system

Abstract

We provide the system. [Solution] A means of receiving information about the user's learning speed and level of comprehension, A means of analyzing received information to generate individual learning plans, A means of selecting and providing learning materials based on the generated learning plan, A means of recording the user's learning progress and adjusting the learning plan based on that information, An education delivery 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No.2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In conventional educational methods, it is difficult to adjust learning content based on the learning speed and comprehension of individual students. In particular, a uniform educational program is often provided for students with different learning abilities. As a result, each student cannot obtain an optimal learning experience, and there is a problem of reduced learning efficiency. 【Means for Solving the Problems】 【0005】 To solve this problem, the present invention provides a system that includes means for receiving information on the user's learning speed and level of understanding, and generates an individualized learning plan based on the received information. It also includes means for selecting and providing learning materials based on the generated learning plan. Furthermore, by including means for recording the user's learning progress and adjusting the learning plan based on that information, it is possible to realize an educational program tailored to each student and improve learning efficiency. 【0006】 "User" refers to an individual who uses the educational delivery system to learn. 【0007】 "Learning speed" refers to the speed at which a user acquires new knowledge or skills. 【0008】 "Understanding level" is an indicator that shows how well a user understands a particular piece of knowledge or skill. 【0009】 "Means of receiving information" refers to the methods and functions by which a system acquires data and information provided by the user. 【0010】 "Means for generating learning plans" refers to a function that designs the optimal learning content and learning methods for the user based on the information received. 【0011】 "Learning materials" refer to educational materials and learning content provided to support the user's learning. 【0012】 "Means of recording progress" refers to a function that collects and stores data related to the user's learning activities. 【0013】 "Means for adjusting the plan" refers to a function that updates and optimizes the existing learning plan based on the user's progress data and feedback. [Brief explanation of the drawing] 【0014】 [Figure 1]It 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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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. 【MODE FOR CARRYING OUT THE INVENTION】 【0015】 \n Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0020】 In the following embodiments, the 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), etc. 【0021】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0029】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0032】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 The system of this invention is designed to provide customized educational programs tailored to the user's learning needs. The system mainly consists of the interaction between a server, a terminal, and the user. 【0036】 First, users access the educational system through their device and create an account. This involves entering basic profile information and taking an initial assessment test. This test is designed to understand the user's current learning speed and comprehension level. 【0037】 The terminal sends information and test results entered by the user to the server. Upon receiving this data, the server uses a data analysis algorithm to evaluate the user's characteristics and generate an individualized learning plan. 【0038】 The generated learning plan takes into account the user's strengths and weaknesses to provide an optimal learning route. Based on this plan, the server selects appropriate learning materials and provides them to the user through the device. The user can then use these materials on the device to proceed with their learning. 【0039】 User learning activities are recorded in real time by the device. The device periodically uploads this progress information to the server. The server analyzes the progress data and adjusts the learning plan as needed. This adjustment involves readjusting the content of the learning materials and the learning process to maximize the user's learning efficiency. 【0040】 As a concrete example, consider a user aiming to improve their mathematical abilities. Initial testing reveals this user is strong in algebra but weak in geometry. Based on this analysis, the server designs a curriculum that reviews the fundamentals of geometry while tackling applied algebra problems. The terminal provides this designed material and continuously monitors the user's progress, adding new challenges as needed. 【0041】 Thus, the system of the present invention aims to improve the quality of learning by providing dynamic educational programs that meet the individual needs of each user. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 Users access the educational system using their devices and create accounts. They enter basic information such as their name, grade level, and subjects of interest. 【0045】 Step 2: 【0046】 The device collects information provided by the user and sends it to the server. The server receives this information and sets up the profile. 【0047】 Step 3: 【0048】 The server generates an assessment test to understand the user's current learning speed and comprehension level. The server sends the test questions to the terminal and displays them to the user. 【0049】 Step 4: 【0050】 The user answers test questions displayed on the device. Through this, the user informs the system of their academic ability. 【0051】 Step 5: 【0052】 The device records the user's responses and sends them to the server. The server analyzes the received test results to identify the user's strengths and weaknesses. 【0053】 Step 6: 【0054】 The server generates individual learning plans based on the analysis results. When creating these plans, the server selects learning materials and assignments. 【0055】 Step 7: 【0056】 The terminal displays the learning plan and materials sent from the server to the user. The user then begins learning using the displayed materials. 【0057】 Step 8: 【0058】 The user's learning activity is recorded in real time by the device. The device sends the user's progress information to the server. 【0059】 Step 9: 【0060】 The server continuously adjusts the learning plan based on progress information. This optimizes the plan to maximize the user's learning efficiency. 【0061】 Step 10: 【0062】 If a user requires feedback or additional support regarding a specific issue, the server will respond by providing individual guidance and advice. 【0063】 (Example 1) 【0064】 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." 【0065】 There is a lack of means to individually assess each user's learning speed and comprehension level, and to promote efficient and effective learning. In particular, uniform educational methods make it difficult to maximize individual abilities and learning characteristics, so there is a need to provide flexible educational programs that meet individual needs. 【0066】 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. 【0067】 In this invention, the server includes means for receiving initial input information and evaluation results from the user and transmitting them to a data processing device; means for utilizing a generated AI model based on the received information to analyze the learning characteristics of each user and generate an individualized learning strategy; and means for selecting the optimal educational resources based on the generated learning strategy and providing them via an information processing device. This makes it possible to provide an effective learning program that meets the individual needs of each user. 【0068】 "User" refers to an individual who uses an educational system to engage in learning activities. 【0069】 "Initial input information" refers to basic information such as name, age, and learning purpose that users provide when creating an account. 【0070】 "Evaluation results" refer to data obtained after the user completes the initial system test, and include numerical values ​​and indicators showing learning speed and comprehension. 【0071】 A "data processing device" refers to a computer or server used to analyze information obtained from users. 【0072】 A "generative AI model" refers to an artificial intelligence algorithm or program used to analyze a user's learning characteristics and generate an optimal learning plan. 【0073】 "Learning characteristics" refer to the individual characteristics and tendencies of a user regarding learning, including learning speed and areas of strength and weakness. 【0074】 An "individualized learning strategy" refers to a plan that determines the optimal learning content and order based on the user's learning characteristics. 【0075】 "Educational resources" refer to content provided to users when they engage in learning activities, such as teaching materials and learning tools. 【0076】 "Information processing equipment" refers to computers and servers used to provide learning resources to users and manage learning activities. 【0077】 An "educational program" refers to a combination of various educational resources and activities designed to achieve the user's learning objectives. 【0078】 This invention is a system for providing customized educational programs based on the learning characteristics of individual users. The system mainly consists of the interaction between a server, a terminal, and the user. 【0079】 Users access the educational system using their devices, enter initial information such as their name, age, and learning objectives when creating an account, and take an initial assessment test. This assessment test measures the user's learning speed and comprehension and includes multiple-choice and written questions. The device transmits this information to a server, which is a data processing unit. SSL / TLS protocol is used for this transmission to ensure data security. 【0080】 The server analyzes the received initial input information and evaluation results using a generative AI model. Specifically, it evaluates the user's learning characteristics (strengths, weaknesses, learning speed, etc.) and generates an individualized learning strategy. This generative AI model can utilize machine learning libraries such as TENSORFLOW® and PyTorch. 【0081】 Based on the generated learning strategy, the server selects the most suitable educational resources and provides them to the user through the terminal. The terminal notifies the user of these resources and makes the learning content accessible. The user progresses through the learning materials provided on the terminal, and their progress is recorded in real time. 【0082】 For example, suppose a user aiming to improve their language skills is found to be strong in grammar but weak in listening comprehension during an initial assessment test. Similarly, based on this analysis, the server designs a curriculum that provides audio materials to strengthen listening fundamentals while also tackling applied grammar problems. 【0083】 The server analyzes learning progress data sent from the terminal at regular intervals and dynamically adjusts the learning strategy as needed. The adjusted new strategy is continuously improved to match the user's understanding and learning pace, providing optimal education. 【0084】 An example of a prompt message might be: "Based on the following profile information and evaluation results, generate a customized learning plan for the user." 【0085】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0086】 Step 1: 【0087】 Users access the educational system using a terminal and enter initial information such as their name, age, and learning objectives through an account creation screen. Next, the terminal presents an initial assessment test, which the user completes. The test consists of multiple-choice and short-answer questions to measure the user's learning speed and comprehension. The entered information and test results are transmitted from the terminal to the server. Input consists of the user's basic information and test results, while output is the data sent to the server. 【0088】 Step 2: 【0089】 The server stores the received initial input information and evaluation results in a database. Next, it analyzes this data using a generative AI model. The purpose of the analysis is to evaluate the user's learning characteristics. The input is user information and test results, and the output is evaluation data that reflects the user's learning needs. The server uses a feature extraction algorithm to extract important patterns from the data. 【0090】 Step 3: 【0091】 The server generates individualized learning strategies based on extracted evaluation data. This process involves an AI recommendation system that creates an optimal learning plan considering the user's strengths and weaknesses. The input is evaluation data, and the output is an individualized learning strategy. The server selects appropriate learning materials from a database of learning resources. 【0092】 Step 4: 【0093】 The server selects appropriate educational resources based on the generated learning strategy and sends them to the terminal. The terminal receives these resources and notifies the user that new learning materials are available. The user then views the materials on the terminal and proceeds with their learning activities. The input is the selected educational resources, and the output is the learning content displayed on the user's terminal. 【0094】 Step 5: 【0095】 User learning activities are recorded in real time by the device and periodically sent to the server as progress data. This data includes learning time, number of correct answers, and assignment completion status. Input is the user's learning activity record, and output is the progress data sent to the server. 【0096】 Step 6: 【0097】 The server analyzes progress data and dynamically adjusts the learning strategy as needed. This adjustment readjusts the difficulty level and order of learning materials based on the user's progress and understanding. The input is progress data, and the output is the updated learning strategy. The server then sends optimized learning material information to the device for the next learning session. 【0098】 (Application Example 1) 【0099】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0100】 Traditional education systems struggle to provide effective learning plans tailored to each learner's progress and level of understanding in real time, and there is a particular lack of responsive learning support, especially through home robots. Therefore, there is a need to develop new systems that maximize each learner's learning efficiency and maintain their continued interest. 【0101】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0102】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension; means for analyzing the received information to generate an individualized learning plan; means for selecting and providing learning materials based on the generated learning plan; means for recording the user's learning progress and adjusting the learning plan based on that information; and means for providing robotic educational assistance to further optimize learning activities based on self-assessment test data. This enables the provision of plans optimized for individual learners and flexible readjustment of learning plans according to progress. 【0103】 A "user" is an individual who uses an educational system to learn. 【0104】 "Learning speed" is an indicator that shows how quickly a user can acquire specific knowledge. 【0105】 "Comprehension level" is a measure used to evaluate the degree to which a user understands educational materials. 【0106】 "Means for receiving information" refers to a method or device for receiving data regarding the user's learning speed and level of comprehension. 【0107】 "Means for analyzing and generating individual learning plans" refers to a system that analyzes received data and creates personalized learning programs based on that analysis. 【0108】 "Means of selecting and providing learning materials" refers to the technology or method of selecting appropriate educational content based on a generated learning plan and presenting it to the user. 【0109】 "Means of recording learning progress" refers to methods of tracking a user's learning process and saving their results and progress. 【0110】 "Means of providing educational assistance" refers to robotic technology that provides additional guidance and support to users based on their learning plans. 【0111】 "Self-assessment test data" refers to test result data based on evaluations conducted by the user themselves, and serves as a standard for determining learning progress and level of understanding. 【0112】 The educational delivery system based on this invention provides flexible and optimal learning plans tailored to individual learners. This system is primarily composed of the interaction between a server, terminals, and users. 【0113】 First, the user accesses the educational system using a terminal. On the terminal, an assessment test is administered to measure the user's learning speed and comprehension. This assessment data is sent from the terminal to the server. The server executes a data analysis algorithm using Python to evaluate the user's characteristics. Based on this evaluation, a personalized learning plan is generated for each user. 【0114】 Next, based on the generated learning plan, the server selects learning materials and provides them to the user via the terminal. The user then proceeds with their learning using these materials. The terminal records the user's learning activity in real time. Progress data is periodically uploaded to the server, which dynamically adjusts the learning plan based on this data. This process aims to improve the quality of learning by adjusting the difficulty level and focus of the learning materials according to the user's progress. 【0115】 Furthermore, robots can be used to provide direct educational assistance. These robots play a role in providing personalized guidance based on self-assessment test data. For example, when learning at home, the robot can improve learning effectiveness by providing intensive support on topics that children struggle with, and track their progress in more detail. 【0116】 As a concrete example, consider a scenario where a user learns mathematics at home. If an initial test determines that the user has difficulty with geometry, the server selects basic geometry materials and provides them to the user via the robot. Once the user reaches a certain progress level, the next level of materials is provided. This supports gradual learning. Using a generative AI model, the prompt "Consider an application where a home educational robot proposes a learning plan based on the user's interests and abilities, and updates the materials according to their progress" is used. 【0117】 This system aims to maximize learning efficiency and effectiveness by providing a customized learning experience tailored to the user's needs. 