system

Abstract

We provide a system that improves students' learning effectiveness. [Solution] A system including means for collecting students' learning history and test results; means for analyzing the collected data and evaluating each student's level of understanding; means for generating individual learning materials based on the evaluation results; means for distributing the generated learning materials to students' terminals; means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data; means for providing the generated feedback to students' terminals; means for analyzing students' learning progress data and generating an optimal learning plan; and means for providing the generated learning plan to students' terminals.

Description

Technical Field

[0004] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional education system, there is a problem that individual learning support according to the learning situation and understanding level of each student is not sufficiently provided. In addition, there is also a lack of a mechanism for grasping the learning progress of students in real time and providing appropriate feedback and learning plans. As a result, the learning efficiency of individual students may decrease, and their learning motivation may be impaired. The present invention aims to solve such problems and improve the learning effect of students.

Means for Solving the Problems

[0005] The present invention solves the above problems with a system that includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for generating individual learning materials based on the evaluation results, means for distributing the generated learning materials to students' terminals, means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to students' terminals, means for analyzing students' learning progress data and generating an optimal learning plan, and means for providing the generated learning plan to students' terminals.

[0006] A "student" is an individual who engages in learning activities at an educational institution.

[0007] "Learning history" refers to a record of the learning activities and related data that a student has undertaken to date.

[0008] "Test results" refer to data on the scores and grades of tests and exams that students have taken.

[0009] "Means of collection" refers to methods and devices for acquiring and recording data.

[0010] "Means of analysis" refer to methods or devices for analyzing collected data and extracting specific information.

[0011] "Means of evaluation" refer to methods or devices that determine students' level of understanding and learning progress based on the analysis results.

[0012] "Educational materials" refer to learning content such as texts, images, videos, and quizzes provided to support students' learning.

[0013] "Means of generation" refers to methods and devices for creating new data or information.

[0014] "Means of distribution" refers to methods or devices for transmitting generated data or information to specific users or devices.

[0015] The "terminal" refers to a learning device (e.g., computer, tablet, smartphone, etc.) used by students.

[0016] The "learning activity" refers to the actions taken by students to acquire knowledge and skills.

[0017] The "means of monitoring" refers to the methods and devices for observing students' actions in real time and recording data.

[0018] "Feedback" refers to the evaluations and advice provided for students' actions and achievements.

[0019] "Real time" means that processing and reactions are carried out immediately.

[0020] The "learning progress" is an indicator showing the degree to which students have achieved the planned learning goals.

[0021] The "learning plan" specifically shows the learning content and schedule for students to achieve their goals.

[0022] The "system" is an aggregate of a series of devices and methods in which the above elements are integrated and function collaboratively.

Brief Explanation of Drawings

[0023] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] [[ID=​​​​​​​​It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0024] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[0045] Creating individualized learning materials for students

[0046] The server collects students' learning history and test results and stores them in a database. Next, the server analyzes the collected data to identify each student's level of understanding, learning strengths, and weaknesses. For example, if past test results for student A reveal a low level of understanding of calculus, the server generates personalized learning materials based on that data. These materials cover calculus from basic to advanced levels in a step-by-step manner.

[0047] The generated individual learning materials are delivered from the server to student A's terminal. The terminal displays these materials to the user (student A) and provides an interface for starting the learning process.

[0048] Real-time feedback

[0049] While the user (student) is engaged in learning activities, the device monitors their activities in real time. Specifically, it records the time the student spends viewing learning materials, the speed at which they solve problems, and correct / incorrect answer data. The monitoring data is immediately sent to the server.

[0050] The server analyzes the received data and generates real-time feedback quickly. For example, if student B enters an incorrect answer in an online quiz, the server generates feedback such as, "This problem may lack the concept of factorization. Please refer to the link below," and sends it to the device. The device displays this feedback to student B and provides advice and resources to help them move forward.

[0051] Providing learning plans based on each student's learning progress

[0052] The server continuously collects and analyzes daily learning activities and test results to understand students' overall learning progress. For example, if student C's learning history reveals that they have a good understanding of English grammar but lack listening skills, the server will propose a learning plan focused on strengthening English listening skills. This plan would include specific details such as listening practice at least three times a week.

[0053] The generated study plan is delivered from the server to student C's terminal, which then displays it to the user (student C). This study plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[0054] Specific example

[0055] For example, consider a case where student D is struggling to remember events from a specific historical period. In this case, the server identifies this trend from the collected data and generates personalized learning materials focused on that particular period. Simultaneously, it also generates quiz-style practice questions and mini-tests to check understanding. These materials are provided via the terminal, and the user (student D) works through them. If mistakes are made during learning, real-time feedback is provided, along with resources and advice for further learning.

[0056] Furthermore, through regular progress analysis, the server determines that student E's understanding of geometry has improved and that a review of algebra is necessary as the next topic to address. The server generates a learning plan focused on algebra, which is then provided to student E via their terminal. This ensures effective and systematic learning.

[0057] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each student with an optimal learning experience, and as a result, an improvement in learning outcomes can be expected.

[0058] The following describes the processing flow.

[0059] Creating individualized learning materials for students (1)

[0060] Step 1:

[0061] The device records students' learning history and test results daily and periodically sends this data to a server.

[0062] Step 2:

[0063] The server stores the received data in a database. This includes learning history, test results, and comprehension trends.

[0064] Step 3:

[0065] The server uses machine learning algorithms for data analysis to identify each student's level of understanding, learning strengths, and weaknesses.

[0066] Step 4:

[0067] Based on the analysis results, the server generates personalized learning materials tailored to each student. These materials include text, images, videos, and quiz-style questions.

[0068] Step 5:

[0069] The server distributes the generated individual learning materials to the students' devices.

[0070] Step 6:

[0071] The terminal displays the received individual learning materials to the user (student) and provides an interface for starting learning.

[0072] Real-time feedback (2)

[0073] Step 1:

[0074] The device monitors the user's (student's) learning activities in real time. Specifically, it records the speed at which problems are solved, correct / incorrect answer data, and time spent viewing learning materials.

[0075] Step 2:

[0076] The terminal sends monitoring data to the server in real time.

[0077] Step 3:

[0078] The server analyzes the received data and generates real-time feedback. This feedback includes the accuracy rate, reasons for incorrect answers, and advice for improving understanding.

[0079] Step 4:

[0080] The server immediately sends the generated feedback to the terminal.

[0081] Step 5:

[0082] The device displays the received feedback to the user (student) and provides additional learning resources and links.

[0083] (3) Providing learning plans based on each student's learning progress

[0084] Step 1:

[0085] The device continuously records the user's (student's) learning activities and test results, and periodically sends them to the server.

[0086] Step 2:

[0087] The server analyzes each student's learning progress based on the received data. Indicators include comprehension level, assignment completion status, and allocation of study time.

[0088] Step 3:

[0089] The server generates an optimal learning plan for each student based on the analysis results. This plan includes the next learning materials, review topics, and time allocation for each study session.

[0090] Step 4:

[0091] The server sends the generated training plan to the terminal.

[0092] Step 5:

[0093] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[0094] The above outlines the specific operational details for each processing step. This system enables the provision of effective learning support tailored to the individual needs of each student.

[0095] (Example 1)

[0096] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] Traditional education systems do not adequately provide individualized learning support based on each student's level of understanding and learning progress. This can lead to decreased student learning efficiency, and some students may fall behind in areas they struggle with. Furthermore, the lack of real-time feedback means students may leave misunderstandings unaddressed, resulting in reduced learning effectiveness.

[0098] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0099] In this invention, the server includes means for generating individual learning materials using a generative AI model, means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for distributing the generated learning materials to students' terminals, means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to students' terminals, means for analyzing students' learning progress data and generating an optimal learning plan using a generative AI model, and means for providing the generated learning plan to students' terminals. This enables the provision of learning materials and learning plans optimized for each student, as well as real-time feedback.

[0100] "Means for collecting students' learning history and test results" refers to a system for collecting data on all learning activities and test results that students have undertaken to date.

[0101] "Methods for analyzing collected data and evaluating each student's level of understanding" refers to algorithms and technologies for analyzing collected learning data and evaluating each student's level of learning comprehension.

[0102] "Means for generating individualized learning materials" refers to software or a system for creating learning materials that are optimal for each student based on their level of learning comprehension.

[0103] "Methods for generating individual learning materials using generative AI models" refers to technologies that use artificial intelligence (AI) to automatically generate learning materials tailored to individual students.

[0104] "Means for distributing generated learning materials to students' devices" refers to a communication system for sending generated learning materials from a server to students' learning devices.

[0105] "Means of monitoring data on students' learning activities in real time" refers to software and sensor technologies that allow for immediate monitoring of what students are doing while learning.

[0106] "Means for generating real-time feedback based on monitoring data" refers to an algorithm that quickly generates appropriate feedback for students based on collected monitoring data.

[0107] "Means for providing generated feedback to students' devices" refers to communication means for displaying real-time generated feedback on students' learning devices.

[0108] "A means of analyzing students' learning progress data and generating an optimal learning plan using a generative AI model" refers to a technology that analyzes students' learning progress and uses an AI model to formulate an optimal learning plan.

[0109] "Means for providing the generated learning plan to students' devices" refers to a communication system that displays the generated learning plan on students' learning devices and encourages them to implement it.

[0110] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[0111] Collection of students' learning history and test results

[0112] The server can collect learning history and test result data from students' learning platforms through specific APIs. This data includes grades for each subject, test results, and the time spent on each problem. The collected data is stored in an SQL database (e.g., MySQL®).

[0113] Data analysis and assessment of understanding

[0114] The server uses Python to retrieve data from the database and analyzes it using machine learning libraries (e.g., Scikit-learn). Specifically, it uses a clustering algorithm to cluster students' levels of understanding. Based on these analysis results, it evaluates each student's level of understanding, learning strengths, and weaknesses.

[0115] Generation of individualized learning materials

[0116] The server generates learning materials for each student using a generative AI model (e.g., GPT-3®). Specifically, it uses prompt statements to instruct the AI ​​model and create materials that correspond to a specific level of understanding. For example, it uses prompt statements like the following:

[0117] "Student A has a poor understanding of calculus. Please create individualized learning materials that cover everything from the basics to advanced topics."

[0118] Distribution of educational materials and provision of interfaces

[0119] The server delivers the generated learning materials to the students' devices using a specific communication protocol (e.g., HTTP). The devices provide an interface that allows the user (student) to view the received materials via a web browser or a dedicated app.

[0120] Monitoring of learning activities

[0121] The device tracks the user's (student's) actions (e.g., visit time, clicks, answer time) in real time using JavaScript® or similar technologies. The collected data is immediately sent to the server.

[0122] Providing real-time feedback

[0123] The server analyzes the received learning activity data and generates real-time feedback using a generative AI model. For example, it generates feedback as follows:

[0124] "If student B answers the quiz incorrectly, the message will read: 'Understanding factorization is necessary. Please refer to the following link.'"

[0125] The server sends the generated feedback to the terminal, and the terminal displays it to the user (student).

[0126] Proposal for a study plan

[0127] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan. For example, it might suggest a plan like this:

[0128] "Student C lacks listening comprehension skills, therefore, we propose a study plan that includes listening practice at least three times a week."

[0129] The generated learning plan is delivered from the server to the student's device, and the device displays it to the user (student).

[0130] Specific example

[0131] If student D is struggling to remember a particular period in history class, the server analyzes all of student D's test results and generates personalized learning materials focused on that specific time period. The terminal immediately displays these materials, and student D uses them to continue their learning.

[0132] If student E answers a specific math problem (e.g., geometry) incorrectly during a lesson, the device immediately sends the data to the server. The server analyzes this data, generates specific feedback such as "This problem was caused by a lack of understanding of factorization," and sends it back to the device. The device then displays this feedback to student E.

[0133] This invention provides each student with learning materials and learning plans optimized for them, and enables real-time feedback. This is expected to maximize students' learning efficiency and results.

[0134] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0135] Step 1:

[0136] The server collects students' learning history and test results from the learning platform. Specifically, it retrieves each student's performance data and test results through a particular API. The collected data is stored in an SQL database (e.g., MySQL).

[0137] Input: Student learning history and test results obtained from the learning platform.

[0138] Output: Learning history and test result data stored in the database

[0139] Step 2:

[0140] The server analyzes the collected data using Python and machine learning libraries (e.g., Scikit-learn). Specifically, it applies clustering algorithms to identify students' comprehension levels, learning strengths, and weaknesses.

[0141] Input: Learning history and test result data retrieved from the database

[0142] Output: Analysis results including each student's level of understanding, strengths, and weaknesses.

[0143] Step 3:

[0144] The server uses a generative AI model (e.g., GPT-3) to generate optimal learning materials for each student. Requests are sent to the AI ​​model using prompts, automatically generating materials corresponding to specific levels of understanding.

[0145] Input: Student-specific comprehension data and generated AI model

[0146] Output: Generated individual learning materials

[0147] Specific action: The prompt message used is "Student A has a low understanding of calculus. Please generate individualized learning materials that cover everything from basic to advanced levels."

[0148] Step 4:

[0149] The server delivers the generated learning materials to students' devices using a specific communication protocol (e.g., HTTP). The generated materials are delivered in web page or application format.

[0150] Input: Generated individual learning materials and student device information

[0151] Output: Learning materials delivered to students' devices

[0152] Step 5:

[0153] The device monitors user (student) behavioral data in real time using JavaScript and other technologies. This includes behavioral information such as viewing time, clicks, and answering time for learning materials.

[0154] Input: Student learning activity data

[0155] Output: Learning activity data collected in real time

[0156] Step 6:

[0157] The device immediately sends the collected learning activity data to the server. Monitoring data is transmitted securely using a communication protocol.

[0158] Input: Learning activity data collected in real time

[0159] Output: Monitoring data sent to the server

[0160] Step 7:

[0161] The server analyzes monitoring data and uses a generative AI model to generate real-time feedback. For example, it might generate specific feedback such as "You lack understanding of factorization" and present it to the user.

[0162] Input: Monitoring data and generated AI models

[0163] Output: Generated real-time feedback

[0164] Step 8:

[0165] The server sends the generated feedback to the terminal, which then displays it to the user (student). The user receives the feedback and can get guidance on how to proceed to the next step.

[0166] Input: Generated real-time feedback

[0167] Output: Feedback displayed on the student's device

[0168] Step 9:

[0169] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan for each student. Specifically, it creates a learning plan that includes the next learning materials and review content to be tackled.

[0170] Input: Learning progress data and generative AI model

[0171] Output: Generated training plan

[0172] Step 10:

[0173] The server delivers the generated study plan to the student's device, and the device displays it to the user (student). The user can then use the study plan as a reference to effectively progress with their studies.

[0174] Input: Generated training plan

[0175] Output: Learning plan displayed on the student's device

[0176] (Application Example 1)

[0177] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] In today's industrial environment, there is a demand for improving employees' technical skills and optimizing operational efficiency. Acquiring skills in robot operation and machine maintenance, particularly in factories, is crucial, but addressing individual learning needs is challenging. Furthermore, the inability of employees to receive appropriate real-time feedback during their learning process leads to decreased learning efficiency, which is a significant problem.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0180] In this invention, the server includes means for collecting students' learning history and test results; means for analyzing the collected data and evaluating each student's level of understanding; means for generating individual learning materials based on the evaluation results; means for distributing the generated learning materials to the students' terminals; means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data; means for providing the generated feedback to the students' terminals; means for analyzing students' learning progress data and generating an optimal learning plan; means for providing the generated learning plan to the students' terminals; means for providing additional resources and explanations according to the user's progress; and means for supplying manuals and video materials necessary for performing the target tasks. This makes it possible for employees to improve their learning efficiency and to provide a comprehensive training environment that meets individual learning needs.

[0181] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[0182] "Test results" refer to the evaluations, test scores, and responses received by the user.

[0183] "Understanding level" indicates the degree to which a user has acquired a particular knowledge or skill.

[0184] "Individualized learning materials" refer to learning materials optimized for a specific user, taking into account their learning history and level of understanding.

[0185] A "terminal" is an electronic device used for displaying learning materials and receiving feedback.

[0186] "Real-time monitoring" means instantly observing and recording a user's actions and reactions while they are engaged in learning activities.

[0187] "Feedback" refers to providing evaluations and advice regarding a user's learning activities.

[0188] A "learning plan" is a schedule and list of learning materials designed to help users learn efficiently.

[0189] "Additional resources" refer to additional learning materials and information provided to assist users in their learning.

[0190] A "procedure manual" is a document that describes the steps required to perform a specific task or operation.

[0191] "Video teaching materials" refer to learning materials in video format that provide information visually.

[0192] This invention is a learning support system for robot operation techniques and machine maintenance in factories. This system mainly consists of a server, terminals, and users (employees).

[0193] Creating individualized training materials for employees

[0194] The server collects employees' learning history and various evaluation results and stores them in a database. Next, the server analyzes the collected data to identify each employee's level of understanding, learning strengths, and weaknesses. For example, if past evaluation results for employee A reveal a low level of understanding of machine maintenance, the server generates personalized learning materials based on that data. These materials cover maintenance from basic to advanced techniques in a step-by-step manner. The generated personalized materials are delivered from the server to employee A's terminal. The terminal (e.g., smart glasses) displays these materials to the user (employee A) and provides an interface for starting the learning process.

[0195] Real-time feedback

[0196] While a user (employee) is engaged in learning activities, the terminal monitors their activities in real time. Specifically, it records the time the employee spends viewing learning materials, the speed at which they complete tasks, and correct / incorrect data. The monitoring data is immediately sent to the server. The server analyzes the received data and quickly generates real-time feedback. For example, if employee B enters an incorrect answer in an online quiz, the server generates feedback such as, "This question may indicate a lack of understanding of the mechanism. Please refer to the link below," and sends it to the terminal. The terminal displays this feedback to employee B and provides advice and resources for taking the next step.

[0197] Providing learning plans based on each employee's learning progress.

[0198] The server continuously collects and analyzes daily learning activities and evaluation results to understand the overall learning progress of employees. For example, if employee C's learning history reveals that they have made progress in understanding robot operation but lack troubleshooting skills, the server will propose a learning plan focused on strengthening troubleshooting. This plan will include specific troubleshooting exercises at least three times a week. The generated learning plan is delivered from the server to employee C's terminal, which then displays it to the user (employee C). This learning plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[0199] Providing learning resources using the Gen AI model

[0200] The server also provides additional resources and explanations depending on the employee's progress. For example, if employee D makes a mistake in the procedure, the server uses a generative AI model to instantly generate and provide easy-to-understand procedural explanations and relevant video materials. An example of a prompt for this purpose is as follows:

[0201] Examples of input prompts for a generative AI model

[0202] User history:

[0203] ID: 12345

[0204] Current skill level: Beginner

[0205] Learning material: Machine overhaul procedure

[0206] Progress: Steps 1 and 2 completed, stuck on step 3.

[0207] question:

[0208] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[0209] In this way, it is possible to provide a comprehensive training environment that meets individual learning needs.

[0210] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0211] Step 1:

[0212] The server collects employees' learning history and evaluation results.

[0213] Input: Employee's past learning data and evaluation data

[0214] Output: Collected learning history and evaluation data are saved to a database.

[0215] Specific operation: The server retrieves and organizes learning history and evaluation data related to employee IDs from the database.

[0216] Step 2:

[0217] The server analyzes the collected data and evaluates each employee's level of understanding.

[0218] Input: Collected learning history and evaluation data

[0219] Output: Comprehension evaluation results

[0220] Specific operation: The server analyzes the data using machine learning algorithms (e.g., linear regression and decision trees) and quantifies each employee's level of understanding.

