System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting answer data, a correct answer rate, an answer time, and a past learning record of a student; means for analyzing the collected data and specifying an unsolvable question and a cause thereof for each student; means for generating an optimal learning path based on an analysis result; means for automatically generating an individually optimized learning material based on the generated learning path; means for transmitting the generated learning material to a terminal of the student and monitoring learning progress; and means for providing timely feedback based on the learning progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In traditional education systems, students are placed into classes based on their grades, but even students with the same grades often have different reasons for being unable to solve problems and different optimal learning paths for understanding. Therefore, simply providing uniform teaching materials and class time makes it difficult to provide effective education optimized for each student's level of understanding and progress. It is also difficult to quickly elicit students' interests and provide individually optimized teaching materials based on those interests. Given these circumstances, there is a need for a system that provides individually optimized learning materials and feedback for each student. [Means for solving the problem]

[0005] The present invention solves the above problems by using the following means: It includes a means for collecting students' answer data, correct answer rate, answer time, and past learning records, and a means for analyzing the collected data to identify problems that each student cannot solve and the causes thereof. It also includes a means for generating an optimal learning path based on the analysis results, and a means for automatically generating individually optimized learning materials based on the generated learning path. The system further includes a means for sending the generated learning materials to the student's device, monitoring the student's learning progress, and providing timely feedback based on the student's learning progress.

[0006] "Answer data" refers to data that indicates the answers submitted by students to questions, the time taken to answer, and information on whether the answers were correct or incorrect.

[0007] The "correct answer rate" is a numerical value that indicates the percentage of questions that a student answered correctly out of all questions that he or she answered.

[0008] "Response time" refers to the amount of time it takes a student to answer a question from the moment they start until they finish.

[0009] "Past learning records" are data that include historical information such as what a student has learned, their progress, and test results.

[0010] "Analysis" is the process of analyzing the collected data in detail to identify each student's weaknesses and the reasons why they cannot solve the problems.

[0011] An "optimal learning path" is a route or steps for effective learning that are designed to take into account a student's individual weaknesses, interests, and level of understanding.

[0012] "Auto-generation" is the process of using artificial intelligence and algorithms to create learning materials and paths without human intervention.

[0013] "Devices" are learning devices used by students, including PCs, tablets, smartphones, etc.

[0014] "Feedback" refers to information such as evaluations and advice provided by the system regarding a student's learning progress and answers.

[0015] "Monitoring" is the process of continually observing students' learning progress and activities and gathering information as needed. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is an online education system that provides learning materials optimized for each student's level of understanding, improving their learning efficiency. This system is mainly composed of a server, terminals, and users.

[0038] Data collection methods

[0039] The device collects data such as answer data, accuracy rate, response time, and past learning records as the student studies. This data is programmed to be automatically recorded the moment the student submits their answer. This recorded data is periodically sent to the server.

[0040] Example: When person A (user) solves a math problem, the answer data (answer content, correct or incorrect, answer time) is collected by the terminal and sent to the server.

[0041] Data analysis format

[0042] The server receives the answer data sent from the device and stores it in a database. AI is then used to analyze this data and identify the types of questions each student is struggling with and what parts they don't understand. The AI ​​model performs its analysis by referring to each student's learning patterns and answer history.

[0043] Example: The server analyzes person A's answer data and determines that person A is having trouble with integer problems.

[0044] Form of optimal learning path generation

[0045] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for each student. Here, a learning path refers to the steps or route that a student takes to efficiently progress through their studies. This learning path is designed taking into account the student's weaknesses, interests, and level of understanding.

[0046] Example: The server generates a learning path for person A that includes steps from the basics to applications of integers.

[0047] Automatic generation of individually optimized teaching materials

[0048] The server automatically generates customized learning materials based on the generated optimal learning path. These materials include explanations and appropriate exercises to help students understand. The learning materials can be provided in a variety of formats, including text, images, and videos.

[0049] Example: The server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems.

[0050] Feedback and progress management forms

[0051] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, who then proceeds with their learning. The progress is recorded again, and data on the degree of completion and understanding of the learning is collected. This progress data is then sent back to the server and used for the next analysis.

[0052] Example: The device displays basic explanations and practice problems about integers to A, and sends progress data to the server as A completes his / her learning.

[0053] Continuous feedback

[0054] The server uses AI to provide students with real-time feedback based on the progress data collected, including advice on how to proceed to the next step and suggestions for supplementary materials to help them revisit areas where they have not fully understood the material.

[0055] Example: The server recognizes from A's progress data that he is ready to tackle applied problems involving integers, and generates and provides new customized learning materials.

[0056] In this way, the system of the present invention realizes efficient learning tailored to the individual needs of each student, and has the effect of improving students' understanding.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. This includes automatically recording data each time a student submits an answer and saving it in local storage.

[0060] Step 2:

[0061] The terminal periodically transmits the collected answer data to the server at regular intervals when a network connection is established.

[0062] Step 3:

[0063] The server receives the answer data sent from the terminal and stores it in a database, which includes receiving the data, checking the format, and writing it to the database.

[0064] Step 4:

[0065] The server analyzes the answer data stored in the database. This analysis involves using an AI model to identify the problems that students are struggling with and the reasons for their difficulties. The AI ​​model performs its analysis by referencing the student's past answer history and learning patterns.

[0066] Step 5:

[0067] Based on the analysis results, the server uses an AI model to generate an optimal learning path for each student, which includes step-by-step instructions for students to learn efficiently.

[0068] Step 6:

[0069] Based on the generated learning path, the server automatically generates individually optimized learning materials, including specific explanations and practice questions to help students deepen their understanding.

[0070] Step 7:

[0071] The server sends the created customized teaching materials to the student's device, where they are displayed in an appropriate format (text, image, video).

[0072] Step 8:

[0073] The device allows the user to study using the provided learning materials and records progress data, including information on how long the user spent studying the materials and how much they understood.

[0074] Step 9:

[0075] The device then sends the collected progress data back to the server either after the learning session ends or periodically.

[0076] Step 10:

[0077] Based on the progress data received, the server uses AI to generate appropriate feedback to students in real time, including advice based on their learning progress and suggestions for supplementary learning materials.

[0078] Step 11:

[0079] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[0080] In this way, all steps work together to create a system that provides students with individually optimized learning.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] With conventional online education systems, it was difficult to provide customized learning materials tailored to each student's level of understanding, which often resulted in insufficient learning outcomes. It was also difficult to grasp students' learning progress in real time and provide timely feedback. To solve these problems, an advanced system capable of effectively collecting and analyzing each student's learning data was required.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes a means for collecting students' answer data, correct answer rate, answer time, and past learning records, a means for periodically sending the collected data to the server, and a means for storing the answer data received by the server in a database and analyzing the data using an artificial intelligence model. This enables the generation of an optimal learning path based on each student's individual learning pattern, the automatic generation of individually optimized learning materials based on the learning path, and real-time feedback based on learning progress data.

[0086] "Answer data" refers to the answers entered by students to questions.

[0087] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly.

[0088] "Response time" is the measurement of the time it takes a student to answer a question.

[0089] "Past learning records" are data that record what a student has learned so far and the results of that learning.

[0090] "Server" refers to a computer system for collecting, storing, analyzing data, and generating educational materials.

[0091] "Device" refers to the device used by students to study, including, for example, a PC, tablet, or smartphone.

[0092] "Artificial intelligence model" refers to the algorithms and machine learning models used to analyze student learning data and generate optimal learning paths and feedback.

[0093] A "learning path" is a set of steps or routes that students can take to efficiently progress through their studies.

[0094] "Customized teaching materials" refers to individually optimized teaching materials that are generated based on each student's level of understanding and learning progress.

[0095] "Learning progress" refers to the process by which students use learning materials and the results of their studies.

[0096] "Feedback" refers to information, including advice and supplementary materials, provided based on learning progress.

[0097] This invention is an online education system for improving student learning efficiency. The system consists of three main components: a server, a terminal, and a user. Each component has a specific function and provides an optimized learning experience for each student.

[0098] First, the terminal is the device that students use to study, and includes PCs, tablets, smartphones, etc. The terminal collects answer data entered by students as they study, as well as the accuracy rate, response time, and past study records. This collected data is automatically recorded each time an answer is entered. Furthermore, the collected data is programmed to be sent to the server periodically.

[0099] Example: When user A answers a math problem using a device, the answer, whether it was correct or not, and the time it took to answer are immediately recorded on the device and sent to the server at regular intervals.

[0100] The server stores the received data in a database and analyzes it using an AI (artificial intelligence) model. The AI ​​model identifies the problems students are struggling with and areas where they lack understanding based on each student's learning patterns and answer history. Based on the results of this analysis, an optimal learning path is generated.

[0101] Example: The server analyzes A's data and identifies that A is struggling with integer problems. Based on this, it generates a systematic learning path from the basics to applications of integers.

[0102] The server then automatically generates customized learning materials that are best suited to each student based on the generated learning path. These materials include various formats, such as text, images, and videos, and also include explanations and practice questions to help students understand. These customized learning materials are then sent from the server to the device.

[0103] Example: The server creates customized learning materials for Mr. A, including basic explanations of integers and practice problems, and sends them to the terminal.

[0104] The device displays the received learning materials to the user, who then uses them to study. The learning progress is recorded again and the data is sent back to the server. This data includes the degree of completion and understanding of the learning.

[0105] Example: The device displays basic explanations and practice problems for person A, who then studies them. After completing the study, the progress data is sent to the server.

[0106] Finally, the server uses the collected progress data to provide real-time AI feedback, including advice on next steps and suggested supplemental materials to help you revisit areas of incomplete understanding.

[0107] Example: The server analyzes A's progress data, recognizes that A is ready to tackle applied problems involving integers, and generates and provides new learning materials.

[0108] The hardware used is a standard server or personal computer terminal, and the software includes an artificial intelligence model and a database management system. Specific software includes machine learning libraries using Python (such as scikit-learn, TensorFlow, and PyTorch). In this way, the system aims to provide an optimal learning experience tailored to each student's needs and improve their understanding of the material.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1: Collect user learning data

[0111] As students study, the device collects answer data, accuracy rate, response time, and past learning records. Input data includes the student's answer content, whether it was correct or incorrect, and the time it took to answer. When this information is entered into the device, it is automatically recorded. Specifically, the device is equipped with an answer form and a timer function, and captures data each time a student enters an answer.

[0112] Step 2: Sending data to the server

[0113] The device periodically sends the collected learning data to the server. The input data is the student's learning data collected in step 1. When sending, the device is programmed to use an appropriate communication protocol. Specifically, the device transfers the data to the server using protocols such as HTTP or WebSocket.

[0114] Step 3: Data storage and analysis

[0115] The server receives the learning data sent from the device and stores it in a database. The input data includes the student's answer data, accuracy rate, answer time, and past learning records. After storing this data, the server analyzes it using an AI model. Specifically, the server stores the data using a database management system, and then analyzes the data using machine learning algorithms implemented in programming languages ​​such as Python.

[0116] Step 4: Generate a learning path

[0117] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. The input data is the analysis results from step 3, which include information on the types of problems the student is struggling with and their level of understanding. The AI ​​model processes the data to generate a learning path, and the output is an individually optimized learning path. Specifically, the AI ​​model calculates the optimal steps and route based on the learning pattern.

[0118] Step 5: Create customized learning materials

[0119] The server automatically generates individually optimized learning materials based on the generated learning path. The input data is the learning path generated in step 4. As data processing, a learning material generation algorithm generates learning materials according to the learning path, and as output, learning materials such as text, images, and videos that are optimal for the student are obtained. Specifically, an algorithm that dynamically combines learning content is executed.

[0120] Step 6: Deliver customized learning materials

[0121] The server sends the created customized teaching materials to the terminal. The input data is the individually optimized teaching materials generated in step 5. Specifically, a file transfer protocol such as an HTTP request or FTP is used to send the teaching material file to the terminal.

[0122] Step 7: Collect user learning and progress data

[0123] The device displays the received learning materials, and the user uses them to progress with their studies. The input data is the customized learning materials sent in step 6. When the user completes their study, the progress data is recorded again on the device. Specifically, the device is equipped with a learning materials display function and a progress data recording function. Each time the user solves a problem, the result is recorded.

[0124] Step 8: Sending progress data to the server and parsing it

[0125] The device again sends the collected progress data to the server. The input data is the learning progress data collected in step 7. The server analyzes the received progress data and uses AI to provide appropriate feedback to the user. Specifically, the AI ​​model analyzes the progress data again and generates feedback suggesting the next task to tackle and supplementary learning materials.

[0126] (Application example 1)

[0127] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0128] Conventional online education systems lack the optimization required to provide learning materials appropriate for each student, resulting in poor learning efficiency. Furthermore, they lacked a mechanism for recommending individually optimized content using users' viewing history and evaluation data. Therefore, there is a need for a system that can improve learner and viewer satisfaction and maximize learning effectiveness.

[0129] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0130] In this invention, the server includes means for collecting students' answer data, correct answer rates, answer times, and past learning records, means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for transmitting the generated learning materials to the student's terminal and monitoring learning progress, means for providing timely feedback based on learning progress, means for collecting and analyzing user viewing history and evaluation data, and means for recommending individually optimized content based on the analysis results. This makes it possible to provide learning materials and content customized for each student or viewer, improving learning efficiency and viewing experience.

[0131] "Student answer data" refers to the content of the answers given by students to questions and information on whether those answers were correct or incorrect.

[0132] The "correct answer rate" is an indicator that indicates the percentage of correct answers among all answers given by a student.

[0133] "Response time" is the time it takes a student to solve a problem.

[0134] "Past learning records" are historical information about the learning a student has done up to now, and are data including progress and level of understanding.

[0135] The "viewing history" is recorded information about the content that the user has viewed up to now.

[0136] "Rating data" refers to data of ratings and feedback given by users to content they have viewed.

[0137] An "optimal learning path" is a set of learning steps or routes designed to help students progress through their studies efficiently.

[0138] "Individually optimized teaching materials" are teaching materials that are customized based on each student's level of understanding and interests.

[0139] The "recommendation means based on analysis results" refers to a method or device for recommending optimal content to a user based on the analysis results of viewing history and evaluation data.

[0140] A "server" is a central computer device that collects and analyzes data and sends the processed results to each terminal.

[0141] "Feedback" refers to timely advice and evaluation of learning or viewing progress.

[0142] This invention includes an online education system that provides learning materials optimized for each learner's level of understanding, improving students' learning efficiency, and a content distribution system that recommends individually optimized content based on the user's viewing history. The details of these systems are described below.

[0143] Data collection methods

[0144] The device collects answer data, accuracy rate, answer time, and past learning records when students study. The device also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment a student or user submits an answer or completes viewing. This recorded data is periodically sent to the server. Specifically, when person A (user) solves a math problem, the device collects answer data (answer content, correct or incorrect, answer time) and sends it to the server. Viewing history data of movies viewed by person A is also collected in a similar manner.

[0145] Data analysis format

[0146] The server receives the answer data and viewing history data sent from the device and stores it in a database. Next, it uses AI to analyze this data and determine what types of problems each student is struggling with or what type of content the user prefers. The AI ​​model performs its analysis by referring to each student's learning patterns, answer history, and user viewing history. For example, the server analyzes Person A's answer data and determines that Person A is struggling with integer problems. It also analyzes Person A's viewing history of science fiction movies that he has previously watched and determines that he prefers the science fiction genre.

[0147] Form of optimal learning path and content recommendation generation

[0148] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. It also recommends the most suitable content to the user. This learning path and content recommendations are designed taking into account the student's weaknesses, interests, and level of understanding. As a specific example, the server generates a learning path for Person A that includes steps from the basics to applications of integers. It also generates a list of recommended new science fiction movies for Person A.

[0149] Automatic generation of individually optimized teaching materials

[0150] The server automatically generates customized learning materials and a list of recommended content based on the generated optimal learning path and content recommendations. The learning materials include explanations and appropriate practice problems to help students understand, and the recommended content list includes content that is likely to interest the user. The learning materials and content can be provided in a variety of formats, including text, images, and videos. As a specific example, the server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems, and also generates a list of recommendations for new science fiction movies.

[0151] Feedback and progress management forms

[0152] The generated customized learning materials and recommended content are sent from the server to the device. The device displays the received learning materials and content to the user, who then proceeds with their learning or viewing. The learning progress and content viewing history are recorded again, and data on the degree of learning completion, level of understanding, and viewing status is collected. This progress data is sent back to the server again and used for the next analysis. As a specific example, the device displays a basic explanation of integers and practice problems to Person A, and then sends progress data and new viewing history data to the server when Person A has completed their learning.

[0153] Continuous feedback

[0154] The server uses AI to provide appropriate feedback to students and users in real time based on the collected progress data and viewing history data. This feedback includes advice on how to proceed to the next step, suggestions for supplementary materials to help students revisit areas where they have insufficient understanding, and recommendations for new content. For example, the server recognizes from student A's progress data that he or she is ready to tackle applied problems involving integers, and provides new customized materials and a list of recommended content.

[0155] Here are some example prompts to input to the generative AI model:

[0156] The sci-fi movies I've seen recently are "A Space Journey Through Time" and "A Faraway Galaxy." Please recommend more movies with similar themes.

[0157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0158] Step 1:

[0159] The device collects answer data, accuracy rate, answer time, and past learning records when students answer questions. It also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment students or users submit their answers or complete viewing. The input data is the student's answer data and viewing history data, and the output data is the collected records.

[0160] Step 2:

[0161] The terminal periodically transmits the collected data to the server. Here, data transmission is performed, and the input data is the answer data and viewing history data collected in step 1. The output data is the recorded data transmitted to the server.

[0162] Step 3:

[0163] The server receives data sent from the terminal and stores it in a database. When storing it, it formats and normalizes the data. The input data is the recorded data sent, and the output data is the data stored in the database.

[0164] Step 4:

[0165] The server analyzes the stored data using an AI model. It references each student's answer history and viewing history to identify which questions they are struggling with and which content they prefer. The input data is the answer data and viewing history data stored in the database. The output data is the analysis results that identify the student's weaknesses and user preferences.

[0166] Step 5:

[0167] The server generates optimal learning paths and content recommendations based on the analysis results. Here, an AI model is used to create learning paths for students and content recommendation lists for users. The input data is the analysis results, and the output data is the generated learning paths and content recommendation lists.

[0168] Step 6:

[0169] The server automatically generates customized learning materials and a list of recommended content based on the generated learning path and content recommendations. The learning materials are provided in the form of text, images, and videos. The input data is the generated learning path and content recommendation list, and the output data is the automatically generated learning materials and content list.