【0118】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0119】 Step 1: 【0120】 The terminal receives basic information and evaluation test results to create the user's learning profile and sends them to the server. Here, the initial learning speed and comprehension level are measured based on the user's input data. The output sent to the server includes the user ID, learning speed, and comprehension level. 【0121】 Step 2: 【0122】 The server analyzes the received data using Python and executes data analysis algorithms. This generates personalized learning plans that take into account the individual characteristics of each user. For example, it determines a user's strengths and weaknesses in specific subjects and designs a curriculum based on that. As output, data for each user's optimized learning plan is generated. 【0123】 Step 3: 【0124】 The server selects appropriate learning materials based on the generated learning plan and provides them to the device. Here, materials are retrieved from the optimal learning library based on each user's plan. Links to the learning materials and their content are provided as output to the device. 【0125】 Step 4: 【0126】 The user progresses through the learning materials provided via their device. In this step, the content is advanced and learning takes place through the user's actions. The user's learning progress is recorded by the device. 【0127】 Step 5: 【0128】 The device periodically uploads the user's learning progress information to the server. Here, data regarding the progress is collected and sent to the server. The output to the server includes the user's learning progress data. 【0129】 Step 6: 【0130】 The server analyzes the uploaded progress data using a generative AI model and adjusts the learning plan as needed. It evaluates changes in learning speed and comprehension through data calculations and generates a newly adjusted learning plan. The updated learning plan is then sent back to the terminal as output. 【0131】 Step 7: 【0132】 The robot provides direct educational assistance based on the user's progress and through self-assessment test data. Specifically, it provides appropriate feedback and additional learning materials to help the user in areas where they struggle. Appropriate feedback and learning support are presented as output to the user. 【0133】 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. 【0134】 This invention is a system for personalizing the user's learning experience and promoting efficient education. The system consists of a server, terminals, and an emotion engine, and dynamically analyzes the user's learning speed, comprehension, and emotions to provide an individualized learning plan. 【0135】 First, the user creates an account via their device and logs into the educational system. The device collects information about the user's learning speed and comprehension level and sends it to the server. The server uses this data to analyze and generate an initial learning plan. The learning plan includes learning materials tailored to the user's strengths and weaknesses, and the content is selected based on the user's individual needs. 【0136】 A notable feature of this system is its use of an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and uses that information to adjust the learning plan in real time. For example, if a user is feeling stressed during learning, the emotion engine detects this and sends data to the server. The server takes the emotion data into consideration and takes appropriate action, such as adjusting the difficulty level, providing relaxing materials, or recommending a break. 【0137】 For example, if a user is learning a language, the server provides a learning plan that focuses particularly on conversational skills, based on initial tests and progress data. During learning, if the emotion engine detects signs of stress in the user, the server receives input from the engine, readjusts the plan, and creates a relaxed learning environment by introducing simple listening exercises and visual learning materials. 【0138】 Furthermore, the server provides a means for users to provide feedback, which is then used to optimize future learning plans. This allows users to continuously enjoy an efficient learning experience. Emotional states are recorded and reflected in long-term learning strategies, enabling more refined and personalized education. 【0139】 In this way, the system grasps the user's learning progress and emotional state in real time and uses that information to provide optimal education. 【0140】 The following describes the processing flow. 【0141】 Step 1: 【0142】 Users log in to the educational system using their device. Upon first use, users create an account and enter basic profile information and learning objectives. 【0143】 Step 2: 【0144】 The device sends information entered by the user and the results of the initial test to the server. This allows the server to receive initial data regarding the user's learning speed and comprehension level. 【0145】 Step 3: 【0146】 The server uses an algorithm based on the received data to evaluate the user's strengths and weaknesses and generate a personalized learning plan. This plan includes recommended learning materials and a learning progression guide. 【0147】 Step 4: 【0148】 The device displays a personalized learning plan and recommended materials sent from the server to the user. The user then begins learning based on the provided plan. 【0149】 Step 5: 【0150】 During learning, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize their emotional state. This information is used to identify whether the user is learning smoothly and what kind of support they need. 【0151】 Step 6: 【0152】 The device sends emotional information obtained from the emotion engine to the server. Based on this information, the server can dynamically adjust the learning plan. 【0153】 Step 7: 【0154】 If the server detects that the user is stressed, it selects learning materials of a lower difficulty level and provides the user with materials and suggestions to help them relax through the device. 【0155】 Step 8: 【0156】 The user records their progress on their device during the learning process. The device sends this progress information to a server, which then analyzes the user's progress data. 【0157】 Step 9: 【0158】 The server considers emotional information and progress data to adjust and optimize the next learning plan. This adjustment is aimed at improving the long-term quality of learning. 【0159】 Step 10: 【0160】 Users provide feedback on completed learning content via their devices, and the server incorporates this feedback into the next plan, ensuring a continuously improved learning experience. 【0161】 (Example 2) 【0162】 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." 【0163】 Traditional education systems considered the user's learning speed and comprehension level, but they did not adequately address the impact of emotional states on learning effectiveness. Furthermore, generating and continuously adjusting individual learning plans made it difficult to provide an optimal learning experience for all users. Therefore, a system was needed to address these issues by adjusting learning plans in real time while considering the user's emotional state. 【0164】 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. 【0165】 In this invention, the server includes means for collecting information on the user's learning speed, comprehension level, and emotional state; means for using a generative AI model to analyze the received information and generate an individualized learning plan; and means for selecting and providing appropriate learning materials based on the generated learning plan. This enables dynamic adjustment of the individualized learning plan, taking into account the user's real-time emotional state. 【0166】 "User" refers to a person who uses the system to engage in learning activities. 【0167】 "Learning speed" is an indicator that shows how quickly a user progresses in their learning. 【0168】 "Comprehension level" is an indicator that shows how well the user understands the learning material. 【0169】 "Emotion" refers to data that reflects the user's emotional state, including stress levels and concentration levels during learning. 【0170】 A "generative AI model" is a computational model that uses artificial intelligence technology to analyze received data and create an optimal learning plan for the user. 【0171】 A "learning plan" refers to a plan that combines the most suitable learning materials and procedures for each individual user. 【0172】 "Learning materials" refer to the textbooks and assignments necessary for users to progress in their studies. 【0173】 "Learning progress" refers to information that shows the user's progress as they engage in learning. 【0174】 This invention provides a system that enables users to learn more effectively. Its main components include a server, a terminal, and an emotion engine. Each of these has a specific role and works together. 【0175】 Users access the system and create an account using an internet-connected device. The device is hardware that collects data on learning speed, comprehension, and emotional state through the user's learning activities. This data is sent from the device to a server for processing. 【0176】 The server uses a generative AI model to analyze the collected training data. This AI model generates a learning plan optimized for each individual user. This process involves processing a large amount of data, but it is executed efficiently by advanced algorithms. This learning plan includes appropriately customized learning materials based on the user's level of understanding and emotions. 【0177】 The emotion engine is software that analyzes the user's emotions in real time, understanding their emotional state from their facial expressions and voice. Changes in emotions detected by the emotion engine are immediately fed back to the server, which then dynamically adjusts the learning plan based on this information. This adjustment allows the system to lower the difficulty level of learning materials or provide relaxing content if the user is experiencing stress. 【0178】 As a concrete example, consider a user learning a new language using a language learning app. The device records the number of questions the user solves and their accuracy rate, while an emotion engine analyzes facial expressions to measure stress levels. The server uses this data to provide the user with an optimal learning plan. For example, it can provide specific guidance such as, "To strengthen your listening skills, please do some simple listening practice." 【0179】 Examples of prompts that can be given to the AI ​​include the following instructions: 【0180】 "Combine the user's learning data and emotional data to create a customized language learning plan." 【0181】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0182】 Step 1: 【0183】 Users create an account and log in to the system. Users access the learning platform via the internet using their device. The information entered during login includes a username and password, which are used to configure their individual learning environment. 【0184】 Step 2: 【0185】 The device collects data on the user's learning speed, comprehension level, and emotional state during learning activities. This data includes task completion time, accuracy rate, facial expressions, and voice data. The device records this data using sensors and learning management software and transmits it to a server. 【0186】 Step 3: 【0187】 The server stores the collected data in a temporary database. The server receives the training data sent from the terminal and saves it in the temporary database as a short-term record. This step involves organizing and temporarily storing the data. 【0188】 Step 4: 【0189】 The server analyzes data using a generative AI model and generates individualized learning plans. The server retrieves data from a temporary database and inputs it into the AI ​​model to generate an optimal learning plan that reflects the user's learning behavior and emotions. This output includes the selection of learning materials and adjustment of difficulty levels. 【0190】 Step 5: 【0191】 The server generates a learning plan and sends it to the device. The learning plan generated as server output is sent to the device, and specific tasks and recommended content that the user should perform are presented. 【0192】 Step 6: 【0193】 The emotion engine evaluates the user's emotions in real time during the learning process. The device uses its camera and microphone to acquire facial expression data and voice, which are then input into the emotion engine. Based on this input, the emotion engine evaluates the user's stress level and concentration level, and sends the results to the server. 【0194】 Step 7: 【0195】 The server adjusts the learning plan in real time based on data from the emotion engine. The server analyzes the received emotional data and modifies the content and progress of the learning plan as needed. This optimizes the learning process so that the user can learn at a more comfortable pace and efficiently. 【0196】 Step 8: 【0197】 The user continues learning based on a tailored learning plan and provides feedback upon completion. The device records the user's feedback and sends it to the server to help improve future learning plans. This step involves a final evaluation of the learning activity. 【0198】 (Application Example 2) 【0199】 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". 【0200】 In modern society, education is becoming individualized and is required to meet the diverse needs of learners. However, traditional learning systems have struggled to provide adaptive learning plans that take into account the user's learning speed, level of comprehension, and emotional state. Ignoring a user's emotional state while proceeding with learning can lead to decreased learning efficiency and loss of motivation. Therefore, flexible adjustments to learning plans that respond to emotional states are necessary. 【0201】 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. 【0202】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension, means for analyzing the received information to generate an individualized learning plan, and means for recognizing the user's emotional state based on their facial expressions and voice, and adaptively adjusting the learning plan using that information. This makes it possible to dynamically change the learning environment and learning materials in real time based on the user's learning progress and emotional state. 【0203】 A "user" refers to an individual user who receives educational content using the learning system. 【0204】 "Learning speed" is an indicator that shows the speed or pace at which a user progresses through the learning process. 【0205】 "Comprehension level" is an indicator that shows how accurately a user understands or grasps the learning content. 【0206】 "Means for receiving information" refers to an interface or protocol for acquiring data from a user and transmitting it to a server. 【0207】 "Individualized learning plan" refers to an educational curriculum and schedule customized for each user. 【0208】 "Emotional state" refers to the user's psychological or emotional condition and can be analyzed from facial expressions and voice. 【0209】 "Means for recognizing emotional states" refers to technologies that analyze the user's facial expressions and voice data to evaluate their current emotional state. 【0210】 "Dynamically changing the learning environment and materials in real time" refers to the process of instantly changing the learning content and materials presented based on collected data and analysis results. 【0211】 This invention is an educational delivery system designed to effectively support user learning. The system primarily consists of a server, terminals, and an emotion engine. 【0212】 The device collects data on the user's learning speed and comprehension. Using the device's camera and microphone, it also captures the user's facial expressions and voice data to gather information for determining their emotional state. This data is transmitted to the server in real time. 【0213】 The server analyzes received information on learning speed and comprehension to generate individualized learning plans. This uses a data analysis engine (e.g., TensorFlow) to select appropriate learning materials based on the user's progress. Furthermore, the server analyzes the user's emotional state using data from an emotion engine and adjusts the learning plan in real time. This adjustment process includes, for example, changing the learning content to something more relaxing if the user is experiencing stress. 【0214】 The emotion engine uses facial recognition and speech recognition technologies (e.g., Emotion API) to understand the user's emotional state. Based on this information, the server personalizes the educational content to help the user maintain an optimal learning state. 【0215】 For example, if a user experiences stress while practicing foreign language conversation, the server can provide easier practice exercises or visual aids to relax the learning environment. By using a generative AI model, user feedback can be accumulated and used to optimize future learning plans. 【0216】 An example of a prompt message is, "If the user is smiling, ask for instructions on how to arrange the learning content." Through such interactions, it is possible to create a more effective and personalized learning experience. 【0217】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0218】 Step 1: 【0219】 The device collects data on the user's learning speed and comprehension. Specifically, it captures facial expressions with its built-in camera and records voice with its microphone to acquire facial features and speech tone. The input is real-time video and audio data from the user, and an initial dataset is generated based on this data. 【0220】 Step 2: 【0221】 The device processes the collected data and sends it to the server as feature data. During this process, it uses facial recognition and voice analysis algorithms to estimate the user's emotional state and also attaches preliminary data on learning speed and comprehension. The output consists of a provisional estimate of the emotional state and an initial evaluation of the learning pace. 【0222】 Step 3: 【0223】 The server receives data sent from the terminal and generates an individualized learning plan based on it. Using a data analysis engine (e.g., TensorFlow), it analyzes the input data and generates a customized learning plan tailored to the user's characteristics. The output includes a list of specific learning materials and a learning schedule. 【0224】 Step 4: 【0225】 The server uses a generative AI model to further analyze sentiment data and adjust the learning plan in real time. Here, prompts (e.g., "If the user is smiling, ask for instructions on how to rearrange the learning content") are used to determine the optimal materials and feedback. The output is the adjusted learning content and additional lesson plans. 【0226】 Step 5: 【0227】 Users progress through their learning based on a customized learning plan provided by the server. Real-time feedback can also be provided, and this information is used to adjust the plan for future sessions. The user's progress and feedback are used as input, and progress data is updated based on this information. 【0228】 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. 【0229】 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. 【0230】 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. 【0231】 [Second Embodiment] 【0232】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0233】 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. 