system

Abstract

We provide the system. [Solution] A data collection method for collecting student learning data, An analytical means for analyzing collected training data to generate an ideal training plan, A distribution method for delivering the generated learning plan to students' devices, A display method for showing the distributed learning plan to students on their devices, A progress management system for tracking students' learning activities, recording their progress, and sending it to a server, A visualization tool for students and instructors to check and analyze learning progress, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 It is difficult to efficiently provide learning support tailored to the progress and understanding of individual learners. In existing educational systems, learning can only be advanced at a uniform teaching material and progress speed, so there is a problem that it is difficult to maximize the learning effect. In addition, there is a problem that the burden on teachers is large and it is difficult to provide an individual learning plan in real time. 【Means for Solving the Problems】 【0005】 This invention utilizes a learning management system equipped with multiple means to collect learning data from individual learners, analyze that data with an AI model, and generate individually optimized learning plans. Furthermore, this plan is delivered to the learner's device, allowing for real-time progress tracking. By providing students and teachers with visualized progress information, efficient feedback and support are enabled. This results in personalized learning support for each learner, improving learning effectiveness and reducing the burden on teachers. 【0006】 "Data collection means" refers to a device or method for collecting data related to students' learning activities. 【0007】 "Analysis means" refers to a device or method for analyzing collected learning data and generating an individually optimized learning plan. 【0008】 "Distribution means" refers to a device or method for sending the generated learning plan to a student's device. 【0009】 "Display means" refers to a device or method for visually presenting a learning plan to students on a terminal. 【0010】 "Progress management means" refers to a device or method for tracking students' learning activities, recording progress data, and transmitting it to a server. 【0011】 A "visualization tool" is a device or method that provides information visually for students and teachers to check and analyze the progress of their learning. [Brief explanation of the drawing] 【0012】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] 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】 【0013】 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. 【0014】 First, the language used in the following description will be explained. 【0015】 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. 【0016】 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. 【0017】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0018】 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), and the like. 【0019】 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." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 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. 【0023】 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). 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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". 【0033】 The learning management system of this invention connects a cloud-based server with student devices via a network, utilizing AI technology to provide an optimal learning plan for each individual student. This system is capable of collecting, analyzing, and providing feedback on student learning activities in real time. 【0034】 Server operation 【0035】 The server receives learning activity data periodically sent from students' devices. This data includes, for example, the results of answering questions, study time, and viewing status of video lectures. The server analyzes the received data using an AI model and generates an optimized learning plan for each student based on the results. The generated plan is then delivered from the server to the student's device at the appropriate time. 【0036】 Terminal operation 【0037】 The device notifies the student of the learning plan received from the server and displays it on the screen. As the student progresses through the learning according to the plan, the device automatically records their progress. The recorded learning data is sent back to the server and used for analysis by the server. Through this iterative process, the learning plan is constantly updated based on the student's latest level of understanding. 【0038】 User actions 【0039】 Students, as users, can progress through their studies based on the learning plan displayed on their devices. By completing the assigned tasks, they can advance to the next learning phase, and their progress can be monitored in real time. Teachers and parents can also monitor students' learning progress through a dashboard and provide guidance and support as needed. 【0040】 Specific example 【0041】 For example, in the case of a student learning mathematics, the server generates a new set of problems, including similar problems, based on problems the student has previously answered incorrectly, and delivers them to the student's device. The device displays the problems, and the student enters their answers. Feedback based on the answers is immediately displayed, and the results are sent to the server. The server analyzes this data and determines the next steps necessary for the student. 【0042】 Thus, this invention provides an individually optimized learning experience using AI technology, thereby improving learning effectiveness. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The device begins collecting learning data from the moment a student starts a learning activity. Specifically, it records the questions answered, the duration of videos watched, and access information for learning materials. 【0046】 Step 2: 【0047】 The device sends the collected learning data to the server at a set time or after the learning session ends. The data is transmitted over the network and received by the server. 【0048】 Step 3: 【0049】 The server inputs the learning data received from the terminal into the AI ​​analysis engine and begins the analysis. This process identifies the student's learning progress and areas of weakness, and structures and stores the data. 【0050】 Step 4: 【0051】 The server generates a personalized learning plan based on the analysis results. This plan includes a new set of problems and recommended learning materials. The generated plan is then prepared for the next learning session. 【0052】 Step 5: 【0053】 The server delivers the generated learning plan to the target student's device. Delivery occurs in real time or upon the next login. 【0054】 Step 6: 【0055】 The device notifies the student of the learning plan received from the server and displays it on the learning screen. The student then resumes learning based on the presented plan. 【0056】 Step 7: 【0057】 The student user solves problems and watches video learning materials according to the displayed learning plan. This allows the device to continuously collect new learning data, initiating the next cycle. 【0058】 By repeating this series of steps, students' learning is always optimized based on updated information. 【0059】 (Example 1) 【0060】 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." 【0061】 Traditional learning management systems have faced challenges in dynamically generating personalized learning plans and updating their content in real time. Furthermore, they have been unable to effectively track learners' progress and provide immediate feedback based on their understanding, limiting learning effectiveness. Additionally, there has been a lack of means for educators and learners themselves to visually monitor and analyze their own learning progress. 【0062】 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. 【0063】 In this invention, the server includes information gathering means for collecting learner learning information, analysis means for analyzing the collected learning information and generating an optimal learning plan using a generative AI model, and transmission means for transmitting the generated learning plan to the learner's device. This enables the dynamic generation and real-time updating of individually optimized learning plans, improving the learner's learning effectiveness, and simultaneously providing visual feedback on progress to educators and the learners themselves. 【0064】 "Learning information" refers to all data generated by learners during their learning activities, including, for example, answer results, study time, and viewing status. 【0065】 "Information gathering means" refers to a mechanism for collecting learning information from the learner's device and transmitting it to a server. 【0066】 "Analysis method" refers to the process of analyzing data using a generated AI model based on collected learning information and generating an optimal learning plan for each learner. 【0067】 A "generative AI model" refers to a model that uses algorithms based on machine learning and deep learning to analyze training data and generate individually optimized learning plans. 【0068】 "Transmission means" refers to the communication means used to deliver the learning plan generated by the analysis means to the learner's device. 【0069】 A "learning plan" refers to a plan generated by an analytical tool that includes the tasks and learning content that learners should undertake. 【0070】 "Device" refers to an electronic device used by learners to receive learning plans and proceed with their studies according to their contents. 【0071】 This invention relates to a learning management system that includes learner devices and a server connected via a network. This system utilizes a generative AI model to generate and provide personalized learning plans optimized for each learner in real time. 【0072】 The server periodically collects learning information from the learner's device. This information includes assignment completion results, time spent learning, and progress on viewed learning content. This data is securely stored using a cloud storage service. A database management system and analysis software are used for specific data processing. 【0073】 The server analyzes the collected training information using a generative AI model. Machine learning frameworks such as TENSORFLOW® and PyTorch are used for the analysis. Based on the analysis results, an optimal training plan is generated. The generated training plan is sent from the server to the learner's device and presented to the learner on the device. 【0074】 One concrete example of using server-generated AI models is suggesting similar problems to a mathematics student based on their previous incorrect answers. In this case, the server inputs a prompt to the AI ​​model such as: "Create the next learning plan based on this student's past learning data." 【0075】 The terminal notifies and displays the learner of the learning plan sent from the server. As the learner progresses according to the plan, their progress is automatically recorded by the terminal. The recorded data is sent back to the server and used as data for the next plan. 【0076】 The user, the learner, can use the device to execute their learning plan and check their progress in real time. Educators can also monitor learning progress through a provided dashboard and provide feedback as needed. In this way, the present invention provides an individually optimized learning experience based on the learner's level of understanding, thereby supporting improved learning effectiveness. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: 【0079】 The server periodically collects learning information from learners' devices. This information includes assignment completion results, time spent learning, and progress on viewed educational content. The input is learner behavior data, which is categorized and organized for each session. The output is the collected learning data set, which is securely stored in a database. A database management system is used for this process. 【0080】 Step 2: 【0081】 The server passes the collected data to the generating AI model for analysis. The input includes the training data organized in Step 1, and the AI ​​generates an optimal training plan based on this data. Machine learning algorithms are used for data processing, and the output is an individualized training plan. This plan includes suggesting the most appropriate next training task for each learner. This process uses a machine learning framework with Python. 【0082】 Step 3: 【0083】 The server sends the generated learning plan to the learner's terminal. The input is the learning plan generated in step 2, and the output is the format received by the terminal. This process uses a network-based communication protocol. The server notifies the terminal that it has received the plan and provides the data necessary for visualization. 【0084】 Step 4: 【0085】 The terminal notifies the learner of the learning plan received from the server and displays it on the screen. The input is the learning plan sent from the server, and the output is the content presented to the learner. The terminal provides the tools and interfaces necessary for the learner to proceed with their learning according to the plan. Specifically, these include question navigation and answer input interfaces. 【0086】 Step 5: 【0087】 The user, the learner, progresses through the learning process based on the provided learning plan. The device automatically records their progress and the completion status of assignments. Input consists of the learner's actions and responses, and output is uploaded to the server as recorded progress data. This progress management ensures that the latest learning status is always available on the server. 【0088】 Step 6: 【0089】 The server re-analyzes the updated progress data and uses it to generate the next learning plan. The input data is the learner's latest progress information, and the AI ​​model uses this to determine the next step. The output is the next learning plan, and this cycle continues. The server can also prepare feedback as needed and send it immediately to the learner's device. 【0090】 (Application Example 1) 【0091】 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." 【0092】 In today's urban environment, there is a need to provide individually optimized learning plans that meet diverse learning needs. However, conventional systems have limitations in providing timely information tailored to the learning progress and understanding of individual residents. Furthermore, there has been a lack of efficient methods for providing learning support linked to local educational facilities and event information. As a result, maximizing learning effectiveness has been difficult. 【0093】 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. 【0094】 In this invention, the server includes data collection means for providing information available to residents, analysis means for analyzing aggregated learning information to generate individually optimized learning plans, and transmission means for delivering the generated learning plans to the user's information terminal. This enables the provision of effective learning plans tailored to the learning progress of each individual user and the optimal use of local educational resources. 【0095】 "Information available to residents" refers to education-related information that residents in an urban environment can access on a daily basis. 【0096】 "Aggregated learning information" refers to a collection of data from individual learners, used for advanced analysis. 【0097】 "Individually optimized learning plans" refer to educational plans customized by AI based on the user's learning progress and level of understanding. 【0098】 "User information terminal" refers to electronic devices such as smartphones and tablets used by individual learners. 【0099】 "Usage status of educational facilities within the region" refers to information indicating the current availability of educational facilities and learning support facilities. 【0100】 A "progress management means" is a function that tracks the user's learning activities, records their progress, and transmits it to an information processing device. 【0101】 A "visualization method" is a visual display system that allows users and instructors to check and evaluate learning progress and status. 【0102】 The system for realizing this invention includes a server, a user's information terminal, and a local educational facility. The server utilizes a cloud computing platform to collect information available to residents and analyze the aggregated learning information. Suitable platforms for this include Amazon Web Services (AWS®) and Google Cloud Platform (GCP). Frameworks such as TensorFlow and PyTorch are used for the AI ​​model. 【0103】 The user's information terminals include smartphones and tablets, and applications are developed using React Native or Flutter®. The terminals display individually optimized training plans sent from the server and process the user's input in real time. 【0104】 The server tracks the user's learning progress through a progress management system and sends the data to the cloud. Based on this progress information, it indicates the next tasks to be addressed. Furthermore, by displaying information on the usage status of local educational facilities and educational events on the terminal in real time, it provides the user with an optimal learning environment. 【0105】 As a concrete example, consider a scenario where students in a certain region learn simultaneous equations online. The device uses AI to suggest learning plans based on past answer data and progress, allowing residents to receive the optimal curriculum from the comfort of their homes. 【0106】 As an example of a prompt, users can input instructions in natural language such as, "Based on past math answer data and progress, please use AI to suggest the next problem to tackle," and the system will then analyze this information using a generative AI model. This system will not only allow users to enjoy a personalized learning experience but also enable them to make the most of local educational resources. 【0107】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0108】 Step 1: 【0109】 The server collects the residents' learning data. 