[0221] Step 3:

[0222] The server generates individual learning materials based on the evaluation results.

[0223] Input: Comprehension assessment results

[0224] Output: Individual learning materials

[0225] Specific operation: The server selects appropriate learning materials from the content management system and configures them according to the employee's level of understanding. The learning materials are provided in various formats, such as text, images, and videos.

[0226] Step 4:

[0227] The server distributes the generated learning materials to employees' terminals.

[0228] Input: Individual learning materials

[0229] Output: Learning materials delivered to the device

[0230] Specific operation: The server uses network protocols (e.g., HTTP, WebSocket) to send educational data to the terminal and displays its contents on the terminal.

[0231] Step 5:

[0232] The terminal monitors data from employees' learning activities in real time and sends the monitored data to the server.

[0233] Input: User's learning activity data (viewing time, correctness of assignment answers, etc.)

[0234] Output: Monitoring data sent to the server

[0235] Specific operation: The terminal acquires learning activity data from sensors and input devices and sends it to the server in real time.

[0236] Step 6:

[0237] The server analyzes the received monitoring data and generates real-time feedback.

[0238] Input: Monitoring data

[0239] Output: Real-time feedback

[0240] Specific operation: Based on monitoring data, the server uses a generative AI model to generate appropriate feedback. For example, it immediately provides "how to correct incorrect procedures."

[0241] Step 7:

[0242] The device provides the generated feedback to employees.

[0243] Input: Real-time feedback

[0244] Output: Feedback displayed to employees

[0245] Specific operation: The terminal displays feedback received from the server on the screen and instructs the user on the appropriate next action. For example, "advise the user to check specific points before proceeding to the next step."

[0246] Step 8:

[0247] The server analyzes employee learning progress data and generates an optimal learning plan.

[0248] Input: Learning progress data

[0249] Output: Optimal learning plan

[0250] Specific operation: The server analyzes the employee's learning history and current learning progress, and generates a learning plan that suggests the optimal schedule and learning materials to achieve specific learning goals.

[0251] Step 9:

[0252] The server provides the generated learning plan to the employee's terminal.

[0253] Input: Learning plan

[0254] Output: Learning plan provided to the device

[0255] Specific operation: The server sends a learning plan to the terminal via the network, and the terminal displays it to the user. The plan includes the next learning materials, review topics, and allocation of study time.

[0256] Step 10:

[0257] The server provides additional resources and explanations based on the employee's progress.

[0258] Input: Employee progress status, generated AI model

[0259] Output: Additional resources and explanations

[0260] Specific operation: The server prompts the generated AI model with a prompt message, generates appropriate resources (e.g., video materials or explanatory text), sends them to the terminal, and provides them to the user.

[0261] Examples of input prompts for a generative AI model

[0262] User history:

[0263] ID: 12345

[0264] Current skill level: Beginner

[0265] Learning material: Machine overhaul procedure

[0266] Progress: Steps 1 and 2 completed, stuck on step 3.

[0267] question:

[0268] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[0269] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0270] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[0271] Creating individualized learning materials for students

[0272] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data on the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotional data. For example, if the emotion engine detects that student A has an insufficient understanding of calculus based on past test results and is experiencing anxiety while studying, the server generates personalized learning materials based on this. These materials cover calculus from basic to advanced levels in a step-by-step manner and also include mental support content to alleviate anxiety.

[0273] The generated individual learning materials are delivered from the server to student A's device, and the device displays them to the user (student A). In this way, learning is supported according to each individual's level of understanding and emotional state.

[0274] Real-time feedback

[0275] While the user (student) is engaged in learning activities, the device monitors the activity in real time and transmits data such as the speed at which problems are solved, correct / incorrect answers, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state. For example, when user (student B) is working on an online quiz, the emotion engine analyzes in real time whether they are focused or hesitant.

[0276] The server analyzes the received data and emotional state to generate real-time feedback. For example, if student B is feeling anxious about a problem, the server generates feedback such as "Relax and tackle the problem. Please refer to the following link," and sends it to the device. The device then displays this feedback to the user (student B) and provides any necessary additional resources or advice.

[0277] Providing learning plans based on each student's learning progress

[0278] The server periodically analyzes students' overall learning progress and also considers emotional data from the emotion engine. For example, if student C is lacking in a specific learning area (e.g., English listening skills) and is experiencing stress during learning, the server will take this into account when creating a learning plan. This plan will include specific study time for improving listening skills and relaxation techniques.

[0279] The generated learning plan is delivered from the server to student C's terminal, which then displays it to the user (student C). Student C can proceed with their studies based on the presented learning plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[0280] Specific example

[0281] For example, consider a case where student D is struggling to remember events from a specific historical period and is experiencing stress. In this case, the server takes into account the stress level detected by the emotion engine and generates personalized learning materials focused on that specific period. These materials would also include tips for reducing stress and advice on creating a more relaxed learning environment.

[0282] In addition, based on regular progress analysis and emotional data, the server determines that student E needs algebra review and emotional support as the next tasks. The server generates a learning plan that focuses on algebra learning and provides follow-up support in terms of emotions, and provides it to student E through the terminal. This enables systematic learning that balances learning efficiency and mental support.

[0283] The above is a form for specifically implementing the system of the present invention. With this system, it is possible to provide an optimal learning experience for each student, and as a result, both an improvement in learning outcomes and a psychological sense of security can be expected.

[0284] The processing flow will be described below.

[0285] Creation of individual teaching materials for students

[0286] Step 1: [[ID=十七]]

[0287] The terminal collects the learning history, test results, facial expressions, and voice data of the student, and periodically sends this data to the server.

[0288] Step 2:

[0289] The server saves the received data in the database. <00009!6> Step 3:

[0291] The server uses a machine learning algorithm for data analysis to identify the level of understanding, learning strengths, and weaknesses of each student.

[0292] Step 4:

[0293] The server also considers the analysis results of the emotion engine and conducts analysis according to the emotional state of the student. For example, when there are emotions such as anxiety or stress, measures to reduce them are considered.

[0294] Step 5:

[0295] Based on the analysis results, the server generates personalized learning materials for the students. The materials include text, images, videos, quiz questions, and emotional support content, etc.

[0296] Step 6:

[0297] The server distributes the generated personalized materials to the students' terminals.

[0298] Step 7:

[0299] The terminal displays the received personalized materials to the user (student) and provides an interface for starting learning.

[0300] Real-time feedback

[0301] Step 1:

[0302] The terminal monitors the students' learning activities in real time and records the speed of solving problems, correct / incorrect data, expressions, tones of voice, etc.

[0303] Step 2:

[0304] The terminal transmits the monitoring data to the server in real time.

[0305] Step 3:

[0306] The emotion engine analyzes the data transmitted from the terminal and identifies the students' emotional states.

[0307] Step 4:

[0308] The server analyzes the emotional data and training data received from the emotion engine to generate real-time feedback. For example, if a student is feeling anxious about a problem, it might generate feedback such as, "Relax and tackle the problem. Please refer to the link below."

[0309] Step 5:

[0310] The server sends the generated feedback to the terminal.

[0311] Step 6:

[0312] The device displays the received feedback to the user (student) and provides additional learning resources and advice.

[0313] Providing a learning plan based on learning progress

[0314] Step 1:

[0315] The device continuously records students' learning activities, test results, and emotional data, and periodically sends this data to a server.

[0316] Step 2:

[0317] The server analyzes each student's learning progress based on the received data. This analysis includes comprehension level, assignment completion status, study time allocation, and emotional state.

[0318] Step 3:

[0319] The server generates an optimal learning plan for each student based on analysis results and emotional data. For example, if a student experiences stress in a particular subject, the server will suggest a plan that includes strategies for dealing with that stress and methods for relaxation.

[0320] Step 4:

[0321] The server sends the generated training plan to the terminal.

[0322] Step 5:

[0323] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[0324] Step 6:

[0325] The device monitors the progress of the learning plan in real time and makes corrections and provides feedback as needed.

[0326] The above outlines the specific processing steps of the system that combines emotional engines. This system makes it possible to simultaneously provide effective learning support and mental support tailored to the individual needs of each student.

[0327] (Example 2)

[0328] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0329] Traditional education systems have struggled to provide individualized support that takes into account not only each student's level of understanding and progress, but also their emotional state. As a result, students have difficulty maintaining motivation for learning and maximizing their learning effectiveness. Furthermore, there has been a lack of technology to provide real-time, emotion-based feedback.

[0330] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for collecting students' facial expressions and voice data and generating emotional data using an emotion engine, and means for generating individual learning materials based on the evaluation results and emotional data. This makes it possible to provide optimal educational support tailored to each student's level of understanding and emotional state.

[0331] "Learning history" refers to a record of learning activities that a student has undertaken in the past.

[0332] "Test results" refer to data showing the grades and answers students received on exams they took.

[0333] "Means of collection" refers to the devices and methods used to collect the target data.

[0334] "Means of analysis" refer to devices and methods for analyzing collected data and extracting meaningful information.

[0335] "Comprehension level" is an indicator that shows how well students understand the learning material.

[0336] "Facial expression and voice data" refers to data that includes facial expressions and vocal characteristics to indicate the emotional state of students.

[0337] An "emotion engine" is a technology or system that analyzes data from students' facial expressions and voices to identify their emotional state.

[0338] "Emotional data" refers to data generated by the emotion engine that represents the emotional state of students.

[0339] "Individualized learning materials" are materials that are optimized for a specific student, generated based on each student's level of understanding and emotional state.

[0340] "Means of distribution" refers to devices and methods for sending generated learning materials and feedback to students' devices.

[0341] "Means of monitoring" refer to devices and methods for observing students' learning activities in real time and collecting data.

[0342] "Feedback" refers to advice and suggestions for improvement given to students based on their progress and results in learning activities.

[0343] "Learning progress data" refers to data that shows the progress of students' learning activities.

[0344] A "learning plan" is a plan that outlines future learning content and schedules, created based on students' learning progress data and emotional data.

[0345] This invention is a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding, progress, and emotional state. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[0346] First, the server collects students' learning history and test results and stores them in a database. This learning history and test results are retrieved from the Learning Management System (LMS). For example, this includes test results and study time logs from the past week. This allows for accurate tracking of each student's learning history and achievements.

[0347] Next, the device collects data on the student's facial expressions and voice using sensors (camera and microphone) and sends it to the emotion engine. The emotion engine analyzes this data to generate emotion data. For example, if a student shows an anxious expression through the camera during a test, the emotion engine will analyze it. The collected data is used in real time to quantify the emotional state.

[0348] The server integrates and analyzes collected learning history, test results, and emotional data to generate learning materials optimized for each student. Using a generative AI model, it creates easy-to-understand learning materials and mental support content. For example, for a student who has insufficient understanding of calculus and feels anxious, the server will generate materials covering everything from basic to advanced levels, as well as mental support content to alleviate their anxiety. This provides education tailored to individual needs.

[0349] The generated individual learning materials are delivered from the server to the students' devices, and the devices display the received materials to the users (students). For example, if learning materials about history are generated, they will be displayed on the student's device screen. This allows students to concentrate on their own learning.

[0350] While students are engaged in learning activities, the device monitors their activities in real time and transmits data on their facial expressions, voice, and learning progress to the emotion engine. The emotion engine analyzes this data to identify the student's current emotional state. For example, while a student is taking an online quiz, their answer speed, accuracy data, facial expressions, and voice tone are transmitted and analyzed.

[0351] The server receives real-time data from the emotion engine and generates real-time feedback based on the emotional state. The generated feedback is sent to the device and displayed to the user (student). For example, feedback such as "Relax and tackle the problem. Please refer to the link below." might be displayed, allowing the student to instantly obtain any necessary additional resources or advice.

[0352] Furthermore, the server periodically analyzes students' learning progress and emotional data, and generates the next learning plan based on this analysis. The generated learning plan is delivered from the server to the student's device, which then displays it to the user (student). Students can then proceed with their learning based on this plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[0353] For example, if a student is experiencing stress while studying history, the server will consider the stress level detected by the emotion engine and generate personalized learning materials focusing on a specific historical period. These materials will also include tips and relaxation techniques to reduce stress. Similarly, if a student is feeling anxious about a math problem, the server will generate real-time feedback addressing that anxiety and display a message such as, "Try to relax."

[0354] Examples of prompt messages include the following:

[0355] "Generate individualized learning materials based on students' recent learning history and emotional state. Focus particularly on calculus in mathematics, and include mental support content to reduce anxiety."

[0356] "Analyze students' emotional states from their facial expressions and audio data while they are participating in online quizzes, and provide real-time feedback as needed."

[0357] "Based on students' overall learning progress and emotional data, create a specific learning plan to improve their English listening skills and distribute that plan to their devices."

[0358] As described above, the system of the present invention makes it possible to provide an optimal learning experience for each student, and both improved learning outcomes and a sense of psychological security can be expected.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1: Collect learning history and test results

[0361] The server collects student learning history and test results from the Learning Management System (LMS). Specifically, it retrieves test results and study time logs from students taken over the past week. The input is student learning data obtained through the LMS API, and the output is learning history and test results stored in the database. Here, API requests are sent, and the data is parsed and saved in JSON format.

[0362] Step 2: Collecting emotional data

[0363] The device collects data on students' facial expressions and voices using a camera and microphone and sends it to an emotion engine. Specifically, the video and audio data captured by the sensors is processed frame by frame and sent to the emotion engine. The input is data on students' facial expressions and voices obtained from the camera and microphone, and the output is emotion data generated by the emotion engine. An emotion analysis algorithm is used to quantify emotional states such as "anxiety" and "concentration."

[0364] Step 3: Data analysis and creation of individualized learning materials

[0365] The server integrates and analyzes collected learning history, test results, and emotional data. Inputs include learning history and test results stored in a database, as well as emotional data sent from the emotion engine. This data is fed into a generative AI model to generate learning materials optimized for each individual student. The output is the generated, personalized learning material. Specifically, the generative AI model automatically generates the material content, creating customized materials based on the student's level of understanding and emotional state.

[0366] Step 4: Delivery and display of individual learning materials

[0367] The server distributes the generated individual learning materials to the students' devices. The input is the individual learning materials generated by the generation AI model, and the output is the distribution of the materials to the devices. The devices display the received materials to the users (students). Specifically, the process involves sending the learning material data to the device and displaying the materials on the device's screen.

[0368] Step 5: Real-time monitoring of learning activities

[0369] The device monitors students' learning activities in real time while they are engaged in them. Inputs include student learning progress, problem-solving speed, correct / incorrect answer data, facial expressions, and voice data, which are sent to the emotion engine. Output is real-time emotion data. Specifically, it sends activity data to the emotion engine, which then analyzes the emotional state in real time.

[0370] Step 6: Real-time feedback generation

[0371] The server analyzes the user's emotional state based on real-time data received from the emotion engine and generates appropriate feedback. The input is real-time emotional data and student learning progress data, while the output is the generated feedback. Specifically, it automatically generates advice such as "Relax and tackle the problem" and sends it to the device.

[0372] Step 7: Regular analysis of learning progress and provision of learning plans

[0373] The server periodically analyzes students' learning progress and sentiment data to generate the next learning plan. The input is past learning progress and sentiment data stored in a database, and the output is the learning plan provided to the student. Specifically, it generates a learning progress report, plans the next learning steps based on it, and delivers it to the terminal. Students can then proceed with their learning based on the presented learning plan.

[0374] (Application Example 2)

[0375] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0376] Traditional education systems struggle to provide not only individualized learning materials tailored to students' progress and understanding, but also real-time feedback that takes into account students' psychological state and emotions. Furthermore, they often lack support to reduce stress and anxiety associated with learning and maximize learning effectiveness. Especially when acquiring specialized skills such as those of a factory operator, psychological state significantly impacts skill acquisition, highlighting the need for a system that can comprehensively manage these factors.

[0377] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for generating individual learning materials based on the evaluation results, means for distributing the generated learning materials to the student's terminal, means for monitoring data in real time when students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to the student's terminal, means for analyzing students' learning progress data and generating an optimal learning plan, means for providing the generated learning plan to the student's terminal, means for analyzing the subject's facial expressions and voice in real time and generating emotional data, means for generating feedback that reduces stress and anxiety based on the emotional data, and means for displaying the generated feedback on the display of a smart device. This makes it possible to provide individualized learning support based on each student's level of understanding and psychological state, enabling effective learning while reducing stress and anxiety associated with skill acquisition.

[0378] "Learning history" refers to data that records what a student has learned in the past and their level of achievement.

[0379] "Test results" refer to data showing students' grades and answers when they took a test.

[0380] "Comprehension level" refers to an assessment that indicates how well a student understands a particular learning topic.

[0381] "Individualized learning materials" refer to learning materials that are created based on each student's learning progress and level of understanding.

[0382] "Real-time monitoring" is the process of collecting and analyzing activity data while students are engaged in learning activities.

[0383] "Feedback" refers to advice and support messages generated based on a student's learning activities and psychological state.

[0384] "Learning progress data" refers to data that shows how well students are progressing according to their learning plan.

[0385] A "study plan" refers to the specific learning content and timetable formulated to help each student achieve their learning goals.

[0386] "Facial expression" refers to the emotions and psychological state that can be interpreted from the movement of facial muscles and facial features.

[0387] "Voice" refers to the audio data produced when speaking, and its tone and changes are used to interpret emotional states.

[0388] "Emotional data" refers to data that indicates the emotional state of students, analyzed from their facial expressions and voices.

[0389] A "smart device" is a portable electronic device that has internet connectivity and high-performance computing capabilities.

[0390] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[0391] Creating individualized learning materials for students

[0392] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data of the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotion data. This emotion data is used to understand changes in emotions that occur during the learning process in real time.

[0393] For example, when an operator is learning how to operate a new robotic arm, the camera analyzes the operator's facial expressions in real time and provides relaxation techniques and specific tips if stress or anxiety is detected. Furthermore, if the operator stumbles at a particular step, detailed explanations and video tutorials are displayed on the smart device's screen.

[0394] Real-time feedback

[0395] While the user (student) is engaged in learning activities, the device monitors their activities in real time and sends data such as the speed at which they solve problems, correctness data, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state.

[0396] For example, when a user (operator) is learning how to operate a new robot online, the emotion engine analyzes in real time whether they are focused or hesitant. The server analyzes the received data and emotional state and generates real-time feedback. For instance, if the operator is feeling anxious about a problem, the server generates feedback such as, "Please relax and learn. Please refer to the following link," and sends it to the terminal.

[0397] Providing learning plans based on each student's learning progress

[0398] The server periodically analyzes the student's overall learning progress and also considers emotional data from the emotion engine. For example, if an operator is lacking in a specific learning item (e.g., robot assembly) and is experiencing stress during learning, the server takes this into account when creating a learning plan. This plan may include specific study time for assembly reinforcement and relaxation techniques.

[0399] The generated learning plan is delivered from the server to the user's terminal, which then displays it to the user. The user can proceed with their learning based on the presented learning plan, and their emotional state is monitored in real time throughout the process, with appropriate feedback provided.

[0400] Examples of prompts for a generative AI model:

[0401] If the operator feels anxious while operating the robotic arm, generate and display feedback that provides relaxation techniques and specific operating procedures.

[0402] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each operator with an optimal learning experience, and as a result, both improved efficiency in skill acquisition and a sense of psychological security can be expected.

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The server collects students' learning history and test results from a database. As input, it retrieves students' past learning data and test scores. As output, the collected data is prepared for analysis.

[0406] Step 2:

[0407] The server analyzes the collected data and evaluates each student's level of understanding. It uses learning history and test score data as input. For data processing, it uses machine learning algorithms to evaluate students' understanding. The output is the understanding evaluation result for each student.