【0234】 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). 【0235】 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. 【0236】 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. 【0237】 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). 【0238】 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. 【0239】 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. 【0240】 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. 【0241】 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. 【0242】 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. 【0243】 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". 【0244】 The system of this invention is designed to provide customized educational programs tailored to the user's learning needs. The system mainly consists of the interaction between a server, a terminal, and the user. 【0245】 First, users access the educational system through their device and create an account. This involves entering basic profile information and taking an initial assessment test. This test is designed to understand the user's current learning speed and comprehension level. 【0246】 The terminal sends information and test results entered by the user to the server. Upon receiving this data, the server uses a data analysis algorithm to evaluate the user's characteristics and generate an individualized learning plan. 【0247】 The generated learning plan takes into account the user's strengths and weaknesses to provide an optimal learning route. Based on this plan, the server selects appropriate learning materials and provides them to the user through the device. The user can then use these materials on the device to proceed with their learning. 【0248】 User learning activities are recorded in real time by the device. The device periodically uploads this progress information to the server. The server analyzes the progress data and adjusts the learning plan as needed. This adjustment involves readjusting the content of the learning materials and the learning process to maximize the user's learning efficiency. 【0249】 As a concrete example, consider a user aiming to improve their mathematical abilities. Initial testing reveals this user is strong in algebra but weak in geometry. Based on this analysis, the server designs a curriculum that reviews the fundamentals of geometry while tackling applied algebra problems. The terminal provides this designed material and continuously monitors the user's progress, adding new challenges as needed. 【0250】 Thus, the system of the present invention aims to improve the quality of learning by providing dynamic educational programs that meet the individual needs of each user. 【0251】 The following describes the processing flow. 【0252】 Step 1: 【0253】 Users access the educational system using their devices and create accounts. They enter basic information such as their name, grade level, and subjects of interest. 【0254】 Step 2: 【0255】 The device collects information provided by the user and sends it to the server. The server receives this information and sets up the profile. 【0256】 Step 3: 【0257】 The server generates an assessment test to understand the user's current learning speed and comprehension level. The server sends the test questions to the terminal and displays them to the user. 【0258】 Step 4: 【0259】 The user answers test questions displayed on the device. Through this, the user informs the system of their academic ability. 【0260】 Step 5: 【0261】 The device records the user's responses and sends them to the server. The server analyzes the received test results to identify the user's strengths and weaknesses. 【0262】 Step 6: 【0263】 The server generates individual learning plans based on the analysis results. When creating these plans, the server selects learning materials and assignments. 【0264】 Step 7: 【0265】 The terminal displays the learning plan and materials sent from the server to the user. The user then begins learning using the displayed materials. 【0266】 Step 8: 【0267】 The user's learning activity is recorded in real time by the device. The device sends the user's progress information to the server. 【0268】 Step 9: 【0269】 The server continuously adjusts the learning plan based on progress information. This optimizes the plan to maximize the user's learning efficiency. 【0270】 Step 10: 【0271】 If a user requires feedback or additional support regarding a specific issue, the server will respond by providing individual guidance and advice. 【0272】 (Example 1) 【0273】 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." 【0274】 There is a lack of means to individually assess each user's learning speed and comprehension level, and to promote efficient and effective learning. In particular, uniform educational methods make it difficult to maximize individual abilities and learning characteristics, so there is a need to provide flexible educational programs that meet individual needs. 【0275】 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. 【0276】 In this invention, the server includes means for receiving initial input information and evaluation results from the user and transmitting them to a data processing device; means for utilizing a generated AI model based on the received information to analyze the learning characteristics of each user and generate an individualized learning strategy; and means for selecting the optimal educational resources based on the generated learning strategy and providing them via an information processing device. This makes it possible to provide an effective learning program that meets the individual needs of each user. 【0277】 "User" refers to an individual who uses an educational system to engage in learning activities. 【0278】 "Initial input information" refers to basic information such as the name, age, and learning purpose provided by the user when creating an account. 【0279】 "Evaluation results" are data obtained after the user completes the initial test by the system, including numerical values and indicators indicating learning speed and comprehension. 【0280】 "Data processing device" refers to a computer or server used to analyze information obtained from the user. 【0281】 "Generative AI model" refers to an artificial intelligence-based algorithm or program used to analyze the learning characteristics of the user and generate an optimal learning plan. 【0282】 "Learning characteristics" refer to the individual characteristics and tendencies of the user related to learning, including learning speed and areas of strength and weakness. 【0283】 "Individual learning strategy" refers to a plan that determines the optimal learning content and order based on the learning characteristics of the user. 【0284】 "Educational resources" refer to content provided to the user during learning activities, such as teaching materials and learning tools. 【0285】 "Information processing device" refers to a computer or server used to provide learning resources to the user and manage learning activities. 【0286】 "Educational program" refers to a combination of various educational resources and activities designed to achieve the learning goals of the user. 【0287】 The present invention is a system for providing an educational program customized based on the learning characteristics of individual users. The system is mainly composed of the interaction between the server, the terminal, and the user. 【0288】 Users access the educational system using their devices, enter initial information such as their name, age, and learning objectives when creating an account, and take an initial assessment test. This assessment test measures the user's learning speed and comprehension and includes multiple-choice and written questions. The device transmits this information to a server, which is a data processing unit. SSL / TLS protocol is used for this transmission to ensure data security. 【0289】 The server analyzes the received initial input information and evaluation results using a generative AI model. Specifically, it evaluates the user's learning characteristics (strengths, weaknesses, learning speed, etc.) and generates an individualized learning strategy. This generative AI model can utilize machine learning libraries such as TensorFlow and PyTorch. 【0290】 Based on the generated learning strategy, the server selects the most suitable educational resources and provides them to the user through the terminal. The terminal notifies the user of these resources and makes the learning content accessible. The user progresses through the learning materials provided on the terminal, and their progress is recorded in real time. 【0291】 For example, suppose a user aiming to improve their language skills is found to be strong in grammar but weak in listening comprehension during an initial assessment test. Similarly, based on this analysis, the server designs a curriculum that provides audio materials to strengthen listening fundamentals while also tackling applied grammar problems. 【0292】 The server analyzes learning progress data sent from the terminal at regular intervals and dynamically adjusts the learning strategy as needed. The adjusted new strategy is continuously improved to match the user's understanding and learning pace, providing optimal education. 【0293】 An example of a prompt message might be: "Based on the following profile information and evaluation results, generate a customized learning plan for the user." 【0294】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0295】 Step 1: 【0296】 Users access the educational system using a terminal and enter initial information such as their name, age, and learning objectives through an account creation screen. Next, the terminal presents an initial assessment test, which the user completes. The test consists of multiple-choice and short-answer questions to measure the user's learning speed and comprehension. The entered information and test results are transmitted from the terminal to the server. Input consists of the user's basic information and test results, while output is the data sent to the server. 【0297】 Step 2: 【0298】 The server stores the received initial input information and evaluation results in a database. Next, it analyzes this data using a generative AI model. The purpose of the analysis is to evaluate the user's learning characteristics. The input is user information and test results, and the output is evaluation data that reflects the user's learning needs. The server uses a feature extraction algorithm to extract important patterns from the data. 【0299】 Step 3: 【0300】 The server generates individualized learning strategies based on extracted evaluation data. This process involves an AI recommendation system that creates an optimal learning plan considering the user's strengths and weaknesses. The input is evaluation data, and the output is an individualized learning strategy. The server selects appropriate learning materials from a database of learning resources. 【0301】 Step 4: 【0302】 The server selects appropriate educational resources based on the generated learning strategy and sends them to the terminal. The terminal receives this and notifies the user that new teaching materials are available. The user views the teaching materials on the terminal and proceeds with the learning activities. The input is the selected educational resources, and the output is the learning content displayed on the user's terminal. 【0303】 Step 5: 【0304】 The user's learning activities are recorded in real time by the terminal and periodically sent to the server as progress data. This data includes learning time, number of correct answers, completion status of tasks, etc. The input is the record of the user's learning activities, and the output is the progress data sent to the server. 【0305】 Step 6: 【0306】 The server analyzes the progress data and dynamically adjusts the learning strategy as needed. In the adjustment, the difficulty level and order of the teaching materials are reset according to the user's progress and understanding level. The input is the progress data, and the output is the updated learning strategy. The server sends the optimized teaching material information to the terminal for the next learning session. 【0307】 (Application Example 1) 【0308】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0309】 In a conventional education system, it is difficult to provide an effective learning plan in real time according to the progress and understanding level of individual learners, and there is a lack of responsive learning support especially through home robots. Therefore, there is a need to develop a new system to maximize the learning efficiency of each learner and maintain continuous interest. 【0310】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0311】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension; means for analyzing the received information to generate an individualized learning plan; means for selecting and providing learning materials based on the generated learning plan; means for recording the user's learning progress and adjusting the learning plan based on that information; and means for providing robotic educational assistance to further optimize learning activities based on self-assessment test data. This enables the provision of plans optimized for individual learners and flexible readjustment of learning plans according to progress. 【0312】 A "user" is an individual who uses an educational system to learn. 【0313】 "Learning speed" is an indicator that shows how quickly a user can acquire specific knowledge. 【0314】 "Comprehension level" is a measure used to evaluate the degree to which a user understands educational materials. 【0315】 "Means for receiving information" refers to a method or device for receiving data regarding the user's learning speed and level of comprehension. 【0316】 "Means for analyzing and generating individual learning plans" refers to a system that analyzes received data and creates personalized learning programs based on that analysis. 【0317】 "Means of selecting and providing learning materials" refers to the technology or method of selecting appropriate educational content based on a generated learning plan and presenting it to the user. 【0318】 "Means of recording learning progress" refers to methods of tracking a user's learning process and saving their results and progress. 【0319】 "Means of providing educational assistance" refers to robotic technology that provides additional guidance and support to users based on their learning plans. 【0320】 "Self-assessment test data" refers to test result data based on evaluations conducted by the user themselves, and serves as a standard for determining learning progress and level of understanding. 【0321】 The educational delivery system based on this invention provides flexible and optimal learning plans tailored to individual learners. This system is primarily composed of the interaction between a server, terminals, and users. 【0322】 First, the user accesses the educational system using a terminal. On the terminal, an assessment test is administered to measure the user's learning speed and comprehension. This assessment data is sent from the terminal to the server. The server executes a data analysis algorithm using Python to evaluate the user's characteristics. Based on this evaluation, a personalized learning plan is generated for each user. 【0323】 Next, based on the generated learning plan, the server selects learning materials and provides them to the user via the terminal. The user then proceeds with their learning using these materials. The terminal records the user's learning activity in real time. Progress data is periodically uploaded to the server, which dynamically adjusts the learning plan based on this data. This process aims to improve the quality of learning by adjusting the difficulty level and focus of the learning materials according to the user's progress. 【0324】 Furthermore, robots can be used to provide direct educational assistance. These robots play a role in providing personalized guidance based on self-assessment test data. For example, when learning at home, the robot can improve learning effectiveness by providing intensive support on topics that children struggle with, and track their progress in more detail. 【0325】 As a concrete example, consider a scenario where a user learns mathematics at home. If an initial test determines that the user has difficulty with geometry, the server selects basic geometry materials and provides them to the user via the robot. Once the user reaches a certain progress level, the next level of materials is provided. This supports gradual learning. Using a generative AI model, the prompt "Consider an application where a home educational robot proposes a learning plan based on the user's interests and abilities, and updates the materials according to their progress" is used. 【0326】 This system aims to maximize learning efficiency and effectiveness by providing a customized learning experience tailored to the user's needs. 【0327】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0328】 Step 1: 【0329】 The terminal receives basic information and evaluation test results to create the user's learning profile and sends them to the server. Here, the initial learning speed and comprehension level are measured based on the user's input data. The output sent to the server includes the user ID, learning speed, and comprehension level. 【0330】 Step 2: 【0331】 The server analyzes the received data using Python and executes data analysis algorithms. This generates personalized learning plans that take into account the individual characteristics of each user. For example, it determines a user's strengths and weaknesses in specific subjects and designs a curriculum based on that. As output, data for each user's optimized learning plan is generated. 【0332】 Step 3: 【0333】 The server selects appropriate learning materials based on the generated learning plan and provides them to the device. Here, materials are retrieved from the optimal learning library based on each user's plan. Links to the learning materials and their content are provided as output to the device. 【0334】 Step 4: 【0335】 The user progresses through the learning materials provided via their device. In this step, the content is advanced and learning takes place through the user's actions. The user's learning progress is recorded by the device. 【0336】 Step 5: 【0337】 The device periodically uploads the user's learning progress information to the server. Here, data regarding the progress is collected and sent to the server. The output to the server includes the user's learning progress data. 【0338】 Step 6: 【0339】 The server analyzes the uploaded progress data using a generative AI model and adjusts the learning plan as needed. It evaluates changes in learning speed and comprehension through data calculations and generates a newly adjusted learning plan. The updated learning plan is then sent back to the terminal as output. 【0340】 Step 7: 【0341】 The robot provides direct educational assistance based on the user's progress and through self-assessment test data. Specifically, it provides appropriate feedback and additional learning materials to help the user in areas where they struggle. Appropriate feedback and learning support are presented as output to the user. 【0342】 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. 