compare the educational content stored in the database with the user profile. Through this process, it generates a lesson plan adapted to the user. Because this generated plan takes into account the user's culture, background, and language, it provides a specific and relevant learning path. 【0666】 Furthermore, users can learn at their own pace, following the provided instructional plan. The device records learning progress in real time and sends it to the server. Based on this information, the server provides feedback tailored to the user's progress and recommends new learning content. This process continuously encourages user growth. 【0667】 For example, if a 10-year-old student is interested in art and mathematics, the server will provide the student with a learning plan that combines art history with math quizzes. New learning themes and content will be added as the student progresses, allowing them to constantly face new challenges. 【0668】 An example of a prompt might be, "A 10-year-old student is interested in both art and mathematics. Please provide the best learning plan for this student." This ensures that high-value education is tailored to individual learners, regardless of region or culture. 【0669】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0670】 Step 1: 【0671】 The server collects educational information using web crawling technology. Specifically, it uses Selenium or Beautiful Soup to retrieve text, video, and audio data from web pages based on URLs and keywords specified as input. As output, it stores the collected data in temporary storage. This process includes classifying the data according to specific academic disciplines and educational purposes. 【0672】 Step 2: 【0673】 The server converts the collected data into a standard format. It receives data in various formats stored in temporary storage as input. Using Python's Pandas library and SQL for data processing, it unifies this data into JSON or XML format. The output is data in a unified standard format, which is then integrated and stored in a database. 【0674】 Step 3: 【0675】 The terminal receives input from the user and forms a user profile. This input includes information about the user's areas of interest, set learning goals, and past learning history. The system analyzes this information as data computation to create an individual profile. The output is a detailed user profile used later to generate a learning plan. 【0676】 Step 4: 【0677】 The server uses a generative AI model to match educational content in the database with user profiles. The input consists of standardized educational content and user profiles retrieved from the database. As a data calculation, the generative AI model generates an appropriate lesson plan based on the prompt statements. The output is a personalized lesson plan, which is provided to the user. 【0678】 Step 5: 【0679】 The user progresses through the learning process based on the provided instructional plan. The input is the instructional plan provided by the server. Specifically, the user learns according to the plan and inputs their progress into the terminal. The output is learning progress information, which is recorded on the terminal. 【0680】 Step 6: 【0681】 The terminal sends learning progress data to the server. It receives learning progress entered by the user as input and sends it to the server in real time. The output is progress information available for the server to monitor the progress. 【0682】 Step 7: 【0683】 The server provides feedback based on learning progress and recommends new learning content as needed. The input is learning progress information sent from the device. As a data computation, the progress information is analyzed, and a generative AI model generates improvements and new content. The output is the feedback and additional learning content provided to the user. 【0684】 (Application Example 1) 【0685】 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". 【0686】 In modern society, there is a need to appropriately provide educational content that caters to diverse cultural backgrounds and individual interests. However, traditional education systems fail to adequately address individual learning needs, and challenges remain, particularly in terms of progress management and motivation. Furthermore, the lack of support for learners to effectively progress at their own pace hinders the improvement of individual learning skills. 【0687】 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. 【0688】 In this invention, the server includes means for collecting educational information from around the world, means for converting the collected educational information into a standard format and integrating it into a database, means for generating a learning plan based on an individual's learning history and goals, means for analyzing the user's interests using a generative AI model and recommending optimal educational content, and means for delivering educational content on smart devices and managing learning interactions. This makes it possible to provide a personalized learning experience tailored to each learner's interests and progress. 【0689】 "Means of collecting educational information from around the world" refers to technologies that acquire diverse education-related data from various sources on the internet. 【0690】 "Means of converting collected educational information into a standard format and integrating it into a database" refers to the process of converting acquired data into a consistent format and aggregating it into an easily manageable database. 