system

Abstract

We provide the system. [Solution] A means to analyze learners' progress in real time and generate individualized learning plans, A means of providing appropriate learning challenges based on the aforementioned learning plan, A means of visually presenting learning outcomes using game elements to maintain motivation. A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 It is to solve the problems of decreased motivation for learning faced by many learners and the difficulty of providing individualized support in a wide range of learning fields. Furthermore, it is to address the issue of realizing efficient and sustainable skill acquisition by providing appropriate challenges based on individual learning progress and promoting the fixation of knowledge. 【Means for Solving the Problems】 【0005】 This system includes a mechanism for analyzing learners' progress in real time and generating personalized learning plans using AI, as well as a mechanism for providing appropriate learning challenges based on these plans. Furthermore, it includes a mechanism for visually presenting learning outcomes as gamified elements to enhance and sustain learner motivation. This system provides an efficient and enjoyable learning experience and promotes further learning through feedback based on individual proficiency levels. 【0006】 A "learner" refers to an individual who aims to improve their skills or retain knowledge by using a system. 【0007】 "Progress" refers to information that indicates the learning outcomes achieved by learners and their current learning status. 【0008】 A "learning plan" is a set of learning plans and assignments designed based on the learner's individual progress and the skills they need to acquire. 【0009】 "AI" refers to all technologies that use artificial intelligence to perform data analysis and decision-making. 【0010】 "Challenge" refers to activities that encourage skill acquisition through specific problems and tasks provided to learners. 【0011】 "Game elements" refer to gamification techniques such as points, badges, and rankings, which are introduced to make learning more enjoyable and maintain motivation. 【0012】 "Feedback" refers to notifications and explanations that help learners retain knowledge by providing information for evaluation and improvement of assignments they have completed. [Brief explanation of the drawing] 【0013】 [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It 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 an 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 an emotion engine is combined. 【Embodiments for Carrying Out the Invention】 【0014】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0017】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0019】 In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 This invention is a gamified learning system aimed at improving learners' abilities, and at its core is AI-powered progress analysis and customized assignment delivery. The specific operation of the system is described below. 【0035】 First, the user logs into the learning system via their device. The device sends the user's authentication information to the server, which verifies the information in its database to authenticate the user. This creates a personalized learning environment. 【0036】 Once learning begins, the device sends user behavior data and progress information to the server. The server analyzes this data in real time through an AI agent to understand the user's level of comprehension and current learning progress. Based on this analysis, the AI ​​agent creates a learning plan for the next steps. 【0037】 The server selects appropriate learning challenges based on the learning plan generated by the AI ​​agent and sends them to the user's device. Users can deepen their knowledge by working on these challenges and completing the tasks. 【0038】 Upon completing the challenge, the device sends the user's answer back to the server, which evaluates the result. The AI ​​agent generates feedback based on the evaluation and returns it to the user. This feedback allows the user to check their level of understanding and clearly identify the next learning steps they need to take. 【0039】 Furthermore, this system incorporates game elements such as points and badges to enhance the sense of accomplishment in learning. The server collects this data and presents it to the user through a visual reward system such as rankings. This allows users to maintain motivation to continue learning while having fun. 【0040】 Through the operations described above, this invention effectively provides a learning environment tailored to individual learners and supports continuous skill improvement. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The user accesses the learning system using a device and enters their ID and password on the login screen. The device collects this information. 【0044】 Step 2: 【0045】 The device sends the user's authentication information to the server. The server checks the database to verify that the entered ID and password are correct and notifies the device of the authentication result. 【0046】 Step 3: 【0047】 After the user logs in, they select learning content via their device. The device sends the selection to the server, and the learning session begins. 【0048】 Step 4: 【0049】 The device continuously collects and sends user learning progress information to the server. This includes tasks completed, completion time, and points earned. 【0050】 Step 5: 【0051】 The server provides the AI ​​agent with the progress data it receives. The AI ​​agent analyzes this data to evaluate the user's understanding and skill level. 【0052】 Step 6: 【0053】 The AI ​​agent generates a learning plan based on the analysis results, which includes new tasks and challenges. 【0054】 Step 7: 【0055】 The server sends the learning plan and challenges generated by the AI ​​agent to the user's device. The user then proceeds with their learning based on this information. 【0056】 Step 8: 【0057】 The user tackles a new challenge and enters the answer into the device. The device then transfers the answer to the server. 【0058】 Step 9: 【0059】 The server evaluates the received answers and determines whether they are correct or incorrect. The AI ​​agent generates feedback and sends the results to the user's device. 【0060】 Step 10: 【0061】 The device displays feedback received from the server to the user. The user uses this feedback to continue learning and prepare to move on to the next step. 【0062】 Step 11: 【0063】 The server compiles user learning progress and updates game elements such as points, badges, and rankings. This visual feedback is sent to the device and helps maintain user motivation. 【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】 This issue stems from learners not receiving adequate learning plans tailored to their individual progress and understanding, leading to decreased learning efficiency and difficulty maintaining motivation. Furthermore, traditional systems have limitations in tracking learning progress in real time and providing individualized feedback. 【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 user learning progress data in real time and analyzing knowledge acquisition status, means for creating personalized learning plans using a generative model based on the analyzed data, and means for evaluating answer results and generating feedback based on the analysis results. This makes it possible to provide an optimized learning experience for each learner and maximize their potential. 【0069】 A "learning plan" is a set of learning plans and assignments designed to match the user's individual progress and level of understanding. 【0070】 A "generative model" is a type of artificial intelligence applied to construct personalized learning plans using the results of data analysis. 【0071】 "Progress data" refers to records of activities and information about results collected during the user's learning process. 【0072】 "Feedback" refers to comments and guidance provided to users to aid their understanding, based on the evaluation of their answers. 【0073】 "Knowledge acquisition status" refers to information that indicates a user's level of understanding of a particular subject or field. 【0074】 "Learning tasks" refer to problems and exercises that users should work on, and are provided based on the learning plan. 【0075】 "Visual representation" refers to presenting learning outcomes to the user in a visible way, and primarily includes points and badges. 【0076】 This invention is a learning support system aimed at improving learners' knowledge, and its core features include individualized progress analysis and learning plan provision utilizing AI technology. The entire system operates with a configuration centered around the user's terminal, a server, and an AI agent. 【0077】 The server receives authentication information entered by the user from the terminal and performs user authentication by matching it against the database. This process provides a customized learning environment based on each user's learning history and current learning status. The server collects the received user learning progress data in real time and performs analysis using a generative AI model. Based on the analysis results, the server uses an AI agent to generate an individualized learning plan and sends it to the terminal. 【0078】 The device presents the user with learning tasks based on a learning plan received from the server. The user works on these tasks and sends their answers to the server via the device. The server evaluates the answers, uses an AI agent to generate feedback, and sends it back to the device. This feedback plays a crucial role in helping the user understand their level of comprehension and move forward with their next learning session. 【0079】 Furthermore, as a means of visually displaying the progress users make as they learn, the server compiles user achievements in the form of points and badges. As a result, the user's achieved level and ranking are displayed on the terminal, and the learning environment, which incorporates game elements, is designed to maintain the user's motivation to learn. 【0080】 As a concrete example, consider a system for learning English grammar. After logging in on their device, users work on a designated set of grammar exercises. Progress and answers are analyzed on the server, and the next steps are suggested as needed. Users can earn points and badges as visual rewards, and can also compare their progress with other learners. 【0081】 An example of a prompt message for a generative AI model is: "The user's progress information is as follows. Please suggest the next task to maximize learning effectiveness." 【0082】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0083】 Step 1: 【0084】 The user logs into the system using a terminal. The terminal sends the authentication information entered by the user to the server. The server verifies the authentication information using a database, and if authentication is successful, prepares a learning environment tailored to the user. The input is the user's authentication information, and the output is the authentication result and the setup of the learning environment. This authentication prepares the user to proceed to the next learning step. 【0085】 Step 2: 【0086】 When a user begins learning, the device sends the user's learning progress data to the server in real time. The server prepares this data for processing by the AI ​​agent. The input is the user's progress data, and the output is learning data organized in list format. The specific operations of the device include acquiring and transmitting data from sensors and interfaces. 【0087】 Step 3: 【0088】 The server uses an AI agent to analyze the received progress data. The AI ​​agent evaluates the data using a generative AI model and determines the user's level of understanding. The input is organized training data, and the output is an analysis report on the user's level of understanding. Based on this analysis, the server determines the next necessary learning action. 【0089】 Step 4: 【0090】 The server builds a personalized learning plan based on the generated comprehension analysis report. The AI ​​agent uses prompts to suggest the next task to tackle. The input is the comprehension analysis report, and the output is a individually customized learning plan. Specific operations include calculations performed by the AI ​​model and task selection based on those calculations. 【0091】 Step 5: 【0092】 The server sends the generated learning plan to the terminal, which then presents it to the user. The user then works on the presented tasks. The input is the individual learning plan, and the output is the task setting resulting from the execution of the learning plan. The terminal then displays the specific learning content that the user should work on. 【0093】 Step 6: 【0094】 Once the user completes the task, the device sends the solution data to the server. The server evaluates the solution and uses an AI agent to generate feedback for the user. The input is the user's solution data, and the output is the solution evaluation result and feedback. The server sends this feedback back to the device, where it is displayed to the user. 【0095】 Step 7: 【0096】 The server aggregates learning outcomes as points and badges and implements game elements. The results are displayed on the device to maintain user engagement. Input is the answer evaluation result, and output is visualized data in the form of points and badges. The device visually displays these results to the user, increasing motivation through rankings and other means. 【0097】 (Application Example 1) 【0098】 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." 【0099】 It is necessary to provide learners with appropriate assignments tailored to their individual learning progress and offer individualized feedback while maintaining their motivation to continue learning. In particular, when learning takes place at home, effective methods are needed to keep learners interested. Traditional systems have the problem of insufficient individualized instruction and interactive dialogue tailored to progress, making it difficult to sustain motivation for learning. 【0100】 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. 【0101】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individualized learning plan, means for providing tasks based on the individualized plan through interaction with a human and generating real-time feedback, and means for promoting learning motivation by awarding points and achievement badges. This makes it possible for learners to maintain their interest and have an individually customized learning experience even at home. 【0102】 A "learning plan" is a plan that reflects the individual progress of the learner and specifically outlines what they should learn next and how to learn it. 【0103】 A "learning challenge" is a task or exercise designed to deepen learners' understanding of specific skills and knowledge. 【0104】 "Game elements" refer to mechanisms that incorporate rewards and competition, such as points, badges, and rankings, to make learning fun and sustainable. 【0105】 "Interactive dialogue" is a form of communication in which a machine interacts with a user, exchanging information and communicating in real time. 【0106】 "Feedback" refers to a system evaluating a learner's behavior and results, and informing them of the results and areas for improvement. 【0107】 "Points and badges" are rewards, such as numerical values ​​or badges, that are awarded as proof of achievement for results and goals reached during the learning process. 【0108】 "In-home life support equipment" refers to mechanical devices that can support the daily lives of residents and provide learning support within a typical household. 【0109】 The embodiment for carrying out the invention is configured as follows: It centers on a life support machine device used in the home, such as a robot with educational support functions. This robot is equipped with various hardware and software to support the user's learning activities. 【0110】 The servers reside in the cloud and are responsible for analyzing learner behavior data and progress information using databases and AI platforms. Specifically, the servers manage data in real time using Firebase Realtime Database and run machine learning models using Google AI Platform. This data processing dynamically generates individual learning plans. 【0111】 The robot, acting as a terminal, is placed within the home and analyzes the learner's voice input using an NLP (Natural Language Processing) engine. For example, the robot converts the user's voice into text and understands the learning content. This processing allows the robot to prepare and present appropriate learning challenges to the user. 【0112】 The robot evaluates learning outcomes and generates and provides feedback to the user based on the acquired data. To make learning more engaging, it includes features that visually reinforce learning achievements using points and badges. 【0113】 As a concrete example, consider a scenario where a robot "presents a multiplication problem." In this scenario, after the user solves the problem, the robot instantly evaluates the result and returns an encouraging message such as "Well done!" Through this interaction, the user's motivation to continue learning increases. 【0114】 Furthermore, an example of a prompt for a generative AI model is: "Please tell me the answer to 3x4. Then, determine if the answer is correct and send a message of encouragement to the user." This allows the robot to engage in natural dialogue while enhancing its learning effectiveness. 【0115】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0116】 Step 1: 【0117】 The user activates the robot and logs in. The robot, which is the user's device, sends login information to the server, and the server authenticates the user using Firebase Realtime Database. The input for this step is the user's authentication information, and the output is the preparation of a learning environment tailored to the user. 