[0408] Step 3:

[0409] The server generates individual learning materials based on evaluation results. It uses student comprehension assessment results as input. As data processing, it applies a material generation algorithm to create materials appropriate to the student's level of comprehension. Individual learning materials are generated as output.

[0410] Step 4:

[0411] The server distributes the generated learning materials to the students' devices. It handles the individually generated learning materials as input. The learning materials are sent to the students' devices as output.

[0412] Step 5:

[0413] The device monitors data in real time as the user engages in learning activities. It captures user learning behavior data (e.g., answer speed, correct / incorrect answers, facial expressions, voice) as input. For data processing, it collects and processes this behavioral data at high frequency. The output is the monitoring data.

[0414] Step 6:

[0415] The device sends monitoring data to the emotion engine, which generates emotion data. Facial expression and voice data are used as input. As data processing, the emotion engine analyzes this data to estimate the real-time emotional state. Emotion data is obtained as output.

[0416] Step 7:

[0417] The server generates real-time feedback based on monitoring data and sentiment data. Sentiment data and learned behavior data are used as input. A feedback generation algorithm is applied as data processing to create appropriate feedback. Feedback data is obtained as output.

[0418] Step 8:

[0419] The device provides the generated feedback to the student. It uses feedback data as input. The feedback message is displayed on the device's screen as output.

[0420] Step 9:

[0421] The server periodically analyzes students' learning progress data and generates an optimal learning plan. It uses learning history and progress data as input. As data processing, it applies a progress analysis algorithm to create the learning plan. The output is the optimal learning plan.

[0422] Step 10:

[0423] The server provides the generated study plan to the student's device. The generated study plan is used as input. The study plan is sent to the student's device as output.

[0424] Step 11:

[0425] The device analyzes the user's facial expressions and voice in real time and generates emotion data. It uses student facial expression and voice data as input. The emotion engine analyzes this data for processing. The output is emotion data.

[0426] Step 12:

[0427] The device generates feedback to reduce stress and anxiety based on emotional data. Emotional data is used as input. A feedback generation algorithm creates feedback tailored to the emotional state as data processing. A feedback message is obtained as output.

[0428] Step 13:

[0429] The terminal displays the generated feedback on the smart device's display. It uses a feedback message as input. The feedback is displayed in the user's field of view as output.

[0430] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0431] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0432] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0433] [Second Embodiment]

[0434] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0435] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0436] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0437] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0438] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0439] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0440] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0441] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0442] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0443] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0444] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0445] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0446] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[0447] Creating individualized learning materials for students

[0448] The server collects students' learning history and test results and stores them in a database. Next, the server analyzes the collected data to identify each student's level of understanding, learning strengths, and weaknesses. For example, if past test results for student A reveal a low level of understanding of calculus, the server generates personalized learning materials based on that data. These materials cover calculus from basic to advanced levels in a step-by-step manner.

[0449] The generated individual learning materials are delivered from the server to student A's terminal. The terminal displays these materials to the user (student A) and provides an interface for starting the learning process.

[0450] Real-time feedback

[0451] While the user (student) is engaged in learning activities, the device monitors their activities in real time. Specifically, it records the time the student spends viewing learning materials, the speed at which they solve problems, and correct / incorrect answer data. The monitoring data is immediately sent to the server.

[0452] The server analyzes the received data and generates real-time feedback quickly. For example, if student B enters an incorrect answer in an online quiz, the server generates feedback such as, "This problem may lack the concept of factorization. Please refer to the link below," and sends it to the device. The device displays this feedback to student B and provides advice and resources to help them move forward.

[0453] Providing learning plans based on each student's learning progress

[0454] The server continuously collects and analyzes daily learning activities and test results to understand students' overall learning progress. For example, if student C's learning history reveals that they have a good understanding of English grammar but lack listening skills, the server will propose a learning plan focused on strengthening English listening skills. This plan would include specific details such as listening practice at least three times a week.

[0455] The generated study plan is delivered from the server to student C's terminal, which then displays it to the user (student C). This study plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[0456] Specific example

[0457] For example, consider a case where student D is struggling to remember events from a specific historical period. In this case, the server identifies this trend from the collected data and generates personalized learning materials focused on that particular period. Simultaneously, it also generates quiz-style practice questions and mini-tests to check understanding. These materials are provided via the terminal, and the user (student D) works through them. If mistakes are made during learning, real-time feedback is provided, along with resources and advice for further learning.

[0458] Furthermore, through regular progress analysis, the server determines that student E's understanding of geometry has improved and that a review of algebra is necessary as the next topic to address. The server generates a learning plan focused on algebra, which is then provided to student E via their terminal. This ensures effective and systematic learning.

[0459] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each student with an optimal learning experience, and as a result, an improvement in learning outcomes can be expected.

[0460] The following describes the processing flow.

[0461] Creating individualized learning materials for students (1)

[0462] Step 1:

[0463] The device records students' learning history and test results daily and periodically sends this data to a server.

[0464] Step 2:

[0465] The server stores the received data in a database. This includes learning history, test results, and comprehension trends.

[0466] Step 3:

[0467] The server uses machine learning algorithms for data analysis to identify each student's level of understanding, learning strengths, and weaknesses.

[0468] Step 4:

[0469] Based on the analysis results, the server generates personalized learning materials tailored to each student. These materials include text, images, videos, and quiz-style questions.

[0470] Step 5:

[0471] The server distributes the generated individual learning materials to the students' devices.

[0472] Step 6:

[0473] The terminal displays the received individual learning materials to the user (student) and provides an interface for starting learning.

[0474] Real-time feedback (2)

[0475] Step 1:

[0476] The device monitors the user's (student's) learning activities in real time. Specifically, it records the speed at which problems are solved, correct / incorrect answer data, and time spent viewing learning materials.

[0477] Step 2:

[0478] The terminal sends monitoring data to the server in real time.

[0479] Step 3:

[0480] The server analyzes the received data and generates real-time feedback. This feedback includes the accuracy rate, reasons for incorrect answers, and advice for improving understanding.

[0481] Step 4:

[0482] The server immediately sends the generated feedback to the terminal.

[0483] Step 5:

[0484] The device displays the received feedback to the user (student) and provides additional learning resources and links.

[0485] (3) Providing learning plans based on each student's learning progress

[0486] Step 1:

[0487] The device continuously records the user's (student's) learning activities and test results, and periodically sends them to the server.

[0488] Step 2:

[0489] The server analyzes each student's learning progress based on the received data. Indicators include comprehension level, assignment completion status, and allocation of study time.

[0490] Step 3:

[0491] The server generates an optimal learning plan for each student based on the analysis results. This plan includes the next learning materials, review topics, and time allocation for each study session.

[0492] Step 4:

[0493] The server sends the generated training plan to the terminal.

[0494] Step 5:

[0495] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[0496] The above outlines the specific operational details for each processing step. This system enables the provision of effective learning support tailored to the individual needs of each student.

[0497] (Example 1)

[0498] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0499] Traditional education systems do not adequately provide individualized learning support based on each student's level of understanding and learning progress. This can lead to decreased student learning efficiency, and some students may fall behind in areas they struggle with. Furthermore, the lack of real-time feedback means students may leave misunderstandings unaddressed, resulting in reduced learning effectiveness.

[0500] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0501] In this invention, the server includes means for generating individual learning materials using a generative AI model, means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for distributing the generated learning materials to students' terminals, means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to students' terminals, means for analyzing students' learning progress data and generating an optimal learning plan using a generative AI model, and means for providing the generated learning plan to students' terminals. This enables the provision of learning materials and learning plans optimized for each student, as well as real-time feedback.

[0502] "Means for collecting students' learning history and test results" refers to a system for collecting data on all learning activities and test results that students have undertaken to date.

[0503] "Methods for analyzing collected data and evaluating each student's level of understanding" refers to algorithms and technologies for analyzing collected learning data and evaluating each student's level of learning comprehension.

[0504] "Means for generating individualized learning materials" refers to software or a system for creating learning materials that are optimal for each student based on their level of learning comprehension.

[0505] "Methods for generating individual learning materials using generative AI models" refers to technologies that use artificial intelligence (AI) to automatically generate learning materials tailored to individual students.

[0506] "Means for distributing generated learning materials to students' devices" refers to a communication system for sending generated learning materials from a server to students' learning devices.

[0507] "Means of monitoring data on students' learning activities in real time" refers to software and sensor technologies that allow for immediate monitoring of what students are doing while learning.

[0508] "Means for generating real-time feedback based on monitoring data" refers to an algorithm that quickly generates appropriate feedback for students based on collected monitoring data.

[0509] "Means for providing generated feedback to students' devices" refers to communication means for displaying real-time generated feedback on students' learning devices.

[0510] "A means of analyzing students' learning progress data and generating an optimal learning plan using a generative AI model" refers to a technology that analyzes students' learning progress and uses an AI model to formulate an optimal learning plan.

[0511] "Means for providing the generated learning plan to students' devices" refers to a communication system that displays the generated learning plan on students' learning devices and encourages them to implement it.

[0512] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[0513] Collection of students' learning history and test results

[0514] The server can collect learning history and test result data from students' learning platforms through specific APIs. This data includes grades for each subject, test results, and the time spent on each problem. The collected data is stored in an SQL database (e.g., MySQL).

[0515] Data analysis and assessment of understanding

[0516] The server uses Python to retrieve data from the database and analyzes it using machine learning libraries (e.g., Scikit-learn). Specifically, it uses a clustering algorithm to cluster students' levels of understanding. Based on these analysis results, it evaluates each student's level of understanding, learning strengths, and weaknesses.

[0517] Generation of individualized learning materials

[0518] The server generates learning materials for each student using a generative AI model (e.g., GPT-3). Specifically, it uses prompt statements to instruct the AI ​​model and create materials that correspond to a specific level of understanding. For example, it uses prompt statements like the following:

[0519] "Student A has a poor understanding of calculus. Please create individualized learning materials that cover everything from the basics to advanced topics."

[0520] Distribution of educational materials and provision of interfaces

[0521] The server delivers the generated learning materials to the students' devices using a specific communication protocol (e.g., HTTP). The devices provide an interface that allows the user (student) to view the received materials via a web browser or a dedicated app.

[0522] Monitoring of learning activities

[0523] The device tracks the user's (student's) actions in real time while they are viewing learning materials (e.g., visit time, clicks, answer time) using JavaScript, etc. The collected data is immediately sent to the server.

[0524] Providing real-time feedback

[0525] The server analyzes the received learning activity data and generates real-time feedback using a generative AI model. For example, it generates feedback as follows:

[0526] "If student B answers the quiz incorrectly, the message will read: 'Understanding factorization is necessary. Please refer to the following link.'"

[0527] The server sends the generated feedback to the terminal, and the terminal displays it to the user (student).

[0528] Proposal for a study plan

[0529] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan. For example, it might suggest a plan like this:

[0530] "Student C lacks listening comprehension skills, therefore, we propose a study plan that includes listening practice at least three times a week."

[0531] The generated learning plan is delivered from the server to the student's device, and the device displays it to the user (student).

[0532] Specific example

[0533] If student D is struggling to remember a particular period in history class, the server analyzes all of student D's test results and generates personalized learning materials focused on that specific time period. The terminal immediately displays these materials, and student D uses them to continue their learning.

[0534] If student E answers a specific math problem (e.g., geometry) incorrectly during a lesson, the device immediately sends the data to the server. The server analyzes this data, generates specific feedback such as "This problem was caused by a lack of understanding of factorization," and sends it back to the device. The device then displays this feedback to student E.

[0535] This invention provides each student with learning materials and learning plans optimized for them, and enables real-time feedback. This is expected to maximize students' learning efficiency and results.

[0536] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0537] Step 1:

[0538] The server collects students' learning history and test results from the learning platform. Specifically, it retrieves each student's performance data and test results through a particular API. The collected data is stored in an SQL database (e.g., MySQL).

[0539] Input: Student learning history and test results obtained from the learning platform.

[0540] Output: Learning history and test result data stored in the database

[0541] Step 2:

[0542] The server analyzes the collected data using Python and machine learning libraries (e.g., Scikit-learn). Specifically, it applies clustering algorithms to identify students' comprehension levels, learning strengths, and weaknesses.

[0543] Input: Learning history and test result data retrieved from the database

[0544] Output: Analysis results including each student's level of understanding, strengths, and weaknesses.

[0545] Step 3:

[0546] The server uses a generative AI model (e.g., GPT-3) to generate optimal learning materials for each student. Requests are sent to the AI ​​model using prompts, automatically generating materials corresponding to specific levels of understanding.

[0547] Input: Student-specific comprehension data and generated AI model

[0548] Output: Generated individual learning materials

[0549] Specific action: The prompt message used is "Student A has a low understanding of calculus. Please generate individualized learning materials that cover everything from basic to advanced levels."

[0550] Step 4:

[0551] The server delivers the generated learning materials to students' devices using a specific communication protocol (e.g., HTTP). The generated materials are delivered in web page or application format.

[0552] Input: Generated individual learning materials and student device information

[0553] Output: Learning materials delivered to students' devices

[0554] Step 5:

[0555] The device monitors user (student) behavioral data in real time using JavaScript and other technologies. This includes behavioral information such as viewing time, clicks, and answering time for learning materials.

[0556] Input: Student learning activity data

[0557] Output: Learning activity data collected in real time

[0558] Step 6:

[0559] The device immediately sends the collected learning activity data to the server. Monitoring data is transmitted securely using a communication protocol.

[0560] Input: Learning activity data collected in real time

[0561] Output: Monitoring data sent to the server

[0562] Step 7:

[0563] The server analyzes monitoring data and uses a generative AI model to generate real-time feedback. For example, it might generate specific feedback such as "You lack understanding of factorization" and present it to the user.

[0564] Input: Monitoring data and generated AI models

[0565] Output: Generated real-time feedback

[0566] Step 8:

[0567] The server sends the generated feedback to the terminal, which then displays it to the user (student). The user receives the feedback and can get guidance on how to proceed to the next step.

[0568] Input: Generated real-time feedback

[0569] Output: Feedback displayed on the student's device

[0570] Step 9:

[0571] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan for each student. Specifically, it creates a learning plan that includes the next learning materials and review content to be tackled.

[0572] Input: Learning progress data and generative AI model

[0573] Output: Generated training plan

[0574] Step 10:

[0575] The server delivers the generated study plan to the student's device, and the device displays it to the user (student). The user can then use the study plan as a reference to effectively progress with their studies.

[0576] Input: Generated training plan

[0577] Output: Learning plan displayed on the student's device

[0578] (Application Example 1)

[0579] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0580] In today's industrial environment, there is a demand for improving employees' technical skills and optimizing operational efficiency. Acquiring skills in robot operation and machine maintenance, particularly in factories, is crucial, but addressing individual learning needs is challenging. Furthermore, the inability of employees to receive appropriate real-time feedback during their learning process leads to decreased learning efficiency, which is a significant problem.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0582] In this invention, the server includes means for collecting students' learning history and test results; means for analyzing the collected data and evaluating each student's level of understanding; means for generating individual learning materials based on the evaluation results; means for distributing the generated learning materials to the students' terminals; means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data; means for providing the generated feedback to the students' terminals; means for analyzing students' learning progress data and generating an optimal learning plan; means for providing the generated learning plan to the students' terminals; means for providing additional resources and explanations according to the user's progress; and means for supplying manuals and video materials necessary for performing the target tasks. This makes it possible for employees to improve their learning efficiency and to provide a comprehensive training environment that meets individual learning needs.

[0583] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[0584] "Test results" refer to the evaluations, test scores, and responses received by the user.

[0585] "Understanding level" indicates the degree to which a user has acquired a particular knowledge or skill.

[0586] "Individualized learning materials" refer to learning materials optimized for a specific user, taking into account their learning history and level of understanding.

[0587] A "terminal" is an electronic device used for displaying learning materials and receiving feedback.

[0588] "Real-time monitoring" means instantly observing and recording a user's actions and reactions while they are engaged in learning activities.

[0589] "Feedback" refers to providing evaluations and advice regarding a user's learning activities.

[0590] A "learning plan" is a schedule and list of learning materials designed to help users learn efficiently.

[0591] "Additional resources" refer to additional learning materials and information provided to assist users in their learning.

[0592] A "procedure manual" is a document that describes the steps required to perform a specific task or operation.

[0593] "Video teaching materials" refer to learning materials in video format that provide information visually.

[0594] This invention is a learning support system for robot operation techniques and machine maintenance in factories. This system mainly consists of a server, terminals, and users (employees).

[0595] Creating individualized training materials for employees

[0596] The server collects employees' learning history and various evaluation results and stores them in a database. Next, the server analyzes the collected data to identify each employee's level of understanding, learning strengths, and weaknesses. For example, if past evaluation results for employee A reveal a low level of understanding of machine maintenance, the server generates personalized learning materials based on that data. These materials cover maintenance from basic to advanced techniques in a step-by-step manner. The generated personalized materials are delivered from the server to employee A's terminal. The terminal (e.g., smart glasses) displays these materials to the user (employee A) and provides an interface for starting the learning process.

[0597] Real-time feedback

[0598] While a user (employee) is engaged in learning activities, the terminal monitors their activities in real time. Specifically, it records the time the employee spends viewing learning materials, the speed at which they complete tasks, and correct / incorrect data. The monitoring data is immediately sent to the server. The server analyzes the received data and quickly generates real-time feedback. For example, if employee B enters an incorrect answer in an online quiz, the server generates feedback such as, "This question may indicate a lack of understanding of the mechanism. Please refer to the link below," and sends it to the terminal. The terminal displays this feedback to employee B and provides advice and resources for taking the next step.

[0599] Providing learning plans based on each employee's learning progress.

[0600] The server continuously collects and analyzes daily learning activities and evaluation results to understand the overall learning progress of employees. For example, if employee C's learning history reveals that they have made progress in understanding robot operation but lack troubleshooting skills, the server will propose a learning plan focused on strengthening troubleshooting. This plan will include specific troubleshooting exercises at least three times a week. The generated learning plan is delivered from the server to employee C's terminal, which then displays it to the user (employee C). This learning plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[0601] Providing learning resources using the Gen AI model

[0602] The server also provides additional resources and explanations depending on the employee's progress. For example, if employee D makes a mistake in the procedure, the server uses a generative AI model to instantly generate and provide easy-to-understand procedural explanations and relevant video materials. An example of a prompt for this purpose is as follows:

[0603] Examples of input prompts for a generative AI model

[0604] User history:

[0605] ID: 12345

[0606] Current skill level: Beginner

[0607] Learning material: Machine overhaul procedure

[0608] Progress: Steps 1 and 2 completed, stuck on step 3.

[0609] question:

[0610] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[0611] In this way, it is possible to provide a comprehensive training environment that meets individual learning needs.

[0612] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0613] Step 1:

[0614] The server collects employees' learning history and evaluation results.

[0615] Input: Employee's past learning data and evaluation data

[0616] Output: Collected learning history and evaluation data are saved to a database.

[0617] Specific operation: The server retrieves and organizes learning history and evaluation data related to employee IDs from the database.

[0618] Step 2:

[0619] The server analyzes the collected data and evaluates each employee's level of understanding.

[0620] Input: Collected learning history and evaluation data

[0621] Output: Comprehension evaluation results

[0622] Specific operation: The server analyzes the data using machine learning algorithms (e.g., linear regression and decision trees) and quantifies each employee's level of understanding.

[0623] Step 3:

[0624] The server generates individual learning materials based on the evaluation results.