【0691】 "Means for generating learning plans based on an individual's learning history and goals" refers to a method for formulating individual learning plans based on a user's past learning information and set goals. 【0692】 "A means of analyzing user interests using generative AI models and recommending optimal educational content" refers to a technology that utilizes artificial intelligence to evaluate user interests and suggest the most suitable learning materials. 【0693】 "Means for delivering educational content and managing learning interactions on smart devices" refers to methods of providing learning materials via digital devices such as smartphones and tablets, and recording and managing learners' behavior. 【0694】 To implement this invention, a coordinated system involving a server, a terminal, and a user is used. The server first accesses educational information sources on the internet and collects educational information in various forms using web crawling technology. This information includes a wide range of data formats, including text, video, and audio. The collected information is converted to a standard format using appropriate software such as Scrapy and integrated into a centralized database. 【0695】 The device creates a profile based on the user's learning history and interest information. This profile data is analyzed by a server-generated AI model to create an optimized learning plan for each user. By using AI technologies such as OpenAI GPT, the system gains a deep understanding of the user's interests and provides recommendations accordingly. 【0696】 Users learn at their own pace using learning content provided through smart devices. Learning progress is monitored in real time, recorded on the device, and periodically sent to the server as feedback. This allows for content updates and plan adjustments as needed. 【0697】 For example, if a middle school student user is interested in science and history, this system will provide relevant educational materials such as archaeology documentaries and science experiment tutorial videos. Furthermore, a prompt could be used to instruct the AI ​​in the following format: "Middle school student user B is interested in science and history. They have previously studied introductory science and basic history. Please suggest content they should learn next." 【0698】 This allows individual learners to receive an educational experience optimized for their own learning needs. 【0699】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0700】 Step 1: 【0701】 The server uses web crawling technology to collect text, video, and audio data from educational resources on the internet. In this process, the server analyzes the collected data using software such as Scrapy, converts it to a standard format, and stores it in a centralized database. The input is raw data obtained from various websites and educational platforms, while the output is data formatted to a standard format. 【0702】 Step 2: 【0703】 The device receives learning history and interest information based on user input and creates a profile. This profile uses the user's personal information and areas of interest as input, and the output is profile data that integrates each learner's learning objectives and past learning activities. 【0704】 Step 3: 【0705】 The server inputs collected educational data and profile data received from the device into a generating AI model to analyze user interests. At this stage, OpenAI GPT or similar AI technology is used to generate data that recommends optimal educational content. The input is standardized educational data and profile data, and the output is a personalized learning plan. 【0706】 Step 4: 【0707】 The server sends the generated learning plan to the device, which then uses it to manage and provide learning content to the user on its smart device. The input is the learning plan received from the server, and the output is the learning content managed on the user's device. This process also records the user's interactions with the selected content. 【0708】 Step 5: 【0709】 Users progress through their learning using the provided learning content. The user's progress is recorded in real time on their device and sent to the server as feedback. Input is progress data obtained from the user's learning activities, and output is feedback information and suggestions for the next learning steps. 【0710】 By combining these steps, an educational experience optimized for each individual learner can be achieved. 【0711】 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. 【0712】 This invention provides a system that recognizes user emotions and uses that information to optimize the learning experience. Specific embodiments are shown below. 【0713】 First, the server continuously collects a variety of education-related content from the internet. This data is converted into a standard format and integrated into a database. This data is then cross-referenced with user profile information and used to generate learning plans. 【0714】 The device incorporates an emotion engine that analyzes the user's facial expressions, voice, and input data. When the user uses the learning materials, this emotion engine analyzes the user's emotional state in real time, detecting their level of concentration, satisfaction, and stress. This allows the system to continuously understand the user's learning state. 