【0118】 Step 2: 【0119】 The user gives instructions to begin learning. The terminal (robot) analyzes the user's voice instructions using an NLP engine to understand the learning content. Here, the user's voice data is the input, and the output is text data resulting from the analysis. Based on the analyzed text, the robot determines the next learning challenge to present. 【0120】 Step 3: 【0121】 The server utilizes Google AI Platform to analyze the user's past learning data and real-time activity information to generate an appropriate learning plan. The input is the user's progress data, and the output is a customized learning plan. Based on the analysis results, the server sets the next task to be addressed and sends it to the device. 【0122】 Step 4: 【0123】 The device presents the user with a corresponding learning challenge. The user works on the presented challenge, and the robot collects the input data in real time. The input here is the problem of the learning challenge, and the user's answer is the output. As the user progresses, the robot provides feedback and moves on to the next step. 【0124】 Step 5: 【0125】 The user's answer is sent to the server for evaluation. The server evaluates the answer using an AI platform and generates feedback based on the user's level of proficiency. The input is the user's answer data, and the output is feedback information. Based on the evaluation results, the server decides to award points or badges and sends data to be displayed visually on the device. 【0126】 Step 6: 【0127】 The device provides feedback to the user and visually presents learning outcomes as a game element. It displays points and badges earned to show the user's progress and motivate them for further learning. The input is feedback data, and the output is the user's visual perception. The robot uses this information to prepare suggestions for the next learning session. 【0128】 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. 【0129】 This invention is a system that not only provides individualized learning plans based on the user's progress, but also, by combining it with an emotion engine, realizes a flexible learning experience that responds to the user's emotional state. The specific operation of the system is described below. 【0130】 When a user logs into the learning system via their device, the device sends their authentication information to the server, which then authenticates the user against the database. After that, the system is ready to generate a personalized learning plan. 【0131】 During learning, the device sends the user's progress and completed tasks to the server. The server then uses an AI agent to analyze the user's learning history in real time and prepare appropriate challenges. The AI ​​agent customizes the challenges it generates, providing the user with the most effective learning experience. 【0132】 Furthermore, the device is equipped with an emotion engine that recognizes emotions through the user's facial expressions and voice. This emotion data is sent to a server, where an AI agent analyzes it and adjusts the learning plan according to the user's emotional state. For example, if the user is feeling frustrated, the difficulty level of the tasks will be lowered. Conversely, if the user is in a good mood, a slightly more challenging task will be suggested. 【0133】 After the learning plan and challenges are adjusted by the emotion engine, the server sends them to the user's device. The user progresses through the tasks and submits their answers to the server. The server evaluates the answers, generates feedback based on the user's level of mastery, and sends it back to the device. 【0134】 Furthermore, game elements such as a point system, visual badges, and rankings have been introduced. Based on this data, the server generates visual rewards to enhance the user's sense of accomplishment and maintain their motivation to learn. 【0135】 Through this series of processes, this invention effectively supports user learning and provides a flexible learning system that takes emotions into consideration. 【0136】 The following describes the processing flow. 【0137】 Step 1: 【0138】 The user accesses the learning system using their device and enters their ID and password on the login screen. The device then sends this information to the server. 【0139】 Step 2: 【0140】 The server compares the received authentication information with the database to authenticate the user. If authentication is successful, a login success message is sent to the terminal. 【0141】 Step 3: 【0142】 After logging in, the user selects learning content. The device sends the selected information to the server, and the creation of a learning plan based on the selection begins. 【0143】 Step 4: 【0144】 The server invokes an AI agent to analyze the user's past learning history and current progress. This generates a personalized learning plan and challenges. 【0145】 Step 5: 【0146】 An emotion engine operates on the device, analyzing the user's facial expressions and voice in real time to acquire emotion data. This data is then sent to a server. 【0147】 Step 6: 【0148】 The server receives data from the emotion engine, and the AI ​​agent evaluates the user's emotional state. Based on this evaluation, the existing learning plan is adjusted. 【0149】 Step 7: 【0150】 The server sends a customized learning plan and challenges to the user's device. The device then presents the user with new tasks. 【0151】 Step 8: 【0152】 The user works on the presented task and enters their answer. The device sends the entered answer to the server. 【0153】 Step 9: 【0154】 The server evaluates the user's responses and generates feedback based on the accuracy rate and level of learning. This feedback is returned to the device and presented to the user. 【0155】 Step 10: 【0156】 The device displays feedback to the user and provides support in deciding the next steps based on the results. 【0157】 Step 11: 【0158】 The server updates the user's progress points and badges as game elements and sends them to the device. The device then displays the user's latest progress, increasing their motivation. 【0159】 (Example 2) 【0160】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0161】 Modern learners have diverse learning needs and varying emotional states, making it necessary to provide them with learning methods optimized for each individual. However, traditional learning systems struggle to provide individualized learning plans and lack the flexibility to take learners' emotional states into account. As a result, learners' motivation decreases, and learning efficiency declines. 【0162】 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. 【0163】 In this invention, the server includes a device means for analyzing the learner's progress using information processing technology and creating an individualized learning plan; a device means for providing appropriate learning tasks based on the learning plan; and a device means for receiving the learner's facial expressions and voice as input and adjusting the learning plan based on their emotional state. This enables the provision of a learning experience optimized for each individual learner and allows for flexible responses that take their emotional state into consideration. 【0164】 "Information processing technology" refers to all technologies used to collect, store, organize, and analyze data. 【0165】 A "learning plan" refers to a plan of learning content and methods that is individually designed according to the learner's progress and level of understanding. 【0166】 "Device" refers to a configuration of mechanical or electronic mechanisms designed to perform a specific function. 【0167】 "Learning tasks" refer to specific tasks or problems that learners are required to work on. 【0168】 "Facial expression" refers to the state of feelings and emotions that are conveyed through the movement and changes in shape of the face. 【0169】 "Sound" refers to sounds that are transmitted as vibrations in the air, such as spoken language and music. 【0170】 "Emotional state" refers to the mood or emotional state that an individual is experiencing temporarily. 【0171】 "Flexible response" refers to the ability or method to appropriately change or adapt depending on the situation and conditions. 【0172】 This invention utilizes a system that provides individualized learning plans tailored to the user's progress and emotional state. The system aims to create a personalized learning experience for the user, maximizing learning efficiency and motivation. 【0173】 When a user logs into the learning system from their device, the device sends the learner's authentication information to the server, which then uses a database to perform the authentication. The server collects progress data from authenticated users, and a program analyzes this data using information processing technology to create a personalized learning plan. The server utilizes AI algorithms along with programming languages ​​such as Python to perform progress analysis using artificial intelligence. 【0174】 During the learning process, users send their progress data and information about the tasks they've completed from their device to the server. The server uses an AI agent to evaluate this data in real time and generate appropriate learning challenges. These challenges are customized based on the user's learning style and sent from the server to the user's device. 【0175】 Furthermore, the user's device utilizes its camera and microphone to capture the user's facial expressions and voice, and this data is analyzed by an emotion engine. The server receives this emotion data, and the AI ​​agent analyzes the user's emotional state. As a result, the learning plan is adjusted based on the user's emotional state, enabling flexible learning. 【0176】 For example, when a user is learning a language, the server detects their level of concentration from their facial expressions and suggests grammar exercises with a slightly higher difficulty level. On the other hand, if the user's facial expressions indicate stress, the system adjusts to return to more basic vocabulary exercises. 【0177】 An example of a prompt is the instruction, "If the user is a beginner in English, create and suggest a customized learning plan based on their progress and sentiment data." By inputting this prompt into the generative AI model, a learning plan tailored to the individual will be generated. 【0178】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0179】 Step 1: 【0180】 The user logs into the learning system from their device. The device sends the entered username and password to the server. The server queries the database to verify the authentication information. If authentication is successful, access to the system is granted. The input is the user's authentication information, and the output is whether authentication was successful or not. 【0181】 Step 2: 【0182】 The server retrieves past learning history from a database to obtain user progress data. Using the Python programming language, an AI algorithm analyzes the data and creates a personalized learning plan. The input is the user's learning history data, and the output is the customized learning plan. 【0183】 Step 3: 【0184】 The device periodically sends the user's progress and completed tasks to the server. The server receives this data in real time, and an AI agent analyzes the data to generate appropriate learning challenges. The input is the user's latest progress data, and the output is a new learning challenge. 【0185】 Step 4: 【0186】 The device captures the user's facial expressions and voice using its camera and microphone. An emotion engine analyzes this data and sends it to a server. The server's AI agent analyzes this emotion data and adjusts the learning plan accordingly. The input is facial and voice data, and the output is an adjusted learning plan based on that emotion. 【0187】 Step 5: 【0188】 A customized learning plan and assignments are sent from the server to the user's device. The user completes the assignments according to the plan and submits their answers from the device to the server. The server grades the answers, generates a score, and sends it back to the user as feedback. The input is the user's assignment answers, and the output is the evaluated score and feedback. 【0189】 Step 6: 【0190】 The server visualizes the user's learning progress and generates visual rewards such as points, badges, and rankings. These visual rewards are sent to the user's device to maintain motivation. The input is the user's cumulative learning progress, and the output is the visual rewards. 【0191】 (Application Example 2) 【0192】 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". 【0193】 While systems exist that provide individualized learning plans and assignments, they lack real-time adjustment features based on learners' emotions, posing challenges to learning efficiency and maintaining motivation. Furthermore, analyzing learners' progress in real time and effectively combining it with motivational elements through visual play is difficult. 【0194】 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. 【0195】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individualized learning plan, means for providing appropriate learning tasks based on the learning plan, and means for continuously recognizing the learner's emotions and adjusting the learning plan accordingly. This enables the provision of a flexible learning experience that takes the learner's emotional state into account, thereby improving motivation. 【0196】 A "learner" is an individual who aims to acquire knowledge and skills in an educational environment. 【0197】 A "learning plan" is a plan that outlines the learning content and sequence, individually formulated based on the learner's progress and abilities. 【0198】 A "learning task" is an educational task that includes specific problems or questions that learners should address. 【0199】 "Progress" refers to the state or process that indicates the extent to which learners are moving towards the educational objectives. 【0200】 "Emotions" refer to the psychological state of a learner and are internal sensations that influence their attitude and motivation towards learning. 【0201】 "Playful elements" refer to visual or experiential features designed to incorporate fun and a sense of competition into the learning environment. 【0202】 "Motivation" refers to the psychological driving force that indicates a learner's willingness or reasons for engaging in learning activities. 【0203】 In the system that implements this invention, the server first runs a program that generates a learning plan based on the learner's individual information. The learner's terminal has software installed to monitor progress and send data back to the server in real time. The software is developed in Python, and uses OpenCV for video processing and a speech emotion recognition library (e.g., librosa) for speech analysis. 【0204】 The device uses its camera and microphone to capture the learner's facial expressions and voice, and sends this data to the server in real time. On the server, this data is analyzed using an AI agent to recognize the learner's emotional state. TENSORFLOW® and other machine learning frameworks are used here. If the server determines that the learner is experiencing stress, it adjusts the learning plan, such as lowering the difficulty level. 【0205】 The server also generates visual feedback incorporating playful elements and sends it to the device. This helps maintain the learner's motivation. For example, the system could involve accumulating points based on learning progress and being awarded badges. 【0206】 For example, when a child is stuck on a math problem, an encouraging video is provided from the device to help them relax and regain their interest in learning. An example of a prompt to the generating AI model at this point would be: "A student is feeling frustrated while working on a math problem. Generate a short motivational video to give her some encouragement and make this moment interactive and fun." 【0207】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0208】 Step 1: 【0209】 The user logs into the learning system via their device. The input is the user's authentication information, which the device sends to the server. The server performs authentication by referring to the database and outputs whether the authentication was successful. 【0210】 Step 2: 【0211】 The server generates a personalized learning plan for authenticated users by referencing past learning data. The input is the user's learning history, and the server outputs the optimal learning plan based on this. An AI algorithm is used to analyze past progress and formulate the plan. 【0212】 Step 3: 【0213】 When a user begins learning, the device sends user progress data to the server in real time. The input is the user's progress information during learning, which the server receives and outputs an evaluation based on that progress. 【0214】 Step 4: 【0215】 The device uses its camera and microphone to capture the user's facial expressions and voice. The input consists of video and audio data, which the device processes with emotion recognition software. The obtained emotional state data is sent to a server, and the emotional state is output. 【0216】 Step 5: 【0217】 The server integrates the received progress data and sentiment data, which the AI ​​agent then analyzes. The input consists of progress and sentiment recognition data, and the server adjusts the learning plan and outputs new learning tasks. 【0218】 Step 6: 【0219】 The server generates and sends feedback, including playful elements, to the device. The input is the user's learning result, and it outputs visual rewards and motivational content. 【0220】 Step 7: 【0221】 The user solves a learning task and submits the results to the server. The input is the learner's answer, which the server evaluates and outputs as response feedback. 【0222】 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. 【0223】 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. 【0224】 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. 