[0625] Input: Comprehension assessment results

[0626] Output: Individual learning materials

[0627] Specific operation: The server selects appropriate learning materials from the content management system and configures them according to the employee's level of understanding. The learning materials are provided in various formats, such as text, images, and videos.

[0628] Step 4:

[0629] The server distributes the generated learning materials to employees' terminals.

[0630] Input: Individual learning materials

[0631] Output: Learning materials delivered to the device

[0632] Specific operation: The server uses network protocols (e.g., HTTP, WebSocket) to send educational data to the terminal and displays its contents on the terminal.

[0633] Step 5:

[0634] The terminal monitors data from employees' learning activities in real time and sends the monitored data to the server.

[0635] Input: User's learning activity data (viewing time, correctness of assignment answers, etc.)

[0636] Output: Monitoring data sent to the server

[0637] Specific operation: The terminal acquires learning activity data from sensors and input devices and sends it to the server in real time.

[0638] Step 6:

[0639] The server analyzes the received monitoring data and generates real-time feedback.

[0640] Input: Monitoring data

[0641] Output: Real-time feedback

[0642] Specific operation: Based on monitoring data, the server uses a generative AI model to generate appropriate feedback. For example, it immediately provides "how to correct incorrect procedures."

[0643] Step 7:

[0644] The device provides the generated feedback to employees.

[0645] Input: Real-time feedback

[0646] Output: Feedback displayed to employees

[0647] Specific operation: The terminal displays feedback received from the server on the screen and instructs the user on the appropriate next action. For example, "advise the user to check specific points before proceeding to the next step."

[0648] Step 8:

[0649] The server analyzes employee learning progress data and generates an optimal learning plan.

[0650] Input: Learning progress data

[0651] Output: Optimal learning plan

[0652] Specific operation: The server analyzes the employee's learning history and current learning progress, and generates a learning plan that suggests the optimal schedule and learning materials to achieve specific learning goals.

[0653] Step 9:

[0654] The server provides the generated learning plan to the employee's terminal.

[0655] Input: Learning plan

[0656] Output: Learning plan provided to the device

[0657] Specific operation: The server sends a learning plan to the terminal via the network, and the terminal displays it to the user. The plan includes the next learning materials, review topics, and allocation of study time.

[0658] Step 10:

[0659] The server provides additional resources and explanations based on the employee's progress.

[0660] Input: Employee progress status, generated AI model

[0661] Output: Additional resources and explanations

[0662] Specific operation: The server prompts the generated AI model with a prompt message, generates appropriate resources (e.g., video materials or explanatory text), sends them to the terminal, and provides them to the user.

[0663] Examples of input prompts for a generative AI model

[0664] User history:

[0665] ID: 12345

[0666] Current skill level: Beginner

[0667] Learning material: Machine overhaul procedure

[0668] Progress: Steps 1 and 2 completed, stuck on step 3.

[0669] question:

[0670] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[0671] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0672] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[0673] Creating individualized learning materials for students

[0674] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data on the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotional data. For example, if the emotion engine detects that student A has an insufficient understanding of calculus based on past test results and is experiencing anxiety while studying, the server generates personalized learning materials based on this. These materials cover calculus from basic to advanced levels in a step-by-step manner and also include mental support content to alleviate anxiety.

[0675] The generated individual learning materials are delivered from the server to student A's device, and the device displays them to the user (student A). In this way, learning is supported according to each individual's level of understanding and emotional state.

[0676] Real-time feedback

[0677] While the user (student) is engaged in learning activities, the device monitors the activity in real time and transmits data such as the speed at which problems are solved, correct / incorrect answers, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state. For example, when user (student B) is working on an online quiz, the emotion engine analyzes in real time whether they are focused or hesitant.

[0678] The server analyzes the received data and emotional state to generate real-time feedback. For example, if student B is feeling anxious about a problem, the server generates feedback such as "Relax and tackle the problem. Please refer to the following link," and sends it to the device. The device then displays this feedback to the user (student B) and provides any necessary additional resources or advice.

[0679] Providing learning plans based on each student's learning progress

[0680] The server periodically analyzes students' overall learning progress and also considers emotional data from the emotion engine. For example, if student C is lacking in a specific learning area (e.g., English listening skills) and is experiencing stress during learning, the server will take this into account when creating a learning plan. This plan will include specific study time for improving listening skills and relaxation techniques.

[0681] The generated learning plan is delivered from the server to student C's terminal, which then displays it to the user (student C). Student C can proceed with their studies based on the presented learning plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[0682] Specific example

[0683] For example, consider a case where student D is struggling to remember events from a specific historical period and is experiencing stress. In this case, the server takes into account the stress level detected by the emotion engine and generates personalized learning materials focused on that specific period. These materials would also include tips for reducing stress and advice on creating a more relaxed learning environment.

[0684] Furthermore, based on regular progress analysis and emotional data, the server determines that student E needs to review algebra and receive emotional support as their next focus. The server generates a learning plan that focuses on algebra while also providing emotional follow-up, and provides this plan to student E via their device. This enables planned learning that balances learning efficiency with mental support.

[0685] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each student with an optimal learning experience, and as a result, both improved learning outcomes and a sense of psychological security can be expected.

[0686] The following describes the processing flow.

[0687] Creating individualized learning materials for students

[0688] Step 1:

[0689] The device collects students' learning history, test results, and data on their facial expressions and voice, and periodically sends this information to a server.

[0690] Step 2:

[0691] The server saves the received data to the database.

[0692] Step 3:

[0693] The server uses machine learning algorithms for data analysis to identify each student's level of understanding, learning strengths, and weaknesses.

[0694] Step 4:

[0695] The server also takes into account the results of the emotion engine's analysis and performs analysis tailored to the student's emotional state. For example, if the student is experiencing feelings of anxiety or stress, it will consider measures to alleviate them.

[0696] Step 5:

[0697] Based on the analysis results, the server generates personalized learning materials tailored to each student. These materials include text, images, videos, quiz-style questions, and emotional support content.

[0698] Step 6:

[0699] The server distributes the generated individual learning materials to the students' devices.

[0700] Step 7:

[0701] The terminal displays the received individual learning materials to the user (student) and provides an interface for starting learning.

[0702] Real-time feedback

[0703] Step 1:

[0704] The device monitors students' learning activities in real time, recording their problem-solving speed, correct / incorrect answer data, facial expressions, and tone of voice.

[0705] Step 2:

[0706] The terminal sends monitoring data to the server in real time.

[0707] Step 3:

[0708] The emotion engine analyzes data transmitted from the device to identify the student's emotional state.

[0709] Step 4:

[0710] The server analyzes the emotional data and training data received from the emotion engine to generate real-time feedback. For example, if a student is feeling anxious about a problem, it might generate feedback such as, "Relax and tackle the problem. Please refer to the link below."

[0711] Step 5:

[0712] The server sends the generated feedback to the terminal.

[0713] Step 6:

[0714] The device displays the received feedback to the user (student) and provides additional learning resources and advice.

[0715] Providing a learning plan based on learning progress

[0716] Step 1:

[0717] The device continuously records students' learning activities, test results, and emotional data, and periodically sends this data to a server.

[0718] Step 2:

[0719] The server analyzes each student's learning progress based on the received data. This analysis includes comprehension level, assignment completion status, study time allocation, and emotional state.

[0720] Step 3:

[0721] The server generates an optimal learning plan for each student based on analysis results and emotional data. For example, if a student experiences stress in a particular subject, the server will suggest a plan that includes strategies for dealing with that stress and methods for relaxation.

[0722] Step 4:

[0723] The server sends the generated training plan to the terminal.

[0724] Step 5:

[0725] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[0726] Step 6:

[0727] The device monitors the progress of the learning plan in real time and makes corrections and provides feedback as needed.

[0728] The above outlines the specific processing steps of the system that combines emotional engines. This system makes it possible to simultaneously provide effective learning support and mental support tailored to the individual needs of each student.

[0729] (Example 2)

[0730] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0731] Traditional education systems have struggled to provide individualized support that takes into account not only each student's level of understanding and progress, but also their emotional state. As a result, students have difficulty maintaining motivation for learning and maximizing their learning effectiveness. Furthermore, there has been a lack of technology to provide real-time, emotion-based feedback.

[0732] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for collecting students' facial expressions and voice data and generating emotional data using an emotion engine, and means for generating individual learning materials based on the evaluation results and emotional data. This makes it possible to provide optimal educational support tailored to each student's level of understanding and emotional state.

[0733] "Learning history" refers to a record of learning activities that a student has undertaken in the past.

[0734] "Test results" refer to data showing the grades and answers students received on exams they took.

[0735] "Means of collection" refers to the devices and methods used to collect the target data.

[0736] "Means of analysis" refer to devices and methods for analyzing collected data and extracting meaningful information.

[0737] "Comprehension level" is an indicator that shows how well students understand the learning material.

[0738] "Facial expression and voice data" refers to data that includes facial expressions and vocal characteristics to indicate the emotional state of students.

[0739] An "emotion engine" is a technology or system that analyzes data from students' facial expressions and voices to identify their emotional state.

[0740] "Emotional data" refers to data generated by the emotion engine that represents the emotional state of students.

[0741] "Individualized learning materials" are materials that are optimized for a specific student, generated based on each student's level of understanding and emotional state.

[0742] "Means of distribution" refers to devices and methods for sending generated learning materials and feedback to students' devices.

[0743] "Means of monitoring" refer to devices and methods for observing students' learning activities in real time and collecting data.

[0744] "Feedback" refers to advice and suggestions for improvement given to students based on their progress and results in learning activities.

[0745] "Learning progress data" refers to data that shows the progress of students' learning activities.

[0746] A "learning plan" is a plan that outlines future learning content and schedules, created based on students' learning progress data and emotional data.

[0747] This invention is a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding, progress, and emotional state. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[0748] First, the server collects students' learning history and test results and stores them in a database. This learning history and test results are retrieved from the Learning Management System (LMS). For example, this includes test results and study time logs from the past week. This allows for accurate tracking of each student's learning history and achievements.

[0749] Next, the device collects data on the student's facial expressions and voice using sensors (camera and microphone) and sends it to the emotion engine. The emotion engine analyzes this data to generate emotion data. For example, if a student shows an anxious expression through the camera during a test, the emotion engine will analyze it. The collected data is used in real time to quantify the emotional state.

[0750] The server integrates and analyzes collected learning history, test results, and emotional data to generate learning materials optimized for each student. Using a generative AI model, it creates easy-to-understand learning materials and mental support content. For example, for a student who has insufficient understanding of calculus and feels anxious, the server will generate materials covering everything from basic to advanced levels, as well as mental support content to alleviate their anxiety. This provides education tailored to individual needs.

[0751] The generated individual learning materials are delivered from the server to the students' devices, and the devices display the received materials to the users (students). For example, if learning materials about history are generated, they will be displayed on the student's device screen. This allows students to concentrate on their own learning.

[0752] While students are engaged in learning activities, the device monitors their activities in real time and transmits data on their facial expressions, voice, and learning progress to the emotion engine. The emotion engine analyzes this data to identify the student's current emotional state. For example, while a student is taking an online quiz, their answer speed, accuracy data, facial expressions, and voice tone are transmitted and analyzed.

[0753] The server receives real-time data from the emotion engine and generates real-time feedback based on the emotional state. The generated feedback is sent to the device and displayed to the user (student). For example, feedback such as "Relax and tackle the problem. Please refer to the link below." might be displayed, allowing the student to instantly obtain any necessary additional resources or advice.

[0754] Furthermore, the server periodically analyzes students' learning progress and emotional data, and generates the next learning plan based on this analysis. The generated learning plan is delivered from the server to the student's device, which then displays it to the user (student). Students can then proceed with their learning based on this plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[0755] For example, if a student is experiencing stress while studying history, the server will consider the stress level detected by the emotion engine and generate personalized learning materials focusing on a specific historical period. These materials will also include tips and relaxation techniques to reduce stress. Similarly, if a student is feeling anxious about a math problem, the server will generate real-time feedback addressing that anxiety and display a message such as, "Try to relax."

[0756] Examples of prompt messages include the following:

[0757] "Generate individualized learning materials based on students' recent learning history and emotional state. Focus particularly on calculus in mathematics, and include mental support content to reduce anxiety."

[0758] "Analyze students' emotional states from their facial expressions and audio data while they are participating in online quizzes, and provide real-time feedback as needed."

[0759] "Based on students' overall learning progress and emotional data, create a specific learning plan to improve their English listening skills and distribute that plan to their devices."

[0760] As described above, the system of the present invention makes it possible to provide an optimal learning experience for each student, and both improved learning outcomes and a sense of psychological security can be expected.

[0761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0762] Step 1: Collect learning history and test results

[0763] The server collects student learning history and test results from the Learning Management System (LMS). Specifically, it retrieves test results and study time logs from students taken over the past week. The input is student learning data obtained through the LMS API, and the output is learning history and test results stored in the database. Here, API requests are sent, and the data is parsed and saved in JSON format.

[0764] Step 2: Collecting emotional data

[0765] The device collects data on students' facial expressions and voices using a camera and microphone and sends it to an emotion engine. Specifically, the video and audio data captured by the sensors is processed frame by frame and sent to the emotion engine. The input is data on students' facial expressions and voices obtained from the camera and microphone, and the output is emotion data generated by the emotion engine. An emotion analysis algorithm is used to quantify emotional states such as "anxiety" and "concentration."

[0766] Step 3: Data analysis and creation of individualized learning materials

[0767] The server integrates and analyzes collected learning history, test results, and emotional data. Inputs include learning history and test results stored in a database, as well as emotional data sent from the emotion engine. This data is fed into a generative AI model to generate learning materials optimized for each individual student. The output is the generated, personalized learning material. Specifically, the generative AI model automatically generates the material content, creating customized materials based on the student's level of understanding and emotional state.

[0768] Step 4: Delivery and display of individual learning materials

[0769] The server distributes the generated individual learning materials to the students' devices. The input is the individual learning materials generated by the generation AI model, and the output is the distribution of the materials to the devices. The devices display the received materials to the users (students). Specifically, the process involves sending the learning material data to the device and displaying the materials on the device's screen.

[0770] Step 5: Real-time monitoring of learning activities

[0771] The device monitors students' learning activities in real time while they are engaged in them. Inputs include student learning progress, problem-solving speed, correct / incorrect answer data, facial expressions, and voice data, which are sent to the emotion engine. Output is real-time emotion data. Specifically, it sends activity data to the emotion engine, which then analyzes the emotional state in real time.

[0772] Step 6: Real-time feedback generation

[0773] The server analyzes the user's emotional state based on real-time data received from the emotion engine and generates appropriate feedback. The input is real-time emotional data and student learning progress data, while the output is the generated feedback. Specifically, it automatically generates advice such as "Relax and tackle the problem" and sends it to the device.

[0774] Step 7: Regular analysis of learning progress and provision of learning plans

[0775] The server periodically analyzes students' learning progress and sentiment data to generate the next learning plan. The input is past learning progress and sentiment data stored in a database, and the output is the learning plan provided to the student. Specifically, it generates a learning progress report, plans the next learning steps based on it, and delivers it to the terminal. Students can then proceed with their learning based on the presented learning plan.

[0776] (Application Example 2)

[0777] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0778] Traditional education systems struggle to provide not only individualized learning materials tailored to students' progress and understanding, but also real-time feedback that takes into account students' psychological state and emotions. Furthermore, they often lack support to reduce stress and anxiety associated with learning and maximize learning effectiveness. Especially when acquiring specialized skills such as those of a factory operator, psychological state significantly impacts skill acquisition, highlighting the need for a system that can comprehensively manage these factors.

[0779] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for generating individual learning materials based on the evaluation results, means for distributing the generated learning materials to the student's terminal, means for monitoring data in real time when students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to the student's terminal, means for analyzing students' learning progress data and generating an optimal learning plan, means for providing the generated learning plan to the student's terminal, means for analyzing the subject's facial expressions and voice in real time and generating emotional data, means for generating feedback that reduces stress and anxiety based on the emotional data, and means for displaying the generated feedback on the display of a smart device. This makes it possible to provide individualized learning support based on each student's level of understanding and psychological state, enabling effective learning while reducing stress and anxiety associated with skill acquisition.

[0780] "Learning history" refers to data that records what a student has learned in the past and their level of achievement.

[0781] "Test results" refer to data showing students' grades and answers when they took a test.

[0782] "Comprehension level" refers to an assessment that indicates how well a student understands a particular learning topic.

[0783] "Individualized learning materials" refer to learning materials that are created based on each student's learning progress and level of understanding.

[0784] "Real-time monitoring" is the process of collecting and analyzing activity data while students are engaged in learning activities.

[0785] "Feedback" refers to advice and support messages generated based on a student's learning activities and psychological state.

[0786] "Learning progress data" refers to data that shows how well students are progressing according to their learning plan.

[0787] A "study plan" refers to the specific learning content and timetable formulated to help each student achieve their learning goals.

[0788] "Facial expression" refers to the emotions and psychological state that can be interpreted from the movement of facial muscles and facial features.

[0789] "Voice" refers to the audio data produced when speaking, and its tone and changes are used to interpret emotional states.

[0790] "Emotional data" refers to data that indicates the emotional state of students, analyzed from their facial expressions and voices.

[0791] A "smart device" is a portable electronic device that has internet connectivity and high-performance computing capabilities.

[0792] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[0793] Creating individualized learning materials for students

[0794] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data of the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotion data. This emotion data is used to understand changes in emotions that occur during the learning process in real time.

[0795] For example, when an operator is learning how to operate a new robotic arm, the camera analyzes the operator's facial expressions in real time and provides relaxation techniques and specific tips if stress or anxiety is detected. Furthermore, if the operator stumbles at a particular step, detailed explanations and video tutorials are displayed on the smart device's screen.

[0796] Real-time feedback

[0797] While the user (student) is engaged in learning activities, the device monitors their activities in real time and sends data such as the speed at which they solve problems, correctness data, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state.

[0798] For example, when a user (operator) is learning how to operate a new robot online, the emotion engine analyzes in real time whether they are focused or hesitant. The server analyzes the received data and emotional state and generates real-time feedback. For instance, if the operator is feeling anxious about a problem, the server generates feedback such as, "Please relax and learn. Please refer to the following link," and sends it to the terminal.

[0799] Providing learning plans based on each student's learning progress

[0800] The server periodically analyzes the student's overall learning progress and also considers emotional data from the emotion engine. For example, if an operator is lacking in a specific learning item (e.g., robot assembly) and is experiencing stress during learning, the server takes this into account when creating a learning plan. This plan may include specific study time for assembly reinforcement and relaxation techniques.

[0801] The generated learning plan is delivered from the server to the user's terminal, which then displays it to the user. The user can proceed with their learning based on the presented learning plan, and their emotional state is monitored in real time throughout the process, with appropriate feedback provided.

[0802] Examples of prompts for a generative AI model:

[0803] If the operator feels anxious while operating the robotic arm, generate and display feedback that provides relaxation techniques and specific operating procedures.

[0804] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each operator with an optimal learning experience, and as a result, both improved efficiency in skill acquisition and a sense of psychological security can be expected.

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The server collects students' learning history and test results from a database. As input, it retrieves students' past learning data and test scores. As output, the collected data is prepared for analysis.

[0808] Step 2:

[0809] The server analyzes the collected data and evaluates each student's level of understanding. It uses learning history and test score data as input. For data processing, it uses machine learning algorithms to evaluate students' understanding. The output is the understanding evaluation result for each student.

[0810] Step 3:

[0811] The server generates individual learning materials based on evaluation results. It uses student comprehension assessment results as input. As data processing, it applies a material generation algorithm to create materials appropriate to the student's level of comprehension. Individual learning materials are generated as output.