【0343】 This invention is a system for personalizing the user's learning experience and promoting efficient education. The system consists of a server, terminals, and an emotion engine, and dynamically analyzes the user's learning speed, comprehension, and emotions to provide an individualized learning plan. 【0344】 First, the user creates an account via their device and logs into the educational system. The device collects information about the user's learning speed and comprehension level and sends it to the server. The server uses this data to analyze and generate an initial learning plan. The learning plan includes learning materials tailored to the user's strengths and weaknesses, and the content is selected based on the user's individual needs. 【0345】 A notable feature of this system is its use of an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and uses that information to adjust the learning plan in real time. For example, if a user is feeling stressed during learning, the emotion engine detects this and sends data to the server. The server takes the emotion data into consideration and takes appropriate action, such as adjusting the difficulty level, providing relaxing materials, or recommending a break. 【0346】 For example, if a user is learning a language, the server provides a learning plan that focuses particularly on conversational skills, based on initial tests and progress data. During learning, if the emotion engine detects signs of stress in the user, the server receives input from the engine, readjusts the plan, and creates a relaxed learning environment by introducing simple listening exercises and visual learning materials. 【0347】 Furthermore, the server provides a means for users to provide feedback, which is then used to optimize future learning plans. This allows users to continuously enjoy an efficient learning experience. Emotional states are recorded and reflected in long-term learning strategies, enabling more refined and personalized education. 【0348】 In this way, the system grasps the user's learning progress and emotional state in real time and uses that information to provide optimal education. 【0349】 The following describes the processing flow. 【0350】 Step 1: 【0351】 Users log in to the educational system using their device. Upon first use, users create an account and enter basic profile information and learning objectives. 【0352】 Step 2: 【0353】 The device sends information entered by the user and the results of the initial test to the server. This allows the server to receive initial data regarding the user's learning speed and comprehension level. 【0354】 Step 3: 【0355】 The server uses an algorithm based on the received data to evaluate the user's strengths and weaknesses and generate a personalized learning plan. This plan includes recommended learning materials and a learning progression guide. 【0356】 Step 4: 【0357】 The device displays a personalized learning plan and recommended materials sent from the server to the user. The user then begins learning based on the provided plan. 【0358】 Step 5: 【0359】 During learning, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize their emotional state. This information is used to identify whether the user is learning smoothly and what kind of support they need. 【0360】 Step 6: 【0361】 The device sends emotional information obtained from the emotion engine to the server. Based on this information, the server can dynamically adjust the learning plan. 【0362】 Step 7: 【0363】 If the server detects that the user is stressed, it selects learning materials of a lower difficulty level and provides the user with materials and suggestions to help them relax through the device. 【0364】 Step 8: 【0365】 The user records their progress on their device during the learning process. The device sends this progress information to a server, which then analyzes the user's progress data. 【0366】 Step 9: 【0367】 The server considers emotional information and progress data to adjust and optimize the next learning plan. This adjustment is aimed at improving the long-term quality of learning. 【0368】 Step 10: 【0369】 Users provide feedback on completed learning content via their devices, and the server incorporates this feedback into the next plan, ensuring a continuously improved learning experience. 【0370】 (Example 2) 【0371】 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". 【0372】 Traditional education systems considered the user's learning speed and comprehension level, but they did not adequately address the impact of emotional states on learning effectiveness. Furthermore, generating and continuously adjusting individual learning plans made it difficult to provide an optimal learning experience for all users. Therefore, a system was needed to address these issues by adjusting learning plans in real time while considering the user's emotional state. 【0373】 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. 【0374】 In this invention, the server includes means for collecting information on the user's learning speed, comprehension level, and emotional state; means for using a generative AI model to analyze the received information and generate an individualized learning plan; and means for selecting and providing appropriate learning materials based on the generated learning plan. This enables dynamic adjustment of the individualized learning plan, taking into account the user's real-time emotional state. 【0375】 "User" refers to a person who uses the system to engage in learning activities. 【0376】 "Learning speed" is an indicator that shows how quickly a user progresses in their learning. 【0377】 "Comprehension level" is an indicator that shows how well the user understands the learning material. 【0378】 "Emotion" refers to data that reflects the user's emotional state, including stress levels and concentration levels during learning. 【0379】 A "generative AI model" is a computational model that uses artificial intelligence technology to analyze received data and create an optimal learning plan for the user. 【0380】 A "learning plan" refers to a plan that combines the most suitable learning materials and procedures for each individual user. 【0381】 "Learning materials" refer to the textbooks and assignments necessary for users to progress in their studies. 【0382】 "Learning progress" refers to information that shows the user's progress as they engage in learning. 【0383】 This invention provides a system that enables users to learn more effectively. Its main components include a server, a terminal, and an emotion engine. Each of these has a specific role and works together. 【0384】 Users access the system and create an account using an internet-connected device. The device is hardware that collects data on learning speed, comprehension, and emotional state through the user's learning activities. This data is sent from the device to a server for processing. 【0385】 The server uses a generative AI model to analyze the collected training data. This AI model generates a learning plan optimized for each individual user. This process involves processing a large amount of data, but it is executed efficiently by advanced algorithms. This learning plan includes appropriately customized learning materials based on the user's level of understanding and emotions. 【0386】 The emotion engine is software that analyzes the user's emotions in real time, understanding their emotional state from their facial expressions and voice. Changes in emotions detected by the emotion engine are immediately fed back to the server, which then dynamically adjusts the learning plan based on this information. This adjustment allows the system to lower the difficulty level of learning materials or provide relaxing content if the user is experiencing stress. 【0387】 As a concrete example, consider a user learning a new language using a language learning app. The device records the number of questions the user solves and their accuracy rate, while an emotion engine analyzes facial expressions to measure stress levels. The server uses this data to provide the user with an optimal learning plan. For example, it can provide specific guidance such as, "To strengthen your listening skills, please do some simple listening practice." 【0388】 Examples of prompts that can be given to the AI ​​include the following instructions: 【0389】 "Combine the user's learning data and emotional data to create a customized language learning plan." 【0390】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0391】 Step 1: 【0392】 Users create an account and log in to the system. Users access the learning platform via the internet using their device. The information entered during login includes a username and password, which are used to configure their individual learning environment. 【0393】 Step 2: 【0394】 The device collects data on the user's learning speed, comprehension level, and emotional state during learning activities. This data includes task completion time, accuracy rate, facial expressions, and voice data. The device records this data using sensors and learning management software and transmits it to a server. 【0395】 Step 3: 【0396】 The server stores the collected data in a temporary database. The server receives the training data sent from the terminal and saves it in the temporary database as a short-term record. This step involves organizing and temporarily storing the data. 【0397】 Step 4: 【0398】 The server analyzes data using a generative AI model and generates individualized learning plans. The server retrieves data from a temporary database and inputs it into the AI ​​model to generate an optimal learning plan that reflects the user's learning behavior and emotions. This output includes the selection of learning materials and adjustment of difficulty levels. 【0399】 Step 5: 【0400】 The server generates a learning plan and sends it to the device. The learning plan generated as server output is sent to the device, and specific tasks and recommended content that the user should perform are presented. 【0401】 Step 6: 【0402】 The emotion engine evaluates the user's emotions in real time during the learning process. The device uses its camera and microphone to acquire facial expression data and voice, which are then input into the emotion engine. Based on this input, the emotion engine evaluates the user's stress level and concentration level, and sends the results to the server. 【0403】 Step 7: 【0404】 The server adjusts the learning plan in real time based on data from the emotion engine. The server analyzes the received emotional data and modifies the content and progress of the learning plan as needed. This optimizes the learning process so that the user can learn at a more comfortable pace and efficiently. 【0405】 Step 8: 【0406】 The user continues learning based on a tailored learning plan and provides feedback upon completion. The device records the user's feedback and sends it to the server to help improve future learning plans. This step involves a final evaluation of the learning activity. 【0407】 (Application Example 2) 【0408】 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." 【0409】 In modern society, education is becoming individualized and is required to meet the diverse needs of learners. However, traditional learning systems have struggled to provide adaptive learning plans that take into account the user's learning speed, level of comprehension, and emotional state. Ignoring a user's emotional state while proceeding with learning can lead to decreased learning efficiency and loss of motivation. Therefore, flexible adjustments to learning plans that respond to emotional states are necessary. 【0410】 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. 【0411】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension, means for analyzing the received information to generate an individualized learning plan, and means for recognizing the user's emotional state based on their facial expressions and voice, and adaptively adjusting the learning plan using that information. This makes it possible to dynamically change the learning environment and learning materials in real time based on the user's learning progress and emotional state. 【0412】 A "user" refers to an individual user who receives educational content using the learning system. 【0413】 "Learning speed" is an indicator that shows the speed or pace at which a user progresses through the learning process. 【0414】 "Comprehension level" is an indicator that shows how accurately a user understands or grasps the learning content. 【0415】 "Means for receiving information" refers to an interface or protocol for acquiring data from a user and transmitting it to a server. 【0416】 "Individualized learning plan" refers to an educational curriculum and schedule customized for each user. 【0417】 "Emotional state" refers to the user's psychological or emotional condition and can be analyzed from facial expressions and voice. 【0418】 "Means for recognizing emotional states" refers to technologies that analyze the user's facial expressions and voice data to evaluate their current emotional state. 【0419】 "Dynamically changing the learning environment and materials in real time" refers to the process of instantly changing the learning content and materials presented based on collected data and analysis results. 【0420】 This invention is an educational delivery system designed to effectively support user learning. The system primarily consists of a server, terminals, and an emotion engine. 【0421】 The device collects data on the user's learning speed and comprehension. Using the device's camera and microphone, it also captures the user's facial expressions and voice data to gather information for determining their emotional state. This data is transmitted to the server in real time. 【0422】 The server analyzes received information on learning speed and comprehension to generate individualized learning plans. This uses a data analysis engine (e.g., TensorFlow) to select appropriate learning materials based on the user's progress. Furthermore, the server analyzes the user's emotional state using data from an emotion engine and adjusts the learning plan in real time. This adjustment process includes, for example, changing the learning content to something more relaxing if the user is experiencing stress. 【0423】 The emotion engine uses facial recognition and speech recognition technologies (e.g., Emotion API) to understand the user's emotional state. Based on this information, the server personalizes the educational content to help the user maintain an optimal learning state. 【0424】 For example, if a user experiences stress while practicing foreign language conversation, the server can provide easier practice exercises or visual aids to relax the learning environment. By using a generative AI model, user feedback can be accumulated and used to optimize future learning plans. 【0425】 An example of a prompt message is, "If the user is smiling, ask for instructions on how to arrange the learning content." Through such interactions, it is possible to create a more effective and personalized learning experience. 【0426】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0427】 Step 1: 【0428】 The device collects data on the user's learning speed and comprehension. Specifically, it captures facial expressions with its built-in camera and records voice with its microphone to acquire facial features and speech tone. The input is real-time video and audio data from the user, and an initial dataset is generated based on this data. 【0429】 Step 2: 【0430】 The device processes the collected data and sends it to the server as feature data. During this process, it uses facial recognition and voice analysis algorithms to estimate the user's emotional state and also attaches preliminary data on learning speed and comprehension. The output consists of a provisional estimate of the emotional state and an initial evaluation of the learning pace. 【0431】 Step 3: 【0432】 The server receives data sent from the terminal and generates an individualized learning plan based on it. Using a data analysis engine (e.g., TensorFlow), it analyzes the input data and generates a customized learning plan tailored to the user's characteristics. The output includes a list of specific learning materials and a learning schedule. 【0433】 Step 4: 【0434】 The server uses a generative AI model to further analyze sentiment data and adjust the learning plan in real time. Here, prompts (e.g., "If the user is smiling, ask for instructions on how to rearrange the learning content") are used to determine the optimal materials and feedback. The output is the adjusted learning content and additional lesson plans. 【0435】 Step 5: 【0436】 Users progress through their learning based on a customized learning plan provided by the server. Real-time feedback can also be provided, and this information is used to adjust the plan for future sessions. The user's progress and feedback are used as input, and progress data is updated based on this information. 【0437】 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. 【0438】 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. 【0439】 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. 【0440】 [Third Embodiment] 【0441】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0442】 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. 【0443】 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). 【0444】 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. 【0445】 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. 【0446】 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). 【0447】 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. 【0448】 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. 【0449】 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. 【0450】 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. 【0451】 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. 【0452】 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". 【0453】 The system of this invention is designed to provide customized educational programs tailored to the user's learning needs. The system mainly consists of the interaction between a server, a terminal, and the user. 【0454】 First, users access the educational system through their device and create an account. This involves entering basic profile information and taking an initial assessment test. This test is designed to understand the user's current learning speed and comprehension level. 【0455】 The terminal sends information and test results entered by the user to the server. Upon receiving this data, the server uses a data analysis algorithm to evaluate the user's characteristics and generate an individualized learning plan. 【0456】 The generated learning plan takes into account the user's strengths and weaknesses to provide an optimal learning route. Based on this plan, the server selects appropriate learning materials and provides them to the user through the device. The user can then use these materials on the device to proceed with their learning. 【0457】 User learning activities are recorded in real time by the device. The device periodically uploads this progress information to the server. The server analyzes the progress data and adjusts the learning plan as needed. This adjustment involves readjusting the content of the learning materials and the learning process to maximize the user's learning efficiency. 【0458】 As a concrete example, consider a user aiming to improve their mathematical abilities. Initial testing reveals this user is strong in algebra but weak in geometry. Based on this analysis, the server designs a curriculum that reviews the fundamentals of geometry while tackling applied algebra problems. The terminal provides this designed material and continuously monitors the user's progress, adding new challenges as needed. 