【0110】 Input includes learning history, answer scores, and usage time. 【0111】 The server collects this data and stores it in a cloud-based database. 【0112】 Step 2: 【0113】 The server analyzes the training data it has collected. 【0114】 The training data stored as input is used. 【0115】 The server uses a generative AI model to analyze this data and generate individually optimized training plans. The output is a training plan tailored to the user. 【0116】 Step 3: 【0117】 The server distributes the generated training plan to the information terminal. 【0118】 The input is the learning plan generated in Step 2. 【0119】 The server sends this plan to the user's device. 【0120】 Step 4: 【0121】 The device displays the received learning plan to the user. 【0122】 The learning plan sent from the server is used as input. 【0123】 The device displays a learning plan through an application, indicating the next task the user should complete. 【0124】 Step 5: 【0125】 The user progresses through the learning process based on the learning plan and inputs their progress into the device. 【0126】 The input consists of the user's learning results, activity time, and answers. 【0127】 The terminal records this data using a progress management system. 【0128】 Step 6: 【0129】 The device sends the recorded learning progress to the server. 【0130】 The input is the progress data recorded in step 5. 【0131】 The device sends this data to the server via a secure channel. 【0132】 Step 7: 【0133】 The server analyzes the transmitted learning progress data and generates an updated learning plan. 【0134】 The latest training progress data is used as input. 【0135】 The server then uses the generated AI model again to analyze the data and generate the optimal learning plan. The output is the new learning plan. 【0136】 Step 8: 【0137】 The server notifies information terminals of the usage status of educational facilities within the region. 【0138】 The input consists of facility usage data and requirements derived from learning plans. 【0139】 The server sends this information to the terminal, which helps users schedule their visits to the facility. 【0140】 Step 9: 【0141】 Based on the information received by the user, the system will maximize the use of local educational resources to advance learning. 【0142】 Users utilize the information presented to them and continue learning in the optimal environment. 【0143】 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. 【0144】 This invention dynamically provides a learning environment that responds to students' emotions by incorporating an emotion engine into a learning management system. This system, which includes a cloud-based server, learning terminals, and emotion recognition capabilities, aims to improve students' learning efficiency. 【0145】 Server operation 【0146】 The server collects and analyzes students' learning and emotional data to generate personalized learning plans. Emotional data is obtained from the user's facial expressions, voice, or behavior, and their emotional state is recognized through AI analysis on the server side. For example, if a student is restless, relaxing materials or music can be incorporated into the learning plan. 【0147】 Terminal operation 【0148】 The device receives learning plans and emotion-based content sent from the server and provides them to the user. It incorporates an emotion engine that monitors the user's emotional state in real time through sensors such as the camera and microphone. This information is sent to the server and used to adjust future learning plans. 【0149】 User actions 【0150】 The student user learns based on the content displayed on the device. If the emotion engine detects tension or anxiety during the learning process, the device immediately sends feedback to the server, which then provides more appropriate learning content based on that feedback. 【0151】 Specific example 【0152】 When a student is learning mathematics and solving a problem, the emotional engine recognizes their seriousness. If the emotional engine determines that the student's concentration has wavered, the device suggests a short game or video for refreshment. Once this response is sent within the feedback loop, the server uses that information to make adjustments in the next session to promote more sustainable concentration. 【0153】 In this form, the present invention can provide a learning plan that incorporates students' emotional states, thereby improving the learning experience and creating an efficient learning environment. 【0154】 The following describes the processing flow. 【0155】 Step 1: 【0156】 As soon as a student begins learning, the device uses its built-in camera and microphone to capture their facial expressions and voice in real time, collecting emotional data. Simultaneously, it also records data from their regular learning activities. 【0157】 Step 2: 【0158】 The device transmits collected emotional data and learning activity data to the server at regular intervals. The emotional data is used as an indicator of the student's current emotional state. 【0159】 Step 3: 【0160】 The server inputs the received emotional data into an AI analysis engine to analyze the student's emotional state. For example, if the student's emotions deviate from a calm state, the server identifies the cause and considers appropriate countermeasures. 【0161】 Step 4: 【0162】 The server dynamically adjusts the learning plan based on the analysis results. If the emotional state is deteriorating, it incorporates tasks and materials that promote relaxation. If it is stable, it maintains the normal learning plan. 【0163】 Step 5: 【0164】 The server delivers the customized learning plan to the student's device. The device then displays this plan to the student in real time. 【0165】 Step 6: 【0166】 The student user continues learning based on the adjusted learning plan displayed on their device. The system continues to monitor data on emotions and learning activities, collecting new information for the next cycle. 【0167】 Step 7: 【0168】 By repeating this process, the server and terminal work together to continuously provide a dynamic learning environment that adapts to the student's emotions. Ultimately, the student's learning experience becomes more personalized and effectively improved. 【0169】 (Example 2) 【0170】 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." 【0171】 Traditional learning management systems provide a uniform learning plan without considering students' emotional states, making it difficult to provide a learning environment optimized for individual students. This results in problems such as decreased student concentration and motivation, and insufficient learning efficiency. 【0172】 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. 【0173】 In this invention, the server includes data acquisition means for collecting student learning data and emotional data, a generative model for analyzing the acquired data to generate individually optimized learning plans, and communication means for transmitting the learning plans to terminals. This makes it possible to provide a dynamic and optimized learning environment that is tailored to each student's emotional state. 【0174】 "Data acquisition means" refers to devices and methods for collecting students' learning data and emotional data. 【0175】 A "generative model" is an algorithm or method that analyzes collected learning data and emotional data to generate learning plans optimized for individual students. 【0176】 "Communication methods" refer to the technologies and means used to send the generated learning plan to the student's device. 【0177】 "Display means" refers to a function that presents a learning plan on a device and dynamically adjusts the content based on the student's emotional state. 【0178】 A "monitoring system" is a mechanism for continuously monitoring students' learning activities and emotional states and sending feedback data to a server. 【0179】 "Adjustment methods" refer to methods and techniques for updating learning plans for subsequent sessions based on feedback data. 【0180】 This invention is a technology that provides an individualized learning environment that takes students' emotions into consideration in a learning management system. The system consists of a server, terminals, and users. 【0181】 The server operates on a cloud-based platform and collects data related to students' learning activities and emotions using data acquisition methods. Emotional data is acquired in real time from terminals using software technology that performs facial expression and voice analysis. The server inputs the acquired data into a generative AI model, which generates a learning plan tailored to each student through data analysis. This generative model selects learning materials and activities according to the student's state based on a specific algorithm. 【0182】 The terminal, used by students, receives learning plans sent from the server and presents them to the user through a display. The terminal incorporates hardware sensors such as a camera and microphone, which are used to monitor the user's emotional state in real time. For example, if a student is not concentrating, the terminal displays relaxation content and sends the effect as feedback data to the server. This allows the server to adjust the next learning plan and provide more effective content. 【0183】 The student user learns the materials according to the provided learning plan. During the learning process, the device monitors the student's responses and sends feedback to the server as needed. 【0184】 For example, in the case of a student studying mathematics, the generative AI model instantly presents the most suitable learning materials based on their level of concentration while working on problems. If it determines that a visual break is needed, the device can suggest light games or videos. 【0185】 An example of a prompt is, "Generate a learning plan that suggests content to help students relax." This prompt is input to a generative model and provides hints for creating an appropriate learning environment. 【0186】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0187】 Step 1: 【0188】 The server collects student learning history data and emotional data. It receives real-time facial expression and voice data transmitted from the terminal as input. This data is stored on the server using data acquisition methods. During this process, facial recognition and voice analysis technologies are used to understand the student's current state. As output, the server constructs organized learning and emotional datasets. 【0189】 Step 2: 【0190】 The server inputs organized learning data and sentiment data into a generative AI model. It performs analysis and generates learning plans tailored to individual students. The input data includes past learning progress, evaluation scores, and sentiment states. The generative AI model uses pattern recognition techniques to develop an optimized plan, resulting in a customized learning plan as output. 【0191】 Step 3: 【0192】 The server sends the generated learning plan to the device. The learning plan is sent as a digital file using a communication method. The transmitted data includes a list of specific learning materials and emotionally responsive activity suggestions. This makes the device ready to use the received plan immediately. 【0193】 Step 4: 【0194】 The device presents the received learning plan to the user. It uses display means to show learning materials and activities in a visually easy-to-understand format. Input information includes the learning plan from the server, and based on this, the device updates the dashboard and presents a visualized learning plan as output. The user's emotional state is also taken into consideration as it may influence the plan. 【0195】 Step 5: 【0196】 The user progresses through the learning process based on the presented learning plan. Inputs include clicking on learning materials and completing tasks. Based on this, the device monitors the student's learning behavior and sends feedback to the server as output. The emotion engine simultaneously evaluates the user's state, such as concentration and stress levels. 【0197】 Step 6: 【0198】 The server receives feedback data sent from the terminal and uses it for subsequent learning sessions. It analyzes the feedback data and updates the learning plan using adjustment mechanisms. This allows for further improvement in student performance in the next session. The server is then ready to send the improved learning plan back to the terminal as output. 【0199】 (Application Example 2) 【0200】 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". 【0201】 Traditional learning management systems could track students' learning progress, but they struggled to provide dynamic educational plans that took emotions into account. Therefore, they couldn't flexibly provide the most suitable learning environment for each individual student. Furthermore, the inability to provide real-time feedback tailored to students' understanding and emotions meant that learning efficiency wasn't adequately improved. 【0202】 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. 【0203】 In this invention, the server includes emotion analysis means for recognizing the emotions of the student and reflecting adjustments to the educational plan accordingly, information acquisition means for collecting information on the student's learning, and analysis means for analyzing the acquired learning information and generating a suitable educational plan. This enables the provision of flexible educational plans that respond to the student's emotional state and real-time feedback that corresponds to their level of understanding. 【0204】 A "student receiving education" is someone who receives educational support during the learning process. 【0205】 "Information acquisition means" refers to devices and methods for collecting information related to the learning of educated individuals. 【0206】 An "analysis tool" is a device that analyzes acquired learning information and generates a suitable educational plan. 【0207】 "Means of transmission" refers to a mechanism or method for transmitting the generated educational plan to the recipient's personal information terminal. 【0208】 "Display means" refers to devices or methods for visually showing a transmitted educational plan to the student on a mobile information terminal. 【0209】 A "progress management method" is a method for tracking the learning activities of students, recording their progress, and transmitting it to a data processing device. 【0210】 "Emotional analysis tools" are tools that recognize the emotions of those being educated and reflect those emotions in the educational plan. 【0211】 A system implementing this invention includes functions such as a server for handling educational support, a portable information terminal for students to learn, and an emotion engine for analyzing emotions. 【0212】 The server collects learning-related information from students through information acquisition methods and analyzes this information using analytical methods to generate an optimal educational plan. This process utilizes a cloud-based platform with powerful processing capabilities for handling data. AI models are used to analyze students' facial expressions and voice data and recognize emotions; for example, Google Cloud's AI models or Amazon Web Services' Rekognition can be used. 【0213】 This educational plan is transmitted to the student's mobile device via a communication device and displayed visually on a display device. The device monitors the student's emotional state using a camera and microphone, and adjusts the educational plan accordingly through an emotion analysis device. Depending on the situation, learning progress and comprehension are also provided as real-time feedback. 【0214】 For example, if a child learning math is losing focus, the emotion engine recognizes this, and the device suggests relaxing music or a short game. A series of feedback data is sent to a server, and adjustments are made based on this information in the next learning session. In this application, an example of a prompt sentence used to indicate a specific emotional state to the generative AI model for educational support would be, "This child is clearly losing focus, judging from their facial expression and tone of voice. Suggest a short break and a fun activity." 【0215】 In this way, this system can provide an optimal learning experience according to the emotional state of the learner, thereby significantly improving the efficiency of learning. 【0216】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0217】 Step 1: 【0218】 Sensor input 【0219】 The server acquires image and audio data from the camera and microphone on the device. This data is sent as input and preprocessed by the AI ​​model for emotion recognition. Specifically, facial features are extracted from the images, and tone and tempo are analyzed from the audio. 【0220】 Step 2: 【0221】 emotion recognition 【0222】 The server inputs the pre-processed data into a generating AI model to estimate the emotions of the student. An example of a prompt is "The facial expression and tone of voice indicate that the student is losing focus," and the model outputs an emotional state such as "decreased concentration." 【0223】 Step 3: 【0224】 Generation of an educational plan 【0225】 The server uses analytical tools to generate an optimal educational plan based on the output of emotional states. Specifically, if concentration levels are low, it creates a plan that includes music or games to suggest breaks. Past data, such as learning progress, is also taken into consideration. 【0226】 Step 4: 【0227】 Plan Communication 【0228】 The server sends the generated lesson plan to the terminal. The terminal receives this plan, and the display device visually presents the plan to the student. For example, it might display a message on the screen suggesting a relaxing activity. 【0229】 Step 5: 【0230】 Real-time feedback 【0231】 The user progresses through the learning process based on the presented plan. The device collects the user's responses again as new image and audio data, sends this to the server, and continues to provide feedback. The server uses this feedback data to make adjustments to improve the next educational plan, thereby enhancing the overall learning efficiency of the system. 【0232】 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. 【0233】 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. 【0234】 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. 【0235】 [Second Embodiment] 【0236】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0237】 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. 