【0715】 During learning, the server updates the user profile based on collected emotional data and dynamically adjusts the learning plan. For example, if the user is feeling stressed, the system provides easier learning materials or relaxing content. Conversely, when the user is highly focused, challenging problems are added to enhance the learning effect. 【0716】 As learning progresses, the device constantly records changes in the user's emotions and sends this data to a server. The server analyzes this information and provides feedback on the user's learning experience. Furthermore, mechanisms are in place to support user motivation through rewards for meeting certain achievement criteria and gamification elements. 【0717】 As a concrete example, consider a 12-year-old student doing math exercises. If the student shows a confused expression, the emotion engine detects this and sends the data to the server. Based on this, the server immediately adjusts the learning plan and presents content, including visual explanations, to support the student's understanding. This allows students to receive optimal education tailored to their individual needs, creating an effective learning environment. 【0718】 Thus, the present invention aims to realize flexible and effective learning tailored to individual needs by having AI recognize the user's emotions and optimizing the learning content using that information. 【0719】 The following describes the processing flow. 【0720】 Step 1: 【0721】 The server collects educational information from educational websites on the internet. Using crawling technology, it automatically extracts content in various formats. All obtained data is converted to a standard format to facilitate subsequent processing. 【0722】 Step 2: 【0723】 The device collects basic information from the user, including the user's age, areas of learning interest, and specific learning goals. This information is important for creating a user profile. 【0724】 Step 3: 【0725】 The emotion engine built into the device analyzes the user's facial expressions and voice to detect their emotional state in real time. The analysis results include the user's level of concentration, satisfaction, and stress level. 【0726】 Step 4: 【0727】 The server receives user sentiment data sent from the sentiment engine and registers it in the user profile. This profile, along with the learning history, is used to optimize the learning plan. 【0728】 Step 5: 【0729】 The server generates a learning plan using a generative AI model based on user profiles and sentiment data. It adjusts the difficulty level of the learning materials and selects new content as needed. 【0730】 Step 6: 【0731】 The server sends the generated learning plan to the device. The device displays this plan to the user and provides the necessary interface to begin learning. 【0732】 Step 7: 【0733】 Users take courses and solve practice problems based on a learning plan provided on their device. An emotion engine continuously monitors the user's emotions during the learning process. 【0734】 Step 8: 【0735】 The server evaluates the effectiveness of the learning plan based on real-time updated sentiment data and generates feedback as needed. For example, it might suggest taking breaks to alleviate stress. 【0736】 Step 9: 【0737】 The device applies a reward system based on the user's progress and emotional data. When a user meets achievement criteria, they are provided with incentives, which strengthens their motivation to learn. 【0738】 (Example 2) 【0739】 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". 【0740】 Traditional education systems have struggled to analyze individual learners' emotional states in real time and dynamically adjust learning plans accordingly. Furthermore, they lacked mechanisms to provide appropriate feedback based on individual learning progress, preventing them from maximizing learning effectiveness. Therefore, it is necessary to provide flexible learning methods tailored to learners' concentration levels and stress levels to enhance motivation. 【0741】 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. 【0742】 In this invention, the server includes means for collecting educational data from around the world, means for converting the collected educational data into a standard format and storing it, means for monitoring an individual's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning plan based on the individual's emotional state. This makes it possible to provide education adapted to individual emotional states and to build a flexible and effective learning environment to enhance learning effectiveness. 【0743】 "Educational data" refers to information and materials related to learners' education, and includes a variety of formats such as text, videos, images, and audio. 【0744】 A "standard format" refers to a conversion format used to unify data of different formats into a common structure, thereby facilitating data access and analysis. 【0745】 "Storage" refers to the process of collecting information and saving it in a way that allows it to be used for later processing and analysis. 【0746】 An "emotion analysis engine" refers to a technology that analyzes a user's facial expressions, voice, and behavioral data in real time and evaluates their emotional state. 【0747】 "Real-time monitoring" refers to a process that instantly detects a user's emotional state and immediately tracks any changes in it. 【0748】 A "learning plan" refers to a plan of learning activities that is optimized according to the individual characteristics of each learner. 【0749】 "Dynamic adjustment" refers to flexibly changing plans and system configurations in response to changes in the environment and conditions. 