[0170] Step 7:

[0171] The server transmits the generated learning materials and content list to the terminal. Here, learning materials and recommended content are transmitted to the user terminal. The input data are the automatically generated learning materials and content list, and the output data are the data transmitted to the terminal.

[0172] Step 8:

[0173] The terminal displays the received learning materials and content to the user, and the user proceeds with their learning or viewing. The learning progress and viewing history are recorded again. The input data is the received learning materials and content list, and the output data is the learning progress and viewing history.

[0174] Step 9:

[0175] The device transmits the re-recorded progress data and viewing history data to the server. Here, data transmission is performed. The input data is the learning progress status and viewing history, and the output data is the recorded data transmitted to the server.

[0176] Step 10:

[0177] The server uses AI to provide feedback based on the collected progress data and viewing history data. The feedback includes study advice and recommendations for new content. The input data is the progress data and viewing history, and the output data is the provided feedback.

[0178] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0179] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. This system is mainly composed of a server, terminals, and users, and also incorporates an emotion engine to utilize students' emotional data.

[0180] Data collection methods

[0181] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[0182] Example: When user A answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[0183] Data transmission format

[0184] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[0185] Data analysis format

[0186] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses AI to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[0187] Example: The server analyzes person A's answer data and emotion data and determines that person A is struggling with integer problems and is feeling frustrated while learning.

[0188] Form of optimal learning path generation

[0189] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[0190] Example: The server generates a learning path for person A, starting with the basics of integers and gradually progressing to applications, taking into account person A's emotional state.

[0191] Automatic generation of individually optimized teaching materials

[0192] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[0193] Example: The server creates customized learning materials for A, including a basic explanation of integers, related practice problems, and interactive content that will attract A's interest.

[0194] Feedback and progress management forms

[0195] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[0196] Example: The device displays basic explanations and practice problems for person A about integers, and after person A has finished learning, it sends progress data and emotional data to the server.

[0197] Continuous feedback

[0198] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, it provides encouragement and warnings based on emotional data to maintain students' motivation.

[0199] Example: The server determines from A's emotional data that she is feeling frustrated while studying, and sends her encouraging messages and messages encouraging her to take a break, and provides additional explanations for areas where she does not fully understand.

[0200] In this way, the system of the present invention provides a learning experience that is optimized to suit the individual needs and emotional state of each student, thereby improving their understanding and increasing their motivation to learn.

[0201] The processing flow will be explained below.

[0202] Step 1:

[0203] The device collects answer data (answer content, accuracy rate, and answer time) in real time when the student (user) answers the questions. In addition, it uses an emotion engine to analyze the student's emotional state (excitement, concentration, frustration, etc.) from facial expressions and voice data and record it as emotional data.

[0204] Specific operation:

[0205] The device uses a camera and microphone to collect emotional data while students answer questions.

[0206] When the answer is submitted, the emotional data at that time is also saved.

[0207] Step 2:

[0208] The device periodically transmits the collected answer data and emotion data to the server. Transmission is performed automatically when a network connection is established.

[0209] Specific operation:

[0210] The terminal processes the collected data in batches at regular intervals and sends them to the server.

[0211] If there is no network connection, data is kept locally until the connection is restored.

[0212] Step 3:

[0213] The server receives the answer data and emotion data sent from the device and stores them in a database. During this process, the data format is checked and reformatted.

[0214] Specific operation:

[0215] The server checks the integrity of the received data and converts the format appropriately.

[0216] Once all the validations are completed, the data is inserted into the database.

[0217] Step 4:

[0218] The server analyzes the answer data and emotional data stored in the database, using an AI model to identify which questions students are struggling with and the reasons for this from the answer data, and also uses the emotional data to understand the student's mental state while studying.

[0219] Specific operation:

[0220] The AI ​​model analyzes the answer data and identifies students' weaknesses.

[0221] Analyze emotional data and record changes in students' emotions during learning.

[0222] Step 5:

[0223] Based on the analysis results, the server uses AI models to generate an optimal learning path for each student, which is designed taking into account the student's weaknesses and emotional state.

[0224] Specific operation:

[0225] The learning path selects the optimal learning order and content for each student based on answer data and emotional data.

[0226] Path generation is dynamic and updates based on real-time data.

[0227] Step 6:

[0228] Based on the generated learning path, the server automatically generates individually optimized learning materials, including explanations and exercises that are optimal for the student's level of understanding and emotional state.

[0229] Specific operation:

[0230] The automatically generated teaching materials include a variety of content, including text, images, and videos.

[0231] Add content that students are likely to be interested in based on emotional data.

[0232] Step 7:

[0233] The server sends the created customized learning materials to the terminal, which displays the learning materials in an appropriate format (text, image, video).

[0234] Specific operation:

[0235] Materials are delivered in a format that suits each individual student.

[0236] The terminal displays the received educational material in a user-friendly interface.

[0237] Step 8:

[0238] The device then uses the provided learning materials to help the user study, recording progress and emotional data, including the user's level of understanding of the learning content, the time required, and the emotional data, which is then analyzed again.

[0239] Examples:

[0240] As students progress through their studies, their progress and emotional state is recorded in real time.

[0241] If the device's emotions change during the learning process, the change is immediately analyzed using the emotion engine.

[0242] Step 9:

[0243] The device then sends the collected progress and emotion data back to the server at the end of the learning session or at other regular intervals.

[0244] Specific operation:

[0245] Progress data and emotion data are batch processed and sent to the server.

[0246] Immediate transmission occurs at the end of the learning session.

[0247] Step 10:

[0248] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data it receives, including assessments of their learning progress, advice, and encouragement and warnings to keep them motivated.

[0249] Specific operation:

[0250] An AI model analyzes progress data to assess understanding and next learning steps.

[0251] Based on the emotional data, messages that provide emotional support and motivation to students are generated.

[0252] Step 11:

[0253] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[0254] Specific operation:

[0255] The feedback is immediately sent to the terminal and set to be displayed to the user.

[0256] The devices display feedback in an easy-to-read format to help students understand.

[0257] Example 2

[0258] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0259] Conventional online education systems are based solely on students' answer data and learning progress, and do not take into account their emotional state. This makes it difficult to appropriately address issues such as frustration and loss of motivation that students experience during learning. The purpose of this invention is to improve students' learning efficiency and comprehension, while also providing optimal learning materials and feedback that take into account their emotional state during learning.

[0260] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0261] In this invention, the server includes means for collecting students' answer data, accuracy rate, answer time, and past learning records, means for collecting emotional data based on facial expressions, voice, and behavior during learning, means for analyzing the collected answer data and emotional data to identify problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for sending the generated learning materials to the student's terminal and monitoring the learning progress, and means for providing timely feedback based on the learning progress and encouraging or warning the student according to their emotional state. This makes it possible to provide appropriate learning materials and feedback that take into account each student's individual level of understanding and emotional state.

[0262] "Answer data" is information about the questions answered by the student, specifically data including correct / incorrect answers, answer time, and selection history of options.

[0263] "Emotional data" refers to data about a student's emotional state collected from facial expressions, voice, behavior, etc. while they are studying.

[0264] "Analysis" is the process of evaluating and analyzing students' learning situations and emotional states based on collected answer data and emotional data.

[0265] A "learning path" is a plan that shows students the optimal order of content and unit learning based on the analysis results.

[0266] "Individually optimized learning materials" are learning materials and content that are customized to best suit a specific student, taking into account the student's level of understanding and emotional state.

[0267] "Progress data" refers to data that indicates the progress and achievement of a student as they progress through their studies.

[0268] "Feedback" refers to guidance and advice provided to students based on their learning progress and analysis results.

[0269] "Encouragement and caution" refers to messages or instructions that take into account the student's emotional state and are intended to increase motivation or draw attention.

[0270] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. The system is mainly composed of a server, terminals, and users. It also incorporates an emotion engine to utilize emotional data.

[0271] Data collection methods

[0272] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[0273] Example: When a user answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[0274] Data transmission format

[0275] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[0276] Data analysis format

[0277] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses an AI model to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[0278] Example: A server analyzes a user's answer data and sentiment data to determine that the user is struggling with integer problems and is frustrated while learning.

[0279] Form of optimal learning path generation

[0280] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[0281] Example: A server generates a learning path for a user, starting with the basics of integers and gradually progressing to applications, taking into account the user's emotional state.

[0282] Automatic generation of individually optimized teaching materials

[0283] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[0284] Example: A server creates customized learning materials for a user, including a basic explanation of integers and related exercises, as well as interactive content that will attract the user's attention.

[0285] Feedback and progress management forms

[0286] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[0287] Example: A device displays basic explanations and practice problems for integers to a user, and sends progress data and emotion data to a server after the user has finished learning.

[0288] Continuous feedback

[0289] The server uses an AI model to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, the server provides encouragement and warnings based on emotional data to maintain students' motivation.

[0290] Example: The server determines from the user's emotional data that they are feeling frustrated while studying, and sends them messages of encouragement or encouragement to take a break, and provides additional explanations for areas where they do not fully understand.

[0291] Example prompts to be input to the generative AI model

[0292] "Analyze users' latest learning outcomes and sentiment data to generate appropriate learning paths."

[0293] "Suggest additional feedback and educational materials based on user progress and sentiment data."

[0294] This allows the system of the present invention to provide a learning experience that is optimized to the individual needs and emotional state of the student, improving comprehension and increasing engagement.

[0295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0296] Step 1:

[0297] Data collection

[0298] The device opens a session when the user begins learning. The input is the question the user answers and its content. Specifically, when the user answers a question, the device collects answer data (correct / incorrect answers, answer time, and choice history) in real time. The device also activates its built-in emotion engine and uses the camera and microphone to record the user's facial expressions, voice, and actions. This allows the user's answer data and emotion data to be collected and stored in local storage.

[0299] Step 2:

[0300] Data transmission

[0301] The device stores the collected data in a buffer and periodically checks the network connection status. The input is the answer data and emotion data stored in the buffer, and the network connection status. If the network is good, the device sends the data in the buffer to the server. The output is the answer data and emotion data sent to the server.

[0302] Step 3:

[0303] Data analysis

[0304] The server receives the answer data and emotion data sent from the device and stores them in a database. The input is the received answer data and emotion data. The server then analyzes the data using an AI model. Specifically, the AI ​​analyzes the answer data and emotion data to identify the problems the user is struggling with, their causes, and their emotional state during learning. The output is an analysis of the user's learning problems and emotional state.

[0305] Step 4:

[0306] Generate optimal learning paths

[0307] Based on the analysis results, the server uses an AI model to generate an optimal learning path for the user. The input is the results of data analysis. Specifically, the AI ​​model uses the analysis results to plan the learning order and content, and designs a learning path that is optimal for the user's level of understanding and emotional state. The output is the generated learning path.

[0308] Step 5:

[0309] Automatic generation of individually optimized teaching materials

[0310] The server automatically generates individually optimized learning materials based on the generated learning path. The input is the generated learning path. Specifically, the server creates learning materials such as text, images, and videos, and customizes them to suit the user's interests and level of understanding. The output is the optimized learning materials.

[0311] Step 6:

[0312] Sending study materials and managing progress

[0313] The server sends the generated learning materials to the device. The input is the created learning materials. Specifically, the device displays the received learning materials to the user and progresses the learning. When the user finishes learning, the device again records the progress data and new emotion data and stores them in local storage.

[0314] Step 7:

[0315] Data retransmission

[0316] The device stores the progress data and emotion data in a buffer and checks the network connection status. The input is the progress data and emotion data stored in the buffer. If the network is good, the device sends the data to the server again. The output is the progress data and emotion data sent to the server.

[0317] Step 8:

[0318] Continuous feedback

[0319] The server then analyzes the progress and emotional data again and provides appropriate feedback to the user in real time. The inputs are the progress and emotional data. Specifically, the AI ​​model evaluates the learning progress, provides additional training materials and advice as needed, and sends encouragement and warnings according to the user's emotional state. The output is feedback and training materials to the user.

[0320] In this way, the system can provide a learning experience that is optimized to each student's individual needs and emotional state, improving comprehension and increasing engagement.

[0321] (Application example 2)

[0322] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0323] Conventional online education systems have the problem that it is difficult to grasp students' level of understanding and learning behavior, making it difficult to adequately manage progress and provide feedback according to individual needs. It is also difficult to grasp customers' emotional state in real time in physical stores, making it difficult to provide appropriate individual support. It is necessary to improve student and customer satisfaction and realize effective learning and customer service.

[0324] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting students' answer data, correct answer rate, answer time, and past learning records; means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof; means for generating an optimal learning path based on the analysis results; means for automatically generating individually optimized learning materials based on the generated learning path; means for sending the generated learning materials to the student's device and monitoring learning progress; means for providing timely feedback based on learning progress; and means for collecting and analyzing customer emotional state and behavioral data and providing optimal support and suggestions in real time. This maximizes students' learning efficiency, provides a learning experience tailored to their individual needs, and makes it possible to provide appropriate support to customers in physical stores.

[0325] "Student answer data" refers to the answers entered by students in response to questions presented in the educational system.

[0326] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly out of all questions that he or she answered.

[0327] "Response time" refers to the time it takes a student to complete a response to a question.

[0328] "Past learning records" are a collection of data that includes the learning content that a student has undertaken and the results of that learning.

[0329] "Means of collection" refers to the methods and equipment necessary to obtain and use data.

[0330] "Means for analyzing data" means methods and techniques for processing collected data and extracting information.

[0331] "Means for generating optimal learning paths" refers to a method for creating the most effective learning sequence and curriculum for students based on the analysis results.

[0332] "Means for automatically generating individually optimized teaching materials" refers to a method for automatically creating learning materials that meet the needs of students.

[0333] A "progress monitoring tool" is a method for tracking students' progress and recording their progress.

[0334] "Means of providing feedback" are ways to provide students with appropriate guidance and advice based on their learning progress.

[0335] "Means for collecting and analyzing customer emotional state and behavioral data" refers to methods and technologies for acquiring customer facial expressions, movements, speech content, etc., and analyzing them to clarify their emotional state and behavioral intentions.

[0336] "Means of providing optimal support and suggestions in real time" refers to a method of providing appropriate responses and services on the spot based on collected and analyzed customer data.

[0337] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state.It is also a system that can analyze the emotional state of customers in physical stores in real time and provide optimal support.

[0338] Data collection methods

[0339] The device (smartphone, smart glasses, robot, etc.) collects student or customer behavior data, answer data, accuracy rate, answer time, past learning records, emotional data, etc. Using an emotion engine, emotional data is extracted from the facial expressions, voice, and behavior of students or customers. This data is automatically recorded and stored in local storage.

[0340] Data transmission format

[0341] The device periodically transmits the collected answer data and emotion data to the server. Transmission is established when the network connection is good.

[0342] Data analysis format

[0343] The server receives the answer data and emotion data sent from the device and stores them in a database. It then analyzes the data using a generative AI model (for example, a model using TensorFlow) to identify the problems the student or customer is struggling with and the reasons for their difficulties. It also identifies the emotional state of the student or customer based on the emotion data.

[0344] Optimal learning paths and support generation

[0345] The server uses an AI model to generate the optimal learning path based on the analysis results. Furthermore, by analyzing the customer's emotional data, it can provide optimal support and suggestions in real time. These learning paths and suggestions are designed taking into account both the student's or customer's answer data and emotional data.

[0346] Automatic generation of individually optimized teaching materials and support

[0347] Based on the generated optimal learning path or support proposal, the server automatically generates individually optimized learning materials for students and appropriate support and proposals for customers. The learning materials are provided in a variety of formats, including text, images, and videos.

[0348] Feedback and progress management forms

[0349] The generated customized learning materials and support suggestions are sent from the server to the device and displayed to the user. The device again records progress data and emotional data as the user progresses with their learning and response, and sends this data to the server.

[0350] Continuous feedback

[0351] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress data and emotional data. For example, if the server determines from the emotional data analyzed that the customer is confused during learning or purchasing, it will send appropriate guidance or support messages.

[0352] Prompt Sentence Examples

[0353] For example, consider the following prompt:

[0354] python

[0355] emotion_data = emotion_model.detect_emotions(frame)

[0356] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[0357] This system enables the provision of learning experiences and customer support that are optimized to suit the individual needs of students and the real-time emotional state of customers, maximizing satisfaction and effectiveness.

[0358] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0359] Step 1:

[0360] The device (smartphone, smart glasses, robot, etc.) collects the user's answer data, accuracy rate, answer time, past learning record, and emotional data obtained from facial expressions and voice. These data are recorded in local storage. The input is the user's behavior and learning data, and the output is the various collected data.

[0361] Step 2:

[0362] The collected answer data and emotion data are periodically transmitted from the device to the server. Transmission is established when the network condition is good. The input is the data recorded on the device, and the output is the data transmitted to the server.

[0363] Step 3:

[0364] The server stores the answer data and emotion data received from the device in a database. The input is the data sent from the device, and the output is stored in the database.

[0365] Step 4:

[0366] The server analyzes the stored data using a generative AI model to identify which problem the user is struggling with and the cause. It also identifies the user's emotional state during learning based on emotional data. The input is the data stored in the database, and the output is the analysis results.

[0367] Step 5:

[0368] The server uses an AI model to generate the optimal learning path based on the analysis results. It can also provide optimal support and suggestions in real time based on customer sentiment data. The input is the analysis results, and the output is the optimal learning path and support suggestions.

[0369] Step 6:

[0370] The server automatically generates individually optimized learning materials or support based on the generated optimal learning path or support proposal. The learning materials are provided in the form of text, images, videos, etc. The input is the optimal learning path or support proposal, and the output is the generated learning materials or support content.

[0371] Step 7:

[0372] The generated customized teaching materials and support suggestions are sent from the server to the terminal and displayed to the user. The input is the generated teaching materials and support content, and the output is the display on the terminal.

[0373] Step 8:

[0374] The device records the progress data and emotion data of the user as they learn and respond, and sends them to the server. The input is the user's learning and behavior data, and the output is the data sent back to the server.

[0375] Step 9:

[0376] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress and emotion data. The input is progress and emotion data, and the output is feedback provided to students or customers.

[0377] For example, consider the following prompt:

[0378] python

[0379] emotion_data = emotion_model.detect_emotions(frame)

[0380] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[0381] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0382] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0383] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0384] [Second embodiment]

[0385] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0386] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0387] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0388] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0389] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0390] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0391] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0392] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0393] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0394] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0395] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0396] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0397] This invention is an online education system that provides learning materials optimized for each student's level of understanding, improving their learning efficiency. This system is mainly composed of a server, terminals, and users.