【0225】 [Second Embodiment] 【0226】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0227】 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. 【0228】 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). 【0229】 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. 【0230】 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. 【0231】 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). 【0232】 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. 【0233】 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. 【0234】 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. 【0235】 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. 【0236】 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. 【0237】 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". 【0238】 This invention is a gamified learning system aimed at improving learners' abilities, and at its core is AI-powered progress analysis and customized assignment delivery. The specific operation of the system is described below. 【0239】 First, the user logs into the learning system via their device. The device sends the user's authentication information to the server, which verifies the information in its database to authenticate the user. This creates a personalized learning environment. 【0240】 Once learning begins, the device sends user behavior data and progress information to the server. The server analyzes this data in real time through an AI agent to understand the user's level of comprehension and current learning progress. Based on this analysis, the AI ​​agent creates a learning plan for the next steps. 【0241】 The server selects appropriate learning challenges based on the learning plan generated by the AI ​​agent and sends them to the user's device. Users can deepen their knowledge by working on these challenges and completing the tasks. 【0242】 Upon completing the challenge, the device sends the user's answer back to the server, which evaluates the result. The AI ​​agent generates feedback based on the evaluation and returns it to the user. This feedback allows the user to check their level of understanding and clearly identify the next learning steps they need to take. 【0243】 Furthermore, this system incorporates game elements such as points and badges to enhance the sense of accomplishment in learning. The server collects this data and presents it to the user through a visual reward system such as rankings. This allows users to maintain motivation to continue learning while having fun. 【0244】 Through the operations described above, this invention effectively provides a learning environment tailored to individual learners and supports continuous skill improvement. 【0245】 The following describes the processing flow. 【0246】 Step 1: 【0247】 The user accesses the learning system using a device and enters their ID and password on the login screen. The device collects this information. 【0248】 Step 2: 【0249】 The device sends the user's authentication information to the server. The server checks the database to verify that the entered ID and password are correct and notifies the device of the authentication result. 【0250】 Step 3: 【0251】 After the user logs in, they select learning content via their device. The device sends the selection to the server, and the learning session begins. 【0252】 Step 4: 【0253】 The device continuously collects and sends user learning progress information to the server. This includes tasks completed, completion time, and points earned. 【0254】 Step 5: 【0255】 The server provides the AI ​​agent with the progress data it receives. The AI ​​agent analyzes this data to evaluate the user's understanding and skill level. 【0256】 Step 6: 【0257】 The AI ​​agent generates a learning plan based on the analysis results, which includes new tasks and challenges. 【0258】 Step 7: 【0259】 The server sends the learning plan and challenges generated by the AI ​​agent to the user's device. The user then proceeds with their learning based on this information. 【0260】 Step 8: 【0261】 The user tackles a new challenge and enters the answer into the device. The device then transfers the answer to the server. 【0262】 Step 9: 【0263】 The server evaluates the received answers and determines whether they are correct or incorrect. The AI ​​agent generates feedback and sends the results to the user's device. 【0264】 Step 10: 【0265】 The device displays feedback received from the server to the user. The user uses this feedback to continue learning and prepare to move on to the next step. 【0266】 Step 11: 【0267】 The server compiles user learning progress and updates game elements such as points, badges, and rankings. This visual feedback is sent to the device and helps maintain user motivation. 【0268】 (Example 1) 【0269】 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." 【0270】 This issue stems from learners not receiving adequate learning plans tailored to their individual progress and understanding, leading to decreased learning efficiency and difficulty maintaining motivation. Furthermore, traditional systems have limitations in tracking learning progress in real time and providing individualized feedback. 【0271】 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. 【0272】 In this invention, the server includes means for collecting user learning progress data in real time and analyzing knowledge acquisition status, means for creating personalized learning plans using a generative model based on the analyzed data, and means for evaluating answer results and generating feedback based on the analysis results. This makes it possible to provide an optimized learning experience for each learner and maximize their potential. 【0273】 A "learning plan" is a set of learning plans and assignments designed to match the user's individual progress and level of understanding. 【0274】 A "generative model" is a type of artificial intelligence applied to construct personalized learning plans using the results of data analysis. 【0275】 "Progress data" refers to records of activities and information about results collected during the user's learning process. 【0276】 "Feedback" refers to comments and guidance provided to users to aid their understanding, based on the evaluation of their answers. 【0277】 "Knowledge acquisition status" refers to information that indicates a user's level of understanding of a particular subject or field. 【0278】 "Learning tasks" refer to problems and exercises that users should work on, and are provided based on the learning plan. 【0279】 "Visual representation" refers to presenting learning outcomes to the user in a visible way, and primarily includes points and badges. 【0280】 This invention is a learning support system aimed at improving learners' knowledge, and its core features include individualized progress analysis and learning plan provision utilizing AI technology. The entire system operates with a configuration centered around the user's terminal, a server, and an AI agent. 【0281】 The server receives the authentication information input by the user from the terminal, verifies it in the database, and performs user authentication. Through this process, a customized learning environment based on the learning history and current learning status of each user is provided. The server collects the received learning progress data of the user in real time and performs analysis using the generated AI model. Based on the analysis results, the server generates an individualized learning plan using the AI agent and sends it to the terminal. 【0282】 The terminal presents learning tasks to the user based on the learning plan received from the server. The user works on these tasks and sends the answers to the server through the terminal. The server evaluates the answers, creates feedback using the AI agent, and sends it back to the terminal. This feedback plays an important role in enabling the user to understand their own understanding status and connect it to the next learning. 【0283】 Furthermore, as a means to visually display the achievements obtained as the user progresses in learning, the server aggregates the user's performance in the form of scores and badges. As a result, the terminal displays the levels and rankings achieved by the user, creating a mechanism to sustain the user's learning motivation through a learning environment incorporating game elements. 【0284】 As a specific example, consider a system for learning English grammar. After the user logs in on the terminal, they work on a specified set of grammar problems. The progress and answer content are analyzed by the server, and the next steps are presented as needed. The user can obtain points and badges as visual rewards and can also compare their progress with other learners. 【0285】 An example of a prompt sentence for the generated AI model is "The progress information of the user is as follows. Please propose the next tasks to maximize the learning effect." 【0286】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0287】 Step 1: 【0288】 The user logs into the system using a terminal. The terminal sends the authentication information entered by the user to the server. The server verifies the authentication information using a database, and if authentication is successful, prepares a learning environment tailored to the user. The input is the user's authentication information, and the output is the authentication result and the setup of the learning environment. This authentication prepares the user to proceed to the next learning step. 【0289】 Step 2: 【0290】 When a user begins learning, the device sends the user's learning progress data to the server in real time. The server prepares this data for processing by the AI ​​agent. The input is the user's progress data, and the output is learning data organized in list format. The specific operations of the device include acquiring and transmitting data from sensors and interfaces. 【0291】 Step 3: 【0292】 The server uses an AI agent to analyze the received progress data. The AI ​​agent evaluates the data using a generative AI model and determines the user's level of understanding. The input is organized training data, and the output is an analysis report on the user's level of understanding. Based on this analysis, the server determines the next necessary learning action. 【0293】 Step 4: 【0294】 The server builds a personalized learning plan based on the generated comprehension analysis report. The AI ​​agent uses prompts to suggest the next task to tackle. The input is the comprehension analysis report, and the output is a individually customized learning plan. Specific operations include calculations performed by the AI ​​model and task selection based on those calculations. 【0295】 Step 5: 【0296】 The server sends the generated learning plan to the terminal, which then presents it to the user. The user then works on the presented tasks. The input is the individual learning plan, and the output is the task setting resulting from the execution of the learning plan. The terminal then displays the specific learning content that the user should work on. 【0297】 Step 6: 【0298】 Once the user completes the task, the device sends the solution data to the server. The server evaluates the solution and uses an AI agent to generate feedback for the user. The input is the user's solution data, and the output is the solution evaluation result and feedback. The server sends this feedback back to the device, where it is displayed to the user. 【0299】 Step 7: 【0300】 The server aggregates learning outcomes as points and badges and implements game elements. The results are displayed on the device to maintain user engagement. Input is the answer evaluation result, and output is visualized data in the form of points and badges. The device visually displays these results to the user, increasing motivation through rankings and other means. 【0301】 (Application Example 1) 【0302】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0303】 It is necessary to provide learners with appropriate assignments tailored to their individual learning progress and offer individualized feedback while maintaining their motivation to continue learning. In particular, when learning takes place at home, effective methods are needed to keep learners interested. Traditional systems have the problem of insufficient individualized instruction and interactive dialogue tailored to progress, making it difficult to sustain motivation for learning. 【0304】 The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means. 【0305】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individual learning plan, means for providing tasks based on the individual plan through interaction with humans and generating real-time feedback, and means for promoting learning motivation by awarding acquisition points and achievement badges. As a result, it becomes possible for the learner to have an individually customized learning experience while maintaining interest even within the home. 【0306】 A "learning plan" is a plan that reflects the individual progress of the learner and specifically shows what and how to learn next. ​​​​​​​​​​​​​​​​​​​​​​​​​ The embodiment for carrying out the invention is configured as follows: It centers on a life support machine device used in the home, such as a robot with educational support functions. This robot is equipped with various hardware and software to support the user's learning activities. 【0314】 The servers reside in the cloud and are responsible for analyzing learner behavior data and progress information using databases and AI platforms. Specifically, the servers manage data in real time using Firebase Realtime Database and run machine learning models using Google AI Platform. This data processing dynamically generates individual learning plans. 【0315】 The robot, acting as a terminal, is placed within the home and analyzes the learner's voice input using an NLP (Natural Language Processing) engine. For example, the robot converts the user's voice into text and understands the learning content. This processing allows the robot to prepare and present appropriate learning challenges to the user. 【0316】 The robot evaluates learning outcomes and generates and provides feedback to the user based on the acquired data. To make learning more engaging, it includes features that visually reinforce learning achievements using points and badges. 【0317】 As a concrete example, consider a scenario where a robot "presents a multiplication problem." In this scenario, after the user solves the problem, the robot instantly evaluates the result and returns an encouraging message such as "Well done!" Through this interaction, the user's motivation to continue learning increases. 【0318】 Furthermore, an example of a prompt for a generative AI model is: "Please tell me the answer to 3x4. Then, determine if the answer is correct and send a message of encouragement to the user." This allows the robot to engage in natural dialogue while enhancing its learning effectiveness. 【0319】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0320】 Step 1: 【0321】 The user activates the robot and logs in. The robot, which is the user's device, sends login information to the server, and the server authenticates the user using Firebase Realtime Database. The input for this step is the user's authentication information, and the output is the preparation of a learning environment tailored to the user. 【0322】 Step 2: 【0323】 The user gives instructions to begin learning. The terminal (robot) analyzes the user's voice instructions using an NLP engine to understand the learning content. Here, the user's voice data is the input, and the output is text data resulting from the analysis. Based on the analyzed text, the robot determines the next learning challenge to present. 【0324】 Step 3: 【0325】 The server utilizes Google AI Platform to analyze the user's past learning data and real-time activity information to generate an appropriate learning plan. The input is the user's progress data, and the output is a customized learning plan. Based on the analysis results, the server sets the next task to be addressed and sends it to the device. 【0326】 Step 4: 【0327】 The device presents the user with a corresponding learning challenge. The user works on the presented challenge, and the robot collects the input data in real time. The input here is the problem of the learning challenge, and the user's answer is the output. As the user progresses, the robot provides feedback and moves on to the next step. 【0328】 Step 5: 【0329】 The user's answer is sent to the server for evaluation. The server evaluates the answer using an AI platform and generates feedback based on the user's level of proficiency. The input is the user's answer data, and the output is feedback information. Based on the evaluation results, the server decides to award points or badges and sends data to be displayed visually on the device. 【0330】 Step 6: 【0331】 The device provides feedback to the user and visually presents learning outcomes as a game element. It displays points and badges earned to show the user's progress and motivate them for further learning. The input is feedback data, and the output is the user's visual perception. The robot uses this information to prepare suggestions for the next learning session. 【0332】 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. 【0333】 This invention is a system that not only provides individualized learning plans based on the user's progress, but also, by combining it with an emotion engine, realizes a flexible learning experience that responds to the user's emotional state. The specific operation of the system is described below. 【0334】 When a user logs into the learning system via their device, the device sends their authentication information to the server, which then authenticates the user against the database. After that, the system is ready to generate a personalized learning plan. 【0335】 During learning, the device sends the user's progress and completed tasks to the server. The server then uses an AI agent to analyze the user's learning history in real time and prepare appropriate challenges. The AI ​​agent customizes the challenges it generates, providing the user with the most effective learning experience. 