[0812] Step 4:

[0813] The server distributes the generated learning materials to the students' devices. It handles the individually generated learning materials as input. The learning materials are sent to the students' devices as output.

[0814] Step 5:

[0815] The device monitors data in real time as the user engages in learning activities. It captures user learning behavior data (e.g., answer speed, correct / incorrect answers, facial expressions, voice) as input. For data processing, it collects and processes this behavioral data at high frequency. The output is the monitoring data.

[0816] Step 6:

[0817] The device sends monitoring data to the emotion engine, which generates emotion data. Facial expression and voice data are used as input. As data processing, the emotion engine analyzes this data to estimate the real-time emotional state. Emotion data is obtained as output.

[0818] Step 7:

[0819] The server generates real-time feedback based on monitoring data and sentiment data. Sentiment data and learned behavior data are used as input. A feedback generation algorithm is applied as data processing to create appropriate feedback. Feedback data is obtained as output.

[0820] Step 8:

[0821] The device provides the generated feedback to the student. It uses feedback data as input. The feedback message is displayed on the device's screen as output.

[0822] Step 9:

[0823] The server periodically analyzes students' learning progress data and generates an optimal learning plan. It uses learning history and progress data as input. As data processing, it applies a progress analysis algorithm to create the learning plan. The output is the optimal learning plan.

[0824] Step 10:

[0825] The server provides the generated study plan to the student's device. The generated study plan is used as input. The study plan is sent to the student's device as output.

[0826] Step 11:

[0827] The device analyzes the user's facial expressions and voice in real time and generates emotion data. It uses student facial expression and voice data as input. The emotion engine analyzes this data for processing. The output is emotion data.

[0828] Step 12:

[0829] The device generates feedback to reduce stress and anxiety based on emotional data. Emotional data is used as input. A feedback generation algorithm creates feedback tailored to the emotional state as data processing. A feedback message is obtained as output.

[0830] Step 13:

[0831] The terminal displays the generated feedback on the smart device's display. It uses a feedback message as input. The feedback is displayed in the user's field of view as output.

[0832] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0833] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0834] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0835] [Third Embodiment]

[0836] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0837] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0838] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0839] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0840] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0841] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0842] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0843] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0844] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0845] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0846] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0847] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0848] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[0849] Creating individualized learning materials for students

[0850] The server collects students' learning history and test results and stores them in a database. Next, the server analyzes the collected data to identify each student's level of understanding, learning strengths, and weaknesses. For example, if past test results for student A reveal a low level of understanding of calculus, the server generates personalized learning materials based on that data. These materials cover calculus from basic to advanced levels in a step-by-step manner.

[0851] The generated individual learning materials are delivered from the server to student A's terminal. The terminal displays these materials to the user (student A) and provides an interface for starting the learning process.

[0852] Real-time feedback

[0853] While the user (student) is engaged in learning activities, the device monitors their activities in real time. Specifically, it records the time the student spends viewing learning materials, the speed at which they solve problems, and correct / incorrect answer data. The monitoring data is immediately sent to the server.

[0854] The server analyzes the received data and generates real-time feedback quickly. For example, if student B enters an incorrect answer in an online quiz, the server generates feedback such as, "This problem may lack the concept of factorization. Please refer to the link below," and sends it to the device. The device displays this feedback to student B and provides advice and resources to help them move forward.

[0855] Providing learning plans based on each student's learning progress

[0856] The server continuously collects and analyzes daily learning activities and test results to understand students' overall learning progress. For example, if student C's learning history reveals that they have a good understanding of English grammar but lack listening skills, the server will propose a learning plan focused on strengthening English listening skills. This plan would include specific details such as listening practice at least three times a week.

[0857] The generated study plan is delivered from the server to student C's terminal, which then displays it to the user (student C). This study plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[0858] Specific example

[0859] For example, consider a case where student D is struggling to remember events from a specific historical period. In this case, the server identifies this trend from the collected data and generates personalized learning materials focused on that particular period. Simultaneously, it also generates quiz-style practice questions and mini-tests to check understanding. These materials are provided via the terminal, and the user (student D) works through them. If mistakes are made during learning, real-time feedback is provided, along with resources and advice for further learning.

[0860] Furthermore, through regular progress analysis, the server determines that student E's understanding of geometry has improved and that a review of algebra is necessary as the next topic to address. The server generates a learning plan focused on algebra, which is then provided to student E via their terminal. This ensures effective and systematic learning.

[0861] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each student with an optimal learning experience, and as a result, an improvement in learning outcomes can be expected.

[0862] The following describes the processing flow.

[0863] Creating individualized learning materials for students (1)

[0864] Step 1:

[0865] The device records students' learning history and test results daily and periodically sends this data to a server.

[0866] Step 2:

[0867] The server stores the received data in a database. This includes learning history, test results, and comprehension trends.

[0868] Step 3:

[0869] The server uses machine learning algorithms for data analysis to identify each student's level of understanding, learning strengths, and weaknesses.

[0870] Step 4:

[0871] Based on the analysis results, the server generates personalized learning materials tailored to each student. These materials include text, images, videos, and quiz-style questions.

[0872] Step 5:

[0873] The server distributes the generated individual learning materials to the students' devices.

[0874] Step 6:

[0875] The terminal displays the received individual learning materials to the user (student) and provides an interface for starting learning.

[0876] Real-time feedback (2)

[0877] Step 1:

[0878] The device monitors the user's (student's) learning activities in real time. Specifically, it records the speed at which problems are solved, correct / incorrect answer data, and time spent viewing learning materials.

[0879] Step 2:

[0880] The terminal sends monitoring data to the server in real time.

[0881] Step 3:

[0882] The server analyzes the received data and generates real-time feedback. This feedback includes the accuracy rate, reasons for incorrect answers, and advice for improving understanding.

[0883] Step 4:

[0884] The server immediately sends the generated feedback to the terminal.

[0885] Step 5:

[0886] The device displays the received feedback to the user (student) and provides additional learning resources and links.

[0887] (3) Providing learning plans based on each student's learning progress

[0888] Step 1:

[0889] The device continuously records the user's (student's) learning activities and test results, and periodically sends them to the server.

[0890] Step 2:

[0891] The server analyzes each student's learning progress based on the received data. Indicators include comprehension level, assignment completion status, and allocation of study time.

[0892] Step 3:

[0893] The server generates an optimal learning plan for each student based on the analysis results. This plan includes the next learning materials, review topics, and time allocation for each study session.

[0894] Step 4:

[0895] The server sends the generated training plan to the terminal.

[0896] Step 5:

[0897] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[0898] The above outlines the specific operational details for each processing step. This system enables the provision of effective learning support tailored to the individual needs of each student.

[0899] (Example 1)

[0900] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0901] Traditional education systems do not adequately provide individualized learning support based on each student's level of understanding and learning progress. This can lead to decreased student learning efficiency, and some students may fall behind in areas they struggle with. Furthermore, the lack of real-time feedback means students may leave misunderstandings unaddressed, resulting in reduced learning effectiveness.

[0902] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0903] In this invention, the server includes means for generating individual learning materials using a generative AI model, means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for distributing the generated learning materials to students' terminals, means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to students' terminals, means for analyzing students' learning progress data and generating an optimal learning plan using a generative AI model, and means for providing the generated learning plan to students' terminals. This enables the provision of learning materials and learning plans optimized for each student, as well as real-time feedback.

[0904] "Means for collecting students' learning history and test results" refers to a system for collecting data on all learning activities and test results that students have undertaken to date.

[0905] "Methods for analyzing collected data and evaluating each student's level of understanding" refers to algorithms and technologies for analyzing collected learning data and evaluating each student's level of learning comprehension.

[0906] "Means for generating individualized learning materials" refers to software or a system for creating learning materials that are optimal for each student based on their level of learning comprehension.

[0907] "Methods for generating individual learning materials using generative AI models" refers to technologies that use artificial intelligence (AI) to automatically generate learning materials tailored to individual students.

[0908] "Means for distributing generated learning materials to students' devices" refers to a communication system for sending generated learning materials from a server to students' learning devices.

[0909] "Means of monitoring data on students' learning activities in real time" refers to software and sensor technologies that allow for immediate monitoring of what students are doing while learning.

[0910] "Means for generating real-time feedback based on monitoring data" refers to an algorithm that quickly generates appropriate feedback for students based on collected monitoring data.

[0911] "Means for providing generated feedback to students' devices" refers to communication means for displaying real-time generated feedback on students' learning devices.

[0912] "A means of analyzing students' learning progress data and generating an optimal learning plan using a generative AI model" refers to a technology that analyzes students' learning progress and uses an AI model to formulate an optimal learning plan.

[0913] "Means for providing the generated learning plan to students' devices" refers to a communication system that displays the generated learning plan on students' learning devices and encourages them to implement it.

[0914] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[0915] Collection of students' learning history and test results

[0916] The server can collect learning history and test result data from students' learning platforms through specific APIs. This data includes grades for each subject, test results, and the time spent on each problem. The collected data is stored in an SQL database (e.g., MySQL).

[0917] Data analysis and assessment of understanding

[0918] The server uses Python to retrieve data from the database and analyzes it using machine learning libraries (e.g., Scikit-learn). Specifically, it uses a clustering algorithm to cluster students' levels of understanding. Based on these analysis results, it evaluates each student's level of understanding, learning strengths, and weaknesses.

[0919] Generation of individualized learning materials

[0920] The server generates learning materials for each student using a generative AI model (e.g., GPT-3). Specifically, it uses prompt statements to instruct the AI ​​model and create materials that correspond to a specific level of understanding. For example, it uses prompt statements like the following:

[0921] "Student A has a poor understanding of calculus. Please create individualized learning materials that cover everything from the basics to advanced topics."

[0922] Distribution of educational materials and provision of interfaces

[0923] The server delivers the generated learning materials to the students' devices using a specific communication protocol (e.g., HTTP). The devices provide an interface that allows the user (student) to view the received materials via a web browser or a dedicated app.

[0924] Monitoring of learning activities

[0925] The device tracks the user's (student's) actions in real time while they are viewing learning materials (e.g., visit time, clicks, answer time) using JavaScript, etc. The collected data is immediately sent to the server.

[0926] Providing real-time feedback

[0927] The server analyzes the received learning activity data and generates real-time feedback using a generative AI model. For example, it generates feedback as follows:

[0928] "If student B answers the quiz incorrectly, the message will read: 'Understanding factorization is necessary. Please refer to the following link.'"

[0929] The server sends the generated feedback to the terminal, and the terminal displays it to the user (student).

[0930] Proposal for a study plan

[0931] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan. For example, it might suggest a plan like this:

[0932] "Student C lacks listening comprehension skills, therefore, we propose a study plan that includes listening practice at least three times a week."

[0933] The generated learning plan is delivered from the server to the student's device, and the device displays it to the user (student).

[0934] Specific example

[0935] If student D is struggling to remember a particular period in history class, the server analyzes all of student D's test results and generates personalized learning materials focused on that specific time period. The terminal immediately displays these materials, and student D uses them to continue their learning.

[0936] If student E answers a specific math problem (e.g., geometry) incorrectly during a lesson, the device immediately sends the data to the server. The server analyzes this data, generates specific feedback such as "This problem was caused by a lack of understanding of factorization," and sends it back to the device. The device then displays this feedback to student E.

[0937] This invention provides each student with learning materials and learning plans optimized for them, and enables real-time feedback. This is expected to maximize students' learning efficiency and results.

[0938] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0939] Step 1:

[0940] The server collects students' learning history and test results from the learning platform. Specifically, it retrieves each student's performance data and test results through a particular API. The collected data is stored in an SQL database (e.g., MySQL).

[0941] Input: Student learning history and test results obtained from the learning platform.

[0942] Output: Learning history and test result data stored in the database

[0943] Step 2:

[0944] The server analyzes the collected data using Python and machine learning libraries (e.g., Scikit-learn). Specifically, it applies clustering algorithms to identify students' comprehension levels, learning strengths, and weaknesses.

[0945] Input: Learning history and test result data retrieved from the database

[0946] Output: Analysis results including each student's level of understanding, strengths, and weaknesses.

[0947] Step 3:

[0948] The server uses a generative AI model (e.g., GPT-3) to generate optimal learning materials for each student. Requests are sent to the AI ​​model using prompts, automatically generating materials corresponding to specific levels of understanding.

[0949] Input: Student-specific comprehension data and generated AI model

[0950] Output: Generated individual learning materials

[0951] Specific action: The prompt message used is "Student A has a low understanding of calculus. Please generate individualized learning materials that cover everything from basic to advanced levels."

[0952] Step 4:

[0953] The server delivers the generated learning materials to students' devices using a specific communication protocol (e.g., HTTP). The generated materials are delivered in web page or application format.

[0954] Input: Generated individual learning materials and student device information

[0955] Output: Learning materials delivered to students' devices

[0956] Step 5:

[0957] The device monitors user (student) behavioral data in real time using JavaScript and other technologies. This includes behavioral information such as viewing time, clicks, and answering time for learning materials.

[0958] Input: Student learning activity data

[0959] Output: Learning activity data collected in real time

[0960] Step 6:

[0961] The device immediately sends the collected learning activity data to the server. Monitoring data is transmitted securely using a communication protocol.

[0962] Input: Learning activity data collected in real time

[0963] Output: Monitoring data sent to the server

[0964] Step 7:

[0965] The server analyzes monitoring data and uses a generative AI model to generate real-time feedback. For example, it might generate specific feedback such as "You lack understanding of factorization" and present it to the user.

[0966] Input: Monitoring data and generated AI models

[0967] Output: Generated real-time feedback

[0968] Step 8:

[0969] The server sends the generated feedback to the terminal, which then displays it to the user (student). The user receives the feedback and can get guidance on how to proceed to the next step.

[0970] Input: Generated real-time feedback

[0971] Output: Feedback displayed on the student's device

[0972] Step 9:

[0973] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan for each student. Specifically, it creates a learning plan that includes the next learning materials and review content to be tackled.

[0974] Input: Learning progress data and generative AI model

[0975] Output: Generated training plan

[0976] Step 10:

[0977] The server delivers the generated study plan to the student's device, and the device displays it to the user (student). The user can then use the study plan as a reference to effectively progress with their studies.

[0978] Input: Generated training plan

[0979] Output: Learning plan displayed on the student's device

[0980] (Application Example 1)

[0981] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0982] In today's industrial environment, there is a demand for improving employees' technical skills and optimizing operational efficiency. Acquiring skills in robot operation and machine maintenance, particularly in factories, is crucial, but addressing individual learning needs is challenging. Furthermore, the inability of employees to receive appropriate real-time feedback during their learning process leads to decreased learning efficiency, which is a significant problem.

[0983] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0984] In this invention, the server includes means for collecting students' learning history and test results; means for analyzing the collected data and evaluating each student's level of understanding; means for generating individual learning materials based on the evaluation results; means for distributing the generated learning materials to the students' terminals; means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data; means for providing the generated feedback to the students' terminals; means for analyzing students' learning progress data and generating an optimal learning plan; means for providing the generated learning plan to the students' terminals; means for providing additional resources and explanations according to the user's progress; and means for supplying manuals and video materials necessary for performing the target tasks. This makes it possible for employees to improve their learning efficiency and to provide a comprehensive training environment that meets individual learning needs.

[0985] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[0986] "Test results" refer to the evaluations, test scores, and responses received by the user.

[0987] "Understanding level" indicates the degree to which a user has acquired a particular knowledge or skill.

[0988] "Individualized learning materials" refer to learning materials optimized for a specific user, taking into account their learning history and level of understanding.

[0989] A "terminal" is an electronic device used for displaying learning materials and receiving feedback.

[0990] "Real-time monitoring" means instantly observing and recording a user's actions and reactions while they are engaged in learning activities.

[0991] "Feedback" refers to providing evaluations and advice regarding a user's learning activities.

[0992] A "learning plan" is a schedule and list of learning materials designed to help users learn efficiently.

[0993] "Additional resources" refer to additional learning materials and information provided to assist users in their learning.

[0994] A "procedure manual" is a document that describes the steps required to perform a specific task or operation.

[0995] "Video teaching materials" refer to learning materials in video format that provide information visually.

[0996] This invention is a learning support system for robot operation techniques and machine maintenance in factories. This system mainly consists of a server, terminals, and users (employees).

[0997] Creating individualized training materials for employees

[0998] The server collects employees' learning history and various evaluation results and stores them in a database. Next, the server analyzes the collected data to identify each employee's level of understanding, learning strengths, and weaknesses. For example, if past evaluation results for employee A reveal a low level of understanding of machine maintenance, the server generates personalized learning materials based on that data. These materials cover maintenance from basic to advanced techniques in a step-by-step manner. The generated personalized materials are delivered from the server to employee A's terminal. The terminal (e.g., smart glasses) displays these materials to the user (employee A) and provides an interface for starting the learning process.

[0999] Real-time feedback

[1000] While a user (employee) is engaged in learning activities, the terminal monitors their activities in real time. Specifically, it records the time the employee spends viewing learning materials, the speed at which they complete tasks, and correct / incorrect data. The monitoring data is immediately sent to the server. The server analyzes the received data and quickly generates real-time feedback. For example, if employee B enters an incorrect answer in an online quiz, the server generates feedback such as, "This question may indicate a lack of understanding of the mechanism. Please refer to the link below," and sends it to the terminal. The terminal displays this feedback to employee B and provides advice and resources for taking the next step.

[1001] Providing learning plans based on each employee's learning progress.

[1002] The server continuously collects and analyzes daily learning activities and evaluation results to understand the overall learning progress of employees. For example, if employee C's learning history reveals that they have made progress in understanding robot operation but lack troubleshooting skills, the server will propose a learning plan focused on strengthening troubleshooting. This plan will include specific troubleshooting exercises at least three times a week. The generated learning plan is delivered from the server to employee C's terminal, which then displays it to the user (employee C). This learning plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[1003] Providing learning resources using the Gen AI model

[1004] The server also provides additional resources and explanations depending on the employee's progress. For example, if employee D makes a mistake in the procedure, the server uses a generative AI model to instantly generate and provide easy-to-understand procedural explanations and relevant video materials. An example of a prompt for this purpose is as follows:

[1005] Examples of input prompts for a generative AI model

[1006] User history:

[1007] ID: 12345

[1008] Current skill level: Beginner

[1009] Learning material: Machine overhaul procedure

[1010] Progress: Steps 1 and 2 completed, stuck on step 3.

[1011] question:

[1012] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[1013] In this way, it is possible to provide a comprehensive training environment that meets individual learning needs.

[1014] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1015] Step 1:

[1016] The server collects employees' learning history and evaluation results.

[1017] Input: Employee's past learning data and evaluation data

[1018] Output: Collected learning history and evaluation data are saved to a database.

[1019] Specific operation: The server retrieves and organizes learning history and evaluation data related to employee IDs from the database.

[1020] Step 2:

[1021] The server analyzes the collected data and evaluates each employee's level of understanding.

[1022] Input: Collected learning history and evaluation data

[1023] Output: Comprehension evaluation results

[1024] Specific operation: The server analyzes the data using machine learning algorithms (e.g., linear regression and decision trees) and quantifies each employee's level of understanding.

[1025] Step 3:

[1026] The server generates individual learning materials based on the evaluation results.

[1027] Input: Comprehension assessment results

[1028] Output: Individual learning materials

[1029] Specific operation: The server selects appropriate learning materials from the content management system and configures them according to the employee's level of understanding. The learning materials are provided in various formats, such as text, images, and videos.

[1030] Step 4:

[1031] The server distributes the generated learning materials to employees' terminals.