【0238】 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). 【0239】 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. 【0240】 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. 【0241】 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). 【0242】 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. 【0243】 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. 【0244】 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. 【0245】 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. 【0246】 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. 【0247】 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". 【0248】 The learning management system of this invention connects a cloud-based server with student devices via a network, utilizing AI technology to provide an optimal learning plan for each individual student. This system is capable of collecting, analyzing, and providing feedback on student learning activities in real time. 【0249】 Server operation 【0250】 The server receives learning activity data periodically sent from students' devices. This data includes, for example, the results of answering questions, study time, and viewing status of video lectures. The server analyzes the received data using an AI model and generates an optimized learning plan for each student based on the results. The generated plan is then delivered from the server to the student's device at the appropriate time. 【0251】 Terminal operation 【0252】 The device notifies the student of the learning plan received from the server and displays it on the screen. As the student progresses through the learning according to the plan, the device automatically records their progress. The recorded learning data is sent back to the server and used for analysis by the server. Through this iterative process, the learning plan is constantly updated based on the student's latest level of understanding. 【0253】 User actions 【0254】 Students, as users, can progress through their studies based on the learning plan displayed on their devices. By completing the assigned tasks, they can advance to the next learning phase, and their progress can be monitored in real time. Teachers and parents can also monitor students' learning progress through a dashboard and provide guidance and support as needed. 【0255】 Specific example 【0256】 For example, in the case of a student learning mathematics, the server generates a new set of problems, including similar problems, based on problems the student has previously answered incorrectly, and delivers them to the student's device. The device displays the problems, and the student enters their answers. Feedback based on the answers is immediately displayed, and the results are sent to the server. The server analyzes this data and determines the next steps necessary for the student. 【0257】 Thus, this invention provides an individually optimized learning experience using AI technology, thereby improving learning effectiveness. 【0258】 The following describes the processing flow. 【0259】 Step 1: 【0260】 The device begins collecting learning data from the moment a student starts a learning activity. Specifically, it records the questions answered, the duration of videos watched, and access information for learning materials. 【0261】 Step 2: 【0262】 The device sends the collected learning data to the server at a set time or after the learning session ends. The data is transmitted over the network and received by the server. 【0263】 Step 3: 【0264】 The server inputs the learning data received from the terminal into the AI ​​analysis engine and begins the analysis. This process identifies the student's learning progress and areas of weakness, and structures and stores the data. 【0265】 Step 4: 【0266】 The server generates a personalized learning plan based on the analysis results. This plan includes a new set of problems and recommended learning materials. The generated plan is then prepared for the next learning session. 【0267】 Step 5: 【0268】 The server delivers the generated learning plan to the target student's device. Delivery occurs in real time or upon the next login. 【0269】 Step 6: 【0270】 The device notifies the student of the learning plan received from the server and displays it on the learning screen. The student then resumes learning based on the presented plan. 【0271】 Step 7: 【0272】 The student user solves problems and watches video learning materials according to the displayed learning plan. This allows the device to continuously collect new learning data, initiating the next cycle. 【0273】 By repeating this series of steps, students' learning is always optimized based on updated information. 【0274】 (Example 1) 【0275】 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." 【0276】 Traditional learning management systems have faced challenges in dynamically generating personalized learning plans and updating their content in real time. Furthermore, they have been unable to effectively track learners' progress and provide immediate feedback based on their understanding, limiting learning effectiveness. Additionally, there has been a lack of means for educators and learners themselves to visually monitor and analyze their own learning progress. 【0277】 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. 【0278】 In this invention, the server includes information gathering means for collecting learner learning information, analysis means for analyzing the collected learning information and generating an optimal learning plan using a generative AI model, and transmission means for transmitting the generated learning plan to the learner's device. This enables the dynamic generation and real-time updating of individually optimized learning plans, improving the learner's learning effectiveness, and simultaneously providing visual feedback on progress to educators and the learners themselves. 【0279】 "Learning information" refers to all data generated by learners in learning activities, including, for example, answer results, learning time, viewing status, etc. 【0280】 "Information collection means" refers to a mechanism for collecting learning information from a learner's device and transmitting it to the server. 【0281】 "Analysis means" refers to a process of analyzing data using a generated AI model based on the collected learning information and generating an optimal learning plan for each learner. 【0282】 "Generated AI model" refers to a model that analyzes learning data using algorithms based on machine learning or deep learning and generates an individually optimized learning plan. 【0283】 "Transmission means" refers to communication means for delivering the learning plan generated by the analysis means to the learner's device. 【0284】 "Learning plan" refers to a plan generated by the analysis means and including tasks and learning content that the learner should engage in. 【0285】 "Device" refers to an electronic device used by a learner to receive a learning plan and proceed with learning according to its content. 【0286】 The present invention is a learning management system including a learner's device and a server connected via a network. This system has a function of generating and providing an individually optimized learning plan for a learner in real time by utilizing a generated AI model. 【0287】 The server periodically collects learning information from the learner's device. The information collected includes answer results of tasks, time required for learning, progress of learning content being viewed, etc. These are safely stored using a cloud storage service. For specific data processing, a database management system and analysis software are used. 【0288】 The server analyzes the collected training information using a generative AI model. Machine learning frameworks such as TensorFlow and PyTorch are used for the analysis. Based on the analysis results, an optimal training plan is generated. The generated training plan is sent from the server to the learner's device and presented to the learner on the device. 【0289】 One concrete example of using server-generated AI models is suggesting similar problems to a mathematics student based on their previous incorrect answers. In this case, the server inputs a prompt to the AI ​​model such as: "Create the next learning plan based on this student's past learning data." 【0290】 The terminal notifies and displays the learner of the learning plan sent from the server. As the learner progresses according to the plan, their progress is automatically recorded by the terminal. The recorded data is sent back to the server and used as data for the next plan. 【0291】 The user, the learner, can use the device to execute their learning plan and check their progress in real time. Educators can also monitor learning progress through a provided dashboard and provide feedback as needed. In this way, the present invention provides an individually optimized learning experience based on the learner's level of understanding, thereby supporting improved learning effectiveness. 【0292】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0293】 Step 1: 【0294】 The server periodically collects learning information from learners' devices. This information includes assignment completion results, time spent learning, and progress on viewed educational content. The input is learner behavior data, which is categorized and organized for each session. The output is the collected learning data set, which is securely stored in a database. A database management system is used for this process. 【0295】 Step 2: 【0296】 The server passes the collected data to the generating AI model for analysis. The input includes the training data organized in Step 1, and the AI ​​generates an optimal training plan based on this data. Machine learning algorithms are used for data processing, and the output is an individualized training plan. This plan includes suggesting the most appropriate next training task for each learner. This process uses a machine learning framework with Python. 【0297】 Step 3: 【0298】 The server sends the generated learning plan to the learner's terminal. The input is the learning plan generated in step 2, and the output is the format received by the terminal. This process uses a network-based communication protocol. The server notifies the terminal that it has received the plan and provides the data necessary for visualization. 【0299】 Step 4: 【0300】 The terminal notifies the learner of the learning plan received from the server and displays it on the screen. The input is the learning plan sent from the server, and the output is the content presented to the learner. The terminal provides the tools and interfaces necessary for the learner to proceed with their learning according to the plan. Specifically, these include question navigation and answer input interfaces. 【0301】 Step 5: 【0302】 The learner, who is the user, progresses with learning based on the presented learning plan. The terminal automatically records the progress and the completion status of tasks. The input is the learner's operations and reactions, and the output is uploaded to the server as the recorded progress data. Through this progress management, the server can always check the latest learning status. 【0303】 Step 6: 【0304】 The server re-analyzes the updated progress data and utilizes it for generating the next learning plan. The input data is the learner's latest progress information, and the AI model determines the next step based on this. The output is the next learning plan, and this cycle continues. The server can also prepare feedback as needed and immediately send it to the learner's device. 【0305】 (Application Example 1) 【0306】 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". 【0307】 In the modern urban environment, it is required to provide individually optimized learning plans according to various learning needs. However, conventional systems have limitations in providing timely information according to the learning progress and understanding levels of individual residents. There was also a lack of an efficient method for providing learning support in conjunction with local educational facilities and event information. As a result, it has been difficult to maximize the learning effect. 【0308】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means. 【0309】 In this invention, the server includes data collection means for providing information available to residents, analysis means for analyzing aggregated learning information to generate individually optimized learning plans, and transmission means for delivering the generated learning plans to the user's information terminal. This enables the provision of effective learning plans tailored to the learning progress of each individual user and the optimal use of local educational resources. 【0310】 "Information available to residents" refers to education-related information that residents in an urban environment can access on a daily basis. 【0311】 "Aggregated learning information" refers to a collection of data from individual learners, used for advanced analysis. 【0312】 "Individually optimized learning plans" refer to educational plans customized by AI based on the user's learning progress and level of understanding. 【0313】 "User information terminal" refers to electronic devices such as smartphones and tablets used by individual learners. 【0314】 "Usage status of educational facilities within the region" refers to information indicating the current availability of educational facilities and learning support facilities. 【0315】 A "progress management means" is a function that tracks the user's learning activities, records their progress, and transmits it to an information processing device. 【0316】 A "visualization method" is a visual display system that allows users and instructors to check and evaluate learning progress and status. 【0317】 The system for realizing this invention includes a server, a user's information terminal, and a local educational facility. The server utilizes a cloud computing platform to collect information available to residents and analyze the aggregated learning information. Suitable platforms for this include Amazon Web Services (AWS) and Google Cloud Platform (GCP). Frameworks such as TensorFlow and PyTorch are used for the AI ​​model. 【0318】 The user's information terminals include smartphones and tablets, and applications are developed using React Native or Flutter. The terminals display individually optimized training plans sent from the server and process the user's input in real time. 【0319】 The server tracks the user's learning progress through a progress management system and sends the data to the cloud. Based on this progress information, it indicates the next tasks to be addressed. Furthermore, by displaying information on the usage status of local educational facilities and educational events on the terminal in real time, it provides the user with an optimal learning environment. 【0320】 As a concrete example, consider a scenario where students in a certain region learn simultaneous equations online. The device uses AI to suggest learning plans based on past answer data and progress, allowing residents to receive the optimal curriculum from the comfort of their homes. 【0321】 As an example of a prompt, users can input instructions in natural language such as, "Based on past math answer data and progress, please use AI to suggest the next problem to tackle," and the system will then analyze this information using a generative AI model. This system will not only allow users to enjoy a personalized learning experience but also enable them to make the most of local educational resources. 【0322】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0323】 Step 1: 【0324】 The server collects the residents' learning data. 【0325】 Input includes learning history, answer scores, and usage time. 【0326】 The server collects this data and stores it in a cloud-based database. 【0327】 Step 2: 【0328】 The server analyzes the training data it has collected. 【0329】 The training data stored as input is used. 【0330】 The server uses a generative AI model to analyze this data and generate individually optimized training plans. The output is a training plan tailored to the user. 【0331】 Step 3: 【0332】 The server distributes the generated training plan to the information terminal. 【0333】 The input is the learning plan generated in Step 2. 【0334】 The server sends this plan to the user's device. 【0335】 Step 4: 【0336】 The device displays the received learning plan to the user. 【0337】 The learning plan sent from the server is used as input. 【0338】 The device displays a learning plan through an application, indicating the next task the user should complete. 【0339】 Step 5: 【0340】 The user progresses through the learning process based on the learning plan and inputs their progress into the device. 【0341】 The input consists of the user's learning results, activity time, and answers. 【0342】 The terminal records this data using a progress management system. 【0343】 Step 6: 【0344】 The device sends the recorded learning progress to the server. 【0345】 The input is the progress data recorded in step 5. 【0346】 The device sends this data to the server via a secure channel. 【0347】 Step 7: 【0348】 The server analyzes the transmitted learning progress data and generates an updated learning plan. 【0349】 The latest training progress data is used as input. 【0350】 The server then uses the generated AI model again to analyze the data and generate the optimal learning plan. The output is the new learning plan. 【0351】 Step 8: 【0352】 The server notifies information terminals of the usage status of educational facilities within the region. 【0353】 The input consists of facility usage data and requirements derived from learning plans. 【0354】 The server sends this information to the terminal, which helps users schedule their visits to the facility. 【0355】 Step 9: 【0356】 Based on the information received by the user, the system will maximize the use of local educational resources to advance learning. 【0357】 Users utilize the information presented to them and continue learning in the optimal environment. 【0358】 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. 【0359】 This invention dynamically provides a learning environment that responds to students' emotions by incorporating an emotion engine into a learning management system. This system, which includes a cloud-based server, learning terminals, and emotion recognition capabilities, aims to improve students' learning efficiency. 