【0459】 Thus, the system of the present invention aims to improve the quality of learning by providing dynamic educational programs that meet the individual needs of each user. 【0460】 The following describes the processing flow. 【0461】 Step 1: 【0462】 Users access the educational system using their devices and create accounts. They enter basic information such as their name, grade level, and subjects of interest. 【0463】 Step 2: 【0464】 The device collects information provided by the user and sends it to the server. The server receives this information and sets up the profile. 【0465】 Step 3: 【0466】 The server generates an assessment test to understand the user's current learning speed and comprehension level. The server sends the test questions to the terminal and displays them to the user. 【0467】 Step 4: 【0468】 The user answers test questions displayed on the device. Through this, the user informs the system of their academic ability. 【0469】 Step 5: 【0470】 The device records the user's responses and sends them to the server. The server analyzes the received test results to identify the user's strengths and weaknesses. 【0471】 Step 6: 【0472】 The server generates individual learning plans based on the analysis results. When creating these plans, the server selects learning materials and assignments. 【0473】 Step 7: 【0474】 The terminal displays the learning plan and materials sent from the server to the user. The user then begins learning using the displayed materials. 【0475】 Step 8: 【0476】 The user's learning activity is recorded in real time by the device. The device sends the user's progress information to the server. 【0477】 Step 9: 【0478】 The server continuously adjusts the learning plan based on progress information. This optimizes the plan to maximize the user's learning efficiency. 【0479】 Step 10: 【0480】 If a user requires feedback or additional support regarding a specific issue, the server will respond by providing individual guidance and advice. 【0481】 (Example 1) 【0482】 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." 【0483】 There is a lack of means to individually assess each user's learning speed and comprehension level, and to promote efficient and effective learning. In particular, uniform educational methods make it difficult to maximize individual abilities and learning characteristics, so there is a need to provide flexible educational programs that meet individual needs. 【0484】 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. 【0485】 In this invention, the server includes means for receiving initial input information and evaluation results from the user and transmitting them to a data processing device; means for utilizing a generated AI model based on the received information to analyze the learning characteristics of each user and generate an individualized learning strategy; and means for selecting the optimal educational resources based on the generated learning strategy and providing them via an information processing device. This makes it possible to provide an effective learning program that meets the individual needs of each user. 【0486】 "User" refers to an individual who uses an educational system to engage in learning activities. 【0487】 "Initial input information" refers to basic information such as name, age, and learning purpose that users provide when creating an account. 【0488】 "Evaluation results" refer to data obtained after the user completes the initial system test, and include numerical values ​​and indicators showing learning speed and comprehension. 【0489】 A "data processing device" refers to a computer or server used to analyze information obtained from users. 【0490】 A "generative AI model" refers to an artificial intelligence algorithm or program used to analyze a user's learning characteristics and generate an optimal learning plan. 【0491】 "Learning characteristics" refer to the individual characteristics and tendencies of a user regarding learning, including learning speed and areas of strength and weakness. 【0492】 An "individualized learning strategy" refers to a plan that determines the optimal learning content and order based on the user's learning characteristics. 【0493】 "Educational resources" refer to content provided to users when they engage in learning activities, such as teaching materials and learning tools. 【0494】 "Information processing equipment" refers to computers and servers used to provide learning resources to users and manage learning activities. 【0495】 An "educational program" refers to a combination of various educational resources and activities designed to achieve the user's learning objectives. 【0496】 This invention is a system for providing customized educational programs based on the learning characteristics of individual users. The system mainly consists of the interaction between a server, a terminal, and the user. 【0497】 Users access the educational system using their devices, enter initial information such as their name, age, and learning objectives when creating an account, and take an initial assessment test. This assessment test measures the user's learning speed and comprehension and includes multiple-choice and written questions. The device transmits this information to a server, which is a data processing unit. SSL / TLS protocol is used for this transmission to ensure data security. 【0498】 The server analyzes the received initial input information and evaluation results using a generative AI model. Specifically, it evaluates the user's learning characteristics (strengths, weaknesses, learning speed, etc.) and generates an individualized learning strategy. This generative AI model can utilize machine learning libraries such as TensorFlow and PyTorch. 【0499】 Based on the generated learning strategy, the server selects the most suitable educational resources and provides them to the user through the terminal. The terminal notifies the user of these resources and makes the learning content accessible. The user progresses through the learning materials provided on the terminal, and their progress is recorded in real time. 【0500】 For example, suppose a user aiming to improve their language skills is found to be strong in grammar but weak in listening comprehension during an initial assessment test. Similarly, based on this analysis, the server designs a curriculum that provides audio materials to strengthen listening fundamentals while also tackling applied grammar problems. 【0501】 The server analyzes learning progress data sent from the terminal at regular intervals and dynamically adjusts the learning strategy as needed. The adjusted new strategy is continuously improved to match the user's understanding and learning pace, providing optimal education. 【0502】 An example of a prompt message might be: "Based on the following profile information and evaluation results, generate a customized learning plan for the user." 【0503】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0504】 Step 1: 【0505】 Users access the educational system using a terminal and enter initial information such as their name, age, and learning objectives through an account creation screen. Next, the terminal presents an initial assessment test, which the user completes. The test consists of multiple-choice and short-answer questions to measure the user's learning speed and comprehension. The entered information and test results are transmitted from the terminal to the server. Input consists of the user's basic information and test results, while output is the data sent to the server. 【0506】 Step 2: 【0507】 The server stores the received initial input information and evaluation results in a database. Next, it analyzes this data using a generative AI model. The purpose of the analysis is to evaluate the user's learning characteristics. The input is user information and test results, and the output is evaluation data that reflects the user's learning needs. The server uses a feature extraction algorithm to extract important patterns from the data. 【0508】 Step 3: 【0509】 The server generates individualized learning strategies based on extracted evaluation data. This process involves an AI recommendation system that creates an optimal learning plan considering the user's strengths and weaknesses. The input is evaluation data, and the output is an individualized learning strategy. The server selects appropriate learning materials from a database of learning resources. 【0510】 Step 4: 【0511】 The server selects appropriate educational resources based on the generated learning strategy and sends them to the terminal. The terminal receives these resources and notifies the user that new learning materials are available. The user then views the materials on the terminal and proceeds with their learning activities. The input is the selected educational resources, and the output is the learning content displayed on the user's terminal. 【0512】 Step 5: 【0513】 User learning activities are recorded in real time by the device and periodically sent to the server as progress data. This data includes learning time, number of correct answers, and assignment completion status. Input is the user's learning activity record, and output is the progress data sent to the server. 【0514】 Step 6: 【0515】 The server analyzes progress data and dynamically adjusts the learning strategy as needed. This adjustment readjusts the difficulty level and order of learning materials based on the user's progress and understanding. The input is progress data, and the output is the updated learning strategy. The server then sends optimized learning material information to the device for the next learning session. 【0516】 (Application Example 1) 【0517】 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." 【0518】 Traditional education systems struggle to provide effective learning plans tailored to each learner's progress and level of understanding in real time, and there is a particular lack of responsive learning support, especially through home robots. Therefore, there is a need to develop new systems that maximize each learner's learning efficiency and maintain their continued interest. 【0519】 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. 【0520】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension; means for analyzing the received information to generate an individualized learning plan; means for selecting and providing learning materials based on the generated learning plan; means for recording the user's learning progress and adjusting the learning plan based on that information; and means for providing robotic educational assistance to further optimize learning activities based on self-assessment test data. This enables the provision of plans optimized for individual learners and flexible readjustment of learning plans according to progress. 【0521】 A "user" is an individual who uses an educational system to learn. 【0522】 "Learning speed" is an indicator that shows how quickly a user can acquire specific knowledge. 【0523】 "Comprehension level" is a measure used to evaluate the degree to which a user understands educational materials. 【0524】 "Means for receiving information" refers to a method or device for receiving data regarding the user's learning speed and level of comprehension. 【0525】 "Means for analyzing and generating individual learning plans" refers to a system that analyzes received data and creates personalized learning programs based on that analysis. 【0526】 "Means of selecting and providing learning materials" refers to the technology or method of selecting appropriate educational content based on a generated learning plan and presenting it to the user. 【0527】 "Means of recording learning progress" refers to methods of tracking a user's learning process and saving their results and progress. 【0528】 "Means of providing educational assistance" refers to robotic technology that provides additional guidance and support to users based on their learning plans. 【0529】 "Self-assessment test data" refers to test result data based on evaluations conducted by the user themselves, and serves as a standard for determining learning progress and level of understanding. 【0530】 The educational delivery system based on this invention provides flexible and optimal learning plans tailored to individual learners. This system is primarily composed of the interaction between a server, terminals, and users. 【0531】 First, the user accesses the educational system using a terminal. On the terminal, an assessment test is administered to measure the user's learning speed and comprehension. This assessment data is sent from the terminal to the server. The server executes a data analysis algorithm using Python to evaluate the user's characteristics. Based on this evaluation, a personalized learning plan is generated for each user. 【0532】 Next, based on the generated learning plan, the server selects learning materials and provides them to the user via the terminal. The user then proceeds with their learning using these materials. The terminal records the user's learning activity in real time. Progress data is periodically uploaded to the server, which dynamically adjusts the learning plan based on this data. This process aims to improve the quality of learning by adjusting the difficulty level and focus of the learning materials according to the user's progress. 【0533】 Furthermore, robots can be used to provide direct educational assistance. These robots play a role in providing personalized guidance based on self-assessment test data. For example, when learning at home, the robot can improve learning effectiveness by providing intensive support on topics that children struggle with, and track their progress in more detail. 【0534】 As a concrete example, consider a scenario where a user learns mathematics at home. If an initial test determines that the user has difficulty with geometry, the server selects basic geometry materials and provides them to the user via the robot. Once the user reaches a certain progress level, the next level of materials is provided. This supports gradual learning. Using a generative AI model, the prompt "Consider an application where a home educational robot proposes a learning plan based on the user's interests and abilities, and updates the materials according to their progress" is used. 【0535】 This system aims to maximize learning efficiency and effectiveness by providing a customized learning experience tailored to the user's needs. 【0536】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0537】 Step 1: 【0538】 The terminal receives basic information and evaluation test results to create the user's learning profile and sends them to the server. Here, the initial learning speed and comprehension level are measured based on the user's input data. The output sent to the server includes the user ID, learning speed, and comprehension level. 【0539】 Step 2: 【0540】 The server analyzes the received data using Python and executes data analysis algorithms. This generates personalized learning plans that take into account the individual characteristics of each user. For example, it determines a user's strengths and weaknesses in specific subjects and designs a curriculum based on that. As output, data for each user's optimized learning plan is generated. 【0541】 Step 3: 【0542】 The server selects appropriate learning materials based on the generated learning plan and provides them to the device. Here, materials are retrieved from the optimal learning library based on each user's plan. Links to the learning materials and their content are provided as output to the device. 【0543】 Step 4: 【0544】 The user progresses through the learning materials provided via their device. In this step, the content is advanced and learning takes place through the user's actions. The user's learning progress is recorded by the device. 【0545】 Step 5: 【0546】 The device periodically uploads the user's learning progress information to the server. Here, data regarding the progress is collected and sent to the server. The output to the server includes the user's learning progress data. 【0547】 Step 6: 【0548】 The server analyzes the uploaded progress data using a generative AI model and adjusts the learning plan as needed. It evaluates changes in learning speed and comprehension through data calculations and generates a newly adjusted learning plan. The updated learning plan is then sent back to the terminal as output. 【0549】 Step 7: 【0550】 The robot provides direct educational assistance based on the user's progress and through self-assessment test data. Specifically, it provides appropriate feedback and additional learning materials to help the user in areas where they struggle. Appropriate feedback and learning support are presented as output to the user. 【0551】 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. 【0552】 This invention is a system for personalizing the user's learning experience and promoting efficient education. The system consists of a server, terminals, and an emotion engine, and dynamically analyzes the user's learning speed, comprehension, and emotions to provide an individualized learning plan. 【0553】 First, the user creates an account via their device and logs into the educational system. The device collects information about the user's learning speed and comprehension level and sends it to the server. The server uses this data to analyze and generate an initial learning plan. The learning plan includes learning materials tailored to the user's strengths and weaknesses, and the content is selected based on the user's individual needs. 【0554】 A notable feature of this system is its use of an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and uses that information to adjust the learning plan in real time. For example, if a user is feeling stressed during learning, the emotion engine detects this and sends data to the server. The server takes the emotion data into consideration and takes appropriate action, such as adjusting the difficulty level, providing relaxing materials, or recommending a break. 【0555】 For example, if a user is learning a language, the server provides a learning plan that focuses particularly on conversational skills, based on initial tests and progress data. During learning, if the emotion engine detects signs of stress in the user, the server receives input from the engine, readjusts the plan, and creates a relaxed learning environment by introducing simple listening exercises and visual learning materials. 【0556】 Furthermore, the server provides a means for users to provide feedback, which is then used to optimize future learning plans. This allows users to continuously enjoy an efficient learning experience. Emotional states are recorded and reflected in long-term learning strategies, enabling more refined and personalized education. 【0557】 In this way, the system grasps the user's learning progress and emotional state in real time and uses that information to provide optimal education. 【0558】 The following describes the processing flow. 【0559】 Step 1: 【0560】 Users log in to the educational system using their device. Upon first use, users create an account and enter basic profile information and learning objectives. 【0561】 Step 2: 【0562】 The device sends information entered by the user and the results of the initial test to the server. This allows the server to receive initial data regarding the user's learning speed and comprehension level. 