[0398] Data collection methods

[0399] The device collects data such as answer data, accuracy rate, response time, and past learning records as the student studies. This data is programmed to be automatically recorded the moment the student submits their answer. This recorded data is periodically sent to the server.

[0400] Example: When person A (user) solves a math problem, the answer data (answer content, correct or incorrect, answer time) is collected by the terminal and sent to the server.

[0401] Data analysis format

[0402] The server receives the answer data sent from the device and stores it in a database. AI is then used to analyze this data and identify the types of questions each student is struggling with and what parts they don't understand. The AI ​​model performs its analysis by referring to each student's learning patterns and answer history.

[0403] Example: The server analyzes person A's answer data and determines that person A is having trouble with integer problems.

[0404] Form of optimal learning path generation

[0405] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for each student. Here, a learning path refers to the steps or route that a student takes to efficiently progress through their studies. This learning path is designed taking into account the student's weaknesses, interests, and level of understanding.

[0406] Example: The server generates a learning path for person A that includes steps from the basics to applications of integers.

[0407] Automatic generation of individually optimized teaching materials

[0408] The server automatically generates customized learning materials based on the generated optimal learning path. These materials include explanations and appropriate exercises to help students understand. The learning materials can be provided in a variety of formats, including text, images, and videos.

[0409] Example: The server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems.

[0410] Feedback and progress management forms

[0411] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, who then proceeds with their learning. The progress is recorded again, and data on the degree of completion and understanding of the learning is collected. This progress data is then sent back to the server and used for the next analysis.

[0412] Example: The device displays basic explanations and practice problems about integers to A, and sends progress data to the server as A completes his / her learning.

[0413] Continuous feedback

[0414] The server uses AI to provide students with real-time feedback based on the progress data collected, including advice on how to proceed to the next step and suggestions for supplementary materials to help them revisit areas where they have not fully understood the material.

[0415] Example: The server recognizes from A's progress data that he is ready to tackle applied problems involving integers, and generates and provides new customized learning materials.

[0416] In this way, the system of the present invention realizes efficient learning tailored to the individual needs of each student, and has the effect of improving students' understanding.

[0417] The processing flow will be explained below.

[0418] Step 1:

[0419] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. This includes automatically recording data each time a student submits an answer and saving it in local storage.

[0420] Step 2:

[0421] The terminal periodically transmits the collected answer data to the server at regular intervals when a network connection is established.

[0422] Step 3:

[0423] The server receives the answer data sent from the terminal and stores it in a database, which includes receiving the data, checking the format, and writing it to the database.

[0424] Step 4:

[0425] The server analyzes the answer data stored in the database. This analysis involves using an AI model to identify the problems that students are struggling with and the reasons for their difficulties. The AI ​​model performs its analysis by referencing the student's past answer history and learning patterns.

[0426] Step 5:

[0427] Based on the analysis results, the server uses an AI model to generate an optimal learning path for each student, which includes step-by-step instructions for students to learn efficiently.

[0428] Step 6:

[0429] Based on the generated learning path, the server automatically generates individually optimized learning materials, including specific explanations and practice questions to help students deepen their understanding.

[0430] Step 7:

[0431] The server sends the created customized teaching materials to the student's device, where they are displayed in an appropriate format (text, image, video).

[0432] Step 8:

[0433] The device allows the user to study using the provided learning materials and records progress data, including information on how long the user spent studying the materials and how much they understood.

[0434] Step 9:

[0435] The device then sends the collected progress data back to the server either after the learning session ends or periodically.

[0436] Step 10:

[0437] Based on the progress data received, the server uses AI to generate appropriate feedback to students in real time, including advice based on their learning progress and suggestions for supplementary learning materials.

[0438] Step 11:

[0439] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[0440] In this way, all steps work together to create a system that provides students with individually optimized learning.

[0441] Example 1

[0442] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0443] With conventional online education systems, it was difficult to provide customized learning materials tailored to each student's level of understanding, which often resulted in insufficient learning outcomes. It was also difficult to grasp students' learning progress in real time and provide timely feedback. To solve these problems, an advanced system capable of effectively collecting and analyzing each student's learning data was required.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0445] In this invention, the server includes a means for collecting students' answer data, correct answer rate, answer time, and past learning records, a means for periodically sending the collected data to the server, and a means for storing the answer data received by the server in a database and analyzing the data using an artificial intelligence model. This enables the generation of an optimal learning path based on each student's individual learning pattern, the automatic generation of individually optimized learning materials based on the learning path, and real-time feedback based on learning progress data.

[0446] "Answer data" refers to the answers entered by students to questions.

[0447] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly.

[0448] "Response time" is the measurement of the time it takes a student to answer a question.

[0449] "Past learning records" are data that record what a student has learned so far and the results of that learning.

[0450] "Server" refers to a computer system for collecting, storing, analyzing data, and generating educational materials.

[0451] "Device" refers to the device used by students to study, including, for example, a PC, tablet, or smartphone.

[0452] "Artificial intelligence model" refers to the algorithms and machine learning models used to analyze student learning data and generate optimal learning paths and feedback.

[0453] A "learning path" is a set of steps or routes that students can take to efficiently progress through their studies.

[0454] "Customized teaching materials" refers to individually optimized teaching materials that are generated based on each student's level of understanding and learning progress.

[0455] "Learning progress" refers to the process by which students use learning materials and the results of their studies.

[0456] "Feedback" refers to information, including advice and supplementary materials, provided based on learning progress.

[0457] This invention is an online education system for improving student learning efficiency. The system consists of three main components: a server, a terminal, and a user. Each component has a specific function and provides an optimized learning experience for each student.

[0458] First, the terminal is the device that students use to study, and includes PCs, tablets, smartphones, etc. The terminal collects answer data entered by students as they study, as well as the accuracy rate, response time, and past study records. This collected data is automatically recorded each time an answer is entered. Furthermore, the collected data is programmed to be sent to the server periodically.

[0459] Example: When user A answers a math problem using a device, the answer, whether it was correct or not, and the time it took to answer are immediately recorded on the device and sent to the server at regular intervals.

[0460] The server stores the received data in a database and analyzes it using an AI (artificial intelligence) model. The AI ​​model identifies the problems students are struggling with and areas where they lack understanding based on each student's learning patterns and answer history. Based on the results of this analysis, an optimal learning path is generated.

[0461] Example: The server analyzes A's data and identifies that A is struggling with integer problems. Based on this, it generates a systematic learning path from the basics to applications of integers.

[0462] The server then automatically generates customized learning materials that are best suited to each student based on the generated learning path. These materials include various formats, such as text, images, and videos, and also include explanations and practice questions to help students understand. These customized learning materials are then sent from the server to the device.

[0463] Example: The server creates customized learning materials for Mr. A, including basic explanations of integers and practice problems, and sends them to the terminal.

[0464] The device displays the received learning materials to the user, who then uses them to study. The learning progress is recorded again and the data is sent back to the server. This data includes the degree of completion and understanding of the learning.

[0465] Example: The device displays basic explanations and practice problems for person A, who then studies them. After completing the study, the progress data is sent to the server.

[0466] Finally, the server uses the collected progress data to provide real-time AI feedback, including advice on next steps and suggested supplemental materials to help you revisit areas of incomplete understanding.

[0467] Example: The server analyzes A's progress data, recognizes that A is ready to tackle applied problems involving integers, and generates and provides new learning materials.

[0468] The hardware used is a standard server or personal computer terminal, and the software includes an artificial intelligence model and a database management system. Specific software includes machine learning libraries using Python (such as scikit-learn, TensorFlow, and PyTorch). In this way, the system aims to provide an optimal learning experience tailored to each student's needs and improve their understanding of the material.

[0469] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0470] Step 1: Collect user learning data

[0471] As students study, the device collects answer data, accuracy rate, response time, and past learning records. Input data includes the student's answer content, whether it was correct or incorrect, and the time it took to answer. When this information is entered into the device, it is automatically recorded. Specifically, the device is equipped with an answer form and a timer function, and captures data each time a student enters an answer.

[0472] Step 2: Sending data to the server

[0473] The device periodically sends the collected learning data to the server. The input data is the student's learning data collected in step 1. When sending, the device is programmed to use an appropriate communication protocol. Specifically, the device transfers the data to the server using protocols such as HTTP or WebSocket.

[0474] Step 3: Data storage and analysis

[0475] The server receives the learning data sent from the device and stores it in a database. The input data includes the student's answer data, accuracy rate, answer time, and past learning records. After storing this data, the server analyzes it using an AI model. Specifically, the server stores the data using a database management system, and then analyzes the data using machine learning algorithms implemented in programming languages ​​such as Python.

[0476] Step 4: Generate a learning path

[0477] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. The input data is the analysis results from step 3, which include information on the types of problems the student is struggling with and their level of understanding. The AI ​​model processes the data to generate a learning path, and the output is an individually optimized learning path. Specifically, the AI ​​model calculates the optimal steps and route based on the learning pattern.

[0478] Step 5: Create customized learning materials

[0479] The server automatically generates individually optimized learning materials based on the generated learning path. The input data is the learning path generated in step 4. As data processing, a learning material generation algorithm generates learning materials according to the learning path, and as output, learning materials such as text, images, and videos that are optimal for the student are obtained. Specifically, an algorithm that dynamically combines learning content is executed.

[0480] Step 6: Deliver customized learning materials

[0481] The server sends the created customized teaching materials to the terminal. The input data is the individually optimized teaching materials generated in step 5. Specifically, a file transfer protocol such as an HTTP request or FTP is used to send the teaching material file to the terminal.

[0482] Step 7: Collect user learning and progress data

[0483] The device displays the received learning materials, and the user uses them to progress with their studies. The input data is the customized learning materials sent in step 6. When the user completes their study, the progress data is recorded again on the device. Specifically, the device is equipped with a learning materials display function and a progress data recording function. Each time the user solves a problem, the result is recorded.

[0484] Step 8: Sending progress data to the server and parsing it

[0485] The device again sends the collected progress data to the server. The input data is the learning progress data collected in step 7. The server analyzes the received progress data and uses AI to provide appropriate feedback to the user. Specifically, the AI ​​model analyzes the progress data again and generates feedback suggesting the next task to tackle and supplementary learning materials.

[0486] (Application example 1)

[0487] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] Conventional online education systems lack the optimization required to provide learning materials appropriate for each student, resulting in poor learning efficiency. Furthermore, they lacked a mechanism for recommending individually optimized content using users' viewing history and evaluation data. Therefore, there is a need for a system that can improve learner and viewer satisfaction and maximize learning effectiveness.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0490] In this invention, the server includes means for collecting students' answer data, correct answer rates, answer times, and past learning records, means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for transmitting the generated learning materials to the student's terminal and monitoring learning progress, means for providing timely feedback based on learning progress, means for collecting and analyzing user viewing history and evaluation data, and means for recommending individually optimized content based on the analysis results. This makes it possible to provide learning materials and content customized for each student or viewer, improving learning efficiency and viewing experience.

[0491] "Student answer data" refers to the content of the answers given by students to questions and information on whether those answers were correct or incorrect.

[0492] The "correct answer rate" is an indicator that indicates the percentage of correct answers among all answers given by a student.

[0493] "Response time" is the time it takes a student to solve a problem.

[0494] "Past learning records" are historical information about the learning a student has done up to now, and are data including progress and level of understanding.

[0495] The "viewing history" is recorded information about the content that the user has viewed up to now.

[0496] "Rating data" refers to data of ratings and feedback given by users to content they have viewed.

[0497] An "optimal learning path" is a set of learning steps or routes designed to help students progress through their studies efficiently.

[0498] "Individually optimized teaching materials" are teaching materials that are customized based on each student's level of understanding and interests.

[0499] The "recommendation means based on analysis results" refers to a method or device for recommending optimal content to a user based on the analysis results of viewing history and evaluation data.

[0500] A "server" is a central computer device that collects and analyzes data and sends the processed results to each terminal.

[0501] "Feedback" refers to timely advice and evaluation of learning or viewing progress.

[0502] This invention includes an online education system that provides learning materials optimized for each learner's level of understanding, improving students' learning efficiency, and a content distribution system that recommends individually optimized content based on the user's viewing history. The details of these systems are described below.

[0503] Data collection methods

[0504] The device collects answer data, accuracy rate, answer time, and past learning records when students study. The device also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment a student or user submits an answer or completes viewing. This recorded data is periodically sent to the server. Specifically, when person A (user) solves a math problem, the device collects answer data (answer content, correct or incorrect, answer time) and sends it to the server. Viewing history data of movies viewed by person A is also collected in a similar manner.

[0505] Data analysis format

[0506] The server receives the answer data and viewing history data sent from the device and stores it in a database. Next, it uses AI to analyze this data and determine what types of problems each student is struggling with or what type of content the user prefers. The AI ​​model performs its analysis by referring to each student's learning patterns, answer history, and user viewing history. For example, the server analyzes Person A's answer data and determines that Person A is struggling with integer problems. It also analyzes Person A's viewing history of science fiction movies that he has previously watched and determines that he prefers the science fiction genre.

[0507] Form of optimal learning path and content recommendation generation

[0508] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. It also recommends the most suitable content to the user. This learning path and content recommendations are designed taking into account the student's weaknesses, interests, and level of understanding. As a specific example, the server generates a learning path for Person A that includes steps from the basics to applications of integers. It also generates a list of recommended new science fiction movies for Person A.

[0509] Automatic generation of individually optimized teaching materials

[0510] The server automatically generates customized learning materials and a list of recommended content based on the generated optimal learning path and content recommendations. The learning materials include explanations and appropriate practice problems to help students understand, and the recommended content list includes content that is likely to interest the user. The learning materials and content can be provided in a variety of formats, including text, images, and videos. As a specific example, the server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems, and also generates a list of recommendations for new science fiction movies.

[0511] Feedback and progress management forms

[0512] The generated customized learning materials and recommended content are sent from the server to the device. The device displays the received learning materials and content to the user, who then proceeds with their learning or viewing. The learning progress and content viewing history are recorded again, and data on the degree of learning completion, level of understanding, and viewing status is collected. This progress data is sent back to the server again and used for the next analysis. As a specific example, the device displays a basic explanation of integers and practice problems to Person A, and then sends progress data and new viewing history data to the server when Person A has completed their learning.

[0513] Continuous feedback

[0514] The server uses AI to provide appropriate feedback to students and users in real time based on the collected progress data and viewing history data. This feedback includes advice on how to proceed to the next step, suggestions for supplementary materials to help students revisit areas where they have insufficient understanding, and recommendations for new content. For example, the server recognizes from student A's progress data that he or she is ready to tackle applied problems involving integers, and provides new customized materials and a list of recommended content.

[0515] Here are some example prompts to input to the generative AI model:

[0516] The sci-fi movies I've seen recently are "A Space Journey Through Time" and "A Faraway Galaxy." Please recommend more movies with similar themes.

[0517] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0518] Step 1:

[0519] The device collects answer data, accuracy rate, answer time, and past learning records when students answer questions. It also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment students or users submit their answers or complete viewing. The input data is the student's answer data and viewing history data, and the output data is the collected records.

[0520] Step 2:

[0521] The terminal periodically transmits the collected data to the server. Here, data transmission is performed, and the input data is the answer data and viewing history data collected in step 1. The output data is the recorded data transmitted to the server.

[0522] Step 3:

[0523] The server receives data sent from the terminal and stores it in a database. When storing it, it formats and normalizes the data. The input data is the recorded data sent, and the output data is the data stored in the database.

[0524] Step 4:

[0525] The server analyzes the stored data using an AI model. It references each student's answer history and viewing history to identify which questions they are struggling with and which content they prefer. The input data is the answer data and viewing history data stored in the database. The output data is the analysis results that identify the student's weaknesses and user preferences.

[0526] Step 5:

[0527] The server generates optimal learning paths and content recommendations based on the analysis results. Here, an AI model is used to create learning paths for students and content recommendation lists for users. The input data is the analysis results, and the output data is the generated learning paths and content recommendation lists.

[0528] Step 6:

[0529] The server automatically generates customized learning materials and a list of recommended content based on the generated learning path and content recommendations. The learning materials are provided in the form of text, images, and videos. The input data is the generated learning path and content recommendation list, and the output data is the automatically generated learning materials and content list.

[0530] Step 7:

[0531] The server transmits the generated learning materials and content list to the terminal. Here, learning materials and recommended content are transmitted to the user terminal. The input data are the automatically generated learning materials and content list, and the output data are the data transmitted to the terminal.

[0532] Step 8:

[0533] The terminal displays the received learning materials and content to the user, and the user proceeds with their learning or viewing. The learning progress and viewing history are recorded again. The input data is the received learning materials and content list, and the output data is the learning progress and viewing history.

[0534] Step 9:

[0535] The device transmits the re-recorded progress data and viewing history data to the server. Here, data transmission is performed. The input data is the learning progress status and viewing history, and the output data is the recorded data transmitted to the server.

[0536] Step 10:

[0537] The server uses AI to provide feedback based on the collected progress data and viewing history data. The feedback includes study advice and recommendations for new content. The input data is the progress data and viewing history, and the output data is the provided feedback.

[0538] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0539] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. This system is mainly composed of a server, terminals, and users, and also incorporates an emotion engine to utilize students' emotional data.

[0540] Data collection methods

[0541] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[0542] Example: When user A answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[0543] Data transmission format

[0544] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[0545] Data analysis format

[0546] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses AI to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[0547] Example: The server analyzes person A's answer data and emotion data and determines that person A is struggling with integer problems and is feeling frustrated while learning.

[0548] Form of optimal learning path generation

[0549] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[0550] Example: The server generates a learning path for person A, starting with the basics of integers and gradually progressing to applications, taking into account person A's emotional state.

[0551] Automatic generation of individually optimized teaching materials

[0552] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[0553] Example: The server creates customized learning materials for A, including a basic explanation of integers, related practice problems, and interactive content that will attract A's interest.

[0554] Feedback and progress management forms

[0555] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[0556] Example: The device displays basic explanations and practice problems for person A about integers, and after person A has finished learning, it sends progress data and emotional data to the server.

[0557] Continuous feedback

[0558] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, it provides encouragement and warnings based on emotional data to maintain students' motivation.

[0559] Example: The server determines from A's emotional data that she is feeling frustrated while studying, and sends her encouraging messages and messages encouraging her to take a break, and provides additional explanations for areas where she does not fully understand.

[0560] In this way, the system of the present invention provides a learning experience that is optimized to suit the individual needs and emotional state of each student, thereby improving their understanding and increasing their motivation to learn.

[0561] The processing flow will be explained below.

[0562] Step 1:

[0563] The device collects answer data (answer content, accuracy rate, and answer time) in real time when the student (user) answers the questions. In addition, it uses an emotion engine to analyze the student's emotional state (excitement, concentration, frustration, etc.) from facial expressions and voice data and record it as emotional data.