【0360】 Server operation 【0361】 The server collects and analyzes students' learning and emotional data to generate personalized learning plans. Emotional data is obtained from the user's facial expressions, voice, or behavior, and their emotional state is recognized through AI analysis on the server side. For example, if a student is restless, relaxing materials or music can be incorporated into the learning plan. 【0362】 Terminal operation 【0363】 The device receives learning plans and emotion-based content sent from the server and provides them to the user. It incorporates an emotion engine that monitors the user's emotional state in real time through sensors such as the camera and microphone. This information is sent to the server and used to adjust future learning plans. 【0364】 User actions 【0365】 The student user learns based on the content displayed on the device. If the emotion engine detects tension or anxiety during the learning process, the device immediately sends feedback to the server, which then provides more appropriate learning content based on that feedback. 【0366】 Specific example 【0367】 When a student is learning mathematics and solving a problem, the emotional engine recognizes their seriousness. If the emotional engine determines that the student's concentration has wavered, the device suggests a short game or video for refreshment. Once this response is sent within the feedback loop, the server uses that information to make adjustments in the next session to promote more sustainable concentration. 【0368】 In this form, the present invention can provide a learning plan that incorporates students' emotional states, thereby improving the learning experience and creating an efficient learning environment. 【0369】 The following describes the processing flow. 【0370】 Step 1: 【0371】 As soon as a student begins learning, the device uses its built-in camera and microphone to capture their facial expressions and voice in real time, collecting emotional data. Simultaneously, it also records data from their regular learning activities. 【0372】 Step 2: 【0373】 The device transmits collected emotional data and learning activity data to the server at regular intervals. The emotional data is used as an indicator of the student's current emotional state. 【0374】 Step 3: 【0375】 The server inputs the received emotional data into an AI analysis engine to analyze the student's emotional state. For example, if the student's emotions deviate from a calm state, the server identifies the cause and considers appropriate countermeasures. 【0376】 Step 4: 【0377】 The server dynamically adjusts the learning plan based on the analysis results. If the emotional state is deteriorating, it incorporates tasks and materials that promote relaxation. If it is stable, it maintains the normal learning plan. 【0378】 Step 5: 【0379】 The server delivers the customized learning plan to the student's device. The device then displays this plan to the student in real time. 【0380】 Step 6: 【0381】 The student user continues learning based on the adjusted learning plan displayed on their device. The system continues to monitor data on emotions and learning activities, collecting new information for the next cycle. 【0382】 Step 7: 【0383】 By repeating this process, the server and terminal work together to continuously provide a dynamic learning environment that adapts to the student's emotions. Ultimately, the student's learning experience becomes more personalized and effectively improved. 【0384】 (Example 2) 【0385】 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". 【0386】 Traditional learning management systems provide a uniform learning plan without considering students' emotional states, making it difficult to provide a learning environment optimized for individual students. This results in problems such as decreased student concentration and motivation, and insufficient learning efficiency. 【0387】 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. 【0388】 In this invention, the server includes data acquisition means for collecting student learning data and emotional data, a generative model for analyzing the acquired data to generate individually optimized learning plans, and communication means for transmitting the learning plans to terminals. This makes it possible to provide a dynamic and optimized learning environment that is tailored to each student's emotional state. 【0389】 "Data acquisition means" refers to devices and methods for collecting students' learning data and emotional data. 【0390】 A "generative model" is an algorithm or method that analyzes collected learning data and emotional data to generate learning plans optimized for individual students. 【0391】 "Communication methods" refer to the technologies and means used to send the generated learning plan to the student's device. 【0392】 "Display means" refers to a function that presents a learning plan on a device and dynamically adjusts the content based on the student's emotional state. 【0393】 A "monitoring system" is a mechanism for continuously monitoring students' learning activities and emotional states and sending feedback data to a server. 【0394】 "Adjustment methods" refer to methods and techniques for updating learning plans for subsequent sessions based on feedback data. 【0395】 This invention is a technology that provides an individualized learning environment that takes students' emotions into consideration in a learning management system. The system consists of a server, terminals, and users. 【0396】 The server operates on a cloud-based platform and collects data related to students' learning activities and emotions using data acquisition methods. Emotional data is acquired in real time from terminals using software technology that performs facial expression and voice analysis. The server inputs the acquired data into a generative AI model, which generates a learning plan tailored to each student through data analysis. This generative model selects learning materials and activities according to the student's state based on a specific algorithm. 【0397】 The terminal, used by students, receives learning plans sent from the server and presents them to the user through a display. The terminal incorporates hardware sensors such as a camera and microphone, which are used to monitor the user's emotional state in real time. For example, if a student is not concentrating, the terminal displays relaxation content and sends the effect as feedback data to the server. This allows the server to adjust the next learning plan and provide more effective content. 【0398】 The student user learns the materials according to the provided learning plan. During the learning process, the device monitors the student's responses and sends feedback to the server as needed. 【0399】 For example, in the case of a student studying mathematics, the generative AI model instantly presents the most suitable learning materials based on their level of concentration while working on problems. If it determines that a visual break is needed, the device can suggest light games or videos. 【0400】 An example of a prompt is, "Generate a learning plan that suggests content to help students relax." This prompt is input to a generative model and provides hints for creating an appropriate learning environment. 【0401】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0402】 Step 1: 【0403】 The server collects student learning history data and emotional data. It receives real-time facial expression and voice data transmitted from the terminal as input. This data is stored on the server using data acquisition methods. During this process, facial recognition and voice analysis technologies are used to understand the student's current state. As output, the server constructs organized learning and emotional datasets. 【0404】 Step 2: 【0405】 The server inputs organized learning data and sentiment data into a generative AI model. It performs analysis and generates learning plans tailored to individual students. The input data includes past learning progress, evaluation scores, and sentiment states. The generative AI model uses pattern recognition techniques to develop an optimized plan, resulting in a customized learning plan as output. 【0406】 Step 3: 【0407】 The server sends the generated learning plan to the device. The learning plan is sent as a digital file using a communication method. The transmitted data includes a list of specific learning materials and emotionally responsive activity suggestions. This makes the device ready to use the received plan immediately. 【0408】 Step 4: 【0409】 The device presents the received learning plan to the user. It uses display means to show learning materials and activities in a visually easy-to-understand format. Input information includes the learning plan from the server, and based on this, the device updates the dashboard and presents a visualized learning plan as output. The user's emotional state is also taken into consideration as it may influence the plan. 【0410】 Step 5: 【0411】 The user progresses through the learning process based on the presented learning plan. Inputs include clicking on learning materials and completing tasks. Based on this, the device monitors the student's learning behavior and sends feedback to the server as output. The emotion engine simultaneously evaluates the user's state, such as concentration and stress levels. 【0412】 Step 6: 【0413】 The server receives feedback data sent from the terminal and uses it for subsequent learning sessions. It analyzes the feedback data and updates the learning plan using adjustment mechanisms. This allows for further improvement in student performance in the next session. The server is then ready to send the improved learning plan back to the terminal as output. 【0414】 (Application Example 2) 【0415】 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." 【0416】 Traditional learning management systems could track students' learning progress, but they struggled to provide dynamic educational plans that took emotions into account. Therefore, they couldn't flexibly provide the most suitable learning environment for each individual student. Furthermore, the inability to provide real-time feedback tailored to students' understanding and emotions meant that learning efficiency wasn't adequately improved. 【0417】 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. 【0418】 In this invention, the server includes emotion analysis means for recognizing the emotions of the student and reflecting adjustments to the educational plan accordingly, information acquisition means for collecting information on the student's learning, and analysis means for analyzing the acquired learning information and generating a suitable educational plan. This enables the provision of flexible educational plans that respond to the student's emotional state and real-time feedback that corresponds to their level of understanding. 【0419】 A "student receiving education" is someone who receives educational support during the learning process. 【0420】 "Information acquisition means" refers to devices and methods for collecting information related to the learning of educated individuals. 【0421】 An "analysis tool" is a device that analyzes acquired learning information and generates a suitable educational plan. 【0422】 "Means of transmission" refers to a mechanism or method for transmitting the generated educational plan to the recipient's personal information terminal. 【0423】 "Display means" refers to devices or methods for visually showing a transmitted educational plan to the student on a mobile information terminal. 【0424】 A "progress management method" is a method for tracking the learning activities of students, recording their progress, and transmitting it to a data processing device. 【0425】 "Emotional analysis tools" are tools that recognize the emotions of those being educated and reflect those emotions in the educational plan. 【0426】 A system implementing this invention includes functions such as a server for handling educational support, a portable information terminal for students to learn, and an emotion engine for analyzing emotions. 【0427】 The server collects learning-related information from students through information acquisition methods and analyzes this information using analytical methods to generate an optimal educational plan. This process utilizes a cloud-based platform with powerful processing capabilities for handling data. AI models are used to analyze students' facial expressions and voice data and recognize emotions; for example, Google Cloud's AI models or Amazon Web Services' Rekognition can be used. 【0428】 This educational plan is transmitted to the student's mobile device via a communication device and displayed visually on a display device. The device monitors the student's emotional state using a camera and microphone, and adjusts the educational plan accordingly through an emotion analysis device. Depending on the situation, learning progress and comprehension are also provided as real-time feedback. 【0429】 For example, if a child learning math is losing focus, the emotion engine recognizes this, and the device suggests relaxing music or a short game. A series of feedback data is sent to a server, and adjustments are made based on this information in the next learning session. In this application, an example of a prompt sentence used to indicate a specific emotional state to the generative AI model for educational support would be, "This child is clearly losing focus, judging from their facial expression and tone of voice. Suggest a short break and a fun activity." 【0430】 In this way, this system can provide an optimal learning experience according to the emotional state of the learner, thereby significantly improving the efficiency of learning. 【0431】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0432】 Step 1: 【0433】 Sensor input 【0434】 The server acquires image and audio data from the camera and microphone on the device. This data is sent as input and preprocessed by the AI ​​model for emotion recognition. Specifically, facial features are extracted from the images, and tone and tempo are analyzed from the audio. 【0435】 Step 2: 【0436】 emotion recognition 【0437】 The server inputs the pre-processed data into a generating AI model to estimate the emotions of the student. An example of a prompt is "The facial expression and tone of voice indicate that the student is losing focus," and the model outputs an emotional state such as "decreased concentration." 【0438】 Step 3: 【0439】 Generation of an educational plan 【0440】 The server uses analytical tools to generate an optimal educational plan based on the output of emotional states. Specifically, if concentration levels are low, it creates a plan that includes music or games to suggest breaks. Past data, such as learning progress, is also taken into consideration. 【0441】 Step 4: 【0442】 Plan Communication 【0443】 The server sends the generated lesson plan to the terminal. The terminal receives this plan, and the display device visually presents the plan to the student. For example, it might display a message on the screen suggesting a relaxing activity. 【0444】 Step 5: 【0445】 Real-time feedback 【0446】 The user progresses through the learning process based on the presented plan. The device collects the user's responses again as new image and audio data, sends this to the server, and continues to provide feedback. The server uses this feedback data to make adjustments to improve the next educational plan, thereby enhancing the overall learning efficiency of the system. 【0447】 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. 【0448】 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. 【0449】 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. 【0450】 [Third Embodiment] 【0451】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0452】 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. 【0453】 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). 【0454】 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. 【0455】 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. 【0456】 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). 【0457】 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. 【0458】 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. 【0459】 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. 【0460】 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. 【0461】 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. 【0462】 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". 【0463】 The learning management system of this invention connects a cloud-based server with student devices via a network, utilizing AI technology to provide an optimal learning plan for each individual student. This system is capable of collecting, analyzing, and providing feedback on student learning activities in real time. 【0464】 Server operation 【0465】 The server receives learning activity data periodically sent from students' devices. This data includes, for example, the results of answering questions, study time, and viewing status of video lectures. The server analyzes the received data using an AI model and generates an optimized learning plan for each student based on the results. The generated plan is then delivered from the server to the student's device at the appropriate time. 【0466】 Terminal operation 【0467】 The device notifies the student of the learning plan received from the server and displays it on the screen. As the student progresses through the learning according to the plan, the device automatically records their progress. The recorded learning data is sent back to the server and used for analysis by the server. Through this iterative process, the learning plan is constantly updated based on the student's latest level of understanding. 【0468】 User actions 【0469】 Students, as users, can progress through their studies based on the learning plan displayed on their devices. By completing the assigned tasks, they can advance to the next learning phase, and their progress can be monitored in real time. Teachers and parents can also monitor students' learning progress through a dashboard and provide guidance and support as needed. 【0470】 Specific example 【0471】 For example, in the case of a student learning mathematics, the server generates a new set of problems, including similar problems, based on problems the student has previously answered incorrectly, and delivers them to the student's device. The device displays the problems, and the student enters their answers. Feedback based on the answers is immediately displayed, and the results are sent to the server. The server analyzes this data and determines the next steps necessary for the student. 