【0563】 Step 3: 【0564】 The server uses an algorithm based on the received data to evaluate the user's strengths and weaknesses and generate a personalized learning plan. This plan includes recommended learning materials and a learning progression guide. 【0565】 Step 4: 【0566】 The device displays a personalized learning plan and recommended materials sent from the server to the user. The user then begins learning based on the provided plan. 【0567】 Step 5: 【0568】 During learning, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize their emotional state. This information is used to identify whether the user is learning smoothly and what kind of support they need. 【0569】 Step 6: 【0570】 The device sends emotional information obtained from the emotion engine to the server. Based on this information, the server can dynamically adjust the learning plan. 【0571】 Step 7: 【0572】 If the server detects that the user is stressed, it selects learning materials of a lower difficulty level and provides the user with materials and suggestions to help them relax through the device. 【0573】 Step 8: 【0574】 The user records their progress on their device during the learning process. The device sends this progress information to a server, which then analyzes the user's progress data. 【0575】 Step 9: 【0576】 The server considers emotional information and progress data to adjust and optimize the next learning plan. This adjustment is aimed at improving the long-term quality of learning. 【0577】 Step 10: 【0578】 Users provide feedback on completed learning content via their devices, and the server incorporates this feedback into the next plan, ensuring a continuously improved learning experience. 【0579】 (Example 2) 【0580】 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." 【0581】 Traditional education systems considered the user's learning speed and comprehension level, but they did not adequately address the impact of emotional states on learning effectiveness. Furthermore, generating and continuously adjusting individual learning plans made it difficult to provide an optimal learning experience for all users. Therefore, a system was needed to address these issues by adjusting learning plans in real time while considering the user's emotional state. 【0582】 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. 【0583】 In this invention, the server includes means for collecting information on the user's learning speed, comprehension level, and emotional state; means for using a generative AI model to analyze the received information and generate an individualized learning plan; and means for selecting and providing appropriate learning materials based on the generated learning plan. This enables dynamic adjustment of the individualized learning plan, taking into account the user's real-time emotional state. 【0584】 "User" refers to a person who uses the system to engage in learning activities. 【0585】 "Learning speed" is an indicator that shows how quickly a user progresses in their learning. 【0586】 "Comprehension level" is an indicator that shows how well the user understands the learning material. 【0587】 "Emotion" refers to data that reflects the user's emotional state, including stress levels and concentration levels during learning. 【0588】 A "generative AI model" is a computational model that uses artificial intelligence technology to analyze received data and create an optimal learning plan for the user. 【0589】 A "learning plan" refers to a plan that combines the most suitable learning materials and procedures for each individual user. 【0590】 "Learning materials" refer to the textbooks and assignments necessary for users to progress in their studies. 【0591】 "Learning progress" refers to information that shows the user's progress as they engage in learning. 【0592】 This invention provides a system that enables users to learn more effectively. Its main components include a server, a terminal, and an emotion engine. Each of these has a specific role and works together. 【0593】 Users access the system and create an account using an internet-connected device. The device is hardware that collects data on learning speed, comprehension, and emotional state through the user's learning activities. This data is sent from the device to a server for processing. 【0594】 The server uses a generative AI model to analyze the collected training data. This AI model generates a learning plan optimized for each individual user. This process involves processing a large amount of data, but it is executed efficiently by advanced algorithms. This learning plan includes appropriately customized learning materials based on the user's level of understanding and emotions. 【0595】 The emotion engine is software that analyzes the user's emotions in real time, understanding their emotional state from their facial expressions and voice. Changes in emotions detected by the emotion engine are immediately fed back to the server, which then dynamically adjusts the learning plan based on this information. This adjustment allows the system to lower the difficulty level of learning materials or provide relaxing content if the user is experiencing stress. 【0596】 As a concrete example, consider a user learning a new language using a language learning app. The device records the number of questions the user solves and their accuracy rate, while an emotion engine analyzes facial expressions to measure stress levels. The server uses this data to provide the user with an optimal learning plan. For example, it can provide specific guidance such as, "To strengthen your listening skills, please do some simple listening practice." 【0597】 Examples of prompts that can be given to the AI ​​include the following instructions: 【0598】 "Combine the user's learning data and emotional data to create a customized language learning plan." 【0599】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0600】 Step 1: 【0601】 Users create an account and log in to the system. Users access the learning platform via the internet using their device. The information entered during login includes a username and password, which are used to configure their individual learning environment. 【0602】 Step 2: 【0603】 The device collects data on the user's learning speed, comprehension level, and emotional state during learning activities. This data includes task completion time, accuracy rate, facial expressions, and voice data. The device records this data using sensors and learning management software and transmits it to a server. 【0604】 Step 3: 【0605】 The server stores the collected data in a temporary database. The server receives the training data sent from the terminal and saves it in the temporary database as a short-term record. This step involves organizing and temporarily storing the data. 【0606】 Step 4: 【0607】 The server analyzes data using a generative AI model and generates individualized learning plans. The server retrieves data from a temporary database and inputs it into the AI ​​model to generate an optimal learning plan that reflects the user's learning behavior and emotions. This output includes the selection of learning materials and adjustment of difficulty levels. 【0608】 Step 5: 【0609】 The server generates a learning plan and sends it to the device. The learning plan generated as server output is sent to the device, and specific tasks and recommended content that the user should perform are presented. 【0610】 Step 6: 【0611】 The emotion engine evaluates the user's emotions in real time during the learning process. The device uses its camera and microphone to acquire facial expression data and voice, which are then input into the emotion engine. Based on this input, the emotion engine evaluates the user's stress level and concentration level, and sends the results to the server. 【0612】 Step 7: 【0613】 The server adjusts the learning plan in real time based on data from the emotion engine. The server analyzes the received emotional data and modifies the content and progress of the learning plan as needed. This optimizes the learning process so that the user can learn at a more comfortable pace and efficiently. 【0614】 Step 8: 【0615】 The user continues learning based on a tailored learning plan and provides feedback upon completion. The device records the user's feedback and sends it to the server to help improve future learning plans. This step involves a final evaluation of the learning activity. 【0616】 (Application Example 2) 【0617】 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." 【0618】 In modern society, education is becoming individualized and is required to meet the diverse needs of learners. However, traditional learning systems have struggled to provide adaptive learning plans that take into account the user's learning speed, level of comprehension, and emotional state. Ignoring a user's emotional state while proceeding with learning can lead to decreased learning efficiency and loss of motivation. Therefore, flexible adjustments to learning plans that respond to emotional states are necessary. 【0619】 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. 【0620】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension, means for analyzing the received information to generate an individualized learning plan, and means for recognizing the user's emotional state based on their facial expressions and voice, and adaptively adjusting the learning plan using that information. This makes it possible to dynamically change the learning environment and learning materials in real time based on the user's learning progress and emotional state. 【0621】 A "user" refers to an individual user who receives educational content using the learning system. 【0622】 "Learning speed" is an indicator that shows the speed or pace at which a user progresses through the learning process. 【0623】 "Comprehension level" is an indicator that shows how accurately a user understands or grasps the learning content. 【0624】 "Means for receiving information" refers to an interface or protocol for acquiring data from a user and transmitting it to a server. 【0625】 "Individualized learning plan" refers to an educational curriculum and schedule customized for each user. 【0626】 "Emotional state" refers to the user's psychological or emotional condition and can be analyzed from facial expressions and voice. 【0627】 "Means for recognizing emotional states" refers to technologies that analyze the user's facial expressions and voice data to evaluate their current emotional state. 【0628】 "Dynamically changing the learning environment and materials in real time" refers to the process of instantly changing the learning content and materials presented based on collected data and analysis results. 【0629】 This invention is an educational delivery system designed to effectively support user learning. The system primarily consists of a server, terminals, and an emotion engine. 【0630】 The device collects data on the user's learning speed and comprehension. Using the device's camera and microphone, it also captures the user's facial expressions and voice data to gather information for determining their emotional state. This data is transmitted to the server in real time. 【0631】 The server analyzes received information on learning speed and comprehension to generate individualized learning plans. This uses a data analysis engine (e.g., TensorFlow) to select appropriate learning materials based on the user's progress. Furthermore, the server analyzes the user's emotional state using data from an emotion engine and adjusts the learning plan in real time. This adjustment process includes, for example, changing the learning content to something more relaxing if the user is experiencing stress. 【0632】 The emotion engine uses facial recognition and speech recognition technologies (e.g., Emotion API) to understand the user's emotional state. Based on this information, the server personalizes the educational content to help the user maintain an optimal learning state. 【0633】 For example, if a user experiences stress while practicing foreign language conversation, the server can provide easier practice exercises or visual aids to relax the learning environment. By using a generative AI model, user feedback can be accumulated and used to optimize future learning plans. 【0634】 An example of a prompt message is, "If the user is smiling, ask for instructions on how to arrange the learning content." Through such interactions, it is possible to create a more effective and personalized learning experience. 【0635】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0636】 Step 1: 【0637】 The device collects data on the user's learning speed and comprehension. Specifically, it captures facial expressions with its built-in camera and records voice with its microphone to acquire facial features and speech tone. The input is real-time video and audio data from the user, and an initial dataset is generated based on this data. 【0638】 Step 2: 【0639】 The device processes the collected data and sends it to the server as feature data. During this process, it uses facial recognition and voice analysis algorithms to estimate the user's emotional state and also attaches preliminary data on learning speed and comprehension. The output consists of a provisional estimate of the emotional state and an initial evaluation of the learning pace. 【0640】 Step 3: 【0641】 The server receives data sent from the terminal and generates an individualized learning plan based on it. Using a data analysis engine (e.g., TensorFlow), it analyzes the input data and generates a customized learning plan tailored to the user's characteristics. The output includes a list of specific learning materials and a learning schedule. 【0642】 Step 4: 【0643】 The server uses a generative AI model to further analyze sentiment data and adjust the learning plan in real time. Here, prompts (e.g., "If the user is smiling, ask for instructions on how to rearrange the learning content") are used to determine the optimal materials and feedback. The output is the adjusted learning content and additional lesson plans. 【0644】 Step 5: 【0645】 Users progress through their learning based on a customized learning plan provided by the server. Real-time feedback can also be provided, and this information is used to adjust the plan for future sessions. The user's progress and feedback are used as input, and progress data is updated based on this information. 【0646】 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. 【0647】 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. 【0648】 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. 【0649】 [Fourth Embodiment] 【0650】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0651】 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. 【0652】 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). 【0653】 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. 【0654】 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. 【0655】 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). 【0656】 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. 【0657】 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. 【0658】 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. 【0659】 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. 【0660】 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. 【0661】 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. 【0662】 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". 【0663】 The system of this invention is designed to provide customized educational programs tailored to the user's learning needs. The system mainly consists of the interaction between a server, a terminal, and the user. 【0664】 First, users access the educational system through their device and create an account. This involves entering basic profile information and taking an initial assessment test. This test is designed to understand the user's current learning speed and comprehension level. 【0665】 The terminal sends information and test results entered by the user to the server. Upon receiving this data, the server uses a data analysis algorithm to evaluate the user's characteristics and generate an individualized learning plan. 【0666】 The generated learning plan takes into account the user's strengths and weaknesses to provide an optimal learning route. Based on this plan, the server selects appropriate learning materials and provides them to the user through the device. The user can then use these materials on the device to proceed with their learning. 【0667】 User learning activities are recorded in real time by the device. The device periodically uploads this progress information to the server. The server analyzes the progress data and adjusts the learning plan as needed. This adjustment involves readjusting the content of the learning materials and the learning process to maximize the user's learning efficiency. 【0668】 As a concrete example, consider a user aiming to improve their mathematical abilities. Initial testing reveals this user is strong in algebra but weak in geometry. Based on this analysis, the server designs a curriculum that reviews the fundamentals of geometry while tackling applied algebra problems. The terminal provides this designed material and continuously monitors the user's progress, adding new challenges as needed. 【0669】 Thus, the system of the present invention aims to improve the quality of learning by providing dynamic educational programs that meet the individual needs of each user. 【0670】 The following describes the processing flow. 【0671】 Step 1: 【0672】 Users access the educational system using their devices and create accounts. They enter basic information such as their name, grade level, and subjects of interest. 【0673】 Step 2: 【0674】 The device collects information provided by the user and sends it to the server. The server receives this information and sets up the profile. 【0675】 Step 3: 【0676】 The server generates an assessment test to understand the user's current learning speed and comprehension level. The server sends the test questions to the terminal and displays them to the user. 【0677】 Step 4: 【0678】 The user answers test questions displayed on the device. Through this, the user informs the system of their academic ability. 【0679】 Step 5: 【0680】 The device records the user's responses and sends them to the server. The server analyzes the received test results to identify the user's strengths and weaknesses. 【0681】 Step 6: 【0682】 The server generates individual learning plans based on the analysis results. When creating these plans, the server selects learning materials and assignments. 【0683】 Step 7: 【0684】 The terminal displays the learning plan and materials sent from the server to the user. The user then begins learning using the displayed materials. 【0685】 Step 8: 【0686】 The user's learning activity is recorded in real time by the device. The device sends the user's progress information to the server. 【0687】 Step 9: 【0688】 The server continuously adjusts the learning plan based on progress information. This optimizes the plan to maximize the user's learning efficiency. 【0689】 Step 10: 【0690】 If a user requires feedback or additional support regarding a specific issue, the server will respond by providing individual guidance and advice. 【0691】 (Example 1) 【0692】 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". 【0693】 There is a lack of means to individually assess each user's learning speed and comprehension level, and to promote efficient and effective learning. In particular, uniform educational methods make it difficult to maximize individual abilities and learning characteristics, so there is a need to provide flexible educational programs that meet individual needs. 