[0564] Specific operation:

[0565] The device uses a camera and microphone to collect emotional data while students answer questions.

[0566] When the answer is submitted, the emotional data at that time is also saved.

[0567] Step 2:

[0568] The device periodically transmits the collected answer data and emotion data to the server. Transmission is performed automatically when a network connection is established.

[0569] Specific operation:

[0570] The terminal processes the collected data in batches at regular intervals and sends them to the server.

[0571] If there is no network connection, data is kept locally until the connection is restored.

[0572] Step 3:

[0573] The server receives the answer data and emotion data sent from the device and stores them in a database. During this process, the data format is checked and reformatted.

[0574] Specific operation:

[0575] The server checks the integrity of the received data and converts the format appropriately.

[0576] Once all the validations are completed, the data is inserted into the database.

[0577] Step 4:

[0578] The server analyzes the answer data and emotional data stored in the database, using an AI model to identify which questions students are struggling with and the reasons for this from the answer data, and also uses the emotional data to understand the student's mental state while studying.

[0579] Specific operation:

[0580] The AI ​​model analyzes the answer data and identifies students' weaknesses.

[0581] Analyze emotional data and record changes in students' emotions during learning.

[0582] Step 5:

[0583] Based on the analysis results, the server uses AI models to generate an optimal learning path for each student, which is designed taking into account the student's weaknesses and emotional state.

[0584] Specific operation:

[0585] The learning path selects the optimal learning order and content for each student based on answer data and emotional data.

[0586] Path generation is dynamic and updates based on real-time data.

[0587] Step 6:

[0588] Based on the generated learning path, the server automatically generates individually optimized learning materials, including explanations and exercises that are optimal for the student's level of understanding and emotional state.

[0589] Specific operation:

[0590] The automatically generated teaching materials include a variety of content, including text, images, and videos.

[0591] Add content that students are likely to be interested in based on emotional data.

[0592] Step 7:

[0593] The server sends the created customized learning materials to the terminal, which displays the learning materials in an appropriate format (text, image, video).

[0594] Specific operation:

[0595] Materials are delivered in a format that suits each individual student.

[0596] The terminal displays the received educational material in a user-friendly interface.

[0597] Step 8:

[0598] The device then uses the provided learning materials to help the user study, recording progress and emotional data, including the user's level of understanding of the learning content, the time required, and the emotional data, which is then analyzed again.

[0599] Examples:

[0600] As students progress through their studies, their progress and emotional state is recorded in real time.

[0601] If the device's emotions change during the learning process, the change is immediately analyzed using the emotion engine.

[0602] Step 9:

[0603] The device then sends the collected progress and emotion data back to the server at the end of the learning session or at other regular intervals.

[0604] Specific operation:

[0605] Progress data and emotion data are batch processed and sent to the server.

[0606] Immediate transmission occurs at the end of the learning session.

[0607] Step 10:

[0608] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data it receives, including assessments of their learning progress, advice, and encouragement and warnings to keep them motivated.

[0609] Specific operation:

[0610] An AI model analyzes progress data to assess understanding and next learning steps.

[0611] Based on the emotional data, messages that provide emotional support and motivation to students are generated.

[0612] Step 11:

[0613] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[0614] Specific operation:

[0615] The feedback is immediately sent to the terminal and set to be displayed to the user.

[0616] The devices display feedback in an easy-to-read format to help students understand.

[0617] Example 2

[0618] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0619] Conventional online education systems are based solely on students' answer data and learning progress, and do not take into account their emotional state. This makes it difficult to appropriately address issues such as frustration and loss of motivation that students experience during learning. The purpose of this invention is to improve students' learning efficiency and comprehension, while also providing optimal learning materials and feedback that take into account their emotional state during learning.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0621] In this invention, the server includes means for collecting students' answer data, accuracy rate, answer time, and past learning records, means for collecting emotional data based on facial expressions, voice, and behavior during learning, means for analyzing the collected answer data and emotional data to identify problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for sending the generated learning materials to the student's terminal and monitoring the learning progress, and means for providing timely feedback based on the learning progress and encouraging or warning the student according to their emotional state. This makes it possible to provide appropriate learning materials and feedback that take into account each student's individual level of understanding and emotional state.

[0622] "Answer data" is information about the questions answered by the student, specifically data including correct / incorrect answers, answer time, and selection history of options.

[0623] "Emotional data" refers to data about a student's emotional state collected from facial expressions, voice, behavior, etc. while they are studying.

[0624] "Analysis" is the process of evaluating and analyzing students' learning situations and emotional states based on collected answer data and emotional data.

[0625] A "learning path" is a plan that shows students the optimal order of content and unit learning based on the analysis results.

[0626] "Individually optimized learning materials" are learning materials and content that are customized to best suit a specific student, taking into account the student's level of understanding and emotional state.

[0627] "Progress data" refers to data that indicates the progress and achievement of a student as they progress through their studies.

[0628] "Feedback" refers to guidance and advice provided to students based on their learning progress and analysis results.

[0629] "Encouragement and caution" refers to messages or instructions that take into account the student's emotional state and are intended to increase motivation or draw attention.

[0630] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. The system is mainly composed of a server, terminals, and users. It also incorporates an emotion engine to utilize emotional data.

[0631] Data collection methods

[0632] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[0633] Example: When a user answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[0634] Data transmission format

[0635] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[0636] Data analysis format

[0637] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses an AI model to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[0638] Example: A server analyzes a user's answer data and sentiment data to determine that the user is struggling with integer problems and is frustrated while learning.

[0639] Form of optimal learning path generation

[0640] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[0641] Example: A server generates a learning path for a user, starting with the basics of integers and gradually progressing to applications, taking into account the user's emotional state.

[0642] Automatic generation of individually optimized teaching materials

[0643] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[0644] Example: A server creates customized learning materials for a user, including a basic explanation of integers and related exercises, as well as interactive content that will attract the user's attention.

[0645] Feedback and progress management forms

[0646] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[0647] Example: A device displays basic explanations and practice problems for integers to a user, and sends progress data and emotion data to a server after the user has finished learning.

[0648] Continuous feedback

[0649] The server uses an AI model to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, the server provides encouragement and warnings based on emotional data to maintain students' motivation.

[0650] Example: The server determines from the user's emotional data that they are feeling frustrated while studying, and sends them messages of encouragement or encouragement to take a break, and provides additional explanations for areas where they do not fully understand.

[0651] Example prompts to be input to the generative AI model

[0652] "Analyze users' latest learning outcomes and sentiment data to generate appropriate learning paths."

[0653] "Suggest additional feedback and educational materials based on user progress and sentiment data."

[0654] This allows the system of the present invention to provide a learning experience that is optimized to the individual needs and emotional state of the student, improving comprehension and increasing engagement.

[0655] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0656] Step 1:

[0657] Data collection

[0658] The device opens a session when the user begins learning. The input is the question the user answers and its content. Specifically, when the user answers a question, the device collects answer data (correct / incorrect answers, answer time, and choice history) in real time. The device also activates its built-in emotion engine and uses the camera and microphone to record the user's facial expressions, voice, and actions. This allows the user's answer data and emotion data to be collected and stored in local storage.

[0659] Step 2:

[0660] Data transmission

[0661] The device stores the collected data in a buffer and periodically checks the network connection status. The input is the answer data and emotion data stored in the buffer, and the network connection status. If the network is good, the device sends the data in the buffer to the server. The output is the answer data and emotion data sent to the server.

[0662] Step 3:

[0663] Data analysis

[0664] The server receives the answer data and emotion data sent from the device and stores them in a database. The input is the received answer data and emotion data. The server then analyzes the data using an AI model. Specifically, the AI ​​analyzes the answer data and emotion data to identify the problems the user is struggling with, their causes, and their emotional state during learning. The output is an analysis of the user's learning problems and emotional state.

[0665] Step 4:

[0666] Generate optimal learning paths

[0667] Based on the analysis results, the server uses an AI model to generate an optimal learning path for the user. The input is the results of data analysis. Specifically, the AI ​​model uses the analysis results to plan the learning order and content, and designs a learning path that is optimal for the user's level of understanding and emotional state. The output is the generated learning path.

[0668] Step 5:

[0669] Automatic generation of individually optimized teaching materials

[0670] The server automatically generates individually optimized learning materials based on the generated learning path. The input is the generated learning path. Specifically, the server creates learning materials such as text, images, and videos, and customizes them to suit the user's interests and level of understanding. The output is the optimized learning materials.

[0671] Step 6:

[0672] Sending study materials and managing progress

[0673] The server sends the generated learning materials to the device. The input is the created learning materials. Specifically, the device displays the received learning materials to the user and progresses the learning. When the user finishes learning, the device again records the progress data and new emotion data and stores them in local storage.

[0674] Step 7:

[0675] Data retransmission

[0676] The device stores the progress data and emotion data in a buffer and checks the network connection status. The input is the progress data and emotion data stored in the buffer. If the network is good, the device sends the data to the server again. The output is the progress data and emotion data sent to the server.

[0677] Step 8:

[0678] Continuous feedback

[0679] The server then analyzes the progress and emotional data again and provides appropriate feedback to the user in real time. The inputs are the progress and emotional data. Specifically, the AI ​​model evaluates the learning progress, provides additional training materials and advice as needed, and sends encouragement and warnings according to the user's emotional state. The output is feedback and training materials to the user.

[0680] In this way, the system can provide a learning experience that is optimized to each student's individual needs and emotional state, improving comprehension and increasing engagement.

[0681] (Application example 2)

[0682] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0683] Conventional online education systems have the problem that it is difficult to grasp students' level of understanding and learning behavior, making it difficult to adequately manage progress and provide feedback according to individual needs. It is also difficult to grasp customers' emotional state in real time in physical stores, making it difficult to provide appropriate individual support. It is necessary to improve student and customer satisfaction and realize effective learning and customer service.

[0684] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting students' answer data, correct answer rate, answer time, and past learning records; means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof; means for generating an optimal learning path based on the analysis results; means for automatically generating individually optimized learning materials based on the generated learning path; means for sending the generated learning materials to the student's device and monitoring learning progress; means for providing timely feedback based on learning progress; and means for collecting and analyzing customer emotional state and behavioral data and providing optimal support and suggestions in real time. This maximizes students' learning efficiency, provides a learning experience tailored to their individual needs, and makes it possible to provide appropriate support to customers in physical stores.

[0685] "Student answer data" refers to the answers entered by students in response to questions presented in the educational system.

[0686] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly out of all questions that he or she answered.

[0687] "Response time" refers to the time it takes a student to complete a response to a question.

[0688] "Past learning records" are a collection of data that includes the learning content that a student has undertaken and the results of that learning.

[0689] "Means of collection" refers to the methods and equipment necessary to obtain and use data.

[0690] "Means for analyzing data" means methods and techniques for processing collected data and extracting information.

[0691] "Means for generating optimal learning paths" refers to a method for creating the most effective learning sequence and curriculum for students based on the analysis results.

[0692] "Means for automatically generating individually optimized teaching materials" refers to a method for automatically creating learning materials that meet the needs of students.

[0693] A "progress monitoring tool" is a method for tracking students' progress and recording their progress.

[0694] "Means of providing feedback" are ways to provide students with appropriate guidance and advice based on their learning progress.

[0695] "Means for collecting and analyzing customer emotional state and behavioral data" refers to methods and technologies for acquiring customer facial expressions, movements, speech content, etc., and analyzing them to clarify their emotional state and behavioral intentions.

[0696] "Means of providing optimal support and suggestions in real time" refers to a method of providing appropriate responses and services on the spot based on collected and analyzed customer data.

[0697] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state.It is also a system that can analyze the emotional state of customers in physical stores in real time and provide optimal support.

[0698] Data collection methods

[0699] The device (smartphone, smart glasses, robot, etc.) collects student or customer behavior data, answer data, accuracy rate, answer time, past learning records, emotional data, etc. Using an emotion engine, emotional data is extracted from the facial expressions, voice, and behavior of students or customers. This data is automatically recorded and stored in local storage.

[0700] Data transmission format

[0701] The device periodically transmits the collected answer data and emotion data to the server. Transmission is established when the network connection is good.

[0702] Data analysis format

[0703] The server receives the answer data and emotion data sent from the device and stores them in a database. It then analyzes the data using a generative AI model (for example, a model using TensorFlow) to identify the problems the student or customer is struggling with and the reasons for their difficulties. It also identifies the emotional state of the student or customer based on the emotion data.

[0704] Optimal learning paths and support generation

[0705] The server uses an AI model to generate the optimal learning path based on the analysis results. Furthermore, by analyzing the customer's emotional data, it can provide optimal support and suggestions in real time. These learning paths and suggestions are designed taking into account both the student's or customer's answer data and emotional data.

[0706] Automatic generation of individually optimized teaching materials and support

[0707] Based on the generated optimal learning path or support proposal, the server automatically generates individually optimized learning materials for students and appropriate support and proposals for customers. The learning materials are provided in a variety of formats, including text, images, and videos.

[0708] Feedback and progress management forms

[0709] The generated customized learning materials and support suggestions are sent from the server to the device and displayed to the user. The device again records progress data and emotional data as the user progresses with their learning and response, and sends this data to the server.

[0710] Continuous feedback

[0711] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress data and emotional data. For example, if the server determines from the emotional data analyzed that the customer is confused during learning or purchasing, it will send appropriate guidance or support messages.

[0712] Prompt Sentence Examples

[0713] For example, consider the following prompt:

[0714] python

[0715] emotion_data = emotion_model.detect_emotions(frame)

[0716] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[0717] This system enables the provision of learning experiences and customer support that are optimized to suit the individual needs of students and the real-time emotional state of customers, maximizing satisfaction and effectiveness.

[0718] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0719] Step 1:

[0720] The device (smartphone, smart glasses, robot, etc.) collects the user's answer data, accuracy rate, answer time, past learning record, and emotional data obtained from facial expressions and voice. These data are recorded in local storage. The input is the user's behavior and learning data, and the output is the various collected data.

[0721] Step 2:

[0722] The collected answer data and emotion data are periodically transmitted from the device to the server. Transmission is established when the network condition is good. The input is the data recorded on the device, and the output is the data transmitted to the server.

[0723] Step 3:

[0724] The server stores the answer data and emotion data received from the device in a database. The input is the data sent from the device, and the output is stored in the database.

[0725] Step 4:

[0726] The server analyzes the stored data using a generative AI model to identify which problem the user is struggling with and the cause. It also identifies the user's emotional state during learning based on emotional data. The input is the data stored in the database, and the output is the analysis results.

[0727] Step 5:

[0728] The server uses an AI model to generate the optimal learning path based on the analysis results. It can also provide optimal support and suggestions in real time based on customer sentiment data. The input is the analysis results, and the output is the optimal learning path and support suggestions.

[0729] Step 6:

[0730] The server automatically generates individually optimized learning materials or support based on the generated optimal learning path or support proposal. The learning materials are provided in the form of text, images, videos, etc. The input is the optimal learning path or support proposal, and the output is the generated learning materials or support content.

[0731] Step 7:

[0732] The generated customized teaching materials and support suggestions are sent from the server to the terminal and displayed to the user. The input is the generated teaching materials and support content, and the output is the display on the terminal.

[0733] Step 8:

[0734] The device records the progress data and emotion data of the user as they learn and respond, and sends them to the server. The input is the user's learning and behavior data, and the output is the data sent back to the server.

[0735] Step 9:

[0736] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress and emotion data. The input is progress and emotion data, and the output is feedback provided to students or customers.

[0737] For example, consider the following prompt:

[0738] python

[0739] emotion_data = emotion_model.detect_emotions(frame)

[0740] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[0741] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0742] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0743] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0744] [Third embodiment]

[0745] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0746] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0747] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0748] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0749] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0750] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0751] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0752] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0753] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0754] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0755] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0756] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0757] This invention is an online education system that provides learning materials optimized for each student's level of understanding, improving their learning efficiency. This system is mainly composed of a server, terminals, and users.

[0758] Data collection methods

[0759] The device collects data such as answer data, accuracy rate, response time, and past learning records as the student studies. This data is programmed to be automatically recorded the moment the student submits their answer. This recorded data is periodically sent to the server.

[0760] Example: When person A (user) solves a math problem, the answer data (answer content, correct or incorrect, answer time) is collected by the terminal and sent to the server.

[0761] Data analysis format

[0762] The server receives the answer data sent from the device and stores it in a database. AI is then used to analyze this data and identify the types of questions each student is struggling with and what parts they don't understand. The AI ​​model performs its analysis by referring to each student's learning patterns and answer history.

[0763] Example: The server analyzes person A's answer data and determines that person A is having trouble with integer problems.

[0764] Form of optimal learning path generation

[0765] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for each student. Here, a learning path refers to the steps or route that a student takes to efficiently progress through their studies. This learning path is designed taking into account the student's weaknesses, interests, and level of understanding.

[0766] Example: The server generates a learning path for person A that includes steps from the basics to applications of integers.

[0767] Automatic generation of individually optimized teaching materials

[0768] The server automatically generates customized learning materials based on the generated optimal learning path. These materials include explanations and appropriate exercises to help students understand. The learning materials can be provided in a variety of formats, including text, images, and videos.

[0769] Example: The server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems.

[0770] Feedback and progress management forms

[0771] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, who then proceeds with their learning. The progress is recorded again, and data on the degree of completion and understanding of the learning is collected. This progress data is then sent back to the server and used for the next analysis.

[0772] Example: The device displays basic explanations and practice problems about integers to A, and sends progress data to the server as A completes his / her learning.

[0773] Continuous feedback

[0774] The server uses AI to provide students with real-time feedback based on the progress data collected, including advice on how to proceed to the next step and suggestions for supplementary materials to help them revisit areas where they have not fully understood the material.

[0775] Example: The server recognizes from A's progress data that he is ready to tackle applied problems involving integers, and generates and provides new customized learning materials.

[0776] In this way, the system of the present invention realizes efficient learning tailored to the individual needs of each student, and has the effect of improving students' understanding.

[0777] The processing flow will be explained below.

[0778] Step 1:

[0779] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. This includes automatically recording data each time a student submits an answer and saving it in local storage.

[0780] Step 2:

[0781] The terminal periodically transmits the collected answer data to the server at regular intervals when a network connection is established.

[0782] Step 3:

[0783] The server receives the answer data sent from the terminal and stores it in a database, which includes receiving the data, checking the format, and writing it to the database.