【0336】 Furthermore, the device is equipped with an emotion engine that recognizes emotions through the user's facial expressions and voice. This emotion data is sent to a server, where an AI agent analyzes it and adjusts the learning plan according to the user's emotional state. For example, if the user is feeling frustrated, the difficulty level of the tasks will be lowered. Conversely, if the user is in a good mood, a slightly more challenging task will be suggested. 【0337】 After the learning plan and challenges are adjusted by the emotion engine, the server sends them to the user's device. The user progresses through the tasks and submits their answers to the server. The server evaluates the answers, generates feedback based on the user's level of mastery, and sends it back to the device. 【0338】 Furthermore, game elements such as a point system, visual badges, and rankings have been introduced. Based on this data, the server generates visual rewards to enhance the user's sense of accomplishment and maintain their motivation to learn. 【0339】 Through this series of processes, this invention effectively supports user learning and provides a flexible learning system that takes emotions into consideration. 【0340】 The following describes the processing flow. 【0341】 Step 1: 【0342】 The user accesses the learning system using their device and enters their ID and password on the login screen. The device then sends this information to the server. 【0343】 Step 2: 【0344】 The server compares the received authentication information with the database to authenticate the user. If authentication is successful, a login success message is sent to the terminal. 【0345】 Step 3: 【0346】 After logging in, the user selects learning content. The device sends the selected information to the server, and the creation of a learning plan based on the selection begins. 【0347】 Step 4: 【0348】 The server invokes an AI agent to analyze the user's past learning history and current progress. This generates a personalized learning plan and challenges. 【0349】 Step 5: 【0350】 An emotion engine operates on the device, analyzing the user's facial expressions and voice in real time to acquire emotion data. This data is then sent to a server. 【0351】 Step 6: 【0352】 The server receives data from the emotion engine, and the AI ​​agent evaluates the user's emotional state. Based on this evaluation, the existing learning plan is adjusted. 【0353】 Step 7: 【0354】 The server sends a customized learning plan and challenges to the user's device. The device then presents the user with new tasks. 【0355】 Step 8: 【0356】 The user works on the presented task and enters their answer. The device sends the entered answer to the server. 【0357】 Step 9: 【0358】 The server evaluates the user's responses and generates feedback based on the accuracy rate and level of learning. This feedback is returned to the device and presented to the user. 【0359】 Step 10: 【0360】 The device displays feedback to the user and provides support in deciding the next steps based on the results. 【0361】 Step 11: 【0362】 The server updates the user's progress points and badges as game elements and sends them to the device. The device then displays the user's latest progress, increasing their motivation. 【0363】 (Example 2) 【0364】 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". 【0365】 Modern learners have diverse learning needs and varying emotional states, making it necessary to provide them with learning methods optimized for each individual. However, traditional learning systems struggle to provide individualized learning plans and lack the flexibility to take learners' emotional states into account. As a result, learners' motivation decreases, and learning efficiency declines. 【0366】 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. 【0367】 In this invention, the server includes a device means for analyzing the learner's progress using information processing technology and creating an individualized learning plan; a device means for providing appropriate learning tasks based on the learning plan; and a device means for receiving the learner's facial expressions and voice as input and adjusting the learning plan based on their emotional state. This enables the provision of a learning experience optimized for each individual learner and allows for flexible responses that take their emotional state into consideration. 【0368】 "Information processing technology" refers to all technologies used to collect, store, organize, and analyze data. 【0369】 A "learning plan" refers to a plan of learning content and methods that is individually designed according to the learner's progress and level of understanding. 【0370】 "Device" refers to a configuration of mechanical or electronic mechanisms designed to perform a specific function. 【0371】 "Learning tasks" refer to specific tasks or problems that learners are required to work on. 【0372】 "Facial expression" refers to the state of feelings and emotions that are conveyed through the movement and changes in shape of the face. 【0373】 "Sound" refers to sounds that are transmitted as vibrations in the air, such as spoken language and music. 【0374】 "Emotional state" refers to the mood or emotional state that an individual is experiencing temporarily. 【0375】 "Flexible response" refers to the ability or method to appropriately change or adapt depending on the situation and conditions. 【0376】 This invention utilizes a system that provides individualized learning plans tailored to the user's progress and emotional state. The system aims to create a personalized learning experience for the user, maximizing learning efficiency and motivation. 【0377】 When a user logs into the learning system from their device, the device sends the learner's authentication information to the server, which then uses a database to perform the authentication. The server collects progress data from authenticated users, and a program analyzes this data using information processing technology to create a personalized learning plan. The server utilizes AI algorithms along with programming languages ​​such as Python to perform progress analysis using artificial intelligence. 【0378】 During the learning process, users send their progress data and information about the tasks they've completed from their device to the server. The server uses an AI agent to evaluate this data in real time and generate appropriate learning challenges. These challenges are customized based on the user's learning style and sent from the server to the user's device. 【0379】 Furthermore, the user's device utilizes its camera and microphone to capture the user's facial expressions and voice, and this data is analyzed by an emotion engine. The server receives this emotion data, and the AI ​​agent analyzes the user's emotional state. As a result, the learning plan is adjusted based on the user's emotional state, enabling flexible learning. 【0380】 For example, when a user is learning a language, the server detects their level of concentration from their facial expressions and suggests grammar exercises with a slightly higher difficulty level. On the other hand, if the user's facial expressions indicate stress, the system adjusts to return to more basic vocabulary exercises. 【0381】 An example of a prompt is the instruction, "If the user is a beginner in English, create and suggest a customized learning plan based on their progress and sentiment data." By inputting this prompt into the generative AI model, a learning plan tailored to the individual will be generated. 【0382】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0383】 Step 1: 【0384】 The user logs into the learning system from their device. The device sends the entered username and password to the server. The server queries the database to verify the authentication information. If authentication is successful, access to the system is granted. The input is the user's authentication information, and the output is whether authentication was successful or not. 【0385】 Step 2: 【0386】 The server retrieves past learning history from a database to obtain user progress data. Using the Python programming language, an AI algorithm analyzes the data and creates a personalized learning plan. The input is the user's learning history data, and the output is the customized learning plan. 【0387】 Step 3: 【0388】 The device periodically sends the user's progress and completed tasks to the server. The server receives this data in real time, and an AI agent analyzes the data to generate appropriate learning challenges. The input is the user's latest progress data, and the output is a new learning challenge. 【0389】 Step 4: 【0390】 The device captures the user's facial expressions and voice using its camera and microphone. An emotion engine analyzes this data and sends it to a server. The server's AI agent analyzes this emotion data and adjusts the learning plan accordingly. The input is facial and voice data, and the output is an adjusted learning plan based on that emotion. 【0391】 Step 5: 【0392】 A customized learning plan and assignments are sent from the server to the user's device. The user completes the assignments according to the plan and submits their answers from the device to the server. The server grades the answers, generates a score, and sends it back to the user as feedback. The input is the user's assignment answers, and the output is the evaluated score and feedback. 【0393】 Step 6: 【0394】 The server visualizes the user's learning progress and generates visual rewards such as points, badges, and rankings. These visual rewards are sent to the user's device to maintain motivation. The input is the user's cumulative learning progress, and the output is the visual rewards. 【0395】 (Application Example 2) 【0396】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0397】 While systems exist that provide individualized learning plans and assignments, they lack real-time adjustment features based on learners' emotions, posing challenges to learning efficiency and maintaining motivation. Furthermore, analyzing learners' progress in real time and effectively combining it with motivational elements through visual play is difficult. 【0398】 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. 【0399】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individualized learning plan, means for providing appropriate learning tasks based on the learning plan, and means for continuously recognizing the learner's emotions and adjusting the learning plan accordingly. This enables the provision of a flexible learning experience that takes the learner's emotional state into account, thereby improving motivation. 【0400】 A "learner" is an individual who aims to acquire knowledge and skills in an educational environment. 【0401】 A "learning plan" is a plan that outlines the learning content and sequence, individually formulated based on the learner's progress and abilities. 【0402】 A "learning task" is an educational task that includes specific problems or questions that learners should address. 【0403】 "Progress" refers to the state or process that indicates the extent to which learners are moving towards the educational objectives. 【0404】 "Emotions" refer to the psychological state of a learner and are internal sensations that influence their attitude and motivation towards learning. 【0405】 "Playful elements" refer to visual or experiential features designed to incorporate fun and a sense of competition into the learning environment. 【0406】 "Motivation" refers to the psychological driving force that indicates a learner's willingness or reasons for engaging in learning activities. 【0407】 In the system that implements this invention, the server first runs a program that generates a learning plan based on the learner's individual information. The learner's terminal has software installed to monitor progress and send data back to the server in real time. The software is developed in Python, and uses OpenCV for video processing and a speech emotion recognition library (e.g., librosa) for speech analysis. 【0408】 The device uses its camera and microphone to capture the learner's facial expressions and voice, and sends this data to the server in real time. On the server, this data is analyzed using an AI agent to recognize the learner's emotional state. TensorFlow and other machine learning frameworks are used here. If the server determines that the learner is experiencing stress, it adjusts the learning plan, such as lowering the difficulty level. 【0409】 The server also generates visual feedback incorporating playful elements and sends it to the device. This helps maintain the learner's motivation. For example, the system could involve accumulating points based on learning progress and being awarded badges. 【0410】 For example, when a child is stuck on a math problem, an encouraging video is provided from the device to help them relax and regain their interest in learning. An example of a prompt to the generating AI model at this point would be: "A student is feeling frustrated while working on a math problem. Generate a short motivational video to give her some encouragement and make this moment interactive and fun." 【0411】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0412】 Step 1: 【0413】 The user logs into the learning system via their device. The input is the user's authentication information, which the device sends to the server. The server performs authentication by referring to the database and outputs whether the authentication was successful. 【0414】 Step 2: 【0415】 The server generates a personalized learning plan for authenticated users by referencing past learning data. The input is the user's learning history, and the server outputs the optimal learning plan based on this. An AI algorithm is used to analyze past progress and formulate the plan. 【0416】 Step 3: 【0417】 When a user begins learning, the device sends user progress data to the server in real time. The input is the user's progress information during learning, which the server receives and outputs an evaluation based on that progress. 【0418】 Step 4: 【0419】 The device uses its camera and microphone to capture the user's facial expressions and voice. The input consists of video and audio data, which the device processes with emotion recognition software. The obtained emotional state data is sent to a server, and the emotional state is output. 【0420】 Step 5: 【0421】 The server integrates the received progress data and sentiment data, which the AI ​​agent then analyzes. The input consists of progress and sentiment recognition data, and the server adjusts the learning plan and outputs new learning tasks. 【0422】 Step 6: 【0423】 The server generates and sends feedback, including playful elements, to the device. The input is the user's learning result, and it outputs visual rewards and motivational content. 【0424】 Step 7: 【0425】 The user solves a learning task and submits the results to the server. The input is the learner's answer, which the server evaluates and outputs as response feedback. 【0426】 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. 【0427】 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. 【0428】 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. 【0429】 [Third Embodiment] 【0430】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0431】 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. 【0432】 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). 【0433】 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. 【0434】 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. 【0435】 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). 【0436】 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. 【0437】 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. 【0438】 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. 【0439】 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. 【0440】 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. 【0441】 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". 【0442】 This invention is a gamified learning system aimed at improving learners' abilities, and at its core is AI-powered progress analysis and customized assignment delivery. The specific operation of the system is described below. 【0443】 First, the user logs into the learning system via their device. The device sends the user's authentication information to the server, which verifies the information in its database to authenticate the user. This creates a personalized learning environment. 【0444】 Once learning begins, the device sends user behavior data and progress information to the server. The server analyzes this data in real time through an AI agent to understand the user's level of comprehension and current learning progress. Based on this analysis, the AI ​​agent creates a learning plan for the next steps. 【0445】 The server selects appropriate learning challenges based on the learning plan generated by the AI ​​agent and sends them to the user's device. Users can deepen their knowledge by working on these challenges and completing the tasks. 【0446】 Upon completing the challenge, the device sends the user's answer back to the server, which evaluates the result. The AI ​​agent generates feedback based on the evaluation and returns it to the user. This feedback allows the user to check their level of understanding and clearly identify the next learning steps they need to take. 【0447】 Furthermore, this system incorporates game elements such as points and badges to enhance the sense of accomplishment in learning. The server collects this data and presents it to the user through a visual reward system such as rankings. This allows users to maintain motivation to continue learning while having fun. 【0448】 Through the operations described above, this invention effectively provides a learning environment tailored to individual learners and supports continuous skill improvement. 【0449】 The following describes the processing flow. 【0450】 Step 1: 【0451】 The user accesses the learning system using a device and enters their ID and password on the login screen. The device collects this information. 