【0694】 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. 【0695】 In this invention, the server includes means for receiving initial input information and evaluation results from the user and transmitting them to a data processing device; means for utilizing a generated AI model based on the received information to analyze the learning characteristics of each user and generate an individualized learning strategy; and means for selecting the optimal educational resources based on the generated learning strategy and providing them via an information processing device. This makes it possible to provide an effective learning program that meets the individual needs of each user. 【0696】 "User" refers to an individual who uses an educational system to engage in learning activities. 【0697】 "Initial input information" refers to basic information such as name, age, and learning purpose that users provide when creating an account. 【0698】 "Evaluation results" refer to data obtained after the user completes the initial system test, and include numerical values ​​and indicators showing learning speed and comprehension. 【0699】 A "data processing device" refers to a computer or server used to analyze information obtained from users. 【0700】 A "generative AI model" refers to an artificial intelligence algorithm or program used to analyze a user's learning characteristics and generate an optimal learning plan. 【0701】 "Learning characteristics" refer to the individual characteristics and tendencies of a user regarding learning, including learning speed and areas of strength and weakness. 【0702】 An "individualized learning strategy" refers to a plan that determines the optimal learning content and order based on the user's learning characteristics. 【0703】 "Educational resources" refer to content provided to users when they engage in learning activities, such as teaching materials and learning tools. 【0704】 "Information processing equipment" refers to computers and servers used to provide learning resources to users and manage learning activities. 【0705】 An "educational program" refers to a combination of various educational resources and activities designed to achieve the user's learning objectives. 【0706】 This invention is a system for providing customized educational programs based on the learning characteristics of individual users. The system mainly consists of the interaction between a server, a terminal, and the user. 【0707】 Users access the educational system using their devices, enter initial information such as their name, age, and learning objectives when creating an account, and take an initial assessment test. This assessment test measures the user's learning speed and comprehension and includes multiple-choice and written questions. The device transmits this information to a server, which is a data processing unit. SSL / TLS protocol is used for this transmission to ensure data security. 【0708】 The server analyzes the received initial input information and evaluation results using a generative AI model. Specifically, it evaluates the user's learning characteristics (strengths, weaknesses, learning speed, etc.) and generates an individualized learning strategy. This generative AI model can utilize machine learning libraries such as TensorFlow and PyTorch. 【0709】 Based on the generated learning strategy, the server selects the most suitable educational resources and provides them to the user through the terminal. The terminal notifies the user of these resources and makes the learning content accessible. The user progresses through the learning materials provided on the terminal, and their progress is recorded in real time. 【0710】 For example, suppose a user aiming to improve their language skills is found to be strong in grammar but weak in listening comprehension during an initial assessment test. Similarly, based on this analysis, the server designs a curriculum that provides audio materials to strengthen listening fundamentals while also tackling applied grammar problems. 【0711】 The server analyzes learning progress data sent from the terminal at regular intervals and dynamically adjusts the learning strategy as needed. The adjusted new strategy is continuously improved to match the user's understanding and learning pace, providing optimal education. 【0712】 An example of a prompt message might be: "Based on the following profile information and evaluation results, generate a customized learning plan for the user." 【0713】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0714】 Step 1: 【0715】 Users access the educational system using a terminal and enter initial information such as their name, age, and learning objectives through an account creation screen. Next, the terminal presents an initial assessment test, which the user completes. The test consists of multiple-choice and short-answer questions to measure the user's learning speed and comprehension. The entered information and test results are transmitted from the terminal to the server. Input consists of the user's basic information and test results, while output is the data sent to the server. 【0716】 Step 2: 【0717】 The server stores the received initial input information and evaluation results in a database. Next, it analyzes this data using a generative AI model. The purpose of the analysis is to evaluate the user's learning characteristics. The input is user information and test results, and the output is evaluation data that reflects the user's learning needs. The server uses a feature extraction algorithm to extract important patterns from the data. 【0718】 Step 3: 【0719】 The server generates individualized learning strategies based on extracted evaluation data. This process involves an AI recommendation system that creates an optimal learning plan considering the user's strengths and weaknesses. The input is evaluation data, and the output is an individualized learning strategy. The server selects appropriate learning materials from a database of learning resources. 【0720】 Step 4: 【0721】 The server selects appropriate educational resources based on the generated learning strategy and sends them to the terminal. The terminal receives these resources and notifies the user that new learning materials are available. The user then views the materials on the terminal and proceeds with their learning activities. The input is the selected educational resources, and the output is the learning content displayed on the user's terminal. 【0722】 Step 5: 【0723】 User learning activities are recorded in real time by the device and periodically sent to the server as progress data. This data includes learning time, number of correct answers, and assignment completion status. Input is the user's learning activity record, and output is the progress data sent to the server. 【0724】 Step 6: 【0725】 The server analyzes progress data and dynamically adjusts the learning strategy as needed. This adjustment readjusts the difficulty level and order of learning materials based on the user's progress and understanding. The input is progress data, and the output is the updated learning strategy. The server then sends optimized learning material information to the device for the next learning session. 【0726】 (Application Example 1) 【0727】 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". 【0728】 Traditional education systems struggle to provide effective learning plans tailored to each learner's progress and level of understanding in real time, and there is a particular lack of responsive learning support, especially through home robots. Therefore, there is a need to develop new systems that maximize each learner's learning efficiency and maintain their continued interest. 【0729】 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. 【0730】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension; means for analyzing the received information to generate an individualized learning plan; means for selecting and providing learning materials based on the generated learning plan; means for recording the user's learning progress and adjusting the learning plan based on that information; and means for providing robotic educational assistance to further optimize learning activities based on self-assessment test data. This enables the provision of plans optimized for individual learners and flexible readjustment of learning plans according to progress. 【0731】 A "user" is an individual who uses an educational system to learn. 【0732】 "Learning speed" is an indicator that shows how quickly a user can acquire specific knowledge. 【0733】 "Comprehension level" is a measure used to evaluate the degree to which a user understands educational materials. 【0734】 "Means for receiving information" refers to a method or device for receiving data regarding the user's learning speed and level of comprehension. 【0735】 "Means for analyzing and generating individual learning plans" refers to a system that analyzes received data and creates personalized learning programs based on that analysis. 【0736】 "Means of selecting and providing learning materials" refers to the technology or method of selecting appropriate educational content based on a generated learning plan and presenting it to the user. 【0737】 "Means of recording learning progress" refers to methods of tracking a user's learning process and saving their results and progress. 【0738】 "Means of providing educational assistance" refers to robotic technology that provides additional guidance and support to users based on their learning plans. 【0739】 "Self-assessment test data" refers to test result data based on evaluations conducted by the user themselves, and serves as a standard for determining learning progress and level of understanding. 【0740】 The educational delivery system based on this invention provides flexible and optimal learning plans tailored to individual learners. This system is primarily composed of the interaction between a server, terminals, and users. 【0741】 First, the user accesses the educational system using a terminal. On the terminal, an assessment test is administered to measure the user's learning speed and comprehension. This assessment data is sent from the terminal to the server. The server executes a data analysis algorithm using Python to evaluate the user's characteristics. Based on this evaluation, a personalized learning plan is generated for each user. 【0742】 Next, based on the generated learning plan, the server selects learning materials and provides them to the user via the terminal. The user then proceeds with their learning using these materials. The terminal records the user's learning activity in real time. Progress data is periodically uploaded to the server, which dynamically adjusts the learning plan based on this data. This process aims to improve the quality of learning by adjusting the difficulty level and focus of the learning materials according to the user's progress. 【0743】 Furthermore, robots can be used to provide direct educational assistance. These robots play a role in providing personalized guidance based on self-assessment test data. For example, when learning at home, the robot can improve learning effectiveness by providing intensive support on topics that children struggle with, and track their progress in more detail. 【0744】 As a concrete example, consider a scenario where a user learns mathematics at home. If an initial test determines that the user has difficulty with geometry, the server selects basic geometry materials and provides them to the user via the robot. Once the user reaches a certain progress level, the next level of materials is provided. This supports gradual learning. Using a generative AI model, the prompt "Consider an application where a home educational robot proposes a learning plan based on the user's interests and abilities, and updates the materials according to their progress" is used. 【0745】 This system aims to maximize learning efficiency and effectiveness by providing a customized learning experience tailored to the user's needs. 【0746】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0747】 Step 1: 【0748】 The terminal receives basic information and evaluation test results to create the user's learning profile and sends them to the server. Here, the initial learning speed and comprehension level are measured based on the user's input data. The output sent to the server includes the user ID, learning speed, and comprehension level. 【0749】 Step 2: 【0750】 The server analyzes the received data using Python and executes data analysis algorithms. This generates personalized learning plans that take into account the individual characteristics of each user. For example, it determines a user's strengths and weaknesses in specific subjects and designs a curriculum based on that. As output, data for each user's optimized learning plan is generated. 【0751】 Step 3: 【0752】 The server selects appropriate learning materials based on the generated learning plan and provides them to the device. Here, materials are retrieved from the optimal learning library based on each user's plan. Links to the learning materials and their content are provided as output to the device. 【0753】 Step 4: 【0754】 The user progresses through the learning materials provided via their device. In this step, the content is advanced and learning takes place through the user's actions. The user's learning progress is recorded by the device. 【0755】 Step 5: 【0756】 The device periodically uploads the user's learning progress information to the server. Here, data regarding the progress is collected and sent to the server. The output to the server includes the user's learning progress data. 【0757】 Step 6: 【0758】 The server analyzes the uploaded progress data using a generative AI model and adjusts the learning plan as needed. It evaluates changes in learning speed and comprehension through data calculations and generates a newly adjusted learning plan. The updated learning plan is then sent back to the terminal as output. 【0759】 Step 7: 【0760】 The robot provides direct educational assistance based on the user's progress and through self-assessment test data. Specifically, it provides appropriate feedback and additional learning materials to help the user in areas where they struggle. Appropriate feedback and learning support are presented as output to the user. 【0761】 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. 【0762】 This invention is a system for personalizing the user's learning experience and promoting efficient education. The system consists of a server, terminals, and an emotion engine, and dynamically analyzes the user's learning speed, comprehension, and emotions to provide an individualized learning plan. 【0763】 First, the user creates an account via their device and logs into the educational system. The device collects information about the user's learning speed and comprehension level and sends it to the server. The server uses this data to analyze and generate an initial learning plan. The learning plan includes learning materials tailored to the user's strengths and weaknesses, and the content is selected based on the user's individual needs. 【0764】 A notable feature of this system is its use of an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and uses that information to adjust the learning plan in real time. For example, if a user is feeling stressed during learning, the emotion engine detects this and sends data to the server. The server takes the emotion data into consideration and takes appropriate action, such as adjusting the difficulty level, providing relaxing materials, or recommending a break. 【0765】 For example, if a user is learning a language, the server provides a learning plan that focuses particularly on conversational skills, based on initial tests and progress data. During learning, if the emotion engine detects signs of stress in the user, the server receives input from the engine, readjusts the plan, and creates a relaxed learning environment by introducing simple listening exercises and visual learning materials. 【0766】 Furthermore, the server provides a means for users to provide feedback, which is then used to optimize future learning plans. This allows users to continuously enjoy an efficient learning experience. Emotional states are recorded and reflected in long-term learning strategies, enabling more refined and personalized education. 【0767】 In this way, the system grasps the user's learning progress and emotional state in real time and uses that information to provide optimal education. 【0768】 The following describes the processing flow. 【0769】 Step 1: 【0770】 Users log in to the educational system using their device. Upon first use, users create an account and enter basic profile information and learning objectives. 【0771】 Step 2: 【0772】 The device sends information entered by the user and the results of the initial test to the server. This allows the server to receive initial data regarding the user's learning speed and comprehension level. 【0773】 Step 3: 【0774】 The server uses an algorithm based on the received data to evaluate the user's strengths and weaknesses and generate a personalized learning plan. This plan includes recommended learning materials and a learning progression guide. 【0775】 Step 4: 【0776】 The device displays a personalized learning plan and recommended materials sent from the server to the user. The user then begins learning based on the provided plan. 【0777】 Step 5: 【0778】 During learning, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize their emotional state. This information is used to identify whether the user is learning smoothly and what kind of support they need. 【0779】 Step 6: 【0780】 The device sends emotional information obtained from the emotion engine to the server. Based on this information, the server can dynamically adjust the learning plan. 【0781】 Step 7: 【0782】 If the server detects that the user is stressed, it selects learning materials of a lower difficulty level and provides the user with materials and suggestions to help them relax through the device. 【0783】 Step 8: 【0784】 The user records their progress on their device during the learning process. The device sends this progress information to a server, which then analyzes the user's progress data. 【0785】 Step 9: 【0786】 The server considers emotional information and progress data to adjust and optimize the next learning plan. This adjustment is aimed at improving the long-term quality of learning. 【0787】 Step 10: 【0788】 Users provide feedback on completed learning content via their devices, and the server incorporates this feedback into the next plan, ensuring a continuously improved learning experience. 【0789】 (Example 2) 【0790】 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". 【0791】 Traditional education systems considered the user's learning speed and comprehension level, but they did not adequately address the impact of emotional states on learning effectiveness. Furthermore, generating and continuously adjusting individual learning plans made it difficult to provide an optimal learning experience for all users. Therefore, a system was needed to address these issues by adjusting learning plans in real time while considering the user's emotional state. 【0792】 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. 