【0472】 Thus, this invention provides an individually optimized learning experience using AI technology, thereby improving learning effectiveness. 【0473】 The following describes the processing flow. 【0474】 Step 1: 【0475】 The device begins collecting learning data from the moment a student starts a learning activity. Specifically, it records the questions answered, the duration of videos watched, and access information for learning materials. 【0476】 Step 2: 【0477】 The device sends the collected learning data to the server at a set time or after the learning session ends. The data is transmitted over the network and received by the server. 【0478】 Step 3: 【0479】 The server inputs the learning data received from the terminal into the AI ​​analysis engine and begins the analysis. This process identifies the student's learning progress and areas of weakness, and structures and stores the data. 【0480】 Step 4: 【0481】 The server generates a personalized learning plan based on the analysis results. This plan includes a new set of problems and recommended learning materials. The generated plan is then prepared for the next learning session. 【0482】 Step 5: 【0483】 The server delivers the generated learning plan to the target student's device. Delivery occurs in real time or upon the next login. 【0484】 Step 6: 【0485】 The device notifies the student of the learning plan received from the server and displays it on the learning screen. The student then resumes learning based on the presented plan. 【0486】 Step 7: 【0487】 The student user solves problems and watches video learning materials according to the displayed learning plan. This allows the device to continuously collect new learning data, initiating the next cycle. 【0488】 By repeating this series of steps, students' learning is always optimized based on updated information. 【0489】 (Example 1) 【0490】 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." 【0491】 Traditional learning management systems have faced challenges in dynamically generating personalized learning plans and updating their content in real time. Furthermore, they have been unable to effectively track learners' progress and provide immediate feedback based on their understanding, limiting learning effectiveness. Additionally, there has been a lack of means for educators and learners themselves to visually monitor and analyze their own learning progress. 【0492】 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. 【0493】 In this invention, the server includes information gathering means for collecting learner learning information, analysis means for analyzing the collected learning information and generating an optimal learning plan using a generative AI model, and transmission means for transmitting the generated learning plan to the learner's device. This enables the dynamic generation and real-time updating of individually optimized learning plans, improving the learner's learning effectiveness, and simultaneously providing visual feedback on progress to educators and the learners themselves. 【0494】 "Learning information" refers to all data generated by learners during their learning activities, including, for example, answer results, study time, and viewing status. 【0495】 "Information gathering means" refers to a mechanism for collecting learning information from the learner's device and transmitting it to a server. 【0496】 "Analysis method" refers to the process of analyzing data using a generated AI model based on collected learning information and generating an optimal learning plan for each learner. 【0497】 A "generative AI model" refers to a model that uses algorithms based on machine learning and deep learning to analyze training data and generate individually optimized learning plans. 【0498】 "Transmission means" refers to the communication means used to deliver the learning plan generated by the analysis means to the learner's device. 【0499】 A "learning plan" refers to a plan generated by an analytical tool that includes the tasks and learning content that learners should undertake. 【0500】 "Device" refers to an electronic device used by learners to receive learning plans and proceed with their studies according to their contents. 【0501】 This invention relates to a learning management system that includes learner devices and a server connected via a network. This system utilizes a generative AI model to generate and provide personalized learning plans optimized for each learner in real time. 【0502】 The server periodically collects learning information from the learner's device. This information includes assignment completion results, time spent learning, and progress on viewed learning content. This data is securely stored using a cloud storage service. A database management system and analysis software are used for specific data processing. 【0503】 The server analyzes the collected training information using a generative AI model. Machine learning frameworks such as TensorFlow and PyTorch are used for the analysis. Based on the analysis results, an optimal training plan is generated. The generated training plan is sent from the server to the learner's device and presented to the learner on the device. 【0504】 One concrete example of using server-generated AI models is suggesting similar problems to a mathematics student based on their previous incorrect answers. In this case, the server inputs a prompt to the AI ​​model such as: "Create the next learning plan based on this student's past learning data." 【0505】 The terminal notifies and displays the learner of the learning plan sent from the server. As the learner progresses according to the plan, their progress is automatically recorded by the terminal. The recorded data is sent back to the server and used as data for the next plan. 【0506】 The user, the learner, can use the device to execute their learning plan and check their progress in real time. Educators can also monitor learning progress through a provided dashboard and provide feedback as needed. In this way, the present invention provides an individually optimized learning experience based on the learner's level of understanding, thereby supporting improved learning effectiveness. 【0507】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0508】 Step 1: 【0509】 The server periodically collects learning information from learners' devices. This information includes assignment completion results, time spent learning, and progress on viewed educational content. The input is learner behavior data, which is categorized and organized for each session. The output is the collected learning data set, which is securely stored in a database. A database management system is used for this process. 【0510】 Step 2: 【0511】 The server passes the collected data to the generating AI model for analysis. The input includes the training data organized in Step 1, and the AI ​​generates an optimal training plan based on this data. Machine learning algorithms are used for data processing, and the output is an individualized training plan. This plan includes suggesting the most appropriate next training task for each learner. This process uses a machine learning framework with Python. 【0512】 Step 3: 【0513】 The server sends the generated learning plan to the learner's terminal. The input is the learning plan generated in step 2, and the output is the format received by the terminal. This process uses a network-based communication protocol. The server notifies the terminal that it has received the plan and provides the data necessary for visualization. 【0514】 Step 4: 【0515】 The terminal notifies the learner of the learning plan received from the server and displays it on the screen. The input is the learning plan sent from the server, and the output is the content presented to the learner. The terminal provides the tools and interfaces necessary for the learner to proceed with their learning according to the plan. Specifically, these include question navigation and answer input interfaces. 【0516】 Step 5: 【0517】 The user, the learner, progresses through the learning process based on the provided learning plan. The device automatically records their progress and the completion status of assignments. Input consists of the learner's actions and responses, and output is uploaded to the server as recorded progress data. This progress management ensures that the latest learning status is always available on the server. 【0518】 Step 6: 【0519】 The server re-analyzes the updated progress data and uses it to generate the next learning plan. The input data is the learner's latest progress information, and the AI ​​model uses this to determine the next step. The output is the next learning plan, and this cycle continues. The server can also prepare feedback as needed and send it immediately to the learner's device. 【0520】 (Application Example 1) 【0521】 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." 【0522】 In today's urban environment, there is a need to provide individually optimized learning plans that meet diverse learning needs. However, conventional systems have limitations in providing timely information tailored to the learning progress and understanding of individual residents. Furthermore, there has been a lack of efficient methods for providing learning support linked to local educational facilities and event information. As a result, maximizing learning effectiveness has been difficult. 【0523】 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. 【0524】 In this invention, the server includes data collection means for providing information available to residents, analysis means for analyzing aggregated learning information to generate individually optimized learning plans, and transmission means for delivering the generated learning plans to the user's information terminal. This enables the provision of effective learning plans tailored to the learning progress of each individual user and the optimal use of local educational resources. 【0525】 "Information available to residents" refers to education-related information that residents in an urban environment can access on a daily basis. 【0526】 "Aggregated learning information" refers to a collection of data from individual learners, used for advanced analysis. 【0527】 "Individually optimized learning plans" refer to educational plans customized by AI based on the user's learning progress and level of understanding. 【0528】 "User information terminal" refers to electronic devices such as smartphones and tablets used by individual learners. 【0529】 "Usage status of educational facilities within the region" refers to information indicating the current availability of educational facilities and learning support facilities. 【0530】 A "progress management means" is a function that tracks the user's learning activities, records their progress, and transmits it to an information processing device. 【0531】 A "visualization method" is a visual display system that allows users and instructors to check and evaluate learning progress and status. 【0532】 The system for realizing this invention includes a server, a user's information terminal, and a local educational facility. The server utilizes a cloud computing platform to collect information available to residents and analyze the aggregated learning information. Suitable platforms for this include Amazon Web Services (AWS) and Google Cloud Platform (GCP). Frameworks such as TensorFlow and PyTorch are used for the AI ​​model. 【0533】 The user's information terminals include smartphones and tablets, and applications are developed using React Native or Flutter. The terminals display individually optimized training plans sent from the server and process the user's input in real time. 【0534】 The server tracks the user's learning progress through a progress management system and sends the data to the cloud. Based on this progress information, it indicates the next tasks to be addressed. Furthermore, by displaying information on the usage status of local educational facilities and educational events on the terminal in real time, it provides the user with an optimal learning environment. 【0535】 As a concrete example, consider a scenario where students in a certain region learn simultaneous equations online. The device uses AI to suggest learning plans based on past answer data and progress, allowing residents to receive the optimal curriculum from the comfort of their homes. 【0536】 As an example of a prompt, users can input instructions in natural language such as, "Based on past math answer data and progress, please use AI to suggest the next problem to tackle," and the system will then analyze this information using a generative AI model. This system will not only allow users to enjoy a personalized learning experience but also enable them to make the most of local educational resources. 【0537】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0538】 Step 1: 【0539】 The server collects the residents' learning data. 【0540】 Input includes learning history, answer scores, and usage time. 【0541】 The server collects this data and stores it in a cloud-based database. 【0542】 Step 2: 【0543】 The server analyzes the training data it has collected. 【0544】 The training data stored as input is used. 【0545】 The server uses a generative AI model to analyze this data and generate individually optimized training plans. The output is a training plan tailored to the user. 【0546】 Step 3: 【0547】 The server distributes the generated training plan to the information terminal. 【0548】 The input is the learning plan generated in Step 2. 【0549】 The server sends this plan to the user's device. 【0550】 Step 4: 【0551】 The device displays the received learning plan to the user. 【0552】 The learning plan sent from the server is used as input. 【0553】 The device displays a learning plan through an application, indicating the next task the user should complete. 【0554】 Step 5: 【0555】 The user progresses through the learning process based on the learning plan and inputs their progress into the device. 【0556】 The input consists of the user's learning results, activity time, and answers. 【0557】 The terminal records this data using a progress management system. 【0558】 Step 6: 【0559】 The device sends the recorded learning progress to the server. 【0560】 The input is the progress data recorded in step 5. 【0561】 The device sends this data to the server via a secure channel. 【0562】 Step 7: 【0563】 The server analyzes the transmitted learning progress data and generates an updated learning plan. 【0564】 The latest training progress data is used as input. 【0565】 The server then uses the generated AI model again to analyze the data and generate the optimal learning plan. The output is the new learning plan. 【0566】 Step 8: 【0567】 The server notifies information terminals of the usage status of educational facilities within the region. 【0568】 The input consists of facility usage data and requirements derived from learning plans. 【0569】 The server sends this information to the terminal, which helps users schedule their visits to the facility. 【0570】 Step 9: 【0571】 Based on the information received by the user, the system will maximize the use of local educational resources to advance learning. 【0572】 Users utilize the information presented to them and continue learning in the optimal environment. 【0573】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0574】 This invention dynamically provides a learning environment that responds to students' emotions by incorporating an emotion engine into a learning management system. This system, which includes a cloud-based server, learning terminals, and emotion recognition capabilities, aims to improve students' learning efficiency. 【0575】 Server operation 【0576】 The server collects and analyzes students' learning and emotional data to generate personalized learning plans. Emotional data is obtained from the user's facial expressions, voice, or behavior, and their emotional state is recognized through AI analysis on the server side. For example, if a student is restless, relaxing materials or music can be incorporated into the learning plan. 【0577】 Terminal operation 【0578】 The device receives learning plans and emotion-based content sent from the server and provides them to the user. It incorporates an emotion engine that monitors the user's emotional state in real time through sensors such as the camera and microphone. This information is sent to the server and used to adjust future learning plans. 【0579】 User actions 【0580】 The student user learns based on the content displayed on the device. If the emotion engine detects tension or anxiety during the learning process, the device immediately sends feedback to the server, which then provides more appropriate learning content based on that feedback. 【0581】 Specific example 【0582】 When a student is learning mathematics and solving a problem, the emotional engine recognizes their seriousness. If the emotional engine determines that the student's concentration has wavered, the device suggests a short game or video for refreshment. Once this response is sent within the feedback loop, the server uses that information to make adjustments in the next session to promote more sustainable concentration. 【0583】 In this form, the present invention can provide a learning plan that incorporates students' emotional states, thereby improving the learning experience and creating an efficient learning environment. 【0584】 The following describes the processing flow. 【0585】 Step 1: 【0586】 As soon as a student begins learning, the device uses its built-in camera and microphone to capture their facial expressions and voice in real time, collecting emotional data. Simultaneously, it also records data from their regular learning activities. 【0587】 Step 2: 【0588】 The device transmits collected emotional data and learning activity data to the server at regular intervals. The emotional data is used as an indicator of the student's current emotional state. 【0589】 Step 3: 【0590】 The server inputs the received emotional data into an AI analysis engine to analyze the student's emotional state. For example, if the student's emotions deviate from a calm state, the server identifies the cause and considers appropriate countermeasures. 【0591】 Step 4: 【0592】 The server dynamically adjusts the learning plan based on the analysis results. If the emotional state is deteriorating, it incorporates tasks and materials that promote relaxation. If it is stable, it maintains the normal learning plan. 