[0784] Step 4:

[0785] The server analyzes the answer data stored in the database. This analysis involves using an AI model to identify the problems that students are struggling with and the reasons for their difficulties. The AI ​​model performs its analysis by referencing the student's past answer history and learning patterns.

[0786] Step 5:

[0787] Based on the analysis results, the server uses an AI model to generate an optimal learning path for each student, which includes step-by-step instructions for students to learn efficiently.

[0788] Step 6:

[0789] Based on the generated learning path, the server automatically generates individually optimized learning materials, including specific explanations and practice questions to help students deepen their understanding.

[0790] Step 7:

[0791] The server sends the created customized teaching materials to the student's device, where they are displayed in an appropriate format (text, image, video).

[0792] Step 8:

[0793] The device allows the user to study using the provided learning materials and records progress data, including information on how long the user spent studying the materials and how much they understood.

[0794] Step 9:

[0795] The device then sends the collected progress data back to the server either after the learning session ends or periodically.

[0796] Step 10:

[0797] Based on the progress data received, the server uses AI to generate appropriate feedback to students in real time, including advice based on their learning progress and suggestions for supplementary learning materials.

[0798] Step 11:

[0799] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[0800] In this way, all steps work together to create a system that provides students with individually optimized learning.

[0801] Example 1

[0802] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0803] With conventional online education systems, it was difficult to provide customized learning materials tailored to each student's level of understanding, which often resulted in insufficient learning outcomes. It was also difficult to grasp students' learning progress in real time and provide timely feedback. To solve these problems, an advanced system capable of effectively collecting and analyzing each student's learning data was required.

[0804] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0805] In this invention, the server includes a means for collecting students' answer data, correct answer rate, answer time, and past learning records, a means for periodically sending the collected data to the server, and a means for storing the answer data received by the server in a database and analyzing the data using an artificial intelligence model. This enables the generation of an optimal learning path based on each student's individual learning pattern, the automatic generation of individually optimized learning materials based on the learning path, and real-time feedback based on learning progress data.

[0806] "Answer data" refers to the answers entered by students to questions.

[0807] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly.

[0808] "Response time" is the measurement of the time it takes a student to answer a question.

[0809] "Past learning records" are data that record what a student has learned so far and the results of that learning.

[0810] "Server" refers to a computer system for collecting, storing, analyzing data, and generating educational materials.

[0811] "Device" refers to the device used by students to study, including, for example, a PC, tablet, or smartphone.

[0812] "Artificial intelligence model" refers to the algorithms and machine learning models used to analyze student learning data and generate optimal learning paths and feedback.

[0813] A "learning path" is a set of steps or routes that students can take to efficiently progress through their studies.

[0814] "Customized teaching materials" refers to individually optimized teaching materials that are generated based on each student's level of understanding and learning progress.

[0815] "Learning progress" refers to the process by which students use learning materials and the results of their studies.

[0816] "Feedback" refers to information, including advice and supplementary materials, provided based on learning progress.

[0817] This invention is an online education system for improving student learning efficiency. The system consists of three main components: a server, a terminal, and a user. Each component has a specific function and provides an optimized learning experience for each student.

[0818] First, the terminal is the device that students use to study, and includes PCs, tablets, smartphones, etc. The terminal collects answer data entered by students as they study, as well as the accuracy rate, response time, and past study records. This collected data is automatically recorded each time an answer is entered. Furthermore, the collected data is programmed to be sent to the server periodically.

[0819] Example: When user A answers a math problem using a device, the answer, whether it was correct or not, and the time it took to answer are immediately recorded on the device and sent to the server at regular intervals.

[0820] The server stores the received data in a database and analyzes it using an AI (artificial intelligence) model. The AI ​​model identifies the problems students are struggling with and areas where they lack understanding based on each student's learning patterns and answer history. Based on the results of this analysis, an optimal learning path is generated.

[0821] Example: The server analyzes A's data and identifies that A is struggling with integer problems. Based on this, it generates a systematic learning path from the basics to applications of integers.

[0822] The server then automatically generates customized learning materials that are best suited to each student based on the generated learning path. These materials include various formats, such as text, images, and videos, and also include explanations and practice questions to help students understand. These customized learning materials are then sent from the server to the device.

[0823] Example: The server creates customized learning materials for Mr. A, including basic explanations of integers and practice problems, and sends them to the terminal.

[0824] The device displays the received learning materials to the user, who then uses them to study. The learning progress is recorded again and the data is sent back to the server. This data includes the degree of completion and understanding of the learning.

[0825] Example: The device displays basic explanations and practice problems for person A, who then studies them. After completing the study, the progress data is sent to the server.

[0826] Finally, the server uses the collected progress data to provide real-time AI feedback, including advice on next steps and suggested supplemental materials to help you revisit areas of incomplete understanding.

[0827] Example: The server analyzes A's progress data, recognizes that A is ready to tackle applied problems involving integers, and generates and provides new learning materials.

[0828] The hardware used is a standard server or personal computer terminal, and the software includes an artificial intelligence model and a database management system. Specific software includes machine learning libraries using Python (such as scikit-learn, TensorFlow, and PyTorch). In this way, the system aims to provide an optimal learning experience tailored to each student's needs and improve their understanding of the material.

[0829] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0830] Step 1: Collect user learning data

[0831] As students study, the device collects answer data, accuracy rate, response time, and past learning records. Input data includes the student's answer content, whether it was correct or incorrect, and the time it took to answer. When this information is entered into the device, it is automatically recorded. Specifically, the device is equipped with an answer form and a timer function, and captures data each time a student enters an answer.

[0832] Step 2: Sending data to the server

[0833] The device periodically sends the collected learning data to the server. The input data is the student's learning data collected in step 1. When sending, the device is programmed to use an appropriate communication protocol. Specifically, the device transfers the data to the server using protocols such as HTTP or WebSocket.

[0834] Step 3: Data storage and analysis

[0835] The server receives the learning data sent from the device and stores it in a database. The input data includes the student's answer data, accuracy rate, answer time, and past learning records. After storing this data, the server analyzes it using an AI model. Specifically, the server stores the data using a database management system, and then analyzes the data using machine learning algorithms implemented in programming languages ​​such as Python.

[0836] Step 4: Generate a learning path

[0837] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. The input data is the analysis results from step 3, which include information on the types of problems the student is struggling with and their level of understanding. The AI ​​model processes the data to generate a learning path, and the output is an individually optimized learning path. Specifically, the AI ​​model calculates the optimal steps and route based on the learning pattern.

[0838] Step 5: Create customized learning materials

[0839] The server automatically generates individually optimized learning materials based on the generated learning path. The input data is the learning path generated in step 4. As data processing, a learning material generation algorithm generates learning materials according to the learning path, and as output, learning materials such as text, images, and videos that are optimal for the student are obtained. Specifically, an algorithm that dynamically combines learning content is executed.

[0840] Step 6: Deliver customized learning materials

[0841] The server sends the created customized teaching materials to the terminal. The input data is the individually optimized teaching materials generated in step 5. Specifically, a file transfer protocol such as an HTTP request or FTP is used to send the teaching material file to the terminal.

[0842] Step 7: Collect user learning and progress data

[0843] The device displays the received learning materials, and the user uses them to progress with their studies. The input data is the customized learning materials sent in step 6. When the user completes their study, the progress data is recorded again on the device. Specifically, the device is equipped with a learning materials display function and a progress data recording function. Each time the user solves a problem, the result is recorded.

[0844] Step 8: Sending progress data to the server and parsing it

[0845] The device again sends the collected progress data to the server. The input data is the learning progress data collected in step 7. The server analyzes the received progress data and uses AI to provide appropriate feedback to the user. Specifically, the AI ​​model analyzes the progress data again and generates feedback suggesting the next task to tackle and supplementary learning materials.

[0846] (Application example 1)

[0847] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0848] Conventional online education systems lack the optimization required to provide learning materials appropriate for each student, resulting in poor learning efficiency. Furthermore, they lacked a mechanism for recommending individually optimized content using users' viewing history and evaluation data. Therefore, there is a need for a system that can improve learner and viewer satisfaction and maximize learning effectiveness.

[0849] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0850] In this invention, the server includes means for collecting students' answer data, correct answer rates, answer times, and past learning records, means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for transmitting the generated learning materials to the student's terminal and monitoring learning progress, means for providing timely feedback based on learning progress, means for collecting and analyzing user viewing history and evaluation data, and means for recommending individually optimized content based on the analysis results. This makes it possible to provide learning materials and content customized for each student or viewer, improving learning efficiency and viewing experience.

[0851] "Student answer data" refers to the content of the answers given by students to questions and information on whether those answers were correct or incorrect.

[0852] The "correct answer rate" is an indicator that indicates the percentage of correct answers among all answers given by a student.

[0853] "Response time" is the time it takes a student to solve a problem.

[0854] "Past learning records" are historical information about the learning a student has done up to now, and are data including progress and level of understanding.

[0855] The "viewing history" is recorded information about the content that the user has viewed up to now.

[0856] "Rating data" refers to data of ratings and feedback given by users to content they have viewed.

[0857] An "optimal learning path" is a set of learning steps or routes designed to help students progress through their studies efficiently.

[0858] "Individually optimized teaching materials" are teaching materials that are customized based on each student's level of understanding and interests.

[0859] The "recommendation means based on analysis results" refers to a method or device for recommending optimal content to a user based on the analysis results of viewing history and evaluation data.

[0860] A "server" is a central computer device that collects and analyzes data and sends the processed results to each terminal.

[0861] "Feedback" refers to timely advice and evaluation of learning or viewing progress.

[0862] This invention includes an online education system that provides learning materials optimized for each learner's level of understanding, improving students' learning efficiency, and a content distribution system that recommends individually optimized content based on the user's viewing history. The details of these systems are described below.

[0863] Data collection methods

[0864] The device collects answer data, accuracy rate, answer time, and past learning records when students study. The device also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment a student or user submits an answer or completes viewing. This recorded data is periodically sent to the server. Specifically, when person A (user) solves a math problem, the device collects answer data (answer content, correct or incorrect, answer time) and sends it to the server. Viewing history data of movies viewed by person A is also collected in a similar manner.

[0865] Data analysis format

[0866] The server receives the answer data and viewing history data sent from the device and stores it in a database. Next, it uses AI to analyze this data and determine what types of problems each student is struggling with or what type of content the user prefers. The AI ​​model performs its analysis by referring to each student's learning patterns, answer history, and user viewing history. For example, the server analyzes Person A's answer data and determines that Person A is struggling with integer problems. It also analyzes Person A's viewing history of science fiction movies that he has previously watched and determines that he prefers the science fiction genre.

[0867] Form of optimal learning path and content recommendation generation

[0868] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. It also recommends the most suitable content to the user. This learning path and content recommendations are designed taking into account the student's weaknesses, interests, and level of understanding. As a specific example, the server generates a learning path for Person A that includes steps from the basics to applications of integers. It also generates a list of recommended new science fiction movies for Person A.

[0869] Automatic generation of individually optimized teaching materials

[0870] The server automatically generates customized learning materials and a list of recommended content based on the generated optimal learning path and content recommendations. The learning materials include explanations and appropriate practice problems to help students understand, and the recommended content list includes content that is likely to interest the user. The learning materials and content can be provided in a variety of formats, including text, images, and videos. As a specific example, the server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems, and also generates a list of recommendations for new science fiction movies.

[0871] Feedback and progress management forms

[0872] The generated customized learning materials and recommended content are sent from the server to the device. The device displays the received learning materials and content to the user, who then proceeds with their learning or viewing. The learning progress and content viewing history are recorded again, and data on the degree of learning completion, level of understanding, and viewing status is collected. This progress data is sent back to the server again and used for the next analysis. As a specific example, the device displays a basic explanation of integers and practice problems to Person A, and then sends progress data and new viewing history data to the server when Person A has completed their learning.

[0873] Continuous feedback

[0874] The server uses AI to provide appropriate feedback to students and users in real time based on the collected progress data and viewing history data. This feedback includes advice on how to proceed to the next step, suggestions for supplementary materials to help students revisit areas where they have insufficient understanding, and recommendations for new content. For example, the server recognizes from student A's progress data that he or she is ready to tackle applied problems involving integers, and provides new customized materials and a list of recommended content.

[0875] Here are some example prompts to input to the generative AI model:

[0876] The sci-fi movies I've seen recently are "A Space Journey Through Time" and "A Faraway Galaxy." Please recommend more movies with similar themes.

[0877] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0878] Step 1:

[0879] The device collects answer data, accuracy rate, answer time, and past learning records when students answer questions. It also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment students or users submit their answers or complete viewing. The input data is the student's answer data and viewing history data, and the output data is the collected records.

[0880] Step 2:

[0881] The terminal periodically transmits the collected data to the server. Here, data transmission is performed, and the input data is the answer data and viewing history data collected in step 1. The output data is the recorded data transmitted to the server.

[0882] Step 3:

[0883] The server receives data sent from the terminal and stores it in a database. When storing it, it formats and normalizes the data. The input data is the recorded data sent, and the output data is the data stored in the database.

[0884] Step 4:

[0885] The server analyzes the stored data using an AI model. It references each student's answer history and viewing history to identify which questions they are struggling with and which content they prefer. The input data is the answer data and viewing history data stored in the database. The output data is the analysis results that identify the student's weaknesses and user preferences.

[0886] Step 5:

[0887] The server generates optimal learning paths and content recommendations based on the analysis results. Here, an AI model is used to create learning paths for students and content recommendation lists for users. The input data is the analysis results, and the output data is the generated learning paths and content recommendation lists.

[0888] Step 6:

[0889] The server automatically generates customized learning materials and a list of recommended content based on the generated learning path and content recommendations. The learning materials are provided in the form of text, images, and videos. The input data is the generated learning path and content recommendation list, and the output data is the automatically generated learning materials and content list.

[0890] Step 7:

[0891] The server transmits the generated learning materials and content list to the terminal. Here, learning materials and recommended content are transmitted to the user terminal. The input data are the automatically generated learning materials and content list, and the output data are the data transmitted to the terminal.

[0892] Step 8:

[0893] The terminal displays the received learning materials and content to the user, and the user proceeds with their learning or viewing. The learning progress and viewing history are recorded again. The input data is the received learning materials and content list, and the output data is the learning progress and viewing history.

[0894] Step 9:

[0895] The device transmits the re-recorded progress data and viewing history data to the server. Here, data transmission is performed. The input data is the learning progress status and viewing history, and the output data is the recorded data transmitted to the server.

[0896] Step 10:

[0897] The server uses AI to provide feedback based on the collected progress data and viewing history data. The feedback includes study advice and recommendations for new content. The input data is the progress data and viewing history, and the output data is the provided feedback.

[0898] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0899] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. This system is mainly composed of a server, terminals, and users, and also incorporates an emotion engine to utilize students' emotional data.

[0900] Data collection methods

[0901] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[0902] Example: When user A answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[0903] Data transmission format

[0904] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[0905] Data analysis format

[0906] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses AI to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[0907] Example: The server analyzes person A's answer data and emotion data and determines that person A is struggling with integer problems and is feeling frustrated while learning.

[0908] Form of optimal learning path generation

[0909] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[0910] Example: The server generates a learning path for person A, starting with the basics of integers and gradually progressing to applications, taking into account person A's emotional state.

[0911] Automatic generation of individually optimized teaching materials

[0912] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[0913] Example: The server creates customized learning materials for A, including a basic explanation of integers, related practice problems, and interactive content that will attract A's interest.

[0914] Feedback and progress management forms

[0915] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[0916] Example: The device displays basic explanations and practice problems for person A about integers, and after person A has finished learning, it sends progress data and emotional data to the server.

[0917] Continuous feedback

[0918] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, it provides encouragement and warnings based on emotional data to maintain students' motivation.

[0919] Example: The server determines from A's emotional data that she is feeling frustrated while studying, and sends her encouraging messages and messages encouraging her to take a break, and provides additional explanations for areas where she does not fully understand.

[0920] In this way, the system of the present invention provides a learning experience that is optimized to suit the individual needs and emotional state of each student, thereby improving their understanding and increasing their motivation to learn.

[0921] The processing flow will be explained below.

[0922] Step 1:

[0923] The device collects answer data (answer content, accuracy rate, and answer time) in real time when the student (user) answers the questions. In addition, it uses an emotion engine to analyze the student's emotional state (excitement, concentration, frustration, etc.) from facial expressions and voice data and record it as emotional data.

[0924] Specific operation:

[0925] The device uses a camera and microphone to collect emotional data while students answer questions.

[0926] When the answer is submitted, the emotional data at that time is also saved.

[0927] Step 2:

[0928] The device periodically transmits the collected answer data and emotion data to the server. Transmission is performed automatically when a network connection is established.

[0929] Specific operation:

[0930] The terminal processes the collected data in batches at regular intervals and sends them to the server.

[0931] If there is no network connection, data is kept locally until the connection is restored.

[0932] Step 3:

[0933] The server receives the answer data and emotion data sent from the device and stores them in a database. During this process, the data format is checked and reformatted.

[0934] Specific operation:

[0935] The server checks the integrity of the received data and converts the format appropriately.

[0936] Once all the validations are completed, the data is inserted into the database.

[0937] Step 4:

[0938] The server analyzes the answer data and emotional data stored in the database, using an AI model to identify which questions students are struggling with and the reasons for this from the answer data, and also uses the emotional data to understand the student's mental state while studying.

[0939] Specific operation:

[0940] The AI ​​model analyzes the answer data and identifies students' weaknesses.

[0941] Analyze emotional data and record changes in students' emotions during learning.

[0942] Step 5:

[0943] Based on the analysis results, the server uses AI models to generate an optimal learning path for each student, which is designed taking into account the student's weaknesses and emotional state.

[0944] Specific operation:

[0945] The learning path selects the optimal learning order and content for each student based on answer data and emotional data.

[0946] Path generation is dynamic and updates based on real-time data.

[0947] Step 6:

[0948] Based on the generated learning path, the server automatically generates individually optimized learning materials, including explanations and exercises that are optimal for the student's level of understanding and emotional state.

[0949] Specific operation:

[0950] The automatically generated teaching materials include a variety of content, including text, images, and videos.

[0951] Add content that students are likely to be interested in based on emotional data.

[0952] Step 7:

[0953] The server sends the created customized learning materials to the terminal, which displays the learning materials in an appropriate format (text, image, video).

[0954] Specific operation:

[0955] Materials are delivered in a format that suits each individual student.

[0956] The terminal displays the received educational material in a user-friendly interface.

[0957] Step 8:

[0958] The device then uses the provided learning materials to help the user study, recording progress and emotional data, including the user's level of understanding of the learning content, the time required, and the emotional data, which is then analyzed again.