【0452】 Step 2: 【0453】 The device sends the user's authentication information to the server. The server checks the database to verify that the entered ID and password are correct and notifies the device of the authentication result. 【0454】 Step 3: 【0455】 After the user logs in, they select learning content via their device. The device sends the selection to the server, and the learning session begins. 【0456】 Step 4: 【0457】 The device continuously collects and sends user learning progress information to the server. This includes tasks completed, completion time, and points earned. 【0458】 Step 5: 【0459】 The server provides the AI ​​agent with the progress data it receives. The AI ​​agent analyzes this data to evaluate the user's understanding and skill level. 【0460】 Step 6: 【0461】 The AI ​​agent generates a learning plan based on the analysis results, which includes new tasks and challenges. 【0462】 Step 7: 【0463】 The server sends the learning plan and challenges generated by the AI ​​agent to the user's device. The user then proceeds with their learning based on this information. 【0464】 Step 8: 【0465】 The user tackles a new challenge and enters the answer into the device. The device then transfers the answer to the server. 【0466】 Step 9: 【0467】 The server evaluates the received answers and determines whether they are correct or incorrect. The AI ​​agent generates feedback and sends the results to the user's device. 【0468】 Step 10: 【0469】 The device displays feedback received from the server to the user. The user uses this feedback to continue learning and prepare to move on to the next step. 【0470】 Step 11: 【0471】 The server compiles user learning progress and updates game elements such as points, badges, and rankings. This visual feedback is sent to the device and helps maintain user motivation. 【0472】 (Example 1) 【0473】 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." 【0474】 This issue stems from learners not receiving adequate learning plans tailored to their individual progress and understanding, leading to decreased learning efficiency and difficulty maintaining motivation. Furthermore, traditional systems have limitations in tracking learning progress in real time and providing individualized feedback. 【0475】 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. 【0476】 In this invention, the server includes means for collecting user learning progress data in real time and analyzing knowledge acquisition status, means for creating personalized learning plans using a generative model based on the analyzed data, and means for evaluating answer results and generating feedback based on the analysis results. This makes it possible to provide an optimized learning experience for each learner and maximize their potential. 【0477】 A "learning plan" is a set of learning plans and assignments designed to match the user's individual progress and level of understanding. 【0478】 A "generative model" is a type of artificial intelligence applied to construct personalized learning plans using the results of data analysis. 【0479】 "Progress data" refers to records of activities and information about results collected during the user's learning process. 【0480】 "Feedback" refers to comments and guidance provided to users to aid their understanding, based on the evaluation of their answers. 【0481】 "Knowledge acquisition status" refers to information that indicates a user's level of understanding of a particular subject or field. 【0482】 "Learning tasks" refer to problems and exercises that users should work on, and are provided based on the learning plan. 【0483】 "Visual representation" refers to presenting learning outcomes to the user in a visible way, and primarily includes points and badges. 【0484】 This invention is a learning support system aimed at improving learners' knowledge, and its core features include individualized progress analysis and learning plan provision utilizing AI technology. The entire system operates with a configuration centered around the user's terminal, a server, and an AI agent. 【0485】 The server receives authentication information entered by the user from the terminal and performs user authentication by matching it against the database. This process provides a customized learning environment based on each user's learning history and current learning status. The server collects the received user learning progress data in real time and performs analysis using a generative AI model. Based on the analysis results, the server uses an AI agent to generate an individualized learning plan and sends it to the terminal. 【0486】 The device presents the user with learning tasks based on a learning plan received from the server. The user works on these tasks and sends their answers to the server via the device. The server evaluates the answers, uses an AI agent to generate feedback, and sends it back to the device. This feedback plays a crucial role in helping the user understand their level of comprehension and move forward with their next learning session. 【0487】 Furthermore, as a means of visually displaying the progress users make as they learn, the server compiles user achievements in the form of points and badges. As a result, the user's achieved level and ranking are displayed on the terminal, and the learning environment, which incorporates game elements, is designed to maintain the user's motivation to learn. 【0488】 As a concrete example, consider a system for learning English grammar. After logging in on their device, users work on a designated set of grammar exercises. Progress and answers are analyzed on the server, and the next steps are suggested as needed. Users can earn points and badges as visual rewards, and can also compare their progress with other learners. 【0489】 An example of a prompt message for a generative AI model is: "The user's progress information is as follows. Please suggest the next task to maximize learning effectiveness." 【0490】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0491】 Step 1: 【0492】 The user logs into the system using a terminal. The terminal sends the authentication information entered by the user to the server. The server verifies the authentication information using a database, and if authentication is successful, prepares a learning environment tailored to the user. The input is the user's authentication information, and the output is the authentication result and the setup of the learning environment. This authentication prepares the user to proceed to the next learning step. 【0493】 Step 2: 【0494】 When a user begins learning, the device sends the user's learning progress data to the server in real time. The server prepares this data for processing by the AI ​​agent. The input is the user's progress data, and the output is learning data organized in list format. The specific operations of the device include acquiring and transmitting data from sensors and interfaces. 【0495】 Step 3: 【0496】 The server uses an AI agent to analyze the received progress data. The AI ​​agent evaluates the data using a generative AI model and determines the user's level of understanding. The input is organized training data, and the output is an analysis report on the user's level of understanding. Based on this analysis, the server determines the next necessary learning action. 【0497】 Step 4: 【0498】 The server builds a personalized learning plan based on the generated comprehension analysis report. The AI ​​agent uses prompts to suggest the next task to tackle. The input is the comprehension analysis report, and the output is a individually customized learning plan. Specific operations include calculations performed by the AI ​​model and task selection based on those calculations. 【0499】 Step 5: 【0500】 The server sends the generated learning plan to the terminal, which then presents it to the user. The user then works on the presented tasks. The input is the individual learning plan, and the output is the task setting resulting from the execution of the learning plan. The terminal then displays the specific learning content that the user should work on. 【0501】 Step 6: 【0502】 Once the user completes the task, the device sends the solution data to the server. The server evaluates the solution and uses an AI agent to generate feedback for the user. The input is the user's solution data, and the output is the solution evaluation result and feedback. The server sends this feedback back to the device, where it is displayed to the user. 【0503】 Step 7: 【0504】 The server aggregates learning outcomes as points and badges and implements game elements. The results are displayed on the device to maintain user engagement. Input is the answer evaluation result, and output is visualized data in the form of points and badges. The device visually displays these results to the user, increasing motivation through rankings and other means. 【0505】 (Application Example 1) 【0506】 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." 【0507】 It is necessary to provide learners with appropriate assignments tailored to their individual learning progress and offer individualized feedback while maintaining their motivation to continue learning. In particular, when learning takes place at home, effective methods are needed to keep learners interested. Traditional systems have the problem of insufficient individualized instruction and interactive dialogue tailored to progress, making it difficult to sustain motivation for learning. 【0508】 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. 【0509】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individualized learning plan, means for providing tasks based on the individualized plan through interaction with a human and generating real-time feedback, and means for promoting learning motivation by awarding points and achievement badges. This makes it possible for learners to maintain their interest and have an individually customized learning experience even at home. 【0510】 A "learning plan" is a plan that reflects the individual progress of the learner and specifically outlines what they should learn next and how to learn it. 【0511】 A "learning challenge" is a task or exercise designed to deepen learners' understanding of specific skills and knowledge. 【0512】 "Game elements" refer to mechanisms that incorporate rewards and competition, such as points, badges, and rankings, to make learning fun and sustainable. 【0513】 "Interactive dialogue" is a form of communication in which a machine interacts with a user, exchanging information and communicating in real time. 【0514】 "Feedback" refers to a system evaluating a learner's behavior and results, and informing them of the results and areas for improvement. 【0515】 "Points and badges" are rewards, such as numerical values ​​or badges, that are awarded as proof of achievement for results and goals reached during the learning process. 【0516】 "In-home life support equipment" refers to mechanical devices that can support the daily lives of residents and provide learning support within a typical household. 【0517】 The embodiment for carrying out the invention is configured as follows: It centers on a life support machine device used in the home, such as a robot with educational support functions. This robot is equipped with various hardware and software to support the user's learning activities. 【0518】 The servers reside in the cloud and are responsible for analyzing learner behavior data and progress information using databases and AI platforms. Specifically, the servers manage data in real time using Firebase Realtime Database and run machine learning models using Google AI Platform. This data processing dynamically generates individual learning plans. 【0519】 The robot, acting as a terminal, is placed within the home and analyzes the learner's voice input using an NLP (Natural Language Processing) engine. For example, the robot converts the user's voice into text and understands the learning content. This processing allows the robot to prepare and present appropriate learning challenges to the user. 【0520】 The robot evaluates learning outcomes and generates and provides feedback to the user based on the acquired data. To make learning more engaging, it includes features that visually reinforce learning achievements using points and badges. 【0521】 As a concrete example, consider a scenario where a robot "presents a multiplication problem." In this scenario, after the user solves the problem, the robot instantly evaluates the result and returns an encouraging message such as "Well done!" Through this interaction, the user's motivation to continue learning increases. 【0522】 Furthermore, an example of a prompt for a generative AI model is: "Please tell me the answer to 3x4. Then, determine if the answer is correct and send a message of encouragement to the user." This allows the robot to engage in natural dialogue while enhancing its learning effectiveness. 【0523】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0524】 Step 1: 【0525】 The user activates the robot and logs in. The robot, which is the user's device, sends login information to the server, and the server authenticates the user using Firebase Realtime Database. The input for this step is the user's authentication information, and the output is the preparation of a learning environment tailored to the user. 【0526】 Step 2: 【0527】 The user gives instructions to begin learning. The terminal (robot) analyzes the user's voice instructions using an NLP engine to understand the learning content. Here, the user's voice data is the input, and the output is text data resulting from the analysis. Based on the analyzed text, the robot determines the next learning challenge to present. 【0528】 Step 3: 【0529】 The server utilizes Google AI Platform to analyze the user's past learning data and real-time activity information to generate an appropriate learning plan. The input is the user's progress data, and the output is a customized learning plan. Based on the analysis results, the server sets the next task to be addressed and sends it to the device. 【0530】 Step 4: 【0531】 The device presents the user with a corresponding learning challenge. The user works on the presented challenge, and the robot collects the input data in real time. The input here is the problem of the learning challenge, and the user's answer is the output. As the user progresses, the robot provides feedback and moves on to the next step. 【0532】 Step 5: 【0533】 The user's answer is sent to the server for evaluation. The server evaluates the answer using an AI platform and generates feedback based on the user's level of proficiency. The input is the user's answer data, and the output is feedback information. Based on the evaluation results, the server decides to award points or badges and sends data to be displayed visually on the device. 【0534】 Step 6: 【0535】 The device provides feedback to the user and visually presents learning outcomes as a game element. It displays points and badges earned to show the user's progress and motivate them for further learning. The input is feedback data, and the output is the user's visual perception. The robot uses this information to prepare suggestions for the next learning session. 【0536】 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. 【0537】 This invention is a system that not only provides individualized learning plans based on the user's progress, but also, by combining it with an emotion engine, realizes a flexible learning experience that responds to the user's emotional state. The specific operation of the system is described below. 【0538】 When a user logs into the learning system via their device, the device sends their authentication information to the server, which then authenticates the user against the database. After that, the system is ready to generate a personalized learning plan. 【0539】 During learning, the device sends the user's progress and completed tasks to the server. The server then uses an AI agent to analyze the user's learning history in real time and prepare appropriate challenges. The AI ​​agent customizes the challenges it generates, providing the user with the most effective learning experience. 【0540】 Furthermore, the device is equipped with an emotion engine that recognizes emotions through the user's facial expressions and voice. This emotion data is sent to a server, where an AI agent analyzes it and adjusts the learning plan according to the user's emotional state. For example, if the user is feeling frustrated, the difficulty level of the tasks will be lowered. Conversely, if the user is in a good mood, a slightly more challenging task will be suggested. 【0541】 After the learning plan and challenges are adjusted by the emotion engine, the server sends them to the user's device. The user progresses through the tasks and submits their answers to the server. The server evaluates the answers, generates feedback based on the user's level of mastery, and sends it back to the device. 【0542】 Furthermore, game elements such as a point system, visual badges, and rankings have been introduced. Based on this data, the server generates visual rewards to enhance the user's sense of accomplishment and maintain their motivation to learn. 【0543】 Through this series of processes, this invention effectively supports user learning and provides a flexible learning system that takes emotions into consideration. 【0544】 The following describes the processing flow. 【0545】 Step 1: 【0546】 The user accesses the learning system using their device and enters their ID and password on the login screen. The device then sends this information to the server. 【0547】 Step 2: 【0548】 The server compares the received authentication information with the database to authenticate the user. If authentication is successful, a login success message is sent to the terminal. 【0549】 Step 3: 【0550】 After logging in, the user selects learning content. The device sends the selected information to the server, and the creation of a learning plan based on the selection begins. 