[1032] Input: Individual learning materials

[1033] Output: Learning materials delivered to the device

[1034] Specific operation: The server uses network protocols (e.g., HTTP, WebSocket) to send educational data to the terminal and displays its contents on the terminal.

[1035] Step 5:

[1036] The terminal monitors data from employees' learning activities in real time and sends the monitored data to the server.

[1037] Input: User's learning activity data (viewing time, correctness of assignment answers, etc.)

[1038] Output: Monitoring data sent to the server

[1039] Specific operation: The terminal acquires learning activity data from sensors and input devices and sends it to the server in real time.

[1040] Step 6:

[1041] The server analyzes the received monitoring data and generates real-time feedback.

[1042] Input: Monitoring data

[1043] Output: Real-time feedback

[1044] Specific operation: Based on monitoring data, the server uses a generative AI model to generate appropriate feedback. For example, it immediately provides "how to correct incorrect procedures."

[1045] Step 7:

[1046] The device provides the generated feedback to employees.

[1047] Input: Real-time feedback

[1048] Output: Feedback displayed to employees

[1049] Specific operation: The terminal displays feedback received from the server on the screen and instructs the user on the appropriate next action. For example, "advise the user to check specific points before proceeding to the next step."

[1050] Step 8:

[1051] The server analyzes employee learning progress data and generates an optimal learning plan.

[1052] Input: Learning progress data

[1053] Output: Optimal learning plan

[1054] Specific operation: The server analyzes the employee's learning history and current learning progress, and generates a learning plan that suggests the optimal schedule and learning materials to achieve specific learning goals.

[1055] Step 9:

[1056] The server provides the generated learning plan to the employee's terminal.

[1057] Input: Learning plan

[1058] Output: Learning plan provided to the device

[1059] Specific operation: The server sends a learning plan to the terminal via the network, and the terminal displays it to the user. The plan includes the next learning materials, review topics, and allocation of study time.

[1060] Step 10:

[1061] The server provides additional resources and explanations based on the employee's progress.

[1062] Input: Employee progress status, generated AI model

[1063] Output: Additional resources and explanations

[1064] Specific operation: The server prompts the generated AI model with a prompt message, generates appropriate resources (e.g., video materials or explanatory text), sends them to the terminal, and provides them to the user.

[1065] Examples of input prompts for a generative AI model

[1066] User history:

[1067] ID: 12345

[1068] Current skill level: Beginner

[1069] Learning material: Machine overhaul procedure

[1070] Progress: Steps 1 and 2 completed, stuck on step 3.

[1071] question:

[1072] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[1073] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1074] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[1075] Creating individualized learning materials for students

[1076] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data on the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotional data. For example, if the emotion engine detects that student A has an insufficient understanding of calculus based on past test results and is experiencing anxiety while studying, the server generates personalized learning materials based on this. These materials cover calculus from basic to advanced levels in a step-by-step manner and also include mental support content to alleviate anxiety.

[1077] The generated individual learning materials are delivered from the server to student A's device, and the device displays them to the user (student A). In this way, learning is supported according to each individual's level of understanding and emotional state.

[1078] Real-time feedback

[1079] While the user (student) is engaged in learning activities, the device monitors the activity in real time and transmits data such as the speed at which problems are solved, correct / incorrect answers, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state. For example, when user (student B) is working on an online quiz, the emotion engine analyzes in real time whether they are focused or hesitant.

[1080] The server analyzes the received data and emotional state to generate real-time feedback. For example, if student B is feeling anxious about a problem, the server generates feedback such as "Relax and tackle the problem. Please refer to the following link," and sends it to the device. The device then displays this feedback to the user (student B) and provides any necessary additional resources or advice.

[1081] Providing learning plans based on each student's learning progress

[1082] The server periodically analyzes students' overall learning progress and also considers emotional data from the emotion engine. For example, if student C is lacking in a specific learning area (e.g., English listening skills) and is experiencing stress during learning, the server will take this into account when creating a learning plan. This plan will include specific study time for improving listening skills and relaxation techniques.

[1083] The generated learning plan is delivered from the server to student C's terminal, which then displays it to the user (student C). Student C can proceed with their studies based on the presented learning plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[1084] Specific example

[1085] For example, consider a case where student D is struggling to remember events from a specific historical period and is experiencing stress. In this case, the server takes into account the stress level detected by the emotion engine and generates personalized learning materials focused on that specific period. These materials would also include tips for reducing stress and advice on creating a more relaxed learning environment.

[1086] Furthermore, based on regular progress analysis and emotional data, the server determines that student E needs to review algebra and receive emotional support as their next focus. The server generates a learning plan that focuses on algebra while also providing emotional follow-up, and provides this plan to student E via their device. This enables planned learning that balances learning efficiency with mental support.

[1087] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each student with an optimal learning experience, and as a result, both improved learning outcomes and a sense of psychological security can be expected.

[1088] The following describes the processing flow.

[1089] Creating individualized learning materials for students

[1090] Step 1:

[1091] The device collects students' learning history, test results, and data on their facial expressions and voice, and periodically sends this information to a server.

[1092] Step 2:

[1093] The server saves the received data to the database.

[1094] Step 3:

[1095] The server uses machine learning algorithms for data analysis to identify each student's level of understanding, learning strengths, and weaknesses.

[1096] Step 4:

[1097] The server also takes into account the results of the emotion engine's analysis and performs analysis tailored to the student's emotional state. For example, if the student is experiencing feelings of anxiety or stress, it will consider measures to alleviate them.

[1098] Step 5:

[1099] Based on the analysis results, the server generates personalized learning materials tailored to each student. These materials include text, images, videos, quiz-style questions, and emotional support content.

[1100] Step 6:

[1101] The server distributes the generated individual learning materials to the students' devices.

[1102] Step 7:

[1103] The terminal displays the received individual learning materials to the user (student) and provides an interface for starting learning.

[1104] Real-time feedback

[1105] Step 1:

[1106] The device monitors students' learning activities in real time, recording their problem-solving speed, correct / incorrect answer data, facial expressions, and tone of voice.

[1107] Step 2:

[1108] The terminal sends monitoring data to the server in real time.

[1109] Step 3:

[1110] The emotion engine analyzes data transmitted from the device to identify the student's emotional state.

[1111] Step 4:

[1112] The server analyzes the emotional data and training data received from the emotion engine to generate real-time feedback. For example, if a student is feeling anxious about a problem, it might generate feedback such as, "Relax and tackle the problem. Please refer to the link below."

[1113] Step 5:

[1114] The server sends the generated feedback to the terminal.

[1115] Step 6:

[1116] The device displays the received feedback to the user (student) and provides additional learning resources and advice.

[1117] Providing a learning plan based on learning progress

[1118] Step 1:

[1119] The device continuously records students' learning activities, test results, and emotional data, and periodically sends this data to a server.

[1120] Step 2:

[1121] The server analyzes each student's learning progress based on the received data. This analysis includes comprehension level, assignment completion status, study time allocation, and emotional state.

[1122] Step 3:

[1123] The server generates an optimal learning plan for each student based on analysis results and emotional data. For example, if a student experiences stress in a particular subject, the server will suggest a plan that includes strategies for dealing with that stress and methods for relaxation.

[1124] Step 4:

[1125] The server sends the generated training plan to the terminal.

[1126] Step 5:

[1127] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[1128] Step 6:

[1129] The device monitors the progress of the learning plan in real time and makes corrections and provides feedback as needed.

[1130] The above outlines the specific processing steps of the system that combines emotional engines. This system makes it possible to simultaneously provide effective learning support and mental support tailored to the individual needs of each student.

[1131] (Example 2)

[1132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1133] Traditional education systems have struggled to provide individualized support that takes into account not only each student's level of understanding and progress, but also their emotional state. As a result, students have difficulty maintaining motivation for learning and maximizing their learning effectiveness. Furthermore, there has been a lack of technology to provide real-time, emotion-based feedback.

[1134] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for collecting students' facial expressions and voice data and generating emotional data using an emotion engine, and means for generating individual learning materials based on the evaluation results and emotional data. This makes it possible to provide optimal educational support tailored to each student's level of understanding and emotional state.

[1135] "Learning history" refers to a record of learning activities that a student has undertaken in the past.

[1136] "Test results" refer to data showing the grades and answers students received on exams they took.

[1137] "Means of collection" refers to the devices and methods used to collect the target data.

[1138] "Means of analysis" refer to devices and methods for analyzing collected data and extracting meaningful information.

[1139] "Comprehension level" is an indicator that shows how well students understand the learning material.

[1140] "Facial expression and voice data" refers to data that includes facial expressions and vocal characteristics to indicate the emotional state of students.

[1141] An "emotion engine" is a technology or system that analyzes data from students' facial expressions and voices to identify their emotional state.

[1142] "Emotional data" refers to data generated by the emotion engine that represents the emotional state of students.

[1143] "Individualized learning materials" are materials that are optimized for a specific student, generated based on each student's level of understanding and emotional state.

[1144] "Means of distribution" refers to devices and methods for sending generated learning materials and feedback to students' devices.

[1145] "Means of monitoring" refer to devices and methods for observing students' learning activities in real time and collecting data.

[1146] "Feedback" refers to advice and suggestions for improvement given to students based on their progress and results in learning activities.

[1147] "Learning progress data" refers to data that shows the progress of students' learning activities.

[1148] A "learning plan" is a plan that outlines future learning content and schedules, created based on students' learning progress data and emotional data.

[1149] This invention is a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding, progress, and emotional state. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[1150] First, the server collects students' learning history and test results and stores them in a database. This learning history and test results are retrieved from the Learning Management System (LMS). For example, this includes test results and study time logs from the past week. This allows for accurate tracking of each student's learning history and achievements.

[1151] Next, the device collects data on the student's facial expressions and voice using sensors (camera and microphone) and sends it to the emotion engine. The emotion engine analyzes this data to generate emotion data. For example, if a student shows an anxious expression through the camera during a test, the emotion engine will analyze it. The collected data is used in real time to quantify the emotional state.

[1152] The server integrates and analyzes collected learning history, test results, and emotional data to generate learning materials optimized for each student. Using a generative AI model, it creates easy-to-understand learning materials and mental support content. For example, for a student who has insufficient understanding of calculus and feels anxious, the server will generate materials covering everything from basic to advanced levels, as well as mental support content to alleviate their anxiety. This provides education tailored to individual needs.

[1153] The generated individual learning materials are delivered from the server to the students' devices, and the devices display the received materials to the users (students). For example, if learning materials about history are generated, they will be displayed on the student's device screen. This allows students to concentrate on their own learning.

[1154] While students are engaged in learning activities, the device monitors their activities in real time and transmits data on their facial expressions, voice, and learning progress to the emotion engine. The emotion engine analyzes this data to identify the student's current emotional state. For example, while a student is taking an online quiz, their answer speed, accuracy data, facial expressions, and voice tone are transmitted and analyzed.

[1155] The server receives real-time data from the emotion engine and generates real-time feedback based on the emotional state. The generated feedback is sent to the device and displayed to the user (student). For example, feedback such as "Relax and tackle the problem. Please refer to the link below." might be displayed, allowing the student to instantly obtain any necessary additional resources or advice.

[1156] Furthermore, the server periodically analyzes students' learning progress and emotional data, and generates the next learning plan based on this analysis. The generated learning plan is delivered from the server to the student's device, which then displays it to the user (student). Students can then proceed with their learning based on this plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[1157] For example, if a student is experiencing stress while studying history, the server will consider the stress level detected by the emotion engine and generate personalized learning materials focusing on a specific historical period. These materials will also include tips and relaxation techniques to reduce stress. Similarly, if a student is feeling anxious about a math problem, the server will generate real-time feedback addressing that anxiety and display a message such as, "Try to relax."

[1158] Examples of prompt messages include the following:

[1159] "Generate individualized learning materials based on students' recent learning history and emotional state. Focus particularly on calculus in mathematics, and include mental support content to reduce anxiety."

[1160] "Analyze students' emotional states from their facial expressions and audio data while they are participating in online quizzes, and provide real-time feedback as needed."

[1161] "Based on students' overall learning progress and emotional data, create a specific learning plan to improve their English listening skills and distribute that plan to their devices."

[1162] As described above, the system of the present invention makes it possible to provide an optimal learning experience for each student, and both improved learning outcomes and a sense of psychological security can be expected.

[1163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1164] Step 1: Collect learning history and test results

[1165] The server collects student learning history and test results from the Learning Management System (LMS). Specifically, it retrieves test results and study time logs from students taken over the past week. The input is student learning data obtained through the LMS API, and the output is learning history and test results stored in the database. Here, API requests are sent, and the data is parsed and saved in JSON format.

[1166] Step 2: Collecting emotional data

[1167] The device collects data on students' facial expressions and voices using a camera and microphone and sends it to an emotion engine. Specifically, the video and audio data captured by the sensors is processed frame by frame and sent to the emotion engine. The input is data on students' facial expressions and voices obtained from the camera and microphone, and the output is emotion data generated by the emotion engine. An emotion analysis algorithm is used to quantify emotional states such as "anxiety" and "concentration."

[1168] Step 3: Data analysis and creation of individualized learning materials

[1169] The server integrates and analyzes collected learning history, test results, and emotional data. Inputs include learning history and test results stored in a database, as well as emotional data sent from the emotion engine. This data is fed into a generative AI model to generate learning materials optimized for each individual student. The output is the generated, personalized learning material. Specifically, the generative AI model automatically generates the material content, creating customized materials based on the student's level of understanding and emotional state.

[1170] Step 4: Delivery and display of individual learning materials

[1171] The server distributes the generated individual learning materials to the students' devices. The input is the individual learning materials generated by the generation AI model, and the output is the distribution of the materials to the devices. The devices display the received materials to the users (students). Specifically, the process involves sending the learning material data to the device and displaying the materials on the device's screen.

[1172] Step 5: Real-time monitoring of learning activities

[1173] The device monitors students' learning activities in real time while they are engaged in them. Inputs include student learning progress, problem-solving speed, correct / incorrect answer data, facial expressions, and voice data, which are sent to the emotion engine. Output is real-time emotion data. Specifically, it sends activity data to the emotion engine, which then analyzes the emotional state in real time.

[1174] Step 6: Real-time feedback generation

[1175] The server analyzes the user's emotional state based on real-time data received from the emotion engine and generates appropriate feedback. The input is real-time emotional data and student learning progress data, while the output is the generated feedback. Specifically, it automatically generates advice such as "Relax and tackle the problem" and sends it to the device.

[1176] Step 7: Regular analysis of learning progress and provision of learning plans

[1177] The server periodically analyzes students' learning progress and sentiment data to generate the next learning plan. The input is past learning progress and sentiment data stored in a database, and the output is the learning plan provided to the student. Specifically, it generates a learning progress report, plans the next learning steps based on it, and delivers it to the terminal. Students can then proceed with their learning based on the presented learning plan.

[1178] (Application Example 2)

[1179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1180] Traditional education systems struggle to provide not only individualized learning materials tailored to students' progress and understanding, but also real-time feedback that takes into account students' psychological state and emotions. Furthermore, they often lack support to reduce stress and anxiety associated with learning and maximize learning effectiveness. Especially when acquiring specialized skills such as those of a factory operator, psychological state significantly impacts skill acquisition, highlighting the need for a system that can comprehensively manage these factors.

[1181] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for generating individual learning materials based on the evaluation results, means for distributing the generated learning materials to the student's terminal, means for monitoring data in real time when students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to the student's terminal, means for analyzing students' learning progress data and generating an optimal learning plan, means for providing the generated learning plan to the student's terminal, means for analyzing the subject's facial expressions and voice in real time and generating emotional data, means for generating feedback that reduces stress and anxiety based on the emotional data, and means for displaying the generated feedback on the display of a smart device. This makes it possible to provide individualized learning support based on each student's level of understanding and psychological state, enabling effective learning while reducing stress and anxiety associated with skill acquisition.

[1182] "Learning history" refers to data that records what a student has learned in the past and their level of achievement.

[1183] "Test results" refer to data showing students' grades and answers when they took a test.

[1184] "Comprehension level" refers to an assessment that indicates how well a student understands a particular learning topic.

[1185] "Individualized learning materials" refer to learning materials that are created based on each student's learning progress and level of understanding.

[1186] "Real-time monitoring" is the process of collecting and analyzing activity data while students are engaged in learning activities.

[1187] "Feedback" refers to advice and support messages generated based on a student's learning activities and psychological state.

[1188] "Learning progress data" refers to data that shows how well students are progressing according to their learning plan.

[1189] A "study plan" refers to the specific learning content and timetable formulated to help each student achieve their learning goals.

[1190] "Facial expression" refers to the emotions and psychological state that can be interpreted from the movement of facial muscles and facial features.

[1191] "Voice" refers to the audio data produced when speaking, and its tone and changes are used to interpret emotional states.

[1192] "Emotional data" refers to data that indicates the emotional state of students, analyzed from their facial expressions and voices.

[1193] A "smart device" is a portable electronic device that has internet connectivity and high-performance computing capabilities.

[1194] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[1195] Creating individualized learning materials for students

[1196] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data of the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotion data. This emotion data is used to understand changes in emotions that occur during the learning process in real time.

[1197] For example, when an operator is learning how to operate a new robotic arm, the camera analyzes the operator's facial expressions in real time and provides relaxation techniques and specific tips if stress or anxiety is detected. Furthermore, if the operator stumbles at a particular step, detailed explanations and video tutorials are displayed on the smart device's screen.

[1198] Real-time feedback

[1199] While the user (student) is engaged in learning activities, the device monitors their activities in real time and sends data such as the speed at which they solve problems, correctness data, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state.

[1200] For example, when a user (operator) is learning how to operate a new robot online, the emotion engine analyzes in real time whether they are focused or hesitant. The server analyzes the received data and emotional state and generates real-time feedback. For instance, if the operator is feeling anxious about a problem, the server generates feedback such as, "Please relax and learn. Please refer to the following link," and sends it to the terminal.

[1201] Providing learning plans based on each student's learning progress

[1202] The server periodically analyzes the student's overall learning progress and also considers emotional data from the emotion engine. For example, if an operator is lacking in a specific learning item (e.g., robot assembly) and is experiencing stress during learning, the server takes this into account when creating a learning plan. This plan may include specific study time for assembly reinforcement and relaxation techniques.

[1203] The generated learning plan is delivered from the server to the user's terminal, which then displays it to the user. The user can proceed with their learning based on the presented learning plan, and their emotional state is monitored in real time throughout the process, with appropriate feedback provided.

[1204] Examples of prompts for a generative AI model:

[1205] If the operator feels anxious while operating the robotic arm, generate and display feedback that provides relaxation techniques and specific operating procedures.

[1206] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each operator with an optimal learning experience, and as a result, both improved efficiency in skill acquisition and a sense of psychological security can be expected.

[1207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1208] Step 1:

[1209] The server collects students' learning history and test results from a database. As input, it retrieves students' past learning data and test scores. As output, the collected data is prepared for analysis.

[1210] Step 2:

[1211] The server analyzes the collected data and evaluates each student's level of understanding. It uses learning history and test score data as input. For data processing, it uses machine learning algorithms to evaluate students' understanding. The output is the understanding evaluation result for each student.

[1212] Step 3:

[1213] The server generates individual learning materials based on evaluation results. It uses student comprehension assessment results as input. As data processing, it applies a material generation algorithm to create materials appropriate to the student's level of comprehension. Individual learning materials are generated as output.

[1214] Step 4:

[1215] The server distributes the generated learning materials to the students' devices. It handles the individually generated learning materials as input. The learning materials are sent to the students' devices as output.