【0593】 Step 5: 【0594】 The server delivers the customized learning plan to the student's device. The device then displays this plan to the student in real time. 【0595】 Step 6: 【0596】 The student user continues learning based on the adjusted learning plan displayed on their device. The system continues to monitor data on emotions and learning activities, collecting new information for the next cycle. 【0597】 Step 7: 【0598】 By repeating this process, the server and terminal work together to continuously provide a dynamic learning environment that adapts to the student's emotions. Ultimately, the student's learning experience becomes more personalized and effectively improved. 【0599】 (Example 2) 【0600】 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." 【0601】 Traditional learning management systems provide a uniform learning plan without considering students' emotional states, making it difficult to provide a learning environment optimized for individual students. This results in problems such as decreased student concentration and motivation, and insufficient learning efficiency. 【0602】 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. 【0603】 In this invention, the server includes data acquisition means for collecting student learning data and emotional data, a generative model for analyzing the acquired data to generate individually optimized learning plans, and communication means for transmitting the learning plans to terminals. This makes it possible to provide a dynamic and optimized learning environment that is tailored to each student's emotional state. 【0604】 "Data acquisition means" refers to devices and methods for collecting students' learning data and emotional data. 【0605】 A "generative model" is an algorithm or method that analyzes collected learning data and emotional data to generate learning plans optimized for individual students. 【0606】 "Communication methods" refer to the technologies and means used to send the generated learning plan to the student's device. 【0607】 "Display means" refers to a function that presents a learning plan on a device and dynamically adjusts the content based on the student's emotional state. 【0608】 A "monitoring system" is a mechanism for continuously monitoring students' learning activities and emotional states and sending feedback data to a server. 【0609】 "Adjustment methods" refer to methods and techniques for updating learning plans for subsequent sessions based on feedback data. 【0610】 This invention is a technology that provides an individualized learning environment that takes students' emotions into consideration in a learning management system. The system consists of a server, terminals, and users. 【0611】 The server operates on a cloud-based platform and collects data related to students' learning activities and emotions using data acquisition methods. Emotional data is acquired in real time from terminals using software technology that performs facial expression and voice analysis. The server inputs the acquired data into a generative AI model, which generates a learning plan tailored to each student through data analysis. This generative model selects learning materials and activities according to the student's state based on a specific algorithm. 【0612】 The terminal, used by students, receives learning plans sent from the server and presents them to the user through a display. The terminal incorporates hardware sensors such as a camera and microphone, which are used to monitor the user's emotional state in real time. For example, if a student is not concentrating, the terminal displays relaxation content and sends the effect as feedback data to the server. This allows the server to adjust the next learning plan and provide more effective content. 【0613】 The student user learns the materials according to the provided learning plan. During the learning process, the device monitors the student's responses and sends feedback to the server as needed. 【0614】 For example, in the case of a student studying mathematics, the generative AI model instantly presents the most suitable learning materials based on their level of concentration while working on problems. If it determines that a visual break is needed, the device can suggest light games or videos. 【0615】 An example of a prompt is, "Generate a learning plan that suggests content to help students relax." This prompt is input to a generative model and provides hints for creating an appropriate learning environment. 【0616】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0617】 Step 1: 【0618】 The server collects student learning history data and emotional data. It receives real-time facial expression and voice data transmitted from the terminal as input. This data is stored on the server using data acquisition methods. During this process, facial recognition and voice analysis technologies are used to understand the student's current state. As output, the server constructs organized learning and emotional datasets. 【0619】 Step 2: 【0620】 The server inputs organized learning data and sentiment data into a generative AI model. It performs analysis and generates learning plans tailored to individual students. The input data includes past learning progress, evaluation scores, and sentiment states. The generative AI model uses pattern recognition techniques to develop an optimized plan, resulting in a customized learning plan as output. 【0621】 Step 3: 【0622】 The server sends the generated learning plan to the device. The learning plan is sent as a digital file using a communication method. The transmitted data includes a list of specific learning materials and emotionally responsive activity suggestions. This makes the device ready to use the received plan immediately. 【0623】 Step 4: 【0624】 The device presents the received learning plan to the user. It uses display means to show learning materials and activities in a visually easy-to-understand format. Input information includes the learning plan from the server, and based on this, the device updates the dashboard and presents a visualized learning plan as output. The user's emotional state is also taken into consideration as it may influence the plan. 【0625】 Step 5: 【0626】 The user progresses through the learning process based on the presented learning plan. Inputs include clicking on learning materials and completing tasks. Based on this, the device monitors the student's learning behavior and sends feedback to the server as output. The emotion engine simultaneously evaluates the user's state, such as concentration and stress levels. 【0627】 Step 6: 【0628】 The server receives feedback data sent from the terminal and uses it for subsequent learning sessions. It analyzes the feedback data and updates the learning plan using adjustment mechanisms. This allows for further improvement in student performance in the next session. The server is then ready to send the improved learning plan back to the terminal as output. 【0629】 (Application Example 2) 【0630】 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." 【0631】 Traditional learning management systems could track students' learning progress, but they struggled to provide dynamic educational plans that took emotions into account. Therefore, they couldn't flexibly provide the most suitable learning environment for each individual student. Furthermore, the inability to provide real-time feedback tailored to students' understanding and emotions meant that learning efficiency wasn't adequately improved. 【0632】 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. 【0633】 In this invention, the server includes emotion analysis means for recognizing the emotions of the student and reflecting adjustments to the educational plan accordingly, information acquisition means for collecting information on the student's learning, and analysis means for analyzing the acquired learning information and generating a suitable educational plan. This enables the provision of flexible educational plans that respond to the student's emotional state and real-time feedback that corresponds to their level of understanding. 【0634】 A "student receiving education" is someone who receives educational support during the learning process. 【0635】 "Information acquisition means" refers to devices and methods for collecting information related to the learning of educated individuals. 【0636】 An "analysis tool" is a device that analyzes acquired learning information and generates a suitable educational plan. 【0637】 "Means of transmission" refers to a mechanism or method for transmitting the generated educational plan to the recipient's personal information terminal. 【0638】 "Display means" refers to devices or methods for visually showing a transmitted educational plan to the student on a mobile information terminal. 【0639】 A "progress management method" is a method for tracking the learning activities of students, recording their progress, and transmitting it to a data processing device. 【0640】 "Emotional analysis tools" are tools that recognize the emotions of those being educated and reflect those emotions in the educational plan. 【0641】 A system implementing this invention includes functions such as a server for handling educational support, a portable information terminal for students to learn, and an emotion engine for analyzing emotions. 【0642】 The server collects learning-related information from students through information acquisition methods and analyzes this information using analytical methods to generate an optimal educational plan. This process utilizes a cloud-based platform with powerful processing capabilities for handling data. AI models are used to analyze students' facial expressions and voice data and recognize emotions; for example, Google Cloud's AI models or Amazon Web Services' Rekognition can be used. 【0643】 This educational plan is transmitted to the student's mobile device via a communication device and displayed visually on a display device. The device monitors the student's emotional state using a camera and microphone, and adjusts the educational plan accordingly through an emotion analysis device. Depending on the situation, learning progress and comprehension are also provided as real-time feedback. 【0644】 For example, if a child learning math is losing focus, the emotion engine recognizes this, and the device suggests relaxing music or a short game. A series of feedback data is sent to a server, and adjustments are made based on this information in the next learning session. In this application, an example of a prompt sentence used to indicate a specific emotional state to the generative AI model for educational support would be, "This child is clearly losing focus, judging from their facial expression and tone of voice. Suggest a short break and a fun activity." 【0645】 In this way, this system can provide an optimal learning experience according to the emotional state of the learner, thereby significantly improving the efficiency of learning. 【0646】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0647】 Step 1: 【0648】 Sensor input 【0649】 The server acquires image and audio data from the camera and microphone on the device. This data is sent as input and preprocessed by the AI ​​model for emotion recognition. Specifically, facial features are extracted from the images, and tone and tempo are analyzed from the audio. 【0650】 Step 2: 【0651】 emotion recognition 【0652】 The server inputs the pre-processed data into a generating AI model to estimate the emotions of the student. An example of a prompt is "The facial expression and tone of voice indicate that the student is losing focus," and the model outputs an emotional state such as "decreased concentration." 【0653】 Step 3: 【0654】 Generation of an educational plan 【0655】 The server uses analytical tools to generate an optimal educational plan based on the output of emotional states. Specifically, if concentration levels are low, it creates a plan that includes music or games to suggest breaks. Past data, such as learning progress, is also taken into consideration. 【0656】 Step 4: 【0657】 Plan Communication 【0658】 The server sends the generated lesson plan to the terminal. The terminal receives this plan, and the display device visually presents the plan to the student. For example, it might display a message on the screen suggesting a relaxing activity. 【0659】 Step 5: 【0660】 Real-time feedback 【0661】 The user progresses through the learning process based on the presented plan. The device collects the user's responses again as new image and audio data, sends this to the server, and continues to provide feedback. The server uses this feedback data to make adjustments to improve the next educational plan, thereby enhancing the overall learning efficiency of the system. 【0662】 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. 【0663】 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. 【0664】 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. 【0665】 [Fourth Embodiment] 【0666】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0667】 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. 【0668】 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). 【0669】 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. 【0670】 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. 【0671】 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). 【0672】 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. 【0673】 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. 【0674】 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. 【0675】 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. 【0676】 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. 【0677】 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. 【0678】 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". 【0679】 The learning management system of this invention connects a cloud-based server with student devices via a network, utilizing AI technology to provide an optimal learning plan for each individual student. This system is capable of collecting, analyzing, and providing feedback on student learning activities in real time. 【0680】 Server operation 【0681】 The server receives learning activity data periodically sent from students' devices. This data includes, for example, the results of answering questions, study time, and viewing status of video lectures. The server analyzes the received data using an AI model and generates an optimized learning plan for each student based on the results. The generated plan is then delivered from the server to the student's device at the appropriate time. 【0682】 Terminal operation 【0683】 The device notifies the student of the learning plan received from the server and displays it on the screen. As the student progresses through the learning according to the plan, the device automatically records their progress. The recorded learning data is sent back to the server and used for analysis by the server. Through this iterative process, the learning plan is constantly updated based on the student's latest level of understanding. 【0684】 User actions 【0685】 Students, as users, can progress through their studies based on the learning plan displayed on their devices. By completing the assigned tasks, they can advance to the next learning phase, and their progress can be monitored in real time. Teachers and parents can also monitor students' learning progress through a dashboard and provide guidance and support as needed. 【0686】 Specific example 【0687】 For example, in the case of a student learning mathematics, the server generates a new set of problems, including similar problems, based on problems the student has previously answered incorrectly, and delivers them to the student's device. The device displays the problems, and the student enters their answers. Feedback based on the answers is immediately displayed, and the results are sent to the server. The server analyzes this data and determines the next steps necessary for the student. 【0688】 Thus, this invention provides an individually optimized learning experience using AI technology, thereby improving learning effectiveness. 【0689】 The following describes the processing flow. 【0690】 Step 1: 【0691】 The device begins collecting learning data from the moment a student starts a learning activity. Specifically, it records the questions answered, the duration of videos watched, and access information for learning materials. 【0692】 Step 2: 【0693】 The device sends the collected learning data to the server at a set time or after the learning session ends. The data is transmitted over the network and received by the server. 【0694】 Step 3: 【0695】 The server inputs the learning data received from the terminal into the AI ​​analysis engine and begins the analysis. This process identifies the student's learning progress and areas of weakness, and structures and stores the data. 【0696】 Step 4: 【0697】 The server generates a personalized learning plan based on the analysis results. This plan includes a new set of problems and recommended learning materials. The generated plan is then prepared for the next learning session. 【0698】 Step 5: 【0699】 The server delivers the generated learning plan to the target student's device. Delivery occurs in real time or upon the next login. 【0700】 Step 6: 【0701】 The device notifies the student of the learning plan received from the server and displays it on the learning screen. The student then resumes learning based on the presented plan. 【0702】 Step 7: 【0703】 The student user solves problems and watches video learning materials according to the displayed learning plan. This allows the device to continuously collect new learning data, initiating the next cycle. 【0704】 By repeating this series of steps, students' learning is always optimized based on updated information. 【0705】 (Example 1) 【0706】 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". 【0707】 Traditional learning management systems have faced challenges in dynamically generating personalized learning plans and updating their content in real time. Furthermore, they have been unable to effectively track learners' progress and provide immediate feedback based on their understanding, limiting learning effectiveness. Additionally, there has been a lack of means for educators and learners themselves to visually monitor and analyze their own learning progress. 【0708】 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. 