【0551】 Step 4: 【0552】 The server invokes an AI agent to analyze the user's past learning history and current progress. This generates a personalized learning plan and challenges. 【0553】 Step 5: 【0554】 An emotion engine operates on the device, analyzing the user's facial expressions and voice in real time to acquire emotion data. This data is then sent to a server. 【0555】 Step 6: 【0556】 The server receives data from the emotion engine, and the AI ​​agent evaluates the user's emotional state. Based on this evaluation, the existing learning plan is adjusted. 【0557】 Step 7: 【0558】 The server sends a customized learning plan and challenges to the user's device. The device then presents the user with new tasks. 【0559】 Step 8: 【0560】 The user works on the presented task and enters their answer. The device sends the entered answer to the server. 【0561】 Step 9: 【0562】 The server evaluates the user's responses and generates feedback based on the accuracy rate and level of learning. This feedback is returned to the device and presented to the user. 【0563】 Step 10: 【0564】 The device displays feedback to the user and provides support in deciding the next steps based on the results. 【0565】 Step 11: 【0566】 The server updates the user's progress points and badges as game elements and sends them to the device. The device then displays the user's latest progress, increasing their motivation. 【0567】 (Example 2) 【0568】 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." 【0569】 Modern learners have diverse learning needs and varying emotional states, making it necessary to provide them with learning methods optimized for each individual. However, traditional learning systems struggle to provide individualized learning plans and lack the flexibility to take learners' emotional states into account. As a result, learners' motivation decreases, and learning efficiency declines. 【0570】 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. 【0571】 In this invention, the server includes a device means for analyzing the learner's progress using information processing technology and creating an individualized learning plan; a device means for providing appropriate learning tasks based on the learning plan; and a device means for receiving the learner's facial expressions and voice as input and adjusting the learning plan based on their emotional state. This enables the provision of a learning experience optimized for each individual learner and allows for flexible responses that take their emotional state into consideration. 【0572】 "Information processing technology" refers to all technologies used to collect, store, organize, and analyze data. 【0573】 A "learning plan" refers to a plan of learning content and methods that is individually designed according to the learner's progress and level of understanding. 【0574】 "Device" refers to a configuration of mechanical or electronic mechanisms designed to perform a specific function. 【0575】 "Learning tasks" refer to specific tasks or problems that learners are required to work on. 【0576】 "Facial expression" refers to the state of feelings and emotions that are conveyed through the movement and changes in shape of the face. 【0577】 "Sound" refers to sounds that are transmitted as vibrations in the air, such as spoken language and music. 【0578】 "Emotional state" refers to the mood or emotional state that an individual is experiencing temporarily. 【0579】 "Flexible response" refers to the ability or method to appropriately change or adapt depending on the situation and conditions. 【0580】 This invention utilizes a system that provides individualized learning plans tailored to the user's progress and emotional state. The system aims to create a personalized learning experience for the user, maximizing learning efficiency and motivation. 【0581】 When a user logs into the learning system from their device, the device sends the learner's authentication information to the server, which then uses a database to perform the authentication. The server collects progress data from authenticated users, and a program analyzes this data using information processing technology to create a personalized learning plan. The server utilizes AI algorithms along with programming languages ​​such as Python to perform progress analysis using artificial intelligence. 【0582】 During the learning process, users send their progress data and information about the tasks they've completed from their device to the server. The server uses an AI agent to evaluate this data in real time and generate appropriate learning challenges. These challenges are customized based on the user's learning style and sent from the server to the user's device. 【0583】 Furthermore, the user's device utilizes its camera and microphone to capture the user's facial expressions and voice, and this data is analyzed by an emotion engine. The server receives this emotion data, and the AI ​​agent analyzes the user's emotional state. As a result, the learning plan is adjusted based on the user's emotional state, enabling flexible learning. 【0584】 For example, when a user is learning a language, the server detects their level of concentration from their facial expressions and suggests grammar exercises with a slightly higher difficulty level. On the other hand, if the user's facial expressions indicate stress, the system adjusts to return to more basic vocabulary exercises. 【0585】 An example of a prompt is the instruction, "If the user is a beginner in English, create and suggest a customized learning plan based on their progress and sentiment data." By inputting this prompt into the generative AI model, a learning plan tailored to the individual will be generated. 【0586】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0587】 Step 1: 【0588】 The user logs into the learning system from their device. The device sends the entered username and password to the server. The server queries the database to verify the authentication information. If authentication is successful, access to the system is granted. The input is the user's authentication information, and the output is whether authentication was successful or not. 【0589】 Step 2: 【0590】 The server retrieves past learning history from a database to obtain user progress data. Using the Python programming language, an AI algorithm analyzes the data and creates a personalized learning plan. The input is the user's learning history data, and the output is the customized learning plan. 【0591】 Step 3: 【0592】 The device periodically sends the user's progress and completed tasks to the server. The server receives this data in real time, and an AI agent analyzes the data to generate appropriate learning challenges. The input is the user's latest progress data, and the output is a new learning challenge. 【0593】 Step 4: 【0594】 The device captures the user's facial expressions and voice using its camera and microphone. An emotion engine analyzes this data and sends it to a server. The server's AI agent analyzes this emotion data and adjusts the learning plan accordingly. The input is facial and voice data, and the output is an adjusted learning plan based on that emotion. 【0595】 Step 5: 【0596】 A customized learning plan and assignments are sent from the server to the user's device. The user completes the assignments according to the plan and submits their answers from the device to the server. The server grades the answers, generates a score, and sends it back to the user as feedback. The input is the user's assignment answers, and the output is the evaluated score and feedback. 【0597】 Step 6: 【0598】 The server visualizes the user's learning progress and generates visual rewards such as points, badges, and rankings. These visual rewards are sent to the user's device to maintain motivation. The input is the user's cumulative learning progress, and the output is the visual rewards. 【0599】 (Application Example 2) 【0600】 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." 【0601】 While systems exist that provide individualized learning plans and assignments, they lack real-time adjustment features based on learners' emotions, posing challenges to learning efficiency and maintaining motivation. Furthermore, analyzing learners' progress in real time and effectively combining it with motivational elements through visual play is difficult. 【0602】 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. 【0603】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individualized learning plan, means for providing appropriate learning tasks based on the learning plan, and means for continuously recognizing the learner's emotions and adjusting the learning plan accordingly. This enables the provision of a flexible learning experience that takes the learner's emotional state into account, thereby improving motivation. 【0604】 A "learner" is an individual who aims to acquire knowledge and skills in an educational environment. 【0605】 A "learning plan" is a plan that outlines the learning content and sequence, individually formulated based on the learner's progress and abilities. 【0606】 A "learning task" is an educational task that includes specific problems or questions that learners should address. 【0607】 "Progress" refers to the state or process that indicates the extent to which learners are moving towards the educational objectives. 【0608】 "Emotions" refer to the psychological state of a learner and are internal sensations that influence their attitude and motivation towards learning. 【0609】 "Playful elements" refer to visual or experiential features designed to incorporate fun and a sense of competition into the learning environment. 【0610】 "Motivation" refers to the psychological driving force that indicates a learner's willingness or reasons for engaging in learning activities. 【0611】 In the system that implements this invention, the server first runs a program that generates a learning plan based on the learner's individual information. The learner's terminal has software installed to monitor progress and send data back to the server in real time. The software is developed in Python, and uses OpenCV for video processing and a speech emotion recognition library (e.g., librosa) for speech analysis. 【0612】 The device uses its camera and microphone to capture the learner's facial expressions and voice, and sends this data to the server in real time. On the server, this data is analyzed using an AI agent to recognize the learner's emotional state. TensorFlow and other machine learning frameworks are used here. If the server determines that the learner is experiencing stress, it adjusts the learning plan, such as lowering the difficulty level. 【0613】 The server also generates visual feedback incorporating playful elements and sends it to the device. This helps maintain the learner's motivation. For example, the system could involve accumulating points based on learning progress and being awarded badges. 【0614】 For example, when a child is stuck on a math problem, an encouraging video is provided from the device to help them relax and regain their interest in learning. An example of a prompt to the generating AI model at this point would be: "A student is feeling frustrated while working on a math problem. Generate a short motivational video to give her some encouragement and make this moment interactive and fun." 【0615】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0616】 Step 1: 【0617】 The user logs into the learning system via their device. The input is the user's authentication information, which the device sends to the server. The server performs authentication by referring to the database and outputs whether the authentication was successful. 【0618】 Step 2: 【0619】 The server generates a personalized learning plan for authenticated users by referencing past learning data. The input is the user's learning history, and the server outputs the optimal learning plan based on this. An AI algorithm is used to analyze past progress and formulate the plan. 【0620】 Step 3: 【0621】 When a user begins learning, the device sends user progress data to the server in real time. The input is the user's progress information during learning, which the server receives and outputs an evaluation based on that progress. 【0622】 Step 4: 【0623】 The device uses its camera and microphone to capture the user's facial expressions and voice. The input consists of video and audio data, which the device processes with emotion recognition software. The obtained emotional state data is sent to a server, and the emotional state is output. 【0624】 Step 5: 【0625】 The server integrates the received progress data and sentiment data, which the AI ​​agent then analyzes. The input consists of progress and sentiment recognition data, and the server adjusts the learning plan and outputs new learning tasks. 【0626】 Step 6: 【0627】 The server generates and sends feedback, including playful elements, to the device. The input is the user's learning result, and it outputs visual rewards and motivational content. 【0628】 Step 7: 【0629】 The user solves a learning task and submits the results to the server. The input is the learner's answer, which the server evaluates and outputs as response feedback. 【0630】 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. 【0631】 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. 【0632】 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. 【0633】 [Fourth Embodiment] 【0634】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0635】 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. 【0636】 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). 【0637】 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. 【0638】 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. 【0639】 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). 【0640】 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. 【0641】 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. 【0642】 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. 【0643】 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. 【0644】 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. 【0645】 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. 【0646】 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". 【0647】 This invention is a gamified learning system aimed at improving learners' abilities, and at its core is AI-powered progress analysis and customized assignment delivery. The specific operation of the system is described below. 【0648】 First, the user logs into the learning system via their device. The device sends the user's authentication information to the server, which verifies the information in its database to authenticate the user. This creates a personalized learning environment. 【0649】 Once learning begins, the device sends user behavior data and progress information to the server. The server analyzes this data in real time through an AI agent to understand the user's level of comprehension and current learning progress. Based on this analysis, the AI ​​agent creates a learning plan for the next steps. 【0650】 The server selects appropriate learning challenges based on the learning plan generated by the AI ​​agent and sends them to the user's device. Users can deepen their knowledge by working on these challenges and completing the tasks. 【0651】 Upon completing the challenge, the device sends the user's answer back to the server, which evaluates the result. The AI ​​agent generates feedback based on the evaluation and returns it to the user. This feedback allows the user to check their level of understanding and clearly identify the next learning steps they need to take. 【0652】 Furthermore, this system incorporates game elements such as points and badges to enhance the sense of accomplishment in learning. The server collects this data and presents it to the user through a visual reward system such as rankings. This allows users to maintain motivation to continue learning while having fun. 【0653】 Through the operations described above, this invention effectively provides a learning environment tailored to individual learners and supports continuous skill improvement. 【0654】 The following describes the processing flow. 【0655】 Step 1: 【0656】 The user accesses the learning system using a device and enters their ID and password on the login screen. The device collects this information. 【0657】 Step 2: 【0658】 The device sends the user's authentication information to the server. The server checks the database to verify that the entered ID and password are correct and notifies the device of the authentication result. 【0659】 Step 3: 【0660】 After the user logs in, they select learning content via their device. The device sends the selection to the server, and the learning session begins. 【0661】 Step 4: 【0662】 The device continuously collects and sends user learning progress information to the server. This includes tasks completed, completion time, and points earned. 【0663】 Step 5: 【0664】 The server provides the AI ​​agent with the progress data it receives. The AI ​​agent analyzes this data to evaluate the user's understanding and skill level. 【0665】 Step 6: 【0666】 The AI ​​agent generates a learning plan based on the analysis results, which includes new tasks and challenges. 【0667】 Step 7: 【0668】 The server sends the learning plan and challenges generated by the AI ​​agent to the user's device. The user then proceeds with their learning based on this information. 【0669】 Step 8: 【0670】 The user tackles a new challenge and enters the answer into the device. The device then transfers the answer to the server. 【0671】 Step 9: 【0672】 The server evaluates the received answers and determines whether they are correct or incorrect. The AI ​​agent generates feedback and sends the results to the user's device. 【0673】 Step 10: 【0674】 The device displays feedback received from the server to the user. The user uses this feedback to continue learning and prepare to move on to the next step. 【0675】 Step 11: 【0676】 The server compiles user learning progress and updates game elements such as points, badges, and rankings. This visual feedback is sent to the device and helps maintain user motivation. 