[1216] Step 5:

[1217] The device monitors data in real time as the user engages in learning activities. It captures user learning behavior data (e.g., answer speed, correct / incorrect answers, facial expressions, voice) as input. For data processing, it collects and processes this behavioral data at high frequency. The output is the monitoring data.

[1218] Step 6:

[1219] The device sends monitoring data to the emotion engine, which generates emotion data. Facial expression and voice data are used as input. As data processing, the emotion engine analyzes this data to estimate the real-time emotional state. Emotion data is obtained as output.

[1220] Step 7:

[1221] The server generates real-time feedback based on monitoring data and sentiment data. Sentiment data and learned behavior data are used as input. A feedback generation algorithm is applied as data processing to create appropriate feedback. Feedback data is obtained as output.

[1222] Step 8:

[1223] The device provides the generated feedback to the student. It uses feedback data as input. The feedback message is displayed on the device's screen as output.

[1224] Step 9:

[1225] The server periodically analyzes students' learning progress data and generates an optimal learning plan. It uses learning history and progress data as input. As data processing, it applies a progress analysis algorithm to create the learning plan. The output is the optimal learning plan.

[1226] Step 10:

[1227] The server provides the generated study plan to the student's device. The generated study plan is used as input. The study plan is sent to the student's device as output.

[1228] Step 11:

[1229] The device analyzes the user's facial expressions and voice in real time and generates emotion data. It uses student facial expression and voice data as input. The emotion engine analyzes this data for processing. The output is emotion data.

[1230] Step 12:

[1231] The device generates feedback to reduce stress and anxiety based on emotional data. Emotional data is used as input. A feedback generation algorithm creates feedback tailored to the emotional state as data processing. A feedback message is obtained as output.

[1232] Step 13:

[1233] The terminal displays the generated feedback on the smart device's display. It uses a feedback message as input. The feedback is displayed in the user's field of view as output.

[1234] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1235] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1236] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1237] [Fourth Embodiment]

[1238] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1239] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1240] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1241] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1242] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1243] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1244] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1245] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1246] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1247] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1248] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1249] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1250] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1251] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[1252] Creating individualized learning materials for students

[1253] The server collects students' learning history and test results and stores them in a database. Next, the server analyzes the collected data to identify each student's level of understanding, learning strengths, and weaknesses. For example, if past test results for student A reveal a low level of understanding of calculus, the server generates personalized learning materials based on that data. These materials cover calculus from basic to advanced levels in a step-by-step manner.

[1254] The generated individual learning materials are delivered from the server to student A's terminal. The terminal displays these materials to the user (student A) and provides an interface for starting the learning process.

[1255] Real-time feedback

[1256] While the user (student) is engaged in learning activities, the device monitors their activities in real time. Specifically, it records the time the student spends viewing learning materials, the speed at which they solve problems, and correct / incorrect answer data. The monitoring data is immediately sent to the server.

[1257] The server analyzes the received data and generates real-time feedback quickly. For example, if student B enters an incorrect answer in an online quiz, the server generates feedback such as, "This problem may lack the concept of factorization. Please refer to the link below," and sends it to the device. The device displays this feedback to student B and provides advice and resources to help them move forward.

[1258] Providing learning plans based on each student's learning progress

[1259] The server continuously collects and analyzes daily learning activities and test results to understand students' overall learning progress. For example, if student C's learning history reveals that they have a good understanding of English grammar but lack listening skills, the server will propose a learning plan focused on strengthening English listening skills. This plan would include specific details such as listening practice at least three times a week.

[1260] The generated study plan is delivered from the server to student C's terminal, which then displays it to the user (student C). This study plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[1261] Specific example

[1262] For example, consider a case where student D is struggling to remember events from a specific historical period. In this case, the server identifies this trend from the collected data and generates personalized learning materials focused on that particular period. Simultaneously, it also generates quiz-style practice questions and mini-tests to check understanding. These materials are provided via the terminal, and the user (student D) works through them. If mistakes are made during learning, real-time feedback is provided, along with resources and advice for further learning.

[1263] Furthermore, through regular progress analysis, the server determines that student E's understanding of geometry has improved and that a review of algebra is necessary as the next topic to address. The server generates a learning plan focused on algebra, which is then provided to student E via their terminal. This ensures effective and systematic learning.

[1264] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each student with an optimal learning experience, and as a result, an improvement in learning outcomes can be expected.

[1265] The following describes the processing flow.

[1266] Creating individualized learning materials for students (1)

[1267] Step 1:

[1268] The device records students' learning history and test results daily and periodically sends this data to a server.

[1269] Step 2:

[1270] The server stores the received data in a database. This includes learning history, test results, and comprehension trends.

[1271] Step 3:

[1272] The server uses machine learning algorithms for data analysis to identify each student's level of understanding, learning strengths, and weaknesses.

[1273] Step 4:

[1274] Based on the analysis results, the server generates personalized learning materials tailored to each student. These materials include text, images, videos, and quiz-style questions.

[1275] Step 5:

[1276] The server distributes the generated individual learning materials to the students' devices.

[1277] Step 6:

[1278] The terminal displays the received individual learning materials to the user (student) and provides an interface for starting learning.

[1279] Real-time feedback (2)

[1280] Step 1:

[1281] The device monitors the user's (student's) learning activities in real time. Specifically, it records the speed at which problems are solved, correct / incorrect answer data, and time spent viewing learning materials.

[1282] Step 2:

[1283] The terminal sends monitoring data to the server in real time.

[1284] Step 3:

[1285] The server analyzes the received data and generates real-time feedback. This feedback includes the accuracy rate, reasons for incorrect answers, and advice for improving understanding.

[1286] Step 4:

[1287] The server immediately sends the generated feedback to the terminal.

[1288] Step 5:

[1289] The device displays the received feedback to the user (student) and provides additional learning resources and links.

[1290] (3) Providing learning plans based on each student's learning progress

[1291] Step 1:

[1292] The device continuously records the user's (student's) learning activities and test results, and periodically sends them to the server.

[1293] Step 2:

[1294] The server analyzes each student's learning progress based on the received data. Indicators include comprehension level, assignment completion status, and allocation of study time.

[1295] Step 3:

[1296] The server generates an optimal learning plan for each student based on the analysis results. This plan includes the next learning materials, review topics, and time allocation for each study session.

[1297] Step 4:

[1298] The server sends the generated training plan to the terminal.

[1299] Step 5:

[1300] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[1301] The above outlines the specific operational details for each processing step. This system enables the provision of effective learning support tailored to the individual needs of each student.

[1302] (Example 1)

[1303] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1304] Traditional education systems do not adequately provide individualized learning support based on each student's level of understanding and learning progress. This can lead to decreased student learning efficiency, and some students may fall behind in areas they struggle with. Furthermore, the lack of real-time feedback means students may leave misunderstandings unaddressed, resulting in reduced learning effectiveness.

[1305] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1306] In this invention, the server includes means for generating individual learning materials using a generative AI model, means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for distributing the generated learning materials to students' terminals, means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to students' terminals, means for analyzing students' learning progress data and generating an optimal learning plan using a generative AI model, and means for providing the generated learning plan to students' terminals. This enables the provision of learning materials and learning plans optimized for each student, as well as real-time feedback.

[1307] "Means for collecting students' learning history and test results" refers to a system for collecting data on all learning activities and test results that students have undertaken to date.

[1308] "Methods for analyzing collected data and evaluating each student's level of understanding" refers to algorithms and technologies for analyzing collected learning data and evaluating each student's level of learning comprehension.

[1309] "Means for generating individualized learning materials" refers to software or a system for creating learning materials that are optimal for each student based on their level of learning comprehension.

[1310] "Methods for generating individual learning materials using generative AI models" refers to technologies that use artificial intelligence (AI) to automatically generate learning materials tailored to individual students.

[1311] "Means for distributing generated learning materials to students' devices" refers to a communication system for sending generated learning materials from a server to students' learning devices.

[1312] "Means of monitoring data on students' learning activities in real time" refers to software and sensor technologies that allow for immediate monitoring of what students are doing while learning.

[1313] "Means for generating real-time feedback based on monitoring data" refers to an algorithm that quickly generates appropriate feedback for students based on collected monitoring data.

[1314] "Means for providing generated feedback to students' devices" refers to communication means for displaying real-time generated feedback on students' learning devices.

[1315] "A means of analyzing students' learning progress data and generating an optimal learning plan using a generative AI model" refers to a technology that analyzes students' learning progress and uses an AI model to formulate an optimal learning plan.

[1316] "Means for providing the generated learning plan to students' devices" refers to a communication system that displays the generated learning plan on students' learning devices and encourages them to implement it.

[1317] This invention is a system that individually supports students' learning activities and provides optimal education based on their level of understanding and progress. This system mainly consists of a server, terminals, and users (students).

[1318] Collection of students' learning history and test results

[1319] The server can collect learning history and test result data from students' learning platforms through specific APIs. This data includes grades for each subject, test results, and the time spent on each problem. The collected data is stored in an SQL database (e.g., MySQL).

[1320] Data analysis and assessment of understanding

[1321] The server uses Python to retrieve data from the database and analyzes it using machine learning libraries (e.g., Scikit-learn). Specifically, it uses a clustering algorithm to cluster students' levels of understanding. Based on these analysis results, it evaluates each student's level of understanding, learning strengths, and weaknesses.

[1322] Generation of individualized learning materials

[1323] The server generates learning materials for each student using a generative AI model (e.g., GPT-3). Specifically, it uses prompt statements to instruct the AI ​​model and create materials that correspond to a specific level of understanding. For example, it uses prompt statements like the following:

[1324] "Student A has a poor understanding of calculus. Please create individualized learning materials that cover everything from the basics to advanced topics."

[1325] Distribution of educational materials and provision of interfaces

[1326] The server delivers the generated learning materials to the students' devices using a specific communication protocol (e.g., HTTP). The devices provide an interface that allows the user (student) to view the received materials via a web browser or a dedicated app.

[1327] Monitoring of learning activities

[1328] The device tracks the user's (student's) actions in real time while they are viewing learning materials (e.g., visit time, clicks, answer time) using JavaScript, etc. The collected data is immediately sent to the server.

[1329] Providing real-time feedback

[1330] The server analyzes the received learning activity data and generates real-time feedback using a generative AI model. For example, it generates feedback as follows:

[1331] "If student B answers the quiz incorrectly, the message will read: 'Understanding factorization is necessary. Please refer to the following link.'"

[1332] The server sends the generated feedback to the terminal, and the terminal displays it to the user (student).

[1333] Proposal for a study plan

[1334] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan. For example, it might suggest a plan like this:

[1335] "Student C lacks listening comprehension skills, therefore, we propose a study plan that includes listening practice at least three times a week."

[1336] The generated learning plan is delivered from the server to the student's device, and the device displays it to the user (student).

[1337] Specific example

[1338] If student D is struggling to remember a particular period in history class, the server analyzes all of student D's test results and generates personalized learning materials focused on that specific time period. The terminal immediately displays these materials, and student D uses them to continue their learning.

[1339] If student E answers a specific math problem (e.g., geometry) incorrectly during a lesson, the device immediately sends the data to the server. The server analyzes this data, generates specific feedback such as "This problem was caused by a lack of understanding of factorization," and sends it back to the device. The device then displays this feedback to student E.

[1340] This invention provides each student with learning materials and learning plans optimized for them, and enables real-time feedback. This is expected to maximize students' learning efficiency and results.

[1341] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1342] Step 1:

[1343] The server collects students' learning history and test results from the learning platform. Specifically, it retrieves each student's performance data and test results through a particular API. The collected data is stored in an SQL database (e.g., MySQL).

[1344] Input: Student learning history and test results obtained from the learning platform.

[1345] Output: Learning history and test result data stored in the database

[1346] Step 2:

[1347] The server analyzes the collected data using Python and machine learning libraries (e.g., Scikit-learn). Specifically, it applies clustering algorithms to identify students' comprehension levels, learning strengths, and weaknesses.

[1348] Input: Learning history and test result data retrieved from the database

[1349] Output: Analysis results including each student's level of understanding, strengths, and weaknesses.

[1350] Step 3:

[1351] The server uses a generative AI model (e.g., GPT-3) to generate optimal learning materials for each student. Requests are sent to the AI ​​model using prompts, automatically generating materials corresponding to specific levels of understanding.

[1352] Input: Student-specific comprehension data and generated AI model

[1353] Output: Generated individual learning materials

[1354] Specific action: The prompt message used is "Student A has a low understanding of calculus. Please generate individualized learning materials that cover everything from basic to advanced levels."

[1355] Step 4:

[1356] The server delivers the generated learning materials to students' devices using a specific communication protocol (e.g., HTTP). The generated materials are delivered in web page or application format.

[1357] Input: Generated individual learning materials and student device information

[1358] Output: Learning materials delivered to students' devices

[1359] Step 5:

[1360] The device monitors user (student) behavioral data in real time using JavaScript and other technologies. This includes behavioral information such as viewing time, clicks, and answering time for learning materials.

[1361] Input: Student learning activity data

[1362] Output: Learning activity data collected in real time

[1363] Step 6:

[1364] The device immediately sends the collected learning activity data to the server. Monitoring data is transmitted securely using a communication protocol.

[1365] Input: Learning activity data collected in real time

[1366] Output: Monitoring data sent to the server

[1367] Step 7:

[1368] The server analyzes monitoring data and uses a generative AI model to generate real-time feedback. For example, it might generate specific feedback such as "You lack understanding of factorization" and present it to the user.

[1369] Input: Monitoring data and generated AI models

[1370] Output: Generated real-time feedback

[1371] Step 8:

[1372] The server sends the generated feedback to the terminal, which then displays it to the user (student). The user receives the feedback and can get guidance on how to proceed to the next step.

[1373] Input: Generated real-time feedback

[1374] Output: Feedback displayed on the student's device

[1375] Step 9:

[1376] The server analyzes students' learning progress data and uses a generative AI model to generate an optimal learning plan for each student. Specifically, it creates a learning plan that includes the next learning materials and review content to be tackled.

[1377] Input: Learning progress data and generative AI model

[1378] Output: Generated training plan

[1379] Step 10:

[1380] The server delivers the generated study plan to the student's device, and the device displays it to the user (student). The user can then use the study plan as a reference to effectively progress with their studies.

[1381] Input: Generated training plan

[1382] Output: Learning plan displayed on the student's device

[1383] (Application Example 1)

[1384] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1385] In today's industrial environment, there is a demand for improving employees' technical skills and optimizing operational efficiency. Acquiring skills in robot operation and machine maintenance, particularly in factories, is crucial, but addressing individual learning needs is challenging. Furthermore, the inability of employees to receive appropriate real-time feedback during their learning process leads to decreased learning efficiency, which is a significant problem.

[1386] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1387] In this invention, the server includes means for collecting students' learning history and test results; means for analyzing the collected data and evaluating each student's level of understanding; means for generating individual learning materials based on the evaluation results; means for distributing the generated learning materials to the students' terminals; means for monitoring data in real time as students engage in learning activities and generating real-time feedback based on the monitoring data; means for providing the generated feedback to the students' terminals; means for analyzing students' learning progress data and generating an optimal learning plan; means for providing the generated learning plan to the students' terminals; means for providing additional resources and explanations according to the user's progress; and means for supplying manuals and video materials necessary for performing the target tasks. This makes it possible for employees to improve their learning efficiency and to provide a comprehensive training environment that meets individual learning needs.

[1388] "Learning history" refers to a record of the learning activities a user has undertaken in the past.

[1389] "Test results" refer to the evaluations, test scores, and responses received by the user.

[1390] "Understanding level" indicates the degree to which a user has acquired a particular knowledge or skill.

[1391] "Individualized learning materials" refer to learning materials optimized for a specific user, taking into account their learning history and level of understanding.

[1392] A "terminal" is an electronic device used for displaying learning materials and receiving feedback.

[1393] "Real-time monitoring" means instantly observing and recording a user's actions and reactions while they are engaged in learning activities.

[1394] "Feedback" refers to providing evaluations and advice regarding a user's learning activities.

[1395] A "learning plan" is a schedule and list of learning materials designed to help users learn efficiently.

[1396] "Additional resources" refer to additional learning materials and information provided to assist users in their learning.

[1397] A "procedure manual" is a document that describes the steps required to perform a specific task or operation.

[1398] "Video teaching materials" refer to learning materials in video format that provide information visually.

[1399] This invention is a learning support system for robot operation techniques and machine maintenance in factories. This system mainly consists of a server, terminals, and users (employees).

[1400] Creating individualized training materials for employees

[1401] The server collects employees' learning history and various evaluation results and stores them in a database. Next, the server analyzes the collected data to identify each employee's level of understanding, learning strengths, and weaknesses. For example, if past evaluation results for employee A reveal a low level of understanding of machine maintenance, the server generates personalized learning materials based on that data. These materials cover maintenance from basic to advanced techniques in a step-by-step manner. The generated personalized materials are delivered from the server to employee A's terminal. The terminal (e.g., smart glasses) displays these materials to the user (employee A) and provides an interface for starting the learning process.

[1402] Real-time feedback

[1403] While a user (employee) is engaged in learning activities, the terminal monitors their activities in real time. Specifically, it records the time the employee spends viewing learning materials, the speed at which they complete tasks, and correct / incorrect data. The monitoring data is immediately sent to the server. The server analyzes the received data and quickly generates real-time feedback. For example, if employee B enters an incorrect answer in an online quiz, the server generates feedback such as, "This question may indicate a lack of understanding of the mechanism. Please refer to the link below," and sends it to the terminal. The terminal displays this feedback to employee B and provides advice and resources for taking the next step.

[1404] Providing learning plans based on each employee's learning progress.

[1405] The server continuously collects and analyzes daily learning activities and evaluation results to understand the overall learning progress of employees. For example, if employee C's learning history reveals that they have made progress in understanding robot operation but lack troubleshooting skills, the server will propose a learning plan focused on strengthening troubleshooting. This plan will include specific troubleshooting exercises at least three times a week. The generated learning plan is delivered from the server to employee C's terminal, which then displays it to the user (employee C). This learning plan includes a timetable and specific learning content, and is provided in an easy-to-follow format.

[1406] Providing learning resources using the Gen AI model

[1407] The server also provides additional resources and explanations depending on the employee's progress. For example, if employee D makes a mistake in the procedure, the server uses a generative AI model to instantly generate and provide easy-to-understand procedural explanations and relevant video materials. An example of a prompt for this purpose is as follows:

[1408] Examples of input prompts for a generative AI model

[1409] User history:

[1410] ID: 12345

[1411] Current skill level: Beginner

[1412] Learning material: Machine overhaul procedure

[1413] Progress: Steps 1 and 2 completed, stuck on step 3.

[1414] question:

[1415] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[1416] In this way, it is possible to provide a comprehensive training environment that meets individual learning needs.

[1417] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1418] Step 1:

[1419] The server collects employees' learning history and evaluation results.

[1420] Input: Employee's past learning data and evaluation data

[1421] Output: Collected learning history and evaluation data are saved to a database.

[1422] Specific operation: The server retrieves and organizes learning history and evaluation data related to employee IDs from the database.

[1423] Step 2:

[1424] The server analyzes the collected data and evaluates each employee's level of understanding.

[1425] Input: Collected learning history and evaluation data

[1426] Output: Comprehension evaluation results

[1427] Specific operation: The server analyzes the data using machine learning algorithms (e.g., linear regression and decision trees) and quantifies each employee's level of understanding.

[1428] Step 3:

[1429] The server generates individual learning materials based on the evaluation results.

[1430] Input: Comprehension assessment results

[1431] Output: Individual learning materials

[1432] Specific operation: The server selects appropriate learning materials from the content management system and configures them according to the employee's level of understanding. The learning materials are provided in various formats, such as text, images, and videos.