[0959] Examples:

[0960] As students progress through their studies, their progress and emotional state is recorded in real time.

[0961] If the device's emotions change during the learning process, the change is immediately analyzed using the emotion engine.

[0962] Step 9:

[0963] The device then sends the collected progress and emotion data back to the server at the end of the learning session or at other regular intervals.

[0964] Specific operation:

[0965] Progress data and emotion data are batch processed and sent to the server.

[0966] Immediate transmission occurs at the end of the learning session.

[0967] Step 10:

[0968] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data it receives, including assessments of their learning progress, advice, and encouragement and warnings to keep them motivated.

[0969] Specific operation:

[0970] An AI model analyzes progress data to assess understanding and next learning steps.

[0971] Based on the emotional data, messages that provide emotional support and motivation to students are generated.

[0972] Step 11:

[0973] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[0974] Specific operation:

[0975] The feedback is immediately sent to the terminal and set to be displayed to the user.

[0976] The devices display feedback in an easy-to-read format to help students understand.

[0977] Example 2

[0978] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0979] Conventional online education systems are based solely on students' answer data and learning progress, and do not take into account their emotional state. This makes it difficult to appropriately address issues such as frustration and loss of motivation that students experience during learning. The purpose of this invention is to improve students' learning efficiency and comprehension, while also providing optimal learning materials and feedback that take into account their emotional state during learning.

[0980] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0981] In this invention, the server includes means for collecting students' answer data, accuracy rate, answer time, and past learning records, means for collecting emotional data based on facial expressions, voice, and behavior during learning, means for analyzing the collected answer data and emotional data to identify problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for sending the generated learning materials to the student's terminal and monitoring the learning progress, and means for providing timely feedback based on the learning progress and encouraging or warning the student according to their emotional state. This makes it possible to provide appropriate learning materials and feedback that take into account each student's individual level of understanding and emotional state.

[0982] "Answer data" is information about the questions answered by the student, specifically data including correct / incorrect answers, answer time, and selection history of options.

[0983] "Emotional data" refers to data about a student's emotional state collected from facial expressions, voice, behavior, etc. while they are studying.

[0984] "Analysis" is the process of evaluating and analyzing students' learning situations and emotional states based on collected answer data and emotional data.

[0985] A "learning path" is a plan that shows students the optimal order of content and unit learning based on the analysis results.

[0986] "Individually optimized learning materials" are learning materials and content that are customized to best suit a specific student, taking into account the student's level of understanding and emotional state.

[0987] "Progress data" refers to data that indicates the progress and achievement of a student as they progress through their studies.

[0988] "Feedback" refers to guidance and advice provided to students based on their learning progress and analysis results.

[0989] "Encouragement and caution" refers to messages or instructions that take into account the student's emotional state and are intended to increase motivation or draw attention.

[0990] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. The system is mainly composed of a server, terminals, and users. It also incorporates an emotion engine to utilize emotional data.

[0991] Data collection methods

[0992] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[0993] Example: When a user answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[0994] Data transmission format

[0995] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[0996] Data analysis format

[0997] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses an AI model to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[0998] Example: A server analyzes a user's answer data and sentiment data to determine that the user is struggling with integer problems and is frustrated while learning.

[0999] Form of optimal learning path generation

[1000] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[1001] Example: A server generates a learning path for a user, starting with the basics of integers and gradually progressing to applications, taking into account the user's emotional state.

[1002] Automatic generation of individually optimized teaching materials

[1003] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[1004] Example: A server creates customized learning materials for a user, including a basic explanation of integers and related exercises, as well as interactive content that will attract the user's attention.

[1005] Feedback and progress management forms

[1006] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[1007] Example: A device displays basic explanations and practice problems for integers to a user, and sends progress data and emotion data to a server after the user has finished learning.

[1008] Continuous feedback

[1009] The server uses an AI model to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, the server provides encouragement and warnings based on emotional data to maintain students' motivation.

[1010] Example: The server determines from the user's emotional data that they are feeling frustrated while studying, and sends them messages of encouragement or encouragement to take a break, and provides additional explanations for areas where they do not fully understand.

[1011] Example prompts to be input to the generative AI model

[1012] "Analyze users' latest learning outcomes and sentiment data to generate appropriate learning paths."

[1013] "Suggest additional feedback and educational materials based on user progress and sentiment data."

[1014] This allows the system of the present invention to provide a learning experience that is optimized to the individual needs and emotional state of the student, improving comprehension and increasing engagement.

[1015] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1016] Step 1:

[1017] Data collection

[1018] The device opens a session when the user begins learning. The input is the question the user answers and its content. Specifically, when the user answers a question, the device collects answer data (correct / incorrect answers, answer time, and choice history) in real time. The device also activates its built-in emotion engine and uses the camera and microphone to record the user's facial expressions, voice, and actions. This allows the user's answer data and emotion data to be collected and stored in local storage.

[1019] Step 2:

[1020] Data transmission

[1021] The device stores the collected data in a buffer and periodically checks the network connection status. The input is the answer data and emotion data stored in the buffer, and the network connection status. If the network is good, the device sends the data in the buffer to the server. The output is the answer data and emotion data sent to the server.

[1022] Step 3:

[1023] Data analysis

[1024] The server receives the answer data and emotion data sent from the device and stores them in a database. The input is the received answer data and emotion data. The server then analyzes the data using an AI model. Specifically, the AI ​​analyzes the answer data and emotion data to identify the problems the user is struggling with, their causes, and their emotional state during learning. The output is an analysis of the user's learning problems and emotional state.

[1025] Step 4:

[1026] Generate optimal learning paths

[1027] Based on the analysis results, the server uses an AI model to generate an optimal learning path for the user. The input is the results of data analysis. Specifically, the AI ​​model uses the analysis results to plan the learning order and content, and designs a learning path that is optimal for the user's level of understanding and emotional state. The output is the generated learning path.

[1028] Step 5:

[1029] Automatic generation of individually optimized teaching materials

[1030] The server automatically generates individually optimized learning materials based on the generated learning path. The input is the generated learning path. Specifically, the server creates learning materials such as text, images, and videos, and customizes them to suit the user's interests and level of understanding. The output is the optimized learning materials.

[1031] Step 6:

[1032] Sending study materials and managing progress

[1033] The server sends the generated learning materials to the device. The input is the created learning materials. Specifically, the device displays the received learning materials to the user and progresses the learning. When the user finishes learning, the device again records the progress data and new emotion data and stores them in local storage.

[1034] Step 7:

[1035] Data retransmission

[1036] The device stores the progress data and emotion data in a buffer and checks the network connection status. The input is the progress data and emotion data stored in the buffer. If the network is good, the device sends the data to the server again. The output is the progress data and emotion data sent to the server.

[1037] Step 8:

[1038] Continuous feedback

[1039] The server then analyzes the progress and emotional data again and provides appropriate feedback to the user in real time. The inputs are the progress and emotional data. Specifically, the AI ​​model evaluates the learning progress, provides additional training materials and advice as needed, and sends encouragement and warnings according to the user's emotional state. The output is feedback and training materials to the user.

[1040] In this way, the system can provide a learning experience that is optimized to each student's individual needs and emotional state, improving comprehension and increasing engagement.

[1041] (Application example 2)

[1042] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1043] Conventional online education systems have the problem that it is difficult to grasp students' level of understanding and learning behavior, making it difficult to adequately manage progress and provide feedback according to individual needs. It is also difficult to grasp customers' emotional state in real time in physical stores, making it difficult to provide appropriate individual support. It is necessary to improve student and customer satisfaction and realize effective learning and customer service.

[1044] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting students' answer data, correct answer rate, answer time, and past learning records; means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof; means for generating an optimal learning path based on the analysis results; means for automatically generating individually optimized learning materials based on the generated learning path; means for sending the generated learning materials to the student's device and monitoring learning progress; means for providing timely feedback based on learning progress; and means for collecting and analyzing customer emotional state and behavioral data and providing optimal support and suggestions in real time. This maximizes students' learning efficiency, provides a learning experience tailored to their individual needs, and makes it possible to provide appropriate support to customers in physical stores.

[1045] "Student answer data" refers to the answers entered by students in response to questions presented in the educational system.

[1046] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly out of all questions that he or she answered.

[1047] "Response time" refers to the time it takes a student to complete a response to a question.

[1048] "Past learning records" are a collection of data that includes the learning content that a student has undertaken and the results of that learning.

[1049] "Means of collection" refers to the methods and equipment necessary to obtain and use data.

[1050] "Means for analyzing data" means methods and techniques for processing collected data and extracting information.

[1051] "Means for generating optimal learning paths" refers to a method for creating the most effective learning sequence and curriculum for students based on the analysis results.

[1052] "Means for automatically generating individually optimized teaching materials" refers to a method for automatically creating learning materials that meet the needs of students.

[1053] A "progress monitoring tool" is a method for tracking students' progress and recording their progress.

[1054] "Means of providing feedback" are ways to provide students with appropriate guidance and advice based on their learning progress.

[1055] "Means for collecting and analyzing customer emotional state and behavioral data" refers to methods and technologies for acquiring customer facial expressions, movements, speech content, etc., and analyzing them to clarify their emotional state and behavioral intentions.

[1056] "Means of providing optimal support and suggestions in real time" refers to a method of providing appropriate responses and services on the spot based on collected and analyzed customer data.

[1057] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state.It is also a system that can analyze the emotional state of customers in physical stores in real time and provide optimal support.

[1058] Data collection methods

[1059] The device (smartphone, smart glasses, robot, etc.) collects student or customer behavior data, answer data, accuracy rate, answer time, past learning records, emotional data, etc. Using an emotion engine, emotional data is extracted from the facial expressions, voice, and behavior of students or customers. This data is automatically recorded and stored in local storage.

[1060] Data transmission format

[1061] The device periodically transmits the collected answer data and emotion data to the server. Transmission is established when the network connection is good.

[1062] Data analysis format

[1063] The server receives the answer data and emotion data sent from the device and stores them in a database. It then analyzes the data using a generative AI model (for example, a model using TensorFlow) to identify the problems the student or customer is struggling with and the reasons for their difficulties. It also identifies the emotional state of the student or customer based on the emotion data.

[1064] Optimal learning paths and support generation

[1065] The server uses an AI model to generate the optimal learning path based on the analysis results. Furthermore, by analyzing the customer's emotional data, it can provide optimal support and suggestions in real time. These learning paths and suggestions are designed taking into account both the student's or customer's answer data and emotional data.

[1066] Automatic generation of individually optimized teaching materials and support

[1067] Based on the generated optimal learning path or support proposal, the server automatically generates individually optimized learning materials for students and appropriate support and proposals for customers. The learning materials are provided in a variety of formats, including text, images, and videos.

[1068] Feedback and progress management forms

[1069] The generated customized learning materials and support suggestions are sent from the server to the device and displayed to the user. The device again records progress data and emotional data as the user progresses with their learning and response, and sends this data to the server.

[1070] Continuous feedback

[1071] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress data and emotional data. For example, if the server determines from the emotional data analyzed that the customer is confused during learning or purchasing, it will send appropriate guidance or support messages.

[1072] Prompt Sentence Examples

[1073] For example, consider the following prompt:

[1074] python

[1075] emotion_data = emotion_model.detect_emotions(frame)

[1076] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[1077] This system enables the provision of learning experiences and customer support that are optimized to suit the individual needs of students and the real-time emotional state of customers, maximizing satisfaction and effectiveness.

[1078] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1079] Step 1:

[1080] The device (smartphone, smart glasses, robot, etc.) collects the user's answer data, accuracy rate, answer time, past learning record, and emotional data obtained from facial expressions and voice. These data are recorded in local storage. The input is the user's behavior and learning data, and the output is the various collected data.

[1081] Step 2:

[1082] The collected answer data and emotion data are periodically transmitted from the device to the server. Transmission is established when the network condition is good. The input is the data recorded on the device, and the output is the data transmitted to the server.

[1083] Step 3:

[1084] The server stores the answer data and emotion data received from the device in a database. The input is the data sent from the device, and the output is stored in the database.

[1085] Step 4:

[1086] The server analyzes the stored data using a generative AI model to identify which problem the user is struggling with and the cause. It also identifies the user's emotional state during learning based on emotional data. The input is the data stored in the database, and the output is the analysis results.

[1087] Step 5:

[1088] The server uses an AI model to generate the optimal learning path based on the analysis results. It can also provide optimal support and suggestions in real time based on customer sentiment data. The input is the analysis results, and the output is the optimal learning path and support suggestions.

[1089] Step 6:

[1090] The server automatically generates individually optimized learning materials or support based on the generated optimal learning path or support proposal. The learning materials are provided in the form of text, images, videos, etc. The input is the optimal learning path or support proposal, and the output is the generated learning materials or support content.

[1091] Step 7:

[1092] The generated customized teaching materials and support suggestions are sent from the server to the terminal and displayed to the user. The input is the generated teaching materials and support content, and the output is the display on the terminal.

[1093] Step 8:

[1094] The device records the progress data and emotion data of the user as they learn and respond, and sends them to the server. The input is the user's learning and behavior data, and the output is the data sent back to the server.

[1095] Step 9:

[1096] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress and emotion data. The input is progress and emotion data, and the output is feedback provided to students or customers.

[1097] For example, consider the following prompt:

[1098] python

[1099] emotion_data = emotion_model.detect_emotions(frame)

[1100] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[1101] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1103] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1104] [Fourth embodiment]

[1105] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1106] 7, a 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.

[1107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1108] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1109] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1112] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1113] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1114] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1116] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1117] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1118] This invention is an online education system that provides learning materials optimized for each student's level of understanding, improving their learning efficiency. This system is mainly composed of a server, terminals, and users.

[1119] Data collection methods

[1120] The device collects data such as answer data, accuracy rate, response time, and past learning records as the student studies. This data is programmed to be automatically recorded the moment the student submits their answer. This recorded data is periodically sent to the server.

[1121] Example: When person A (user) solves a math problem, the answer data (answer content, correct or incorrect, answer time) is collected by the terminal and sent to the server.

[1122] Data analysis format

[1123] The server receives the answer data sent from the device and stores it in a database. AI is then used to analyze this data and identify the types of questions each student is struggling with and what parts they don't understand. The AI ​​model performs its analysis by referring to each student's learning patterns and answer history.

[1124] Example: The server analyzes person A's answer data and determines that person A is having trouble with integer problems.

[1125] Form of optimal learning path generation

[1126] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for each student. Here, a learning path refers to the steps or route that a student takes to efficiently progress through their studies. This learning path is designed taking into account the student's weaknesses, interests, and level of understanding.

[1127] Example: The server generates a learning path for person A that includes steps from the basics to applications of integers.

[1128] Automatic generation of individually optimized teaching materials

[1129] The server automatically generates customized learning materials based on the generated optimal learning path. These materials include explanations and appropriate exercises to help students understand. The learning materials can be provided in a variety of formats, including text, images, and videos.

[1130] Example: The server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems.

[1131] Feedback and progress management forms

[1132] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, who then proceeds with their learning. The progress is recorded again, and data on the degree of completion and understanding of the learning is collected. This progress data is then sent back to the server and used for the next analysis.

[1133] Example: The device displays basic explanations and practice problems about integers to A, and sends progress data to the server as A completes his / her learning.

[1134] Continuous feedback

[1135] The server uses AI to provide students with real-time feedback based on the progress data collected, including advice on how to proceed to the next step and suggestions for supplementary materials to help them revisit areas where they have not fully understood the material.

[1136] Example: The server recognizes from A's progress data that he is ready to tackle applied problems involving integers, and generates and provides new customized learning materials.

[1137] In this way, the system of the present invention realizes efficient learning tailored to the individual needs of each student, and has the effect of improving students' understanding.

[1138] The processing flow will be explained below.

[1139] Step 1:

[1140] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. This includes automatically recording data each time a student submits an answer and saving it in local storage.

[1141] Step 2:

[1142] The terminal periodically transmits the collected answer data to the server at regular intervals when a network connection is established.

[1143] Step 3:

[1144] The server receives the answer data sent from the terminal and stores it in a database, which includes receiving the data, checking the format, and writing it to the database.

[1145] Step 4:

[1146] The server analyzes the answer data stored in the database. This analysis involves using an AI model to identify the problems that students are struggling with and the reasons for their difficulties. The AI ​​model performs its analysis by referencing the student's past answer history and learning patterns.

[1147] Step 5:

[1148] Based on the analysis results, the server uses an AI model to generate an optimal learning path for each student, which includes step-by-step instructions for students to learn efficiently.

[1149] Step 6:

[1150] Based on the generated learning path, the server automatically generates individually optimized learning materials, including specific explanations and practice questions to help students deepen their understanding.

[1151] Step 7:

[1152] The server sends the created customized teaching materials to the student's device, where they are displayed in an appropriate format (text, image, video).

[1153] Step 8:

[1154] The device allows the user to study using the provided learning materials and records progress data, including information on how long the user spent studying the materials and how much they understood.

[1155] Step 9:

[1156] The device then sends the collected progress data back to the server either after the learning session ends or periodically.

[1157] Step 10:

[1158] Based on the progress data received, the server uses AI to generate appropriate feedback to students in real time, including advice based on their learning progress and suggestions for supplementary learning materials.

[1159] Step 11:

[1160] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[1161] In this way, all steps work together to create a system that provides students with individually optimized learning.

[1162] Example 1

[1163] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1164] With conventional online education systems, it was difficult to provide customized learning materials tailored to each student's level of understanding, which often resulted in insufficient learning outcomes. It was also difficult to grasp students' learning progress in real time and provide timely feedback. To solve these problems, an advanced system capable of effectively collecting and analyzing each student's learning data was required.

[1165] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1166] In this invention, the server includes a means for collecting students' answer data, correct answer rate, answer time, and past learning records, a means for periodically sending the collected data to the server, and a means for storing the answer data received by the server in a database and analyzing the data using an artificial intelligence model. This enables the generation of an optimal learning path based on each student's individual learning pattern, the automatic generation of individually optimized learning materials based on the learning path, and real-time feedback based on learning progress data.

[1167] "Answer data" refers to the answers entered by students to questions.

[1168] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly.

[1169] "Response time" is the measurement of the time it takes a student to answer a question.

[1170] "Past learning records" are data that record what a student has learned so far and the results of that learning.

[1171] "Server" refers to a computer system for collecting, storing, analyzing data, and generating educational materials.

[1172] "Device" refers to the device used by students to study, including, for example, a PC, tablet, or smartphone.

[1173] "Artificial intelligence model" refers to the algorithms and machine learning models used to analyze student learning data and generate optimal learning paths and feedback.

[1174] A "learning path" is a set of steps or routes that students can take to efficiently progress through their studies.