【0750】 "Feedback" refers to the evaluation and guidance provided to learners regarding the progress and outcomes of educational activities, and to reflect these findings in subsequent learning activities. 【0751】 This invention is an educational system that optimizes the user's learning experience by utilizing emotion analysis technology. This system consists of three components: a server, a terminal, and a user. 【0752】 The server is responsible for automatically collecting educational data from the internet, converting the collected data into a standard format, and integrating it. This process utilizes a high-speed database system and data analysis software. As a result, it can generate appropriate learning plans based on the user's learning history and profile, and provide optimal educational content to each individual user. 【0753】 The device incorporates an emotion analysis engine that collects the user's facial expressions and voice in real time. This allows for immediate analysis of the user's emotional state and evaluation of stress levels and concentration. Specifically, it collects emotional data using the camera and microphone, and uses an analysis algorithm to understand the emotional state. Based on the user's emotions, the server can dynamically adjust the learning plan and provide the user with the most suitable content. 【0754】 As a concrete example, consider a case where a student shows a confused expression while working on a math problem. The device's emotion analysis engine recognizes this expression and sends the data to the server. Based on this information, the server adjusts the learning plan to immediately provide clearer learning materials and visual guides, thereby helping to deepen the student's understanding. 【0755】 This invention enables the provision of a flexible and effective learning environment based on real-time user emotion data. By utilizing a generative AI model, the accuracy of learning plans and user feedback can be improved. An example of a prompt to the generative AI model might be, "Please tell me a specific way to adjust the learning plan based on the user's emotional state." 【0756】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0757】 Step 1: 【0758】 The server collects educational data from the internet. This process uses APIs to retrieve data such as text, videos, and images from online educational resources. It accepts data in various formats as input and integrates it into a database by converting it into a common standard format (e.g., JSON). The output is a database of educational content unified in a standard format. 【0759】 Step 2: 【0760】 The device collects the user's facial expressions and voice in real time and inputs them into an emotion analysis engine. Using the camera and microphone, it acquires video and audio data from the user, and by applying this data to an emotion analysis algorithm, it analyzes the user's emotional state, such as stress level and concentration level. Using the collected emotion data as input, it generates the user's emotion parameters as output. 【0761】 Step 3: 【0762】 The server receives emotion parameters sent from the terminal and evaluates the user's current learning plan. Using the user's learning history data and emotion parameters as input, it generates an optimal learning plan tailored to the user's emotional state. As output, the adjusted learning plan is created and provided to the terminal. 【0763】 Step 4: 【0764】 The device displays appropriate content to the user based on a pre-configured learning plan provided by the server. It receives a new learning plan as input and presents the user with learning materials and exercises aligned with that plan. The output displays the specific learning materials and exercises the user will use for their studies. 【0765】 Step 5: 【0766】 Users learn using the provided learning materials and exercises. Emotional data may be collected again during the learning process; the device analyzes the data each time and sends it to the server as needed. This ensures that changes in the user's emotional state during learning are constantly reflected. 【0767】 (Application Example 2) 【0768】 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". 【0769】 In today's information-saturated society, appropriately acquiring the information each user needs and providing an optimal experience based on that information is an extremely difficult challenge. Furthermore, understanding user emotions and adjusting services accordingly is a crucial element in enabling the provision of more personalized experiences. This invention aims to optimize purchasing and learning experiences by providing appropriate information based on user emotions. 【0770】 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. 【0771】 In this invention, the server includes means for collecting information from around the world, means for converting the collected information into a standard format and integrating it into memory, means for generating a plan based on an individual's history and goals, means for analyzing an individual's emotional state and providing information based on the analysis results, and means for generating an appropriate experience based on the analyzed emotions and behaviors. This makes it possible to provide a personalized experience for each user and improve the efficiency of information acquisition and utilization. 【0772】 "Information" refers to data and knowledge that are collected, processed, and analyzed for a specific purpose. 