【0709】 In this invention, the server includes information gathering means for collecting learner learning information, analysis means for analyzing the collected learning information and generating an optimal learning plan using a generative AI model, and transmission means for transmitting the generated learning plan to the learner's device. This enables the dynamic generation and real-time updating of individually optimized learning plans, improving the learner's learning effectiveness, and simultaneously providing visual feedback on progress to educators and the learners themselves. 【0710】 "Learning information" refers to all data generated by learners during their learning activities, including, for example, answer results, study time, and viewing status. 【0711】 "Information gathering means" refers to a mechanism for collecting learning information from the learner's device and transmitting it to a server. 【0712】 "Analysis method" refers to the process of analyzing data using a generated AI model based on collected learning information and generating an optimal learning plan for each learner. 【0713】 A "generative AI model" refers to a model that uses algorithms based on machine learning and deep learning to analyze training data and generate individually optimized learning plans. 【0714】 "Transmission means" refers to the communication means used to deliver the learning plan generated by the analysis means to the learner's device. 【0715】 A "learning plan" refers to a plan generated by an analytical tool that includes the tasks and learning content that learners should undertake. 【0716】 "Device" refers to an electronic device used by learners to receive learning plans and proceed with their studies according to their contents. 【0717】 This invention relates to a learning management system that includes learner devices and a server connected via a network. This system utilizes a generative AI model to generate and provide personalized learning plans optimized for each learner in real time. 【0718】 The server periodically collects learning information from the learner's device. This information includes assignment completion results, time spent learning, and progress on viewed learning content. This data is securely stored using a cloud storage service. A database management system and analysis software are used for specific data processing. 【0719】 The server analyzes the collected training information using a generative AI model. Machine learning frameworks such as TensorFlow and PyTorch are used for the analysis. Based on the analysis results, an optimal training plan is generated. The generated training plan is sent from the server to the learner's device and presented to the learner on the device. 【0720】 One concrete example of using server-generated AI models is suggesting similar problems to a mathematics student based on their previous incorrect answers. In this case, the server inputs a prompt to the AI ​​model such as: "Create the next learning plan based on this student's past learning data." 【0721】 The terminal notifies and displays the learner of the learning plan sent from the server. As the learner progresses according to the plan, their progress is automatically recorded by the terminal. The recorded data is sent back to the server and used as data for the next plan. 【0722】 The user, the learner, can use the device to execute their learning plan and check their progress in real time. Educators can also monitor learning progress through a provided dashboard and provide feedback as needed. In this way, the present invention provides an individually optimized learning experience based on the learner's level of understanding, thereby supporting improved learning effectiveness. 【0723】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0724】 Step 1: 【0725】 The server periodically collects learning information from learners' devices. This information includes assignment completion results, time spent learning, and progress on viewed educational content. The input is learner behavior data, which is categorized and organized for each session. The output is the collected learning data set, which is securely stored in a database. A database management system is used for this process. 【0726】 Step 2: 【0727】 The server passes the collected data to the generating AI model for analysis. The input includes the training data organized in Step 1, and the AI ​​generates an optimal training plan based on this data. Machine learning algorithms are used for data processing, and the output is an individualized training plan. This plan includes suggesting the most appropriate next training task for each learner. This process uses a machine learning framework with Python. 【0728】 Step 3: 【0729】 The server sends the generated learning plan to the learner's terminal. The input is the learning plan generated in step 2, and the output is the format received by the terminal. This process uses a network-based communication protocol. The server notifies the terminal that it has received the plan and provides the data necessary for visualization. 【0730】 Step 4: 【0731】 The terminal notifies the learner of the learning plan received from the server and displays it on the screen. The input is the learning plan sent from the server, and the output is the content presented to the learner. The terminal provides the tools and interfaces necessary for the learner to proceed with their learning according to the plan. Specifically, these include question navigation and answer input interfaces. 【0732】 Step 5: 【0733】 The user, the learner, progresses through the learning process based on the provided learning plan. The device automatically records their progress and the completion status of assignments. Input consists of the learner's actions and responses, and output is uploaded to the server as recorded progress data. This progress management ensures that the latest learning status is always available on the server. 【0734】 Step 6: 【0735】 The server re-analyzes the updated progress data and uses it to generate the next learning plan. The input data is the learner's latest progress information, and the AI ​​model uses this to determine the next step. The output is the next learning plan, and this cycle continues. The server can also prepare feedback as needed and send it immediately to the learner's device. 【0736】 (Application Example 1) 【0737】 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". 【0738】 In today's urban environment, there is a need to provide individually optimized learning plans that meet diverse learning needs. However, conventional systems have limitations in providing timely information tailored to the learning progress and understanding of individual residents. Furthermore, there has been a lack of efficient methods for providing learning support linked to local educational facilities and event information. As a result, maximizing learning effectiveness has been difficult. 【0739】 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. 【0740】 In this invention, the server includes data collection means for providing information available to residents, analysis means for analyzing aggregated learning information to generate individually optimized learning plans, and transmission means for delivering the generated learning plans to the user's information terminal. This enables the provision of effective learning plans tailored to the learning progress of each individual user and the optimal use of local educational resources. 【0741】 "Information available to residents" refers to education-related information that residents in an urban environment can access on a daily basis. 【0742】 "Aggregated learning information" refers to a collection of data from individual learners, used for advanced analysis. 【0743】 "Individually optimized learning plans" refer to educational plans customized by AI based on the user's learning progress and level of understanding. 【0744】 "User information terminal" refers to electronic devices such as smartphones and tablets used by individual learners. 【0745】 "Usage status of educational facilities within the region" refers to information indicating the current availability of educational facilities and learning support facilities. 【0746】 A "progress management means" is a function that tracks the user's learning activities, records their progress, and transmits it to an information processing device. 【0747】 A "visualization method" is a visual display system that allows users and instructors to check and evaluate learning progress and status. 【0748】 The system for realizing this invention includes a server, a user's information terminal, and a local educational facility. The server utilizes a cloud computing platform to collect information available to residents and analyze the aggregated learning information. Suitable platforms for this include Amazon Web Services (AWS) and Google Cloud Platform (GCP). Frameworks such as TensorFlow and PyTorch are used for the AI ​​model. 【0749】 The user's information terminals include smartphones and tablets, and applications are developed using React Native or Flutter. The terminals display individually optimized training plans sent from the server and process the user's input in real time. 【0750】 The server tracks the user's learning progress through a progress management system and sends the data to the cloud. Based on this progress information, it indicates the next tasks to be addressed. Furthermore, by displaying information on the usage status of local educational facilities and educational events on the terminal in real time, it provides the user with an optimal learning environment. 【0751】 As a concrete example, consider a scenario where students in a certain region learn simultaneous equations online. The device uses AI to suggest learning plans based on past answer data and progress, allowing residents to receive the optimal curriculum from the comfort of their homes. 【0752】 As an example of a prompt, users can input instructions in natural language such as, "Based on past math answer data and progress, please use AI to suggest the next problem to tackle," and the system will then analyze this information using a generative AI model. This system will not only allow users to enjoy a personalized learning experience but also enable them to make the most of local educational resources. 【0753】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0754】 Step 1: 【0755】 The server collects the residents' learning data. 【0756】 Input includes learning history, answer scores, and usage time. 【0757】 The server collects this data and stores it in a cloud-based database. 【0758】 Step 2: 【0759】 The server analyzes the training data it has collected. 【0760】 The training data stored as input is used. 【0761】 The server uses a generative AI model to analyze this data and generate individually optimized training plans. The output is a training plan tailored to the user. 【0762】 Step 3: 【0763】 The server distributes the generated training plan to the information terminal. 【0764】 The input is the learning plan generated in Step 2. 【0765】 The server sends this plan to the user's device. 【0766】 Step 4: 【0767】 The device displays the received learning plan to the user. 【0768】 The learning plan sent from the server is used as input. 【0769】 The device displays a learning plan through an application, indicating the next task the user should complete. 【0770】 Step 5: 【0771】 The user progresses through the learning process based on the learning plan and inputs their progress into the device. 【0772】 The input consists of the user's learning results, activity time, and answers. 【0773】 The terminal records this data using a progress management system. 【0774】 Step 6: 【0775】 The device sends the recorded learning progress to the server. 【0776】 The input is the progress data recorded in step 5. 【0777】 The device sends this data to the server via a secure channel. 【0778】 Step 7: 【0779】 The server analyzes the transmitted learning progress data and generates an updated learning plan. 【0780】 The latest training progress data is used as input. 【0781】 The server then uses the generated AI model again to analyze the data and generate the optimal learning plan. The output is the new learning plan. 【0782】 Step 8: 【0783】 The server notifies information terminals of the usage status of educational facilities within the region. 【0784】 The input consists of facility usage data and requirements derived from learning plans. 【0785】 The server sends this information to the terminal, which helps users schedule their visits to the facility. 【0786】 Step 9: 【0787】 Based on the information received by the user, the system will maximize the use of local educational resources to advance learning. 【0788】 Users utilize the information presented to them and continue learning in the optimal environment. 【0789】 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. 【0790】 This invention dynamically provides a learning environment that responds to students' emotions by incorporating an emotion engine into a learning management system. This system, which includes a cloud-based server, learning terminals, and emotion recognition capabilities, aims to improve students' learning efficiency. 【0791】 Server operation 【0792】 The server collects and analyzes students' learning and emotional data to generate personalized learning plans. Emotional data is obtained from the user's facial expressions, voice, or behavior, and their emotional state is recognized through AI analysis on the server side. For example, if a student is restless, relaxing materials or music can be incorporated into the learning plan. 【0793】 Terminal operation 【0794】 The device receives learning plans and emotion-based content sent from the server and provides them to the user. It incorporates an emotion engine that monitors the user's emotional state in real time through sensors such as the camera and microphone. This information is sent to the server and used to adjust future learning plans. 【0795】 User actions 【0796】 The student user learns based on the content displayed on the device. If the emotion engine detects tension or anxiety during the learning process, the device immediately sends feedback to the server, which then provides more appropriate learning content based on that feedback. 【0797】 Specific example 【0798】 When a student is learning mathematics and solving a problem, the emotional engine recognizes their seriousness. If the emotional engine determines that the student's concentration has wavered, the device suggests a short game or video for refreshment. Once this response is sent within the feedback loop, the server uses that information to make adjustments in the next session to promote more sustainable concentration. 【0799】 In this form, the present invention can provide a learning plan that incorporates students' emotional states, thereby improving the learning experience and creating an efficient learning environment. 【0800】 The following describes the processing flow. 【0801】 Step 1: 【0802】 As soon as a student begins learning, the device uses its built-in camera and microphone to capture their facial expressions and voice in real time, collecting emotional data. Simultaneously, it also records data from their regular learning activities. 【0803】 Step 2: 【0804】 The device transmits collected emotional data and learning activity data to the server at regular intervals. The emotional data is used as an indicator of the student's current emotional state. 【0805】 Step 3: 【0806】 The server inputs the received emotional data into an AI analysis engine to analyze the student's emotional state. For example, if the student's emotions deviate from a calm state, the server identifies the cause and considers appropriate countermeasures. 【0807】 Step 4: 【0808】 The server dynamically adjusts the learning plan based on the analysis results. If the emotional state is deteriorating, it incorporates tasks and materials that promote relaxation. If it is stable, it maintains the normal learning plan. 【0809】 Step 5: 【0810】 The server delivers the customized learning plan to the student's device. The device then displays this plan to the student in real time. 【0811】 Step 6: 【0812】 The student user continues learning based on the adjusted learning plan displayed on their device. The system continues to monitor data on emotions and learning activities, collecting new information for the next cycle. 【0813】 Step 7: 【0814】 By repeating this process, the server and terminal work together to continuously provide a dynamic learning environment that adapts to the student's emotions. Ultimately, the student's learning experience becomes more personalized and effectively improved. 【0815】 (Example 2) 【0816】 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". 【0817】 Traditional learning management systems provide a uniform learning plan without considering students' emotional states, making it difficult to provide a learning environment optimized for individual students. This results in problems such as decreased student concentration and motivation, and insufficient learning efficiency. 【0818】 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. 【0819】 In this invention, the server includes data acquisition means for collecting student learning data and emotional data, a generative model for analyzing the acquired data to generate individually optimized learning plans, and communication means for transmitting the learning plans to terminals. This makes it possible to provide a dynamic and optimized learning environment that is tailored to each student's emotional state. 【0820】 "Data acquisition means" refers to devices and methods for collecting students' learning data and emotional data. 【0821】 A "generative model" is an algorithm or method that analyzes collected learning data and emotional data to generate learning plans optimized for individual students. 【0822】 "Communication methods" refer to the technologies and means used to send the generated learning plan to the student's device. 【0823】 "Display means" refers to a function that presents a learning plan on a device and dynamically adjusts the content based on the student's emotional state. 【0824】 A "monitoring system" is a mechanism for continuously monitoring students' learning activities and emotional states and sending feedback data to a server. 【0825】 "Adjustment methods" refer to methods and techniques for updating learning plans for subsequent sessions based on feedback data. 