[1175] "Customized teaching materials" refers to individually optimized teaching materials that are generated based on each student's level of understanding and learning progress.

[1176] "Learning progress" refers to the process by which students use learning materials and the results of their studies.

[1177] "Feedback" refers to information, including advice and supplementary materials, provided based on learning progress.

[1178] This invention is an online education system for improving student learning efficiency. The system consists of three main components: a server, a terminal, and a user. Each component has a specific function and provides an optimized learning experience for each student.

[1179] First, the terminal is the device that students use to study, and includes PCs, tablets, smartphones, etc. The terminal collects answer data entered by students as they study, as well as the accuracy rate, response time, and past study records. This collected data is automatically recorded each time an answer is entered. Furthermore, the collected data is programmed to be sent to the server periodically.

[1180] Example: When user A answers a math problem using a device, the answer, whether it was correct or not, and the time it took to answer are immediately recorded on the device and sent to the server at regular intervals.

[1181] The server stores the received data in a database and analyzes it using an AI (artificial intelligence) model. The AI ​​model identifies the problems students are struggling with and areas where they lack understanding based on each student's learning patterns and answer history. Based on the results of this analysis, an optimal learning path is generated.

[1182] Example: The server analyzes A's data and identifies that A is struggling with integer problems. Based on this, it generates a systematic learning path from the basics to applications of integers.

[1183] The server then automatically generates customized learning materials that are best suited to each student based on the generated learning path. These materials include various formats, such as text, images, and videos, and also include explanations and practice questions to help students understand. These customized learning materials are then sent from the server to the device.

[1184] Example: The server creates customized learning materials for Mr. A, including basic explanations of integers and practice problems, and sends them to the terminal.

[1185] The device displays the received learning materials to the user, who then uses them to study. The learning progress is recorded again and the data is sent back to the server. This data includes the degree of completion and understanding of the learning.

[1186] Example: The device displays basic explanations and practice problems for person A, who then studies them. After completing the study, the progress data is sent to the server.

[1187] Finally, the server uses the collected progress data to provide real-time AI feedback, including advice on next steps and suggested supplemental materials to help you revisit areas of incomplete understanding.

[1188] Example: The server analyzes A's progress data, recognizes that A is ready to tackle applied problems involving integers, and generates and provides new learning materials.

[1189] The hardware used is a standard server or personal computer terminal, and the software includes an artificial intelligence model and a database management system. Specific software includes machine learning libraries using Python (such as scikit-learn, TensorFlow, and PyTorch). In this way, the system aims to provide an optimal learning experience tailored to each student's needs and improve their understanding of the material.

[1190] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1191] Step 1: Collect user learning data

[1192] As students study, the device collects answer data, accuracy rate, response time, and past learning records. Input data includes the student's answer content, whether it was correct or incorrect, and the time it took to answer. When this information is entered into the device, it is automatically recorded. Specifically, the device is equipped with an answer form and a timer function, and captures data each time a student enters an answer.

[1193] Step 2: Sending data to the server

[1194] The device periodically sends the collected learning data to the server. The input data is the student's learning data collected in step 1. When sending, the device is programmed to use an appropriate communication protocol. Specifically, the device transfers the data to the server using protocols such as HTTP or WebSocket.

[1195] Step 3: Data storage and analysis

[1196] The server receives the learning data sent from the device and stores it in a database. The input data includes the student's answer data, accuracy rate, answer time, and past learning records. After storing this data, the server analyzes it using an AI model. Specifically, the server stores the data using a database management system, and then analyzes the data using machine learning algorithms implemented in programming languages ​​such as Python.

[1197] Step 4: Generate a learning path

[1198] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. The input data is the analysis results from step 3, which include information on the types of problems the student is struggling with and their level of understanding. The AI ​​model processes the data to generate a learning path, and the output is an individually optimized learning path. Specifically, the AI ​​model calculates the optimal steps and route based on the learning pattern.

[1199] Step 5: Create customized learning materials

[1200] The server automatically generates individually optimized learning materials based on the generated learning path. The input data is the learning path generated in step 4. As data processing, a learning material generation algorithm generates learning materials according to the learning path, and as output, learning materials such as text, images, and videos that are optimal for the student are obtained. Specifically, an algorithm that dynamically combines learning content is executed.

[1201] Step 6: Deliver customized learning materials

[1202] The server sends the created customized teaching materials to the terminal. The input data is the individually optimized teaching materials generated in step 5. Specifically, a file transfer protocol such as an HTTP request or FTP is used to send the teaching material file to the terminal.

[1203] Step 7: Collect user learning and progress data

[1204] The device displays the received learning materials, and the user uses them to progress with their studies. The input data is the customized learning materials sent in step 6. When the user completes their study, the progress data is recorded again on the device. Specifically, the device is equipped with a learning materials display function and a progress data recording function. Each time the user solves a problem, the result is recorded.

[1205] Step 8: Sending progress data to the server and parsing it

[1206] The device again sends the collected progress data to the server. The input data is the learning progress data collected in step 7. The server analyzes the received progress data and uses AI to provide appropriate feedback to the user. Specifically, the AI ​​model analyzes the progress data again and generates feedback suggesting the next task to tackle and supplementary learning materials.

[1207] (Application example 1)

[1208] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1209] Conventional online education systems lack the optimization required to provide learning materials appropriate for each student, resulting in poor learning efficiency. Furthermore, they lacked a mechanism for recommending individually optimized content using users' viewing history and evaluation data. Therefore, there is a need for a system that can improve learner and viewer satisfaction and maximize learning effectiveness.

[1210] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1211] In this invention, the server includes means for collecting students' answer data, correct answer rates, answer times, and past learning records, means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for transmitting the generated learning materials to the student's terminal and monitoring learning progress, means for providing timely feedback based on learning progress, means for collecting and analyzing user viewing history and evaluation data, and means for recommending individually optimized content based on the analysis results. This makes it possible to provide learning materials and content customized for each student or viewer, improving learning efficiency and viewing experience.

[1212] "Student answer data" refers to the content of the answers given by students to questions and information on whether those answers were correct or incorrect.

[1213] The "correct answer rate" is an indicator that indicates the percentage of correct answers among all answers given by a student.

[1214] "Response time" is the time it takes a student to solve a problem.

[1215] "Past learning records" are historical information about the learning a student has done up to now, and are data including progress and level of understanding.

[1216] The "viewing history" is recorded information about the content that the user has viewed up to now.

[1217] "Rating data" refers to data of ratings and feedback given by users to content they have viewed.

[1218] An "optimal learning path" is a set of learning steps or routes designed to help students progress through their studies efficiently.

[1219] "Individually optimized teaching materials" are teaching materials that are customized based on each student's level of understanding and interests.

[1220] The "recommendation means based on analysis results" refers to a method or device for recommending optimal content to a user based on the analysis results of viewing history and evaluation data.

[1221] A "server" is a central computer device that collects and analyzes data and sends the processed results to each terminal.

[1222] "Feedback" refers to timely advice and evaluation of learning or viewing progress.

[1223] This invention includes an online education system that provides learning materials optimized for each learner's level of understanding, improving students' learning efficiency, and a content distribution system that recommends individually optimized content based on the user's viewing history. The details of these systems are described below.

[1224] Data collection methods

[1225] The device collects answer data, accuracy rate, answer time, and past learning records when students study. The device also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment a student or user submits an answer or completes viewing. This recorded data is periodically sent to the server. Specifically, when person A (user) solves a math problem, the device collects answer data (answer content, correct or incorrect, answer time) and sends it to the server. Viewing history data of movies viewed by person A is also collected in a similar manner.

[1226] Data analysis format

[1227] The server receives the answer data and viewing history data sent from the device and stores it in a database. Next, it uses AI to analyze this data and determine what types of problems each student is struggling with or what type of content the user prefers. The AI ​​model performs its analysis by referring to each student's learning patterns, answer history, and user viewing history. For example, the server analyzes Person A's answer data and determines that Person A is struggling with integer problems. It also analyzes Person A's viewing history of science fiction movies that he has previously watched and determines that he prefers the science fiction genre.

[1228] Form of optimal learning path and content recommendation generation

[1229] Based on the analysis results, the server uses an AI model to generate the most suitable learning path for the student. It also recommends the most suitable content to the user. This learning path and content recommendations are designed taking into account the student's weaknesses, interests, and level of understanding. As a specific example, the server generates a learning path for Person A that includes steps from the basics to applications of integers. It also generates a list of recommended new science fiction movies for Person A.

[1230] Automatic generation of individually optimized teaching materials

[1231] The server automatically generates customized learning materials and a list of recommended content based on the generated optimal learning path and content recommendations. The learning materials include explanations and appropriate practice problems to help students understand, and the recommended content list includes content that is likely to interest the user. The learning materials and content can be provided in a variety of formats, including text, images, and videos. As a specific example, the server creates customized learning materials for Person A, including a basic explanation of integers and related practice problems, and also generates a list of recommendations for new science fiction movies.

[1232] Feedback and progress management forms

[1233] The generated customized learning materials and recommended content are sent from the server to the device. The device displays the received learning materials and content to the user, who then proceeds with their learning or viewing. The learning progress and content viewing history are recorded again, and data on the degree of learning completion, level of understanding, and viewing status is collected. This progress data is sent back to the server again and used for the next analysis. As a specific example, the device displays a basic explanation of integers and practice problems to Person A, and then sends progress data and new viewing history data to the server when Person A has completed their learning.

[1234] Continuous feedback

[1235] The server uses AI to provide appropriate feedback to students and users in real time based on the collected progress data and viewing history data. This feedback includes advice on how to proceed to the next step, suggestions for supplementary materials to help students revisit areas where they have insufficient understanding, and recommendations for new content. For example, the server recognizes from student A's progress data that he or she is ready to tackle applied problems involving integers, and provides new customized materials and a list of recommended content.

[1236] Here are some example prompts to input to the generative AI model:

[1237] The sci-fi movies I've seen recently are "A Space Journey Through Time" and "A Faraway Galaxy." Please recommend more movies with similar themes.

[1238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1239] Step 1:

[1240] The device collects answer data, accuracy rate, answer time, and past learning records when students answer questions. It also collects the history of content viewed by users and evaluation data. This data is automatically recorded the moment students or users submit their answers or complete viewing. The input data is the student's answer data and viewing history data, and the output data is the collected records.

[1241] Step 2:

[1242] The terminal periodically transmits the collected data to the server. Here, data transmission is performed, and the input data is the answer data and viewing history data collected in step 1. The output data is the recorded data transmitted to the server.

[1243] Step 3:

[1244] The server receives data sent from the terminal and stores it in a database. When storing it, it formats and normalizes the data. The input data is the recorded data sent, and the output data is the data stored in the database.

[1245] Step 4:

[1246] The server analyzes the stored data using an AI model. It references each student's answer history and viewing history to identify which questions they are struggling with and which content they prefer. The input data is the answer data and viewing history data stored in the database. The output data is the analysis results that identify the student's weaknesses and user preferences.

[1247] Step 5:

[1248] The server generates optimal learning paths and content recommendations based on the analysis results. Here, an AI model is used to create learning paths for students and content recommendation lists for users. The input data is the analysis results, and the output data is the generated learning paths and content recommendation lists.

[1249] Step 6:

[1250] The server automatically generates customized learning materials and a list of recommended content based on the generated learning path and content recommendations. The learning materials are provided in the form of text, images, and videos. The input data is the generated learning path and content recommendation list, and the output data is the automatically generated learning materials and content list.

[1251] Step 7:

[1252] The server transmits the generated learning materials and content list to the terminal. Here, learning materials and recommended content are transmitted to the user terminal. The input data are the automatically generated learning materials and content list, and the output data are the data transmitted to the terminal.

[1253] Step 8:

[1254] The terminal displays the received learning materials and content to the user, and the user proceeds with their learning or viewing. The learning progress and viewing history are recorded again. The input data is the received learning materials and content list, and the output data is the learning progress and viewing history.

[1255] Step 9:

[1256] The device transmits the re-recorded progress data and viewing history data to the server. Here, data transmission is performed. The input data is the learning progress status and viewing history, and the output data is the recorded data transmitted to the server.

[1257] Step 10:

[1258] The server uses AI to provide feedback based on the collected progress data and viewing history data. The feedback includes study advice and recommendations for new content. The input data is the progress data and viewing history, and the output data is the provided feedback.

[1259] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1260] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. This system is mainly composed of a server, terminals, and users, and also incorporates an emotion engine to utilize students' emotional data.

[1261] Data collection methods

[1262] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[1263] Example: When user A answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[1264] Data transmission format

[1265] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[1266] Data analysis format

[1267] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses AI to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[1268] Example: The server analyzes person A's answer data and emotion data and determines that person A is struggling with integer problems and is feeling frustrated while learning.

[1269] Form of optimal learning path generation

[1270] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[1271] Example: The server generates a learning path for person A, starting with the basics of integers and gradually progressing to applications, taking into account person A's emotional state.

[1272] Automatic generation of individually optimized teaching materials

[1273] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[1274] Example: The server creates customized learning materials for A, including a basic explanation of integers, related practice problems, and interactive content that will attract A's interest.

[1275] Feedback and progress management forms

[1276] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[1277] Example: The device displays basic explanations and practice problems for person A about integers, and after person A has finished learning, it sends progress data and emotional data to the server.

[1278] Continuous feedback

[1279] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, it provides encouragement and warnings based on emotional data to maintain students' motivation.

[1280] Example: The server determines from A's emotional data that she is feeling frustrated while studying, and sends her encouraging messages and messages encouraging her to take a break, and provides additional explanations for areas where she does not fully understand.

[1281] In this way, the system of the present invention provides a learning experience that is optimized to suit the individual needs and emotional state of each student, thereby improving their understanding and increasing their motivation to learn.

[1282] The processing flow will be explained below.

[1283] Step 1:

[1284] The device collects answer data (answer content, accuracy rate, and answer time) in real time when the student (user) answers the questions. In addition, it uses an emotion engine to analyze the student's emotional state (excitement, concentration, frustration, etc.) from facial expressions and voice data and record it as emotional data.

[1285] Specific operation:

[1286] The device uses a camera and microphone to collect emotional data while students answer questions.

[1287] When the answer is submitted, the emotional data at that time is also saved.

[1288] Step 2:

[1289] The device periodically transmits the collected answer data and emotion data to the server. Transmission is performed automatically when a network connection is established.

[1290] Specific operation:

[1291] The terminal processes the collected data in batches at regular intervals and sends them to the server.

[1292] If there is no network connection, data is kept locally until the connection is restored.

[1293] Step 3:

[1294] The server receives the answer data and emotion data sent from the device and stores them in a database. During this process, the data format is checked and reformatted.

[1295] Specific operation:

[1296] The server checks the integrity of the received data and converts the format appropriately.

[1297] Once all the validations are completed, the data is inserted into the database.

[1298] Step 4:

[1299] The server analyzes the answer data and emotional data stored in the database, using an AI model to identify which questions students are struggling with and the reasons for this from the answer data, and also uses the emotional data to understand the student's mental state while studying.

[1300] Specific operation:

[1301] The AI ​​model analyzes the answer data and identifies students' weaknesses.

[1302] Analyze emotional data and record changes in students' emotions during learning.

[1303] Step 5:

[1304] Based on the analysis results, the server uses AI models to generate an optimal learning path for each student, which is designed taking into account the student's weaknesses and emotional state.

[1305] Specific operation:

[1306] The learning path selects the optimal learning order and content for each student based on answer data and emotional data.

[1307] Path generation is dynamic and updates based on real-time data.

[1308] Step 6:

[1309] Based on the generated learning path, the server automatically generates individually optimized learning materials, including explanations and exercises that are optimal for the student's level of understanding and emotional state.

[1310] Specific operation:

[1311] The automatically generated teaching materials include a variety of content, including text, images, and videos.

[1312] Add content that students are likely to be interested in based on emotional data.

[1313] Step 7:

[1314] The server sends the created customized learning materials to the terminal, which displays the learning materials in an appropriate format (text, image, video).

[1315] Specific operation:

[1316] Materials are delivered in a format that suits each individual student.

[1317] The terminal displays the received educational material in a user-friendly interface.

[1318] Step 8:

[1319] The device then uses the provided learning materials to help the user study, recording progress and emotional data, including the user's level of understanding of the learning content, the time required, and the emotional data, which is then analyzed again.

[1320] Examples:

[1321] As students progress through their studies, their progress and emotional state is recorded in real time.

[1322] If the device's emotions change during the learning process, the change is immediately analyzed using the emotion engine.

[1323] Step 9:

[1324] The device then sends the collected progress and emotion data back to the server at the end of the learning session or at other regular intervals.

[1325] Specific operation:

[1326] Progress data and emotion data are batch processed and sent to the server.

[1327] Immediate transmission occurs at the end of the learning session.

[1328] Step 10:

[1329] The server uses AI to provide students with appropriate feedback in real time based on the progress and emotional data it receives, including assessments of their learning progress, advice, and encouragement and warnings to keep them motivated.

[1330] Specific operation:

[1331] An AI model analyzes progress data to assess understanding and next learning steps.

[1332] Based on the emotional data, messages that provide emotional support and motivation to students are generated.

[1333] Step 11:

[1334] The server sends the generated feedback to the device, which then displays it to the user, allowing the student to understand their own learning progress and identify areas for improvement or next steps in their learning.

[1335] Specific operation:

[1336] The feedback is immediately sent to the terminal and set to be displayed to the user.

[1337] The devices display feedback in an easy-to-read format to help students understand.

[1338] Example 2

[1339] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1340] Conventional online education systems are based solely on students' answer data and learning progress, and do not take into account their emotional state. This makes it difficult to appropriately address issues such as frustration and loss of motivation that students experience during learning. The purpose of this invention is to improve students' learning efficiency and comprehension, while also providing optimal learning materials and feedback that take into account their emotional state during learning.

[1341] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1342] In this invention, the server includes means for collecting students' answer data, accuracy rate, answer time, and past learning records, means for collecting emotional data based on facial expressions, voice, and behavior during learning, means for analyzing the collected answer data and emotional data to identify problems that each student cannot solve and the causes thereof, means for generating an optimal learning path based on the analysis results, means for automatically generating individually optimized learning materials based on the generated learning path, means for sending the generated learning materials to the student's terminal and monitoring the learning progress, and means for providing timely feedback based on the learning progress and encouraging or warning the student according to their emotional state. This makes it possible to provide appropriate learning materials and feedback that take into account each student's individual level of understanding and emotional state.

[1343] "Answer data" is information about the questions answered by the student, specifically data including correct / incorrect answers, answer time, and selection history of options.