【0677】 (Example 1) 【0678】 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". 【0679】 This issue stems from learners not receiving adequate learning plans tailored to their individual progress and understanding, leading to decreased learning efficiency and difficulty maintaining motivation. Furthermore, traditional systems have limitations in tracking learning progress in real time and providing individualized feedback. 【0680】 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. 【0681】 In this invention, the server includes means for collecting user learning progress data in real time and analyzing knowledge acquisition status, means for creating personalized learning plans using a generative model based on the analyzed data, and means for evaluating answer results and generating feedback based on the analysis results. This makes it possible to provide an optimized learning experience for each learner and maximize their potential. 【0682】 A "learning plan" is a set of learning plans and assignments designed to match the user's individual progress and level of understanding. 【0683】 A "generative model" is a type of artificial intelligence applied to construct personalized learning plans using the results of data analysis. 【0684】 "Progress data" refers to records of activities and information about results collected during the user's learning process. 【0685】 "Feedback" refers to comments and guidance provided to users to aid their understanding, based on the evaluation of their answers. 【0686】 "Knowledge acquisition status" refers to information that indicates a user's level of understanding of a particular subject or field. 【0687】 "Learning tasks" refer to problems and exercises that users should work on, and are provided based on the learning plan. 【0688】 "Visual representation" refers to presenting learning outcomes to the user in a visible way, and primarily includes points and badges. 【0689】 This invention is a learning support system aimed at improving learners' knowledge, and its core features include individualized progress analysis and learning plan provision utilizing AI technology. The entire system operates with a configuration centered around the user's terminal, a server, and an AI agent. 【0690】 The server receives authentication information entered by the user from the terminal and performs user authentication by matching it against the database. This process provides a customized learning environment based on each user's learning history and current learning status. The server collects the received user learning progress data in real time and performs analysis using a generative AI model. Based on the analysis results, the server uses an AI agent to generate an individualized learning plan and sends it to the terminal. 【0691】 The device presents the user with learning tasks based on a learning plan received from the server. The user works on these tasks and sends their answers to the server via the device. The server evaluates the answers, uses an AI agent to generate feedback, and sends it back to the device. This feedback plays a crucial role in helping the user understand their level of comprehension and move forward with their next learning session. 【0692】 Furthermore, as a means of visually displaying the progress users make as they learn, the server compiles user achievements in the form of points and badges. As a result, the user's achieved level and ranking are displayed on the terminal, and the learning environment, which incorporates game elements, is designed to maintain the user's motivation to learn. 【0693】 As a concrete example, consider a system for learning English grammar. After logging in on their device, users work on a designated set of grammar exercises. Progress and answers are analyzed on the server, and the next steps are suggested as needed. Users can earn points and badges as visual rewards, and can also compare their progress with other learners. 【0694】 An example of a prompt message for a generative AI model is: "The user's progress information is as follows. Please suggest the next task to maximize learning effectiveness." 【0695】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0696】 Step 1: 【0697】 The user logs into the system using a terminal. The terminal sends the authentication information entered by the user to the server. The server verifies the authentication information using a database, and if authentication is successful, prepares a learning environment tailored to the user. The input is the user's authentication information, and the output is the authentication result and the setup of the learning environment. This authentication prepares the user to proceed to the next learning step. 【0698】 Step 2: 【0699】 When a user begins learning, the device sends the user's learning progress data to the server in real time. The server prepares this data for processing by the AI ​​agent. The input is the user's progress data, and the output is learning data organized in list format. The specific operations of the device include acquiring and transmitting data from sensors and interfaces. 【0700】 Step 3: 【0701】 The server uses an AI agent to analyze the received progress data. The AI ​​agent evaluates the data using a generative AI model and determines the user's level of understanding. The input is organized training data, and the output is an analysis report on the user's level of understanding. Based on this analysis, the server determines the next necessary learning action. 【0702】 Step 4: 【0703】 The server builds a personalized learning plan based on the generated comprehension analysis report. The AI ​​agent uses prompts to suggest the next task to tackle. The input is the comprehension analysis report, and the output is a individually customized learning plan. Specific operations include calculations performed by the AI ​​model and task selection based on those calculations. 【0704】 Step 5: 【0705】 The server sends the generated learning plan to the terminal, which then presents it to the user. The user then works on the presented tasks. The input is the individual learning plan, and the output is the task setting resulting from the execution of the learning plan. The terminal then displays the specific learning content that the user should work on. 【0706】 Step 6: 【0707】 Once the user completes the task, the device sends the solution data to the server. The server evaluates the solution and uses an AI agent to generate feedback for the user. The input is the user's solution data, and the output is the solution evaluation result and feedback. The server sends this feedback back to the device, where it is displayed to the user. 【0708】 Step 7: 【0709】 The server aggregates learning outcomes as points and badges and implements game elements. The results are displayed on the device to maintain user engagement. Input is the answer evaluation result, and output is visualized data in the form of points and badges. The device visually displays these results to the user, increasing motivation through rankings and other means. 【0710】 (Application Example 1) 【0711】 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". 【0712】 It is necessary to provide learners with appropriate assignments tailored to their individual learning progress and offer individualized feedback while maintaining their motivation to continue learning. In particular, when learning takes place at home, effective methods are needed to keep learners interested. Traditional systems have the problem of insufficient individualized instruction and interactive dialogue tailored to progress, making it difficult to sustain motivation for learning. 【0713】 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. 【0714】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individualized learning plan, means for providing tasks based on the individualized plan through interaction with a human and generating real-time feedback, and means for promoting learning motivation by awarding points and achievement badges. This makes it possible for learners to maintain their interest and have an individually customized learning experience even at home. 【0715】 A "learning plan" is a plan that reflects the individual progress of the learner and specifically outlines what they should learn next and how to learn it. 【0716】 A "learning challenge" is a task or exercise designed to deepen learners' understanding of specific skills and knowledge. 【0717】 "Game elements" refer to mechanisms that incorporate rewards and competition, such as points, badges, and rankings, to make learning fun and sustainable. 【0718】 "Interactive dialogue" is a form of communication in which a machine interacts with a user, exchanging information and communicating in real time. 【0719】 "Feedback" refers to a system evaluating a learner's behavior and results, and informing them of the results and areas for improvement. 【0720】 "Points and badges" are rewards, such as numerical values ​​or badges, that are awarded as proof of achievement for results and goals reached during the learning process. 【0721】 "In-home life support equipment" refers to mechanical devices that can support the daily lives of residents and provide learning support within a typical household. 【0722】 The embodiment for carrying out the invention is configured as follows: It centers on a life support machine device used in the home, such as a robot with educational support functions. This robot is equipped with various hardware and software to support the user's learning activities. 【0723】 The servers reside in the cloud and are responsible for analyzing learner behavior data and progress information using databases and AI platforms. Specifically, the servers manage data in real time using Firebase Realtime Database and run machine learning models using Google AI Platform. This data processing dynamically generates individual learning plans. 【0724】 The robot, acting as a terminal, is placed within the home and analyzes the learner's voice input using an NLP (Natural Language Processing) engine. For example, the robot converts the user's voice into text and understands the learning content. This processing allows the robot to prepare and present appropriate learning challenges to the user. 【0725】 The robot evaluates learning outcomes and generates and provides feedback to the user based on the acquired data. To make learning more engaging, it includes features that visually reinforce learning achievements using points and badges. 【0726】 As a concrete example, consider a scenario where a robot "presents a multiplication problem." In this scenario, after the user solves the problem, the robot instantly evaluates the result and returns an encouraging message such as "Well done!" Through this interaction, the user's motivation to continue learning increases. 【0727】 Furthermore, an example of a prompt for a generative AI model is: "Please tell me the answer to 3x4. Then, determine if the answer is correct and send a message of encouragement to the user." This allows the robot to engage in natural dialogue while enhancing its learning effectiveness. 【0728】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0729】 Step 1: 【0730】 The user activates the robot and logs in. The robot, which is the user's device, sends login information to the server, and the server authenticates the user using Firebase Realtime Database. The input for this step is the user's authentication information, and the output is the preparation of a learning environment tailored to the user. 【0731】 Step 2: 【0732】 The user gives instructions to begin learning. The terminal (robot) analyzes the user's voice instructions using an NLP engine to understand the learning content. Here, the user's voice data is the input, and the output is text data resulting from the analysis. Based on the analyzed text, the robot determines the next learning challenge to present. 【0733】 Step 3: 【0734】 The server utilizes Google AI Platform to analyze the user's past learning data and real-time activity information to generate an appropriate learning plan. The input is the user's progress data, and the output is a customized learning plan. Based on the analysis results, the server sets the next task to be addressed and sends it to the device. 【0735】 Step 4: 【0736】 The device presents the user with a corresponding learning challenge. The user works on the presented challenge, and the robot collects the input data in real time. The input here is the problem of the learning challenge, and the user's answer is the output. As the user progresses, the robot provides feedback and moves on to the next step. 【0737】 Step 5: 【0738】 The user's answer is sent to the server for evaluation. The server evaluates the answer using an AI platform and generates feedback based on the user's level of proficiency. The input is the user's answer data, and the output is feedback information. Based on the evaluation results, the server decides to award points or badges and sends data to be displayed visually on the device. 【0739】 Step 6: 【0740】 The device provides feedback to the user and visually presents learning outcomes as a game element. It displays points and badges earned to show the user's progress and motivate them for further learning. The input is feedback data, and the output is the user's visual perception. The robot uses this information to prepare suggestions for the next learning session. 【0741】 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. 【0742】 This invention is a system that not only provides individualized learning plans based on the user's progress, but also, by combining it with an emotion engine, realizes a flexible learning experience that responds to the user's emotional state. The specific operation of the system is described below. 【0743】 When a user logs into the learning system via their device, the device sends their authentication information to the server, which then authenticates the user against the database. After that, the system is ready to generate a personalized learning plan. 【0744】 During learning, the device sends the user's progress and completed tasks to the server. The server then uses an AI agent to analyze the user's learning history in real time and prepare appropriate challenges. The AI ​​agent customizes the challenges it generates, providing the user with the most effective learning experience. 【0745】 Furthermore, the device is equipped with an emotion engine that recognizes emotions through the user's facial expressions and voice. This emotion data is sent to a server, where an AI agent analyzes it and adjusts the learning plan according to the user's emotional state. For example, if the user is feeling frustrated, the difficulty level of the tasks will be lowered. Conversely, if the user is in a good mood, a slightly more challenging task will be suggested. 【0746】 After the learning plan and challenges are adjusted by the emotion engine, the server sends them to the user's device. The user progresses through the tasks and submits their answers to the server. The server evaluates the answers, generates feedback based on the user's level of mastery, and sends it back to the device. 【0747】 Furthermore, game elements such as a point system, visual badges, and rankings have been introduced. Based on this data, the server generates visual rewards to enhance the user's sense of accomplishment and maintain their motivation to learn. 【0748】 Through this series of processes, this invention effectively supports user learning and provides a flexible learning system that takes emotions into consideration. 【0749】 The following describes the processing flow. 【0750】 Step 1: 【0751】 The user accesses the learning system using their device and enters their ID and password on the login screen. The device then sends this information to the server. 【0752】 Step 2: 【0753】 The server compares the received authentication information with the database to authenticate the user. If authentication is successful, a login success message is sent to the terminal. 【0754】 Step 3: 【0755】 After logging in, the user selects learning content. The device sends the selected information to the server, and the creation of a learning plan based on the selection begins. 【0756】 Step 4: 【0757】 The server invokes an AI agent to analyze the user's past learning history and current progress. This generates a personalized learning plan and challenges. 【0758】 Step 5: 【0759】 An emotion engine operates on the device, analyzing the user's facial expressions and voice in real time to acquire emotion data. This data is then sent to a server. 【0760】 Step 6: 【0761】 The server receives data from the emotion engine, and the AI ​​agent evaluates the user's emotional state. Based on this evaluation, the existing learning plan is adjusted. 【0762】 Step 7: 【0763】 The server sends a customized learning plan and challenges to the user's device. The device then presents the user with new tasks. 【0764】 Step 8: 【0765】 The user works on the presented task and enters their answer. The device sends the entered answer to the server. 【0766】 Step 9: 【0767】 The server evaluates the user's responses and generates feedback based on the accuracy rate and level of learning. This feedback is returned to the device and presented to the user. 【0768】 Step 10: 【0769】 The device displays feedback to the user and provides support in deciding the next steps based on the results. 【0770】 Step 11: 【0771】 The server updates the user's progress points and badges as game elements and sends them to the device. The device then displays the user's latest progress, increasing their motivation. 【0772】 (Example 2) 【0773】 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". 【0774】 Modern learners have diverse learning needs and varying emotional states, making it necessary to provide them with learning methods optimized for each individual. However, traditional learning systems struggle to provide individualized learning plans and lack the flexibility to take learners' emotional states into account. As a result, learners' motivation decreases, and learning efficiency declines. 