【0773】 "Means" refers to the methods or techniques used to achieve a certain objective. 【0774】 "Memory" refers to a mechanism for storing information and retrieving and using it as needed. 【0775】 "History" refers to records and data about past actions, achievements, and events. 【0776】 A "goal" refers to a destination that represents a specific state or result that should be achieved. 【0777】 A "plan" refers to a set of plans or concepts developed to achieve a specific goal. 【0778】 "Emotional state" refers to the psychological reactions and emotional conditions an individual exhibits in a particular situation. 【0779】 "Analysis results" refer to conclusions and information derived from analyzing data and circumstances. 【0780】 "Information provision" refers to the act of transmitting data or knowledge to others and making it available for their use. 【0781】 "Experience generation" refers to the process of designing and providing events and experiences that individuals will experience in specific situations or environments. 【0782】 "Experience" refers to a series of events or activities that an individual directly experiences in a particular situation or environment. 【0783】 The server continuously collects information from around the world via the internet, converts this information into a standard format, and integrates it into a database. It primarily uses high-performance server equipment for data processing and analysis, possessing the ability to quickly organize and store large amounts of data. Software used in this process includes database management systems and APIs for information collection. 【0784】 The device uses cameras and sensors to collect user facial expressions, voice, and input data in real time. Based on this, an emotion engine analyzes the user's emotional state and determines their level of concentration, satisfaction, and stress. The device uses OpenCV as its image processing library and employs dedicated speech recognition technology for voice analysis. 【0785】 When a user interacts with the system, analyzed emotional data is sent to the server. The server uses this information to update the user profile and provide optimized information and services. For example, if a user is feeling stressed, the system will present relaxing content, and if the user is in a good mood, it will provide promotional information. In this way, the user experience is personalized, and satisfaction is improved. 【0786】 As a concrete example, consider a scenario where a user is online shopping. In this situation, a camera is used to read the user's facial expressions, and based on the analysis results, special offers such as a "10% discount coupon usable now" are displayed. This can increase the user's desire to purchase and provide a satisfying experience. 【0787】 An example of a prompt message to send to a generative AI model is, "Analyze the user's emotions in real time and suggest ways to provide a suitable experience." 【0788】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0789】 Step 1: 【0790】 The server collects information from around the world via the internet. Inputs are various data sources on the internet, and output is information converted into a standard format. The server uses data collection APIs to systematically collect information and prepare it for storage in a database. 【0791】 Step 2: 【0792】 The server converts the collected information into a standard format and integrates it into the database. The input is the raw data collected in step 1, and the output is structured data in a unified format. Through the data conversion process, data in different formats is organized into a consistent format and stored in the database. 【0793】 Step 3: 【0794】 The device uses cameras and sensors to capture the user's facial expressions and voice in real time. The input is perceptual data from the user's physical presence, and the output is analyzable digital data. The device processes the data using image processing libraries such as OpenCV and sends it to the emotion engine. 【0795】 Step 4: 【0796】 The emotion engine analyzes the user's emotional state to determine their level of concentration, satisfaction, and stress. The input is the digital data acquired in step 3, and the output is metadata indicating the user's emotional state. The emotion analysis algorithm is used to evaluate the emotional data in real time. 【0797】 Step 5: 【0798】 The server receives data sent from the emotion engine and updates the user profile. The input is information about the emotional state, and the output is the updated user profile. The server uses a machine learning model to refine the profile and prepare for the next interaction. 【0799】 Step 6: 【0800】 Users receive optimized information and services based on the analyzed data. The input is the updated information determined in step 5, and the output is a customized experience and information. Promotional and support information is presented to the user on the device, providing a personalized experience. 【0801】 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. 【0802】 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. 【0803】 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 robot 414. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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." 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 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. 【0815】 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. 【0816】 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 this memory. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0822】 The following is further disclosed regarding the embodiments described above. 【0823】 (Claim 1) 【0824】 Means of collecting educational information from around the world, 【0825】 A means of converting collected educational information into a standard format and integrating it into a database, 【0826】 A means of generating a learning plan based on an individual's learning history and goals, 【0827】 A means of providing the generated learning plan to an individual, 【0828】 A system that includes means for monitoring individual learning progress and providing feedback. 【0829】 (Claim 2) 【0830】 The system according to claim 1 for generating learning plans that are appropriate for the region and culture. 【0831】 (Claim 3) 【0832】 The system according to claim 1, which introduces a reward system based on learning progress to motivate learning. 【0833】 "Example 1" 【0834】 (Claim 1) 【0835】 Means of collecting educational information, 【0836】 A means for converting the collected information into a standard format and integrating it into a recording device, 【0837】 A means of generating instructional plans based on an individual's learning history and goals, 【0838】 A means of providing individualized instructional plans by comparing instructional plans and information using a generative AI model, 【0839】 A means of monitoring individual learning progress and updating the information provided, 【0840】 A system that includes means of providing incentives based on the progress made. 【0841】 (Claim 2) 【0842】 The system according to claim 1 for generating instructional plans that are appropriate for the region and culture. 【0843】 (Claim 3) 【0844】 The system according to claim 1, which introduces a reward system based on learning progress to improve motivation to learn. 【0845】 "Application Example 1" 【0846】 (Claim 1) 【0847】 Means of collecting educational information from around the world, 【0848】 A means of converting collected educational information into a standard format and integrating it into a database, 【0849】 A means of generating a learning plan based on an individual's learning history and goals, 【0850】 A means of providing the generated learning plan to an individual, 【0851】 A means of monitoring individual learning progress and providing feedback, 【0852】 A means of analyzing user interests using generative AI models and recommending the most suitable educational content, 【0853】 A system that includes means for delivering educational content on smart devices and managing learning interactions. 【0854】 (Claim 2) 【0855】 The system according to claim 1 for generating learning plans that are appropriate for the region and culture. 【0856】 (Claim 3) 【0857】 The system according to claim 1, which introduces a reward system based on learning progress to motivate learning. 【0858】 "Example 2 of combining an emotion engine" 【0859】 (Claim 1) 【0860】 Means of collecting educational data from around the world, 【0861】 A means of converting collected educational data into a standard format and storing it, 【0862】 A means of monitoring an individual's emotional state in real time using an emotion analysis engine, 【0863】 A means of dynamically adjusting the learning plan based on an individual's emotional state, 【0864】 A means of providing personalized learning plans, 【0865】 A system that includes means for monitoring individual learning progress and providing feedback based on analysis results. 【0866】 (Claim 2) 【0867】 The system according to claim 1, which generates a learning plan that is appropriate for the region and culture using the results of emotion analysis. 【0868】 (Claim 3) 【0869】 The system according to claim 1, which motivates learning by introducing a reward system based on emotional data. 【0870】 "Application example 2 when combining with an emotional engine" 【0871】 (Claim 1) 【0872】 Means of collecting information from around the world, 【0873】 A means of converting the collected information into a standard format and integrating it into memory, 【0874】 A means of generating a plan based on an individual's history and goals, 【0875】 Means of providing the generated plan to individuals, 【0876】 A means of monitoring individual progress and providing feedback, 【0877】 A means of analyzing an individual's emotional state and providing information based on the analysis results, 【0878】 A system that includes means for generating appropriate experiences based on analyzed emotions and behaviors. 【0879】 (Claim 2) 【0880】 The system according to claim 1 for generating plans that are appropriate for the region and culture. 【0881】 (Claim 3) 【0882】 The system described in claim 1, which introduces a progress-based reward system to provide motivation. [Explanation of Symbols] 【0883】 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] Means of collecting educational information from around the world, A means of converting collected educational information into a standard format and integrating it into a database, A means of generating a learning plan based on an individual's learning history and goals, A means of providing the generated learning plan to an individual, A system that includes means for monitoring individual learning progress and providing feedback. [Claim 2] The system according to claim 1, which generates learning plans that are appropriate for the region and culture. [Claim 3] The system according to claim 1, which introduces a reward system based on learning progress to motivate learning.

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