[1344] "Emotional data" refers to data about a student's emotional state collected from facial expressions, voice, behavior, etc. while they are studying.

[1345] "Analysis" is the process of evaluating and analyzing students' learning situations and emotional states based on collected answer data and emotional data.

[1346] A "learning path" is a plan that shows students the optimal order of content and unit learning based on the analysis results.

[1347] "Individually optimized learning materials" are learning materials and content that are customized to best suit a specific student, taking into account the student's level of understanding and emotional state.

[1348] "Progress data" refers to data that indicates the progress and achievement of a student as they progress through their studies.

[1349] "Feedback" refers to guidance and advice provided to students based on their learning progress and analysis results.

[1350] "Encouragement and caution" refers to messages or instructions that take into account the student's emotional state and are intended to increase motivation or draw attention.

[1351] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state. The system is mainly composed of a server, terminals, and users. It also incorporates an emotion engine to utilize emotional data.

[1352] Data collection methods

[1353] The device collects answer data, accuracy rate, answer time, and past learning records when students (users) answer questions. In addition, it uses an emotion engine to collect emotional data based on the student's facial expressions, voice, and behavior while studying. This data is automatically recorded and saved in local storage.

[1354] Example: When a user answers a math problem, the device collects emotional data obtained from facial expressions and voice along with the answer data.

[1355] Data transmission format

[1356] The device periodically transmits the collected answer data and emotion data to the server. The transmission is performed periodically and is established when the network connection is good.

[1357] Data analysis format

[1358] The server receives the answer data and emotional data sent from the device and stores them in a database. It then uses an AI model to analyze this data and identify the problems the student is struggling with, the reasons for this, and the student's emotional state while studying. The AI ​​model performs its analysis by referencing the student's past answer history and emotional data.

[1359] Example: A server analyzes a user's answer data and sentiment data to determine that the user is struggling with integer problems and is frustrated while learning.

[1360] Form of optimal learning path generation

[1361] Based on the analysis results, the server uses an AI model to generate the optimal learning path for each student. This learning path is designed taking into account both the student's answer data and emotional data, thereby setting a route to maximize learning efficiency.

[1362] Example: A server generates a learning path for a user, starting with the basics of integers and gradually progressing to applications, taking into account the user's emotional state.

[1363] Automatic generation of individually optimized teaching materials

[1364] The server automatically generates customized learning materials based on the generated optimal learning path. These materials are optimized taking into account the student's level of understanding and emotional state. The materials are provided in a variety of formats, including text, images, and videos.

[1365] Example: A server creates customized learning materials for a user, including a basic explanation of integers and related exercises, as well as interactive content that will attract the user's attention.

[1366] Feedback and progress management forms

[1367] The generated customized learning materials are sent from the server to the device. The device displays the received learning materials to the user, and the user proceeds with their learning. The learning progress data and emotion data are recorded again and sent back to the server.

[1368] Example: A device displays basic explanations and practice problems for integers to a user, and sends progress data and emotion data to a server after the user has finished learning.

[1369] Continuous feedback

[1370] The server uses an AI model to provide students with appropriate feedback in real time based on the progress and emotional data received. This feedback includes advice based on their learning progress and suggestions for supplementary materials for areas where they lack understanding. Furthermore, the server provides encouragement and warnings based on emotional data to maintain students' motivation.

[1371] Example: The server determines from the user's emotional data that they are feeling frustrated while studying, and sends them messages of encouragement or encouragement to take a break, and provides additional explanations for areas where they do not fully understand.

[1372] Example prompts to be input to the generative AI model

[1373] "Analyze users' latest learning outcomes and sentiment data to generate appropriate learning paths."

[1374] "Suggest additional feedback and educational materials based on user progress and sentiment data."

[1375] This allows the system of the present invention to provide a learning experience that is optimized to the individual needs and emotional state of the student, improving comprehension and increasing engagement.

[1376] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1377] Step 1:

[1378] Data collection

[1379] The device opens a session when the user begins learning. The input is the question the user answers and its content. Specifically, when the user answers a question, the device collects answer data (correct / incorrect answers, answer time, and choice history) in real time. The device also activates its built-in emotion engine and uses the camera and microphone to record the user's facial expressions, voice, and actions. This allows the user's answer data and emotion data to be collected and stored in local storage.

[1380] Step 2:

[1381] Data transmission

[1382] The device stores the collected data in a buffer and periodically checks the network connection status. The input is the answer data and emotion data stored in the buffer, and the network connection status. If the network is good, the device sends the data in the buffer to the server. The output is the answer data and emotion data sent to the server.

[1383] Step 3:

[1384] Data analysis

[1385] The server receives the answer data and emotion data sent from the device and stores them in a database. The input is the received answer data and emotion data. The server then analyzes the data using an AI model. Specifically, the AI ​​analyzes the answer data and emotion data to identify the problems the user is struggling with, their causes, and their emotional state during learning. The output is an analysis of the user's learning problems and emotional state.

[1386] Step 4:

[1387] Generate optimal learning paths

[1388] Based on the analysis results, the server uses an AI model to generate an optimal learning path for the user. The input is the results of data analysis. Specifically, the AI ​​model uses the analysis results to plan the learning order and content, and designs a learning path that is optimal for the user's level of understanding and emotional state. The output is the generated learning path.

[1389] Step 5:

[1390] Automatic generation of individually optimized teaching materials

[1391] The server automatically generates individually optimized learning materials based on the generated learning path. The input is the generated learning path. Specifically, the server creates learning materials such as text, images, and videos, and customizes them to suit the user's interests and level of understanding. The output is the optimized learning materials.

[1392] Step 6:

[1393] Sending study materials and managing progress

[1394] The server sends the generated learning materials to the device. The input is the created learning materials. Specifically, the device displays the received learning materials to the user and progresses the learning. When the user finishes learning, the device again records the progress data and new emotion data and stores them in local storage.

[1395] Step 7:

[1396] Data retransmission

[1397] The device stores the progress data and emotion data in a buffer and checks the network connection status. The input is the progress data and emotion data stored in the buffer. If the network is good, the device sends the data to the server again. The output is the progress data and emotion data sent to the server.

[1398] Step 8:

[1399] Continuous feedback

[1400] The server then analyzes the progress and emotional data again and provides appropriate feedback to the user in real time. The inputs are the progress and emotional data. Specifically, the AI ​​model evaluates the learning progress, provides additional training materials and advice as needed, and sends encouragement and warnings according to the user's emotional state. The output is feedback and training materials to the user.

[1401] In this way, the system can provide a learning experience that is optimized to each student's individual needs and emotional state, improving comprehension and increasing engagement.

[1402] (Application example 2)

[1403] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1404] Conventional online education systems have the problem that it is difficult to grasp students' level of understanding and learning behavior, making it difficult to adequately manage progress and provide feedback according to individual needs. It is also difficult to grasp customers' emotional state in real time in physical stores, making it difficult to provide appropriate individual support. It is necessary to improve student and customer satisfaction and realize effective learning and customer service.

[1405] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting students' answer data, correct answer rate, answer time, and past learning records; means for analyzing the collected data and identifying problems that each student cannot solve and the causes thereof; means for generating an optimal learning path based on the analysis results; means for automatically generating individually optimized learning materials based on the generated learning path; means for sending the generated learning materials to the student's device and monitoring learning progress; means for providing timely feedback based on learning progress; and means for collecting and analyzing customer emotional state and behavioral data and providing optimal support and suggestions in real time. This maximizes students' learning efficiency, provides a learning experience tailored to their individual needs, and makes it possible to provide appropriate support to customers in physical stores.

[1406] "Student answer data" refers to the answers entered by students in response to questions presented in the educational system.

[1407] The "correct answer rate" is an indicator that indicates the percentage of questions that a student answered correctly out of all questions that he or she answered.

[1408] "Response time" refers to the time it takes a student to complete a response to a question.

[1409] "Past learning records" are a collection of data that includes the learning content that a student has undertaken and the results of that learning.

[1410] "Means of collection" refers to the methods and equipment necessary to obtain and use data.

[1411] "Means for analyzing data" means methods and techniques for processing collected data and extracting information.

[1412] "Means for generating optimal learning paths" refers to a method for creating the most effective learning sequence and curriculum for students based on the analysis results.

[1413] "Means for automatically generating individually optimized teaching materials" refers to a method for automatically creating learning materials that meet the needs of students.

[1414] A "progress monitoring tool" is a method for tracking students' progress and recording their progress.

[1415] "Means of providing feedback" are ways to provide students with appropriate guidance and advice based on their learning progress.

[1416] "Means for collecting and analyzing customer emotional state and behavioral data" refers to methods and technologies for acquiring customer facial expressions, movements, speech content, etc., and analyzing them to clarify their emotional state and behavioral intentions.

[1417] "Means of providing optimal support and suggestions in real time" refers to a method of providing appropriate responses and services on the spot based on collected and analyzed customer data.

[1418] This invention is an online education system that improves students' learning efficiency by providing optimized learning materials taking into account the students' level of understanding and emotional state.It is also a system that can analyze the emotional state of customers in physical stores in real time and provide optimal support.

[1419] Data collection methods

[1420] The device (smartphone, smart glasses, robot, etc.) collects student or customer behavior data, answer data, accuracy rate, answer time, past learning records, emotional data, etc. Using an emotion engine, emotional data is extracted from the facial expressions, voice, and behavior of students or customers. This data is automatically recorded and stored in local storage.

[1421] Data transmission format

[1422] The device periodically transmits the collected answer data and emotion data to the server. Transmission is established when the network connection is good.

[1423] Data analysis format

[1424] The server receives the answer data and emotion data sent from the device and stores them in a database. It then analyzes the data using a generative AI model (for example, a model using TensorFlow) to identify the problems the student or customer is struggling with and the reasons for their difficulties. It also identifies the emotional state of the student or customer based on the emotion data.

[1425] Optimal learning paths and support generation

[1426] The server uses an AI model to generate the optimal learning path based on the analysis results. Furthermore, by analyzing the customer's emotional data, it can provide optimal support and suggestions in real time. These learning paths and suggestions are designed taking into account both the student's or customer's answer data and emotional data.

[1427] Automatic generation of individually optimized teaching materials and support

[1428] Based on the generated optimal learning path or support proposal, the server automatically generates individually optimized learning materials for students and appropriate support and proposals for customers. The learning materials are provided in a variety of formats, including text, images, and videos.

[1429] Feedback and progress management forms

[1430] The generated customized learning materials and support suggestions are sent from the server to the device and displayed to the user. The device again records progress data and emotional data as the user progresses with their learning and response, and sends this data to the server.

[1431] Continuous feedback

[1432] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress data and emotional data. For example, if the server determines from the emotional data analyzed that the customer is confused during learning or purchasing, it will send appropriate guidance or support messages.

[1433] Prompt Sentence Examples

[1434] For example, consider the following prompt:

[1435] python

[1436] emotion_data = emotion_model.detect_emotions(frame)

[1437] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[1438] This system enables the provision of learning experiences and customer support that are optimized to suit the individual needs of students and the real-time emotional state of customers, maximizing satisfaction and effectiveness.

[1439] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1440] Step 1:

[1441] The device (smartphone, smart glasses, robot, etc.) collects the user's answer data, accuracy rate, answer time, past learning record, and emotional data obtained from facial expressions and voice. These data are recorded in local storage. The input is the user's behavior and learning data, and the output is the various collected data.

[1442] Step 2:

[1443] The collected answer data and emotion data are periodically transmitted from the device to the server. Transmission is established when the network condition is good. The input is the data recorded on the device, and the output is the data transmitted to the server.

[1444] Step 3:

[1445] The server stores the answer data and emotion data received from the device in a database. The input is the data sent from the device, and the output is stored in the database.

[1446] Step 4:

[1447] The server analyzes the stored data using a generative AI model to identify which problem the user is struggling with and the cause. It also identifies the user's emotional state during learning based on emotional data. The input is the data stored in the database, and the output is the analysis results.

[1448] Step 5:

[1449] The server uses an AI model to generate the optimal learning path based on the analysis results. It can also provide optimal support and suggestions in real time based on customer sentiment data. The input is the analysis results, and the output is the optimal learning path and support suggestions.

[1450] Step 6:

[1451] The server automatically generates individually optimized learning materials or support based on the generated optimal learning path or support proposal. The learning materials are provided in the form of text, images, videos, etc. The input is the optimal learning path or support proposal, and the output is the generated learning materials or support content.

[1452] Step 7:

[1453] The generated customized teaching materials and support suggestions are sent from the server to the terminal and displayed to the user. The input is the generated teaching materials and support content, and the output is the display on the terminal.

[1454] Step 8:

[1455] The device records the progress data and emotion data of the user as they learn and respond, and sends them to the server. The input is the user's learning and behavior data, and the output is the data sent back to the server.

[1456] Step 9:

[1457] The server uses a generative AI model to provide appropriate feedback in real time based on the received progress and emotion data. The input is progress and emotion data, and the output is feedback provided to students or customers.

[1458] For example, consider the following prompt:

[1459] python

[1460] emotion_data = emotion_model.detect_emotions(frame)

[1461] recommended_action = customer_data_processor.analyze_emotion_data(emotion_data)

[1462] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1463] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1464] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1465] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1466] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1467] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1468] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1469] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1470] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1471] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1472] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1473] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1474] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1475] 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.

[1476] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1477] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1478] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1479] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1480] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1481] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1482] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1483] The following is further disclosed regarding the above embodiment.

[1484] (Claim 1)

[1485] A means of collecting students' answer data, correct answer rate, answer time, and past learning records;

[1486] A method for analyzing the collected data and identifying the problems each student is unable to solve and the reasons for them.

[1487] A means for generating an optimal learning path based on the analysis results;

[1488] A means for automatically generating individually optimized learning materials based on the generated learning path;

[1489] A means for transmitting the generated teaching materials to students' devices and monitoring their learning progress;

[1490] a means of providing timely feedback based on learning progress;

[1491] A system including:

[1492] (Claim 2)

[1493] 10. The system of claim 1, further comprising means for storing the collected answer data in a database.

[1494] (Claim 3)

[1495] 10. The system of claim 1, further comprising means for analyzing the collected data using an artificial intelligence model.

[1496] "Example 1"

[1497] (Claim 1)

[1498] A means of collecting students' answer data, correct answer rate, answer time, and past learning records;

[1499] means for periodically transmitting the collected data to a server;

[1500] a means for storing the received answer data in a database by the server and analyzing the data using an artificial intelligence model;

[1501] A method to identify the problems and causes of each student's difficulties based on each student's learning pattern and answer history,

[1502] a means for generating an optimal learning path using an artificial intelligence model based on the analysis results;

[1503] A method to automatically generate individually optimized learning materials based on the generated learning path and provide them in various formats such as text, images, and videos.

[1504] a means for transmitting the generated teaching materials to a student's terminal, and for the terminal to display the provided teaching materials to the user and record the learning progress;

[1505] a means for transmitting the learning progress data to the server again and providing timely feedback based on the results of analysis using an artificial intelligence model;

[1506] A system including:

[1507] (Claim 2)

[1508] 10. The system of claim 1, further comprising means for storing the collected answer data in a database.

[1509] (Claim 3)

[1510] 10. The system of claim 1, further comprising means for analyzing the collected data using an artificial intelligence model.

[1511] "Application Example 1"

[1512] (Claim 1)

[1513] A means of collecting students' answer data, correct answer rate, answer time, and past learning records;

[1514] A method for analyzing the collected data and identifying the problems each student is unable to solve and the reasons for them.

[1515] A means for generating an optimal learning path based on the analysis results;

[1516] A means for automatically generating individually optimized learning materials based on the generated learning path;

[1517] A means for transmitting the generated teaching materials to students' devices and monitoring their learning progress;

[1518] a means of providing timely feedback based on learning progress;

[1519] A means for collecting and analyzing user viewing history and evaluation data;

[1520] A means of recommending individually optimized content based on the analysis results;

[1521] A system including:

[1522] (Claim 2)

[1523] 10. The system of claim 1, further comprising means for storing the collected answer data and viewing history data in a database.

[1524] (Claim 3)

[1525] 10. The system of claim 1, further comprising means for analyzing the collected data using a generative AI model.

[1526] "Example 2: Combining Emotion Engines"

[1527] (Claim 1)

[1528] A means of collecting students' answer data, correct answer rate, answer time, and past learning records;

[1529] A means of collecting emotion data based on facial expressions, voice, and behavior during training;

[1530] A method for analyzing collected answer data and emotion data to identify problems that each student cannot solve and the reasons for their inability to solve them.

[1531] A means for generating an optimal learning path based on the analysis results;

[1532] A means for automatically generating individually optimized learning materials based on the generated learning path;

[1533] A means for transmitting the generated teaching materials to students' devices and monitoring their learning progress;

[1534] A means of providing timely feedback based on learning progress and encouraging and reminding students according to their emotional state;

[1535] A system including:

[1536] (Claim 2)

[1537] 10. The system of claim 1, further comprising means for storing the collected answer data and emotion data in a database.

[1538] (Claim 3)

[1539] 10. The system of claim 1, further comprising means for analyzing the collected answer data and emotion data using an artificial intelligence model.

[1540] "Application example 2 when combining emotion engines"

[1541] (Claim 1)

[1542] A means of collecting students' answer data, correct answer rate, answer time, and past learning records;

[1543] A method for analyzing the collected data and identifying the problems each student is unable to solve and the reasons for them.

[1544] A means for generating an optimal learning path based on the analysis results;

[1545] A means for automatically generating individually optimized learning materials based on the generated learning path;

[1546] A means for transmitting the generated teaching materials to students' devices and monitoring their learning progress;

[1547] a means of providing timely feedback based on learning progress;

[1548] A means to collect and analyze customer emotional states and behavioral data and provide optimal support and suggestions in real time.

[1549] A system including:

[1550] (Claim 2)

[1551] 10. The system of claim 1, further comprising means for storing the collected answer data in a database.

[1552] (Claim 3)

[1553] 10. The system of claim 1, further comprising means for analyzing the collected data using an artificial intelligence model. [Explanation of symbols]

[1554] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting students' answer data, correct answer rate, answer time, and past learning records; A method for analyzing the collected data and identifying the problems each student is unable to solve and the reasons for them. A means for generating an optimal learning path based on the analysis results; A means for automatically generating individually optimized learning materials based on the generated learning path; A means for transmitting the generated teaching materials to students' devices and monitoring their learning progress; a means of providing timely feedback based on learning progress; A system including:

2. 10. The system of claim 1, further comprising means for storing the collected answer data in a database.

3. 10. The system of claim 1, further comprising means for using an artificial intelligence model to analyze the collected data.

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