【0793】 In this invention, the server includes means for collecting information on the user's learning speed, comprehension level, and emotional state; means for using a generative AI model to analyze the received information and generate an individualized learning plan; and means for selecting and providing appropriate learning materials based on the generated learning plan. This enables dynamic adjustment of the individualized learning plan, taking into account the user's real-time emotional state. 【0794】 "User" refers to a person who uses the system to engage in learning activities. 【0795】 "Learning speed" is an indicator that shows how quickly a user progresses in their learning. 【0796】 "Comprehension level" is an indicator that shows how well the user understands the learning material. 【0797】 "Emotion" refers to data that reflects the user's emotional state, including stress levels and concentration levels during learning. 【0798】 A "generative AI model" is a computational model that uses artificial intelligence technology to analyze received data and create an optimal learning plan for the user. 【0799】 A "learning plan" refers to a plan that combines the most suitable learning materials and procedures for each individual user. 【0800】 "Learning materials" refer to the textbooks and assignments necessary for users to progress in their studies. 【0801】 "Learning progress" refers to information that shows the user's progress as they engage in learning. 【0802】 This invention provides a system that enables users to learn more effectively. Its main components include a server, a terminal, and an emotion engine. Each of these has a specific role and works together. 【0803】 Users access the system and create an account using an internet-connected device. The device is hardware that collects data on learning speed, comprehension, and emotional state through the user's learning activities. This data is sent from the device to a server for processing. 【0804】 The server uses a generative AI model to analyze the collected training data. This AI model generates a learning plan optimized for each individual user. This process involves processing a large amount of data, but it is executed efficiently by advanced algorithms. This learning plan includes appropriately customized learning materials based on the user's level of understanding and emotions. 【0805】 The emotion engine is software that analyzes the user's emotions in real time, understanding their emotional state from their facial expressions and voice. Changes in emotions detected by the emotion engine are immediately fed back to the server, which then dynamically adjusts the learning plan based on this information. This adjustment allows the system to lower the difficulty level of learning materials or provide relaxing content if the user is experiencing stress. 【0806】 As a concrete example, consider a user learning a new language using a language learning app. The device records the number of questions the user solves and their accuracy rate, while an emotion engine analyzes facial expressions to measure stress levels. The server uses this data to provide the user with an optimal learning plan. For example, it can provide specific guidance such as, "To strengthen your listening skills, please do some simple listening practice." 【0807】 Examples of prompts that can be given to the AI ​​include the following instructions: 【0808】 "Combine the user's learning data and emotional data to create a customized language learning plan." 【0809】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0810】 Step 1: 【0811】 Users create an account and log in to the system. Users access the learning platform via the internet using their device. The information entered during login includes a username and password, which are used to configure their individual learning environment. 【0812】 Step 2: 【0813】 The device collects data on the user's learning speed, comprehension level, and emotional state during learning activities. This data includes task completion time, accuracy rate, facial expressions, and voice data. The device records this data using sensors and learning management software and transmits it to a server. 【0814】 Step 3: 【0815】 The server stores the collected data in a temporary database. The server receives the training data sent from the terminal and saves it in the temporary database as a short-term record. This step involves organizing and temporarily storing the data. 【0816】 Step 4: 【0817】 The server analyzes data using a generative AI model and generates individualized learning plans. The server retrieves data from a temporary database and inputs it into the AI ​​model to generate an optimal learning plan that reflects the user's learning behavior and emotions. This output includes the selection of learning materials and adjustment of difficulty levels. 【0818】 Step 5: 【0819】 The server generates a learning plan and sends it to the device. The learning plan generated as server output is sent to the device, and specific tasks and recommended content that the user should perform are presented. 【0820】 Step 6: 【0821】 The emotion engine evaluates the user's emotions in real time during the learning process. The device uses its camera and microphone to acquire facial expression data and voice, which are then input into the emotion engine. Based on this input, the emotion engine evaluates the user's stress level and concentration level, and sends the results to the server. 【0822】 Step 7: 【0823】 The server adjusts the learning plan in real time based on data from the emotion engine. The server analyzes the received emotional data and modifies the content and progress of the learning plan as needed. This optimizes the learning process so that the user can learn at a more comfortable pace and efficiently. 【0824】 Step 8: 【0825】 The user continues learning based on a tailored learning plan and provides feedback upon completion. The device records the user's feedback and sends it to the server to help improve future learning plans. This step involves a final evaluation of the learning activity. 【0826】 (Application Example 2) 【0827】 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". 【0828】 In modern society, education is becoming individualized and is required to meet the diverse needs of learners. However, traditional learning systems have struggled to provide adaptive learning plans that take into account the user's learning speed, level of comprehension, and emotional state. Ignoring a user's emotional state while proceeding with learning can lead to decreased learning efficiency and loss of motivation. Therefore, flexible adjustments to learning plans that respond to emotional states are necessary. 【0829】 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. 【0830】 In this invention, the server includes means for receiving information on the user's learning speed and level of comprehension, means for analyzing the received information to generate an individualized learning plan, and means for recognizing the user's emotional state based on their facial expressions and voice, and adaptively adjusting the learning plan using that information. This makes it possible to dynamically change the learning environment and learning materials in real time based on the user's learning progress and emotional state. 【0831】 A "user" refers to an individual user who receives educational content using the learning system. 【0832】 "Learning speed" is an indicator that shows the speed or pace at which a user progresses through the learning process. 【0833】 "Comprehension level" is an indicator that shows how accurately a user understands or grasps the learning content. 【0834】 "Means for receiving information" refers to an interface or protocol for acquiring data from a user and transmitting it to a server. 【0835】 "Individualized learning plan" refers to an educational curriculum and schedule customized for each user. 【0836】 "Emotional state" refers to the user's psychological or emotional condition and can be analyzed from facial expressions and voice. 【0837】 "Means for recognizing emotional states" refers to technologies that analyze the user's facial expressions and voice data to evaluate their current emotional state. 【0838】 "Dynamically changing the learning environment and materials in real time" refers to the process of instantly changing the learning content and materials presented based on collected data and analysis results. 【0839】 This invention is an educational delivery system designed to effectively support user learning. The system primarily consists of a server, terminals, and an emotion engine. 【0840】 The device collects data on the user's learning speed and comprehension. Using the device's camera and microphone, it also captures the user's facial expressions and voice data to gather information for determining their emotional state. This data is transmitted to the server in real time. 【0841】 The server analyzes received information on learning speed and comprehension to generate individualized learning plans. This uses a data analysis engine (e.g., TensorFlow) to select appropriate learning materials based on the user's progress. Furthermore, the server analyzes the user's emotional state using data from an emotion engine and adjusts the learning plan in real time. This adjustment process includes, for example, changing the learning content to something more relaxing if the user is experiencing stress. 【0842】 The emotion engine uses facial recognition and speech recognition technologies (e.g., Emotion API) to understand the user's emotional state. Based on this information, the server personalizes the educational content to help the user maintain an optimal learning state. 【0843】 For example, if a user experiences stress while practicing foreign language conversation, the server can provide easier practice exercises or visual aids to relax the learning environment. By using a generative AI model, user feedback can be accumulated and used to optimize future learning plans. 【0844】 An example of a prompt message is, "If the user is smiling, ask for instructions on how to arrange the learning content." Through such interactions, it is possible to create a more effective and personalized learning experience. 【0845】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0846】 Step 1: 【0847】 The device collects data on the user's learning speed and comprehension. Specifically, it captures facial expressions with its built-in camera and records voice with its microphone to acquire facial features and speech tone. The input is real-time video and audio data from the user, and an initial dataset is generated based on this data. 【0848】 Step 2: 【0849】 The device processes the collected data and sends it to the server as feature data. During this process, it uses facial recognition and voice analysis algorithms to estimate the user's emotional state and also attaches preliminary data on learning speed and comprehension. The output consists of a provisional estimate of the emotional state and an initial evaluation of the learning pace. 【0850】 Step 3: 【0851】 The server receives data sent from the terminal and generates an individualized learning plan based on it. Using a data analysis engine (e.g., TensorFlow), it analyzes the input data and generates a customized learning plan tailored to the user's characteristics. The output includes a list of specific learning materials and a learning schedule. 【0852】 Step 4: 【0853】 The server uses a generative AI model to further analyze sentiment data and adjust the learning plan in real time. Here, prompts (e.g., "If the user is smiling, ask for instructions on how to rearrange the learning content") are used to determine the optimal materials and feedback. The output is the adjusted learning content and additional lesson plans. 【0854】 Step 5: 【0855】 Users progress through their learning based on a customized learning plan provided by the server. Real-time feedback can also be provided, and this information is used to adjust the plan for future sessions. The user's progress and feedback are used as input, and progress data is updated based on this information. 【0856】 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. 【0857】 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. 【0858】 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. 【0859】 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. 【0860】 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. 【0861】 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. 【0862】 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. 【0863】 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. 【0864】 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." 【0865】 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. 【0866】 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. 【0867】 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. 【0868】 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. 【0869】 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. 【0870】 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. 【0871】 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. 【0872】 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. 【0873】 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. 【0874】 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. 【0875】 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. 【0876】 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. 【0877】 The following is further disclosed regarding the embodiments described above. 【0878】 (Claim 1) 【0879】 A means of receiving information about the user's learning speed and level of comprehension, 【0880】 A means of analyzing received information to generate individual learning plans, 【0881】 A means of selecting and providing learning materials based on the generated learning plan, 【0882】 A means of recording the user's learning progress and adjusting the learning plan based on that information, 【0883】 An education delivery system that includes this. 【0884】 (Claim 2) 【0885】 The system according to claim 1, which analyzes recorded learning progress to identify areas that need reinforcement and presents learning materials suitable for those areas. 【0886】 (Claim 3) 【0887】 The system according to claim 1, which receives feedback from the user on the generated learning plan and readjusts the next learning plan based on that feedback. 【0888】 "Example 1" 【0889】 (Claim 1) 【0890】 A means for receiving the user's initial input information and evaluation results, and transmitting them to a data processing device, 【0891】 A means of generating individual learning strategies by utilizing an AI model based on received information and analyzing the learning characteristics of each user, 【0892】 A means of selecting the optimal educational resources based on the generated learning strategy and providing them via an information processing device, 【0893】 A means to record the user's educational activities in real time and dynamically modify the learning strategy based on that information as needed, 【0894】 A system that includes this. 【0895】 (Claim 2) 【0896】 The system according to claim 1, which analyzes recorded educational activity information, identifies areas that need strengthening, and provides educational resources suitable for those areas. 【0897】 (Claim 3) 【0898】 The system according to claim 1, which receives feedback from the user on the generated learning strategy and dynamically reconstructs the next learning strategy based on that feedback. 【0899】 "Application Example 1" 【0900】 (Claim 1) 【0901】 A means of receiving information about the user's learning speed and level of comprehension, 【0902】 A means of analyzing received information to generate individual learning plans, 【0903】 A means of selecting and providing learning materials based on the generated learning plan, 【0904】 A means of recording the user's learning progress and adjusting the learning plan based on that information, 【0905】 A means of providing robotic educational assistance to further optimize learning activities based on self-assessment test data, 【0906】 A system that includes this. 【0907】 (Claim 2) 【0908】 The system according to claim 1, which analyzes recorded learning progress to identify areas that need reinforcement, presents appropriate learning materials for those areas, and updates the educational content according to the progress. 【0909】 (Claim 3) 【0910】 The system according to claim 1, which receives feedback from the user on the generated learning plan and readjusts the next learning plan based on the areas that have been strengthened. 【0911】 "Example 2 of combining an emotion engine" 【0912】 (Claim 1) 【0913】 A means of collecting information regarding the user's learning speed, comprehension level, and emotional state, 【0914】 A means of using a generative AI model to analyze received information and generate individual learning plans, 【0915】 A means of selecting and providing appropriate learning materials based on the generated learning plan, 【0916】 A means for recording the user's learning progress and emotions, and dynamically adjusting the learning plan based on that information, 【0917】 A system that includes this. 【0918】 (Claim 2) 【0919】 The system according to claim 1, which analyzes recorded learning progress and emotional data to identify areas that need reinforcement and presents teaching materials suitable for those areas. 【0920】 (Claim 3) 【0921】 The system according to claim 1, which receives feedback from the user on the generated learning plan and optimizes the next learning plan based on that feedback. 【0922】 "Application example 2 when combining with an emotional engine" 【0923】 (Claim 1) 【0924】 A means of receiving information about the user's learning speed and level of comprehension, 【0925】 A means of analyzing received information to generate individual learning plans, 【0926】 A means of selecting and providing learning materials based on the generated learning plan, 【0927】 A means of recognizing the user's emotional state based on their facial expressions and voice, and using that information to adaptively adjust the learning plan, 【0928】 A means of dynamically changing the learning environment and materials based on learning progress and emotional state in real time, 【0929】 A system that includes this. 【0930】 (Claim 2) 【0931】 The system according to claim 1, which analyzes recorded learning progress to identify areas that need reinforcement, presents learning materials suitable for those areas, and also provides learning materials that respond to the user's emotions. 【0932】 (Claim 3) 【0933】 The system according to claim 1, which receives feedback from the user on the generated learning plan and readjusts the next learning plan based on that feedback and sentiment data. [Explanation of Symbols] 【0934】 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 of receiving information about the user's learning speed and level of comprehension, A means of analyzing received information to generate individual learning plans, A means of selecting and providing learning materials based on the generated learning plan, A means of recording the user's learning progress and adjusting the learning plan based on that information, An education delivery system that includes this. [Claim 2] The system according to claim 1, which analyzes recorded learning progress to identify areas that need reinforcement and presents learning materials suitable for those areas. [Claim 3] The system according to claim 1, which receives feedback from the user on the generated learning plan and readjusts the next learning plan based on that feedback.

Images