【0775】 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. 【0776】 In this invention, the server includes a device means for analyzing the learner's progress using information processing technology and creating an individualized learning plan; a device means for providing appropriate learning tasks based on the learning plan; and a device means for receiving the learner's facial expressions and voice as input and adjusting the learning plan based on their emotional state. This enables the provision of a learning experience optimized for each individual learner and allows for flexible responses that take their emotional state into consideration. 【0777】 "Information processing technology" refers to all technologies used to collect, store, organize, and analyze data. 【0778】 A "learning plan" refers to a plan of learning content and methods that is individually designed according to the learner's progress and level of understanding. 【0779】 "Device" refers to a configuration of mechanical or electronic mechanisms designed to perform a specific function. 【0780】 "Learning tasks" refer to specific tasks or problems that learners are required to work on. 【0781】 "Facial expression" refers to the state of feelings and emotions that are conveyed through the movement and changes in shape of the face. 【0782】 "Sound" refers to sounds that are transmitted as vibrations in the air, such as spoken language and music. 【0783】 "Emotional state" refers to the mood or emotional state that an individual is experiencing temporarily. 【0784】 "Flexible response" refers to the ability or method to appropriately change or adapt depending on the situation and conditions. 【0785】 This invention utilizes a system that provides individualized learning plans tailored to the user's progress and emotional state. The system aims to create a personalized learning experience for the user, maximizing learning efficiency and motivation. 【0786】 When a user logs into the learning system from their device, the device sends the learner's authentication information to the server, which then uses a database to perform the authentication. The server collects progress data from authenticated users, and a program analyzes this data using information processing technology to create a personalized learning plan. The server utilizes AI algorithms along with programming languages ​​such as Python to perform progress analysis using artificial intelligence. 【0787】 During the learning process, users send their progress data and information about the tasks they've completed from their device to the server. The server uses an AI agent to evaluate this data in real time and generate appropriate learning challenges. These challenges are customized based on the user's learning style and sent from the server to the user's device. 【0788】 Furthermore, the user's device utilizes its camera and microphone to capture the user's facial expressions and voice, and this data is analyzed by an emotion engine. The server receives this emotion data, and the AI ​​agent analyzes the user's emotional state. As a result, the learning plan is adjusted based on the user's emotional state, enabling flexible learning. 【0789】 For example, when a user is learning a language, the server detects their level of concentration from their facial expressions and suggests grammar exercises with a slightly higher difficulty level. On the other hand, if the user's facial expressions indicate stress, the system adjusts to return to more basic vocabulary exercises. 【0790】 An example of a prompt is the instruction, "If the user is a beginner in English, create and suggest a customized learning plan based on their progress and sentiment data." By inputting this prompt into the generative AI model, a learning plan tailored to the individual will be generated. 【0791】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0792】 Step 1: 【0793】 The user logs into the learning system from their device. The device sends the entered username and password to the server. The server queries the database to verify the authentication information. If authentication is successful, access to the system is granted. The input is the user's authentication information, and the output is whether authentication was successful or not. 【0794】 Step 2: 【0795】 The server retrieves past learning history from a database to obtain user progress data. Using the Python programming language, an AI algorithm analyzes the data and creates a personalized learning plan. The input is the user's learning history data, and the output is the customized learning plan. 【0796】 Step 3: 【0797】 The device periodically sends the user's progress and completed tasks to the server. The server receives this data in real time, and an AI agent analyzes the data to generate appropriate learning challenges. The input is the user's latest progress data, and the output is a new learning challenge. 【0798】 Step 4: 【0799】 The device captures the user's facial expressions and voice using its camera and microphone. An emotion engine analyzes this data and sends it to a server. The server's AI agent analyzes this emotion data and adjusts the learning plan accordingly. The input is facial and voice data, and the output is an adjusted learning plan based on that emotion. 【0800】 Step 5: 【0801】 A customized learning plan and assignments are sent from the server to the user's device. The user completes the assignments according to the plan and submits their answers from the device to the server. The server grades the answers, generates a score, and sends it back to the user as feedback. The input is the user's assignment answers, and the output is the evaluated score and feedback. 【0802】 Step 6: 【0803】 The server visualizes the user's learning progress and generates visual rewards such as points, badges, and rankings. These visual rewards are sent to the user's device to maintain motivation. The input is the user's cumulative learning progress, and the output is the visual rewards. 【0804】 (Application Example 2) 【0805】 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". 【0806】 While systems exist that provide individualized learning plans and assignments, they lack real-time adjustment features based on learners' emotions, posing challenges to learning efficiency and maintaining motivation. Furthermore, analyzing learners' progress in real time and effectively combining it with motivational elements through visual play is difficult. 【0807】 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. 【0808】 In this invention, the server includes means for analyzing the learner's progress in real time and generating an individualized learning plan, means for providing appropriate learning tasks based on the learning plan, and means for continuously recognizing the learner's emotions and adjusting the learning plan accordingly. This enables the provision of a flexible learning experience that takes the learner's emotional state into account, thereby improving motivation. 【0809】 A "learner" is an individual who aims to acquire knowledge and skills in an educational environment. 【0810】 A "learning plan" is a plan that outlines the learning content and sequence, individually formulated based on the learner's progress and abilities. 【0811】 A "learning task" is an educational task that includes specific problems or questions that learners should address. 【0812】 "Progress" refers to the state or process that indicates the extent to which learners are moving towards the educational objectives. 【0813】 "Emotions" refer to the psychological state of a learner and are internal sensations that influence their attitude and motivation towards learning. 【0814】 "Playful elements" refer to visual or experiential features designed to incorporate fun and a sense of competition into the learning environment. 【0815】 "Motivation" refers to the psychological driving force that indicates a learner's willingness or reasons for engaging in learning activities. 【0816】 In the system that implements this invention, the server first runs a program that generates a learning plan based on the learner's individual information. The learner's terminal has software installed to monitor progress and send data back to the server in real time. The software is developed in Python, and uses OpenCV for video processing and a speech emotion recognition library (e.g., librosa) for speech analysis. 【0817】 The device uses its camera and microphone to capture the learner's facial expressions and voice, and sends this data to the server in real time. On the server, this data is analyzed using an AI agent to recognize the learner's emotional state. TensorFlow and other machine learning frameworks are used here. If the server determines that the learner is experiencing stress, it adjusts the learning plan, such as lowering the difficulty level. 【0818】 The server also generates visual feedback incorporating playful elements and sends it to the device. This helps maintain the learner's motivation. For example, the system could involve accumulating points based on learning progress and being awarded badges. 【0819】 For example, when a child is stuck on a math problem, an encouraging video is provided from the device to help them relax and regain their interest in learning. An example of a prompt to the generating AI model at this point would be: "A student is feeling frustrated while working on a math problem. Generate a short motivational video to give her some encouragement and make this moment interactive and fun." 【0820】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0821】 Step 1: 【0822】 The user logs into the learning system via their device. The input is the user's authentication information, which the device sends to the server. The server performs authentication by referring to the database and outputs whether the authentication was successful. 【0823】 Step 2: 【0824】 The server generates a personalized learning plan for authenticated users by referencing past learning data. The input is the user's learning history, and the server outputs the optimal learning plan based on this. An AI algorithm is used to analyze past progress and formulate the plan. 【0825】 Step 3: 【0826】 When a user begins learning, the device sends user progress data to the server in real time. The input is the user's progress information during learning, which the server receives and outputs an evaluation based on that progress. 【0827】 Step 4: 【0828】 The device uses its camera and microphone to capture the user's facial expressions and voice. The input consists of video and audio data, which the device processes with emotion recognition software. The obtained emotional state data is sent to a server, and the emotional state is output. 【0829】 Step 5: 【0830】 The server integrates the received progress data and sentiment data, which the AI ​​agent then analyzes. The input consists of progress and sentiment recognition data, and the server adjusts the learning plan and outputs new learning tasks. 【0831】 Step 6: 【0832】 The server generates and sends feedback, including playful elements, to the device. The input is the user's learning result, and it outputs visual rewards and motivational content. 【0833】 Step 7: 【0834】 The user solves a learning task and submits the results to the server. The input is the learner's answer, which the server evaluates and outputs as response feedback. 【0835】 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. 【0836】 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. 【0837】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0838】 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. 【0839】 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. 【0840】 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. 【0841】 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. 【0842】 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. 【0843】 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." 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 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. 【0848】 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. 【0849】 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. 【0850】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0851】 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. 【0852】 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. 【0853】 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. 【0854】 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. 【0855】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0856】 The following is further disclosed regarding the embodiments described above. 【0857】 (Claim 1) 【0858】 A means to analyze learners' progress in real time and generate individualized learning plans, 【0859】 A means of providing appropriate learning challenges based on the aforementioned learning plan, 【0860】 A means of visually presenting learning outcomes using game elements to maintain motivation. 【0861】 A system that includes this. 【0862】 (Claim 2) 【0863】 The system according to claim 1, further comprising means for authenticating learners and managing data in a personalized manner. 【0864】 (Claim 3) 【0865】 The system according to claim 1, further comprising means for evaluating the results of an individual's efforts to complete a challenge and generating feedback based on the individual's level of mastery. 【0866】 "Example 1" 【0867】 (Claim 1) 【0868】 A means of collecting user learning progress data in real time and analyzing knowledge acquisition status, 【0869】 A means of creating an individualized learning plan using a generative model based on the analyzed data, 【0870】 A means of providing knowledge acquisition tasks based on a learning plan, 【0871】 A means for evaluating the answer results and generating feedback based on the analysis results, 【0872】 A way to visually display achievements as points or badges to increase motivation to learn. 【0873】 A system that includes this. 【0874】 (Claim 2) 【0875】 The system according to claim 1, further comprising means for authenticating users and performing specialized information management. 【0876】 (Claim 3) 【0877】 The system according to claim 1, further comprising means for evaluating the results of efforts toward learning tasks and creating feedback tailored to the individual's knowledge acquisition status. 【0878】 "Application Example 1" 【0879】 (Claim 1) 【0880】 A means to analyze learners' progress in real time and generate individualized learning plans, 【0881】 A means of providing appropriate learning challenges based on the aforementioned learning plan, 【0882】 A means of visually presenting learning outcomes using game elements to maintain motivation, 【0883】 A means of providing tasks based on individual plans through interaction with humans and generating real-time feedback. 【0884】 A system that includes this. 【0885】 (Claim 2) 【0886】 A means of authenticating learners and managing data in a personalized manner, 【0887】 A means of providing educational materials interactively using in-home life support devices. 【0888】 The system according to claim 1, further comprising: 【0889】 (Claim 3) 【0890】 A means of evaluating the results of efforts toward a challenge and generating feedback based on the individual's level of mastery, 【0891】 A means of promoting learning motivation by awarding points and achievement badges. 【0892】 The system according to claim 1, further comprising: 【0893】 "Example 2 of combining an emotion engine" 【0894】 (Claim 1) 【0895】 A device and means for analyzing learners' progress using information processing technology and creating individual learning plans, 【0896】 A device means for providing appropriate learning tasks based on the aforementioned learning plan, 【0897】 A device that receives the learner's facial expressions and voice as input and adjusts the learning plan based on their emotional state, 【0898】 A device that visually displays learning results using entertainment elements to maintain motivation, 【0899】 A system that includes this. 【0900】 (Claim 2) 【0901】 The system according to claim 1, further comprising a device for performing learner authentication and personal information management. 【0902】 (Claim 3) 【0903】 The system according to claim 1, further comprising a device that evaluates the results of an individual's efforts to complete a task and generates a response based on the individual's level of proficiency. 【0904】 "Application example 2 when combining with an emotional engine" 【0905】 (Claim 1) 【0906】 A device that analyzes learners' progress in real time and generates individualized learning plans, 【0907】 A device that provides appropriate learning tasks based on the aforementioned learning plan, 【0908】 A device that continuously recognizes the learner's emotions and adjusts the learning plan accordingly, 【0909】 A device that visually presents learning outcomes using playful elements, thereby maintaining motivation. 【0910】 A system that includes this. 【0911】 (Claim 2) 【0912】 The system according to claim 1, further comprising a device for authenticating learners and managing personalized information. 【0913】 (Claim 3) 【0914】 The system according to claim 1, further comprising a device that evaluates the results of an individual's efforts to complete a task and generates a response based on the individual's level of proficiency. [Explanation of symbols] 【0915】 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] A means to analyze learners' progress in real time and generate individualized learning plans, A means of providing appropriate learning challenges based on the aforementioned learning plan, A means of visually presenting learning outcomes using game elements to maintain motivation. A system that includes this. [Claim 2] The system according to claim 1, further comprising means for authenticating learners and managing data in a personalized manner. [Claim 3] The system according to claim 1, further comprising means for evaluating the results of an individual's efforts to complete a challenge and generating feedback based on the individual's level of mastery.

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