[1433] Step 4:

[1434] The server distributes the generated learning materials to employees' terminals.

[1435] Input: Individual learning materials

[1436] Output: Learning materials delivered to the device

[1437] Specific operation: The server uses network protocols (e.g., HTTP, WebSocket) to send educational data to the terminal and displays its contents on the terminal.

[1438] Step 5:

[1439] The terminal monitors data from employees' learning activities in real time and sends the monitored data to the server.

[1440] Input: User's learning activity data (viewing time, correctness of assignment answers, etc.)

[1441] Output: Monitoring data sent to the server

[1442] Specific operation: The terminal acquires learning activity data from sensors and input devices and sends it to the server in real time.

[1443] Step 6:

[1444] The server analyzes the received monitoring data and generates real-time feedback.

[1445] Input: Monitoring data

[1446] Output: Real-time feedback

[1447] Specific operation: Based on monitoring data, the server uses a generative AI model to generate appropriate feedback. For example, it immediately provides "how to correct incorrect procedures."

[1448] Step 7:

[1449] The device provides the generated feedback to employees.

[1450] Input: Real-time feedback

[1451] Output: Feedback displayed to employees

[1452] Specific operation: The terminal displays feedback received from the server on the screen and instructs the user on the appropriate next action. For example, "advise the user to check specific points before proceeding to the next step."

[1453] Step 8:

[1454] The server analyzes employee learning progress data and generates an optimal learning plan.

[1455] Input: Learning progress data

[1456] Output: Optimal learning plan

[1457] Specific operation: The server analyzes the employee's learning history and current learning progress, and generates a learning plan that suggests the optimal schedule and learning materials to achieve specific learning goals.

[1458] Step 9:

[1459] The server provides the generated learning plan to the employee's terminal.

[1460] Input: Learning plan

[1461] Output: Learning plan provided to the device

[1462] Specific operation: The server sends a learning plan to the terminal via the network, and the terminal displays it to the user. The plan includes the next learning materials, review topics, and allocation of study time.

[1463] Step 10:

[1464] The server provides additional resources and explanations based on the employee's progress.

[1465] Input: Employee progress status, generated AI model

[1466] Output: Additional resources and explanations

[1467] Specific operation: The server prompts the generated AI model with a prompt message, generates appropriate resources (e.g., video materials or explanatory text), sends them to the terminal, and provides them to the user.

[1468] Examples of input prompts for a generative AI model

[1469] User history:

[1470] ID: 12345

[1471] Current skill level: Beginner

[1472] Learning material: Machine overhaul procedure

[1473] Progress: Steps 1 and 2 completed, stuck on step 3.

[1474] question:

[1475] To help users improve their skills, please provide an easy-to-understand explanation of Step 3 and additional resources. Please also include a link to a simple checklist in your feedback.

[1476] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1477] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[1478] Creating individualized learning materials for students

[1479] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data on the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotional data. For example, if the emotion engine detects that student A has an insufficient understanding of calculus based on past test results and is experiencing anxiety while studying, the server generates personalized learning materials based on this. These materials cover calculus from basic to advanced levels in a step-by-step manner and also include mental support content to alleviate anxiety.

[1480] The generated individual learning materials are delivered from the server to student A's device, and the device displays them to the user (student A). In this way, learning is supported according to each individual's level of understanding and emotional state.

[1481] Real-time feedback

[1482] While the user (student) is engaged in learning activities, the device monitors the activity in real time and transmits data such as the speed at which problems are solved, correct / incorrect answers, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state. For example, when user (student B) is working on an online quiz, the emotion engine analyzes in real time whether they are focused or hesitant.

[1483] The server analyzes the received data and emotional state to generate real-time feedback. For example, if student B is feeling anxious about a problem, the server generates feedback such as "Relax and tackle the problem. Please refer to the following link," and sends it to the device. The device then displays this feedback to the user (student B) and provides any necessary additional resources or advice.

[1484] Providing learning plans based on each student's learning progress

[1485] The server periodically analyzes students' overall learning progress and also considers emotional data from the emotion engine. For example, if student C is lacking in a specific learning area (e.g., English listening skills) and is experiencing stress during learning, the server will take this into account when creating a learning plan. This plan will include specific study time for improving listening skills and relaxation techniques.

[1486] The generated learning plan is delivered from the server to student C's terminal, which then displays it to the user (student C). Student C can proceed with their studies based on the presented learning plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[1487] Specific example

[1488] For example, consider a case where student D is struggling to remember events from a specific historical period and is experiencing stress. In this case, the server takes into account the stress level detected by the emotion engine and generates personalized learning materials focused on that specific period. These materials would also include tips for reducing stress and advice on creating a more relaxed learning environment.

[1489] Furthermore, based on regular progress analysis and emotional data, the server determines that student E needs to review algebra and receive emotional support as their next focus. The server generates a learning plan that focuses on algebra while also providing emotional follow-up, and provides this plan to student E via their device. This enables planned learning that balances learning efficiency with mental support.

[1490] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each student with an optimal learning experience, and as a result, both improved learning outcomes and a sense of psychological security can be expected.

[1491] The following describes the processing flow.

[1492] Creating individualized learning materials for students

[1493] Step 1:

[1494] The device collects students' learning history, test results, and data on their facial expressions and voice, and periodically sends this information to a server.

[1495] Step 2:

[1496] The server saves the received data to the database.

[1497] Step 3:

[1498] The server uses machine learning algorithms for data analysis to identify each student's level of understanding, learning strengths, and weaknesses.

[1499] Step 4:

[1500] The server also takes into account the results of the emotion engine's analysis and performs analysis tailored to the student's emotional state. For example, if the student is experiencing feelings of anxiety or stress, it will consider measures to alleviate them.

[1501] Step 5:

[1502] Based on the analysis results, the server generates personalized learning materials tailored to each student. These materials include text, images, videos, quiz-style questions, and emotional support content.

[1503] Step 6:

[1504] The server distributes the generated individual learning materials to the students' devices.

[1505] Step 7:

[1506] The terminal displays the received individual learning materials to the user (student) and provides an interface for starting learning.

[1507] Real-time feedback

[1508] Step 1:

[1509] The device monitors students' learning activities in real time, recording their problem-solving speed, correct / incorrect answer data, facial expressions, and tone of voice.

[1510] Step 2:

[1511] The terminal sends monitoring data to the server in real time.

[1512] Step 3:

[1513] The emotion engine analyzes data transmitted from the device to identify the student's emotional state.

[1514] Step 4:

[1515] The server analyzes the emotional data and training data received from the emotion engine to generate real-time feedback. For example, if a student is feeling anxious about a problem, it might generate feedback such as, "Relax and tackle the problem. Please refer to the link below."

[1516] Step 5:

[1517] The server sends the generated feedback to the terminal.

[1518] Step 6:

[1519] The device displays the received feedback to the user (student) and provides additional learning resources and advice.

[1520] Providing a learning plan based on learning progress

[1521] Step 1:

[1522] The device continuously records students' learning activities, test results, and emotional data, and periodically sends this data to a server.

[1523] Step 2:

[1524] The server analyzes each student's learning progress based on the received data. This analysis includes comprehension level, assignment completion status, study time allocation, and emotional state.

[1525] Step 3:

[1526] The server generates an optimal learning plan for each student based on analysis results and emotional data. For example, if a student experiences stress in a particular subject, the server will suggest a plan that includes strategies for dealing with that stress and methods for relaxation.

[1527] Step 4:

[1528] The server sends the generated training plan to the terminal.

[1529] Step 5:

[1530] The terminal displays the received learning plan to the user (student) and provides an interface to support learning activities based on the plan.

[1531] Step 6:

[1532] The device monitors the progress of the learning plan in real time and makes corrections and provides feedback as needed.

[1533] The above outlines the specific processing steps of the system that combines emotional engines. This system makes it possible to simultaneously provide effective learning support and mental support tailored to the individual needs of each student.

[1534] (Example 2)

[1535] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1536] Traditional education systems have struggled to provide individualized support that takes into account not only each student's level of understanding and progress, but also their emotional state. As a result, students have difficulty maintaining motivation for learning and maximizing their learning effectiveness. Furthermore, there has been a lack of technology to provide real-time, emotion-based feedback.

[1537] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for collecting students' facial expressions and voice data and generating emotional data using an emotion engine, and means for generating individual learning materials based on the evaluation results and emotional data. This makes it possible to provide optimal educational support tailored to each student's level of understanding and emotional state.

[1538] "Learning history" refers to a record of learning activities that a student has undertaken in the past.

[1539] "Test results" refer to data showing the grades and answers students received on exams they took.

[1540] "Means of collection" refers to the devices and methods used to collect the target data.

[1541] "Means of analysis" refer to devices and methods for analyzing collected data and extracting meaningful information.

[1542] "Comprehension level" is an indicator that shows how well students understand the learning material.

[1543] "Facial expression and voice data" refers to data that includes facial expressions and vocal characteristics to indicate the emotional state of students.

[1544] An "emotion engine" is a technology or system that analyzes data from students' facial expressions and voices to identify their emotional state.

[1545] "Emotional data" refers to data generated by the emotion engine that represents the emotional state of students.

[1546] "Individualized learning materials" are materials that are optimized for a specific student, generated based on each student's level of understanding and emotional state.

[1547] "Means of distribution" refers to devices and methods for sending generated learning materials and feedback to students' devices.

[1548] "Means of monitoring" refer to devices and methods for observing students' learning activities in real time and collecting data.

[1549] "Feedback" refers to advice and suggestions for improvement given to students based on their progress and results in learning activities.

[1550] "Learning progress data" refers to data that shows the progress of students' learning activities.

[1551] A "learning plan" is a plan that outlines future learning content and schedules, created based on students' learning progress data and emotional data.

[1552] This invention is a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding, progress, and emotional state. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[1553] First, the server collects students' learning history and test results and stores them in a database. This learning history and test results are retrieved from the Learning Management System (LMS). For example, this includes test results and study time logs from the past week. This allows for accurate tracking of each student's learning history and achievements.

[1554] Next, the device collects data on the student's facial expressions and voice using sensors (camera and microphone) and sends it to the emotion engine. The emotion engine analyzes this data to generate emotion data. For example, if a student shows an anxious expression through the camera during a test, the emotion engine will analyze it. The collected data is used in real time to quantify the emotional state.

[1555] The server integrates and analyzes collected learning history, test results, and emotional data to generate learning materials optimized for each student. Using a generative AI model, it creates easy-to-understand learning materials and mental support content. For example, for a student who has insufficient understanding of calculus and feels anxious, the server will generate materials covering everything from basic to advanced levels, as well as mental support content to alleviate their anxiety. This provides education tailored to individual needs.

[1556] The generated individual learning materials are delivered from the server to the students' devices, and the devices display the received materials to the users (students). For example, if learning materials about history are generated, they will be displayed on the student's device screen. This allows students to concentrate on their own learning.

[1557] While students are engaged in learning activities, the device monitors their activities in real time and transmits data on their facial expressions, voice, and learning progress to the emotion engine. The emotion engine analyzes this data to identify the student's current emotional state. For example, while a student is taking an online quiz, their answer speed, accuracy data, facial expressions, and voice tone are transmitted and analyzed.

[1558] The server receives real-time data from the emotion engine and generates real-time feedback based on the emotional state. The generated feedback is sent to the device and displayed to the user (student). For example, feedback such as "Relax and tackle the problem. Please refer to the link below." might be displayed, allowing the student to instantly obtain any necessary additional resources or advice.

[1559] Furthermore, the server periodically analyzes students' learning progress and emotional data, and generates the next learning plan based on this analysis. The generated learning plan is delivered from the server to the student's device, which then displays it to the user (student). Students can then proceed with their learning based on this plan, and their emotional state is monitored in real time throughout the process, providing appropriate feedback.

[1560] For example, if a student is experiencing stress while studying history, the server will consider the stress level detected by the emotion engine and generate personalized learning materials focusing on a specific historical period. These materials will also include tips and relaxation techniques to reduce stress. Similarly, if a student is feeling anxious about a math problem, the server will generate real-time feedback addressing that anxiety and display a message such as, "Try to relax."

[1561] Examples of prompt messages include the following:

[1562] "Generate individualized learning materials based on students' recent learning history and emotional state. Focus particularly on calculus in mathematics, and include mental support content to reduce anxiety."

[1563] "Analyze students' emotional states from their facial expressions and audio data while they are participating in online quizzes, and provide real-time feedback as needed."

[1564] "Based on students' overall learning progress and emotional data, create a specific learning plan to improve their English listening skills and distribute that plan to their devices."

[1565] As described above, the system of the present invention makes it possible to provide an optimal learning experience for each student, and both improved learning outcomes and a sense of psychological security can be expected.

[1566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1567] Step 1: Collect learning history and test results

[1568] The server collects student learning history and test results from the Learning Management System (LMS). Specifically, it retrieves test results and study time logs from students taken over the past week. The input is student learning data obtained through the LMS API, and the output is learning history and test results stored in the database. Here, API requests are sent, and the data is parsed and saved in JSON format.

[1569] Step 2: Collecting emotional data

[1570] The device collects data on students' facial expressions and voices using a camera and microphone and sends it to an emotion engine. Specifically, the video and audio data captured by the sensors is processed frame by frame and sent to the emotion engine. The input is data on students' facial expressions and voices obtained from the camera and microphone, and the output is emotion data generated by the emotion engine. An emotion analysis algorithm is used to quantify emotional states such as "anxiety" and "concentration."

[1571] Step 3: Data analysis and creation of individualized learning materials

[1572] The server integrates and analyzes collected learning history, test results, and emotional data. Inputs include learning history and test results stored in a database, as well as emotional data sent from the emotion engine. This data is fed into a generative AI model to generate learning materials optimized for each individual student. The output is the generated, personalized learning material. Specifically, the generative AI model automatically generates the material content, creating customized materials based on the student's level of understanding and emotional state.

[1573] Step 4: Delivery and display of individual learning materials

[1574] The server distributes the generated individual learning materials to the students' devices. The input is the individual learning materials generated by the generation AI model, and the output is the distribution of the materials to the devices. The devices display the received materials to the users (students). Specifically, the process involves sending the learning material data to the device and displaying the materials on the device's screen.

[1575] Step 5: Real-time monitoring of learning activities

[1576] The device monitors students' learning activities in real time while they are engaged in them. Inputs include student learning progress, problem-solving speed, correct / incorrect answer data, facial expressions, and voice data, which are sent to the emotion engine. Output is real-time emotion data. Specifically, it sends activity data to the emotion engine, which then analyzes the emotional state in real time.

[1577] Step 6: Real-time feedback generation

[1578] The server analyzes the user's emotional state based on real-time data received from the emotion engine and generates appropriate feedback. The input is real-time emotional data and student learning progress data, while the output is the generated feedback. Specifically, it automatically generates advice such as "Relax and tackle the problem" and sends it to the device.

[1579] Step 7: Regular analysis of learning progress and provision of learning plans

[1580] The server periodically analyzes students' learning progress and sentiment data to generate the next learning plan. The input is past learning progress and sentiment data stored in a database, and the output is the learning plan provided to the student. Specifically, it generates a learning progress report, plans the next learning steps based on it, and delivers it to the terminal. Students can then proceed with their learning based on the presented learning plan.

[1581] (Application Example 2)

[1582] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1583] Traditional education systems struggle to provide not only individualized learning materials tailored to students' progress and understanding, but also real-time feedback that takes into account students' psychological state and emotions. Furthermore, they often lack support to reduce stress and anxiety associated with learning and maximize learning effectiveness. Especially when acquiring specialized skills such as those of a factory operator, psychological state significantly impacts skill acquisition, highlighting the need for a system that can comprehensively manage these factors.

[1584] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting students' learning history and test results, means for analyzing the collected data and evaluating each student's level of understanding, means for generating individual learning materials based on the evaluation results, means for distributing the generated learning materials to the student's terminal, means for monitoring data in real time when students engage in learning activities and generating real-time feedback based on the monitoring data, means for providing the generated feedback to the student's terminal, means for analyzing students' learning progress data and generating an optimal learning plan, means for providing the generated learning plan to the student's terminal, means for analyzing the subject's facial expressions and voice in real time and generating emotional data, means for generating feedback that reduces stress and anxiety based on the emotional data, and means for displaying the generated feedback on the display of a smart device. This makes it possible to provide individualized learning support based on each student's level of understanding and psychological state, enabling effective learning while reducing stress and anxiety associated with skill acquisition.

[1585] "Learning history" refers to data that records what a student has learned in the past and their level of achievement.

[1586] "Test results" refer to data showing students' grades and answers when they took a test.

[1587] "Comprehension level" refers to an assessment that indicates how well a student understands a particular learning topic.

[1588] "Individualized learning materials" refer to learning materials that are created based on each student's learning progress and level of understanding.

[1589] "Real-time monitoring" is the process of collecting and analyzing activity data while students are engaged in learning activities.

[1590] "Feedback" refers to advice and support messages generated based on a student's learning activities and psychological state.

[1591] "Learning progress data" refers to data that shows how well students are progressing according to their learning plan.

[1592] A "study plan" refers to the specific learning content and timetable formulated to help each student achieve their learning goals.

[1593] "Facial expression" refers to the emotions and psychological state that can be interpreted from the movement of facial muscles and facial features.

[1594] "Voice" refers to the audio data produced when speaking, and its tone and changes are used to interpret emotional states.

[1595] "Emotional data" refers to data that indicates the emotional state of students, analyzed from their facial expressions and voices.

[1596] A "smart device" is a portable electronic device that has internet connectivity and high-performance computing capabilities.

[1597] This invention combines an emotion engine with a system that individually supports students' learning activities and provides optimal education based on each student's level of understanding and progress. This system mainly consists of a server, terminals, users (students), and an emotion engine.

[1598] Creating individualized learning materials for students

[1599] The server collects students' learning history and test results and stores them in a database. In addition, the terminal sends data of the user's (student's) facial expressions and voice to an emotion engine, which analyzes this data and generates emotion data. This emotion data is used to understand changes in emotions that occur during the learning process in real time.

[1600] For example, when an operator is learning how to operate a new robotic arm, the camera analyzes the operator's facial expressions in real time and provides relaxation techniques and specific tips if stress or anxiety is detected. Furthermore, if the operator stumbles at a particular step, detailed explanations and video tutorials are displayed on the smart device's screen.

[1601] Real-time feedback

[1602] While the user (student) is engaged in learning activities, the device monitors their activities in real time and sends data such as the speed at which they solve problems, correctness data, facial expressions, and tone of voice to the emotion engine. The emotion engine analyzes this data to identify the user's current emotional state.

[1603] For example, when a user (operator) is learning how to operate a new robot online, the emotion engine analyzes in real time whether they are focused or hesitant. The server analyzes the received data and emotional state and generates real-time feedback. For instance, if the operator is feeling anxious about a problem, the server generates feedback such as, "Please relax and learn. Please refer to the following link," and sends it to the terminal.

[1604] Providing learning plans based on each student's learning progress

[1605] The server periodically analyzes the student's overall learning progress and also considers emotional data from the emotion engine. For example, if an operator is lacking in a specific learning item (e.g., robot assembly) and is experiencing stress during learning, the server takes this into account when creating a learning plan. This plan may include specific study time for assembly reinforcement and relaxation techniques.

[1606] The generated learning plan is delivered from the server to the user's terminal, which then displays it to the user. The user can proceed with their learning based on the presented learning plan, and their emotional state is monitored in real time throughout the process, with appropriate feedback provided.

[1607] Examples of prompts for a generative AI model:

[1608