【0826】 This invention is a technology that provides an individualized learning environment that takes students' emotions into consideration in a learning management system. The system consists of a server, terminals, and users. 【0827】 The server operates on a cloud-based platform and collects data related to students' learning activities and emotions using data acquisition methods. Emotional data is acquired in real time from terminals using software technology that performs facial expression and voice analysis. The server inputs the acquired data into a generative AI model, which generates a learning plan tailored to each student through data analysis. This generative model selects learning materials and activities according to the student's state based on a specific algorithm. 【0828】 The terminal, used by students, receives learning plans sent from the server and presents them to the user through a display. The terminal incorporates hardware sensors such as a camera and microphone, which are used to monitor the user's emotional state in real time. For example, if a student is not concentrating, the terminal displays relaxation content and sends the effect as feedback data to the server. This allows the server to adjust the next learning plan and provide more effective content. 【0829】 The student user learns the materials according to the provided learning plan. During the learning process, the device monitors the student's responses and sends feedback to the server as needed. 【0830】 For example, in the case of a student studying mathematics, the generative AI model instantly presents the most suitable learning materials based on their level of concentration while working on problems. If it determines that a visual break is needed, the device can suggest light games or videos. 【0831】 An example of a prompt is, "Generate a learning plan that suggests content to help students relax." This prompt is input to a generative model and provides hints for creating an appropriate learning environment. 【0832】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0833】 Step 1: 【0834】 The server collects student learning history data and emotional data. It receives real-time facial expression and voice data transmitted from the terminal as input. This data is stored on the server using data acquisition methods. During this process, facial recognition and voice analysis technologies are used to understand the student's current state. As output, the server constructs organized learning and emotional datasets. 【0835】 Step 2: 【0836】 The server inputs organized learning data and sentiment data into a generative AI model. It performs analysis and generates learning plans tailored to individual students. The input data includes past learning progress, evaluation scores, and sentiment states. The generative AI model uses pattern recognition techniques to develop an optimized plan, resulting in a customized learning plan as output. 【0837】 Step 3: 【0838】 The server sends the generated learning plan to the device. The learning plan is sent as a digital file using a communication method. The transmitted data includes a list of specific learning materials and emotionally responsive activity suggestions. This makes the device ready to use the received plan immediately. 【0839】 Step 4: 【0840】 The device presents the received learning plan to the user. It uses display means to show learning materials and activities in a visually easy-to-understand format. Input information includes the learning plan from the server, and based on this, the device updates the dashboard and presents a visualized learning plan as output. The user's emotional state is also taken into consideration as it may influence the plan. 【0841】 Step 5: 【0842】 The user progresses through the learning process based on the presented learning plan. Inputs include clicking on learning materials and completing tasks. Based on this, the device monitors the student's learning behavior and sends feedback to the server as output. The emotion engine simultaneously evaluates the user's state, such as concentration and stress levels. 【0843】 Step 6: 【0844】 The server receives feedback data sent from the terminal and uses it for subsequent learning sessions. It analyzes the feedback data and updates the learning plan using adjustment mechanisms. This allows for further improvement in student performance in the next session. The server is then ready to send the improved learning plan back to the terminal as output. 【0845】 (Application Example 2) 【0846】 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". 【0847】 Traditional learning management systems could track students' learning progress, but they struggled to provide dynamic educational plans that took emotions into account. Therefore, they couldn't flexibly provide the most suitable learning environment for each individual student. Furthermore, the inability to provide real-time feedback tailored to students' understanding and emotions meant that learning efficiency wasn't adequately improved. 【0848】 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. 【0849】 In this invention, the server includes emotion analysis means for recognizing the emotions of the student and reflecting adjustments to the educational plan accordingly, information acquisition means for collecting information on the student's learning, and analysis means for analyzing the acquired learning information and generating a suitable educational plan. This enables the provision of flexible educational plans that respond to the student's emotional state and real-time feedback that corresponds to their level of understanding. 【0850】 A "student receiving education" is someone who receives educational support during the learning process. 【0851】 "Information acquisition means" refers to devices and methods for collecting information related to the learning of educated individuals. 【0852】 An "analysis tool" is a device that analyzes acquired learning information and generates a suitable educational plan. 【0853】 "Means of transmission" refers to a mechanism or method for transmitting the generated educational plan to the recipient's personal information terminal. 【0854】 "Display means" refers to devices or methods for visually showing a transmitted educational plan to the student on a mobile information terminal. 【0855】 A "progress management method" is a method for tracking the learning activities of students, recording their progress, and transmitting it to a data processing device. 【0856】 "Emotional analysis tools" are tools that recognize the emotions of those being educated and reflect those emotions in the educational plan. 【0857】 A system implementing this invention includes functions such as a server for handling educational support, a portable information terminal for students to learn, and an emotion engine for analyzing emotions. 【0858】 The server collects learning-related information from students through information acquisition methods and analyzes this information using analytical methods to generate an optimal educational plan. This process utilizes a cloud-based platform with powerful processing capabilities for handling data. AI models are used to analyze students' facial expressions and voice data and recognize emotions; for example, Google Cloud's AI models or Amazon Web Services' Rekognition can be used. 【0859】 This educational plan is transmitted to the student's mobile device via a communication device and displayed visually on a display device. The device monitors the student's emotional state using a camera and microphone, and adjusts the educational plan accordingly through an emotion analysis device. Depending on the situation, learning progress and comprehension are also provided as real-time feedback. 【0860】 For example, if a child learning math is losing focus, the emotion engine recognizes this, and the device suggests relaxing music or a short game. A series of feedback data is sent to a server, and adjustments are made based on this information in the next learning session. In this application, an example of a prompt sentence used to indicate a specific emotional state to the generative AI model for educational support would be, "This child is clearly losing focus, judging from their facial expression and tone of voice. Suggest a short break and a fun activity." 【0861】 In this way, this system can provide an optimal learning experience according to the emotional state of the learner, thereby significantly improving the efficiency of learning. 【0862】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0863】 Step 1: 【0864】 Sensor input 【0865】 The server acquires image and audio data from the camera and microphone on the device. This data is sent as input and preprocessed by the AI ​​model for emotion recognition. Specifically, facial features are extracted from the images, and tone and tempo are analyzed from the audio. 【0866】 Step 2: 【0867】 emotion recognition 【0868】 The server inputs the pre-processed data into a generating AI model to estimate the emotions of the student. An example of a prompt is "The facial expression and tone of voice indicate that the student is losing focus," and the model outputs an emotional state such as "decreased concentration." 【0869】 Step 3: 【0870】 Generation of an educational plan 【0871】 The server uses analytical tools to generate an optimal educational plan based on the output of emotional states. Specifically, if concentration levels are low, it creates a plan that includes music or games to suggest breaks. Past data, such as learning progress, is also taken into consideration. 【0872】 Step 4: 【0873】 Plan Communication 【0874】 The server sends the generated lesson plan to the terminal. The terminal receives this plan, and the display device visually presents the plan to the student. For example, it might display a message on the screen suggesting a relaxing activity. 【0875】 Step 5: 【0876】 Real-time feedback 【0877】 The user progresses through the learning process based on the presented plan. The device collects the user's responses again as new image and audio data, sends this to the server, and continues to provide feedback. The server uses this feedback data to make adjustments to improve the next educational plan, thereby enhancing the overall learning efficiency of the system. 【0878】 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. 【0879】 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. 【0880】 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. 【0881】 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. 【0882】 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. 【0883】 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. 【0884】 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. 【0885】 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. 【0886】 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." 【0887】 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. 【0888】 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. 【0889】 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. 【0890】 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. 【0891】 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. 【0892】 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. 【0893】 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. 【0894】 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. 【0895】 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. 【0896】 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. 【0897】 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. 【0898】 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. 【0899】 The following is further disclosed regarding the embodiments described above. 【0900】 (Claim 1) 【0901】 A data collection method for collecting student learning data, 【0902】 An analytical means for analyzing collected training data to generate an ideal training plan, 【0903】 A distribution method for delivering the generated learning plan to students' devices, 【0904】 A display method for showing the distributed learning plan to students on their devices, 【0905】 A progress management system for tracking students' learning activities, recording their progress, and sending it to a server, 【0906】 A visualization tool for students and instructors to check and analyze learning progress, 【0907】 A system that includes this. 【0908】 (Claim 2) 【0909】 The system according to claim 1, wherein the analysis means adjusts the learning plan based on the student's health status data. 【0910】 (Claim 3) 【0911】 The system according to claim 1, wherein the display means presents the next task to be tackled in real time and provides feedback according to the student's level of understanding. 【0912】 "Example 1" 【0913】 (Claim 1) 【0914】 Information gathering means for collecting learners' learning information, 【0915】 An analytical means for analyzing collected learning information and generating an optimal learning plan using a generative AI model, 【0916】 A transmission means for sending the generated learning plan to the learner's device, 【0917】 A display means for presenting the transmitted learning plan to the learner on the device, 【0918】 A progress management system for tracking learners' learning behavior, recording their progress, and sending it to a server, 【0919】 A visualization tool for learners and educators to check and analyze learning progress, 【0920】 A system that includes this. 【0921】 (Claim 2) 【0922】 The system according to claim 1, wherein the analysis means dynamically adjusts the learning plan using a generated AI model based on the learner's latest learning data. 【0923】 (Claim 3) 【0924】 The system according to claim 1, wherein the display means presents the next task to be addressed in real time and provides immediate feedback according to the learner's level of understanding. 【0925】 "Application Example 1" 【0926】 (Claim 1) 【0927】 Data collection means for providing information available to residents, 【0928】 An analysis means for analyzing aggregated learning information and generating individually optimized learning plans, 【0929】 A means of distributing the generated learning plan to the user's information terminal, 【0930】 A display means for presenting the distributed learning plan to the user on a device, 【0931】 A progress management means for tracking the user's learning behavior, recording its progress, and transmitting it to an information processing device, 【0932】 A means of visualization for users and instructors to check and evaluate learning progress, 【0933】 A means of displaying the next task according to the level of understanding of the learning material, and showing the usage status of educational facilities in the region, 【0934】 A system that includes this. 【0935】 (Claim 2) 【0936】 The system according to claim 1, wherein the analysis means adjusts the learning plan based on the user's health status data. 【0937】 (Claim 3) 【0938】 The system according to claim 1, wherein the display means immediately presents the next task to be addressed, provides answers according to the user's level of understanding, and notifies the user of local learning events and facility status. 【0939】 "Example 2 of combining an emotion engine" 【0940】 (Claim 1) 【0941】 A data acquisition method for collecting student learning data and emotional data, 【0942】 A generative model for generating an optimized learning plan for each student by analyzing acquired learning data and emotional data, 【0943】 A means of communication for sending the generated learning plan to the terminal, 【0944】 A display means for presenting the submitted learning plan on the device and dynamically adjusting the content based on the student's emotional state, 【0945】 A monitoring system for continuously monitoring students' learning activities and emotional states and sending feedback data to a server, 【0946】 A means of adjusting the learning plan for subsequent sessions based on feedback data, 【0947】 A system that includes this. 【0948】 (Claim 2) 【0949】 The system according to claim 1, wherein the analysis means adjusts the learning plan based on the emotional state of the students and provides an optimal learning environment. 【0950】 (Claim 3) 【0951】 The system according to claim 1, wherein the display means senses the real-time emotional state of a student and presents corresponding content to promote concentration. 【0952】 "Application example 2 when combining with an emotional engine" 【0953】 (Claim 1) 【0954】 Information acquisition methods for collecting information on the learning of educated individuals, 【0955】 An analytical means for analyzing acquired learning information to generate a suitable educational plan, 【0956】 A means of transmitting the generated educational plan to the student's mobile device, 【0957】 A display means for showing the transmitted educational plan to the student on a mobile information terminal, 【0958】 A progress management means for tracking the learning activities of educated individuals, recording their progress, and transmitting it to a data processing device, 【0959】 A means of emotional analysis that recognizes the emotions of those being educated and reflects adjustments to those emotions in the educational plan, 【0960】 A system that includes this. 【0961】 (Claim 2) 【0962】 The system according to claim 1, wherein the analysis means adjusts the educational plan based on information regarding the health and emotions of the person being educated. 【0963】 (Claim 3) 【0964】 The system according to claim 1, wherein the display means provides real-time feedback according to the student's level of understanding and emotional state. [Explanation of Symbols] 【0965】 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 data collection method for collecting student learning data, An analytical means for analyzing collected training data to generate an ideal training plan, A distribution method for delivering the generated learning plan to students' devices, A display method for showing the distributed learning plan to students on their devices, A progress management system for tracking students' learning activities, recording their progress, and sending it to a server, A visualization tool for students and instructors to check and analyze learning progress, A system that includes this. [Claim 2] The system according to claim 1, wherein the analysis means adjusts the learning plan based on the student's health status data. [Claim 3] The system according to claim 1, wherein the display means presents the next task to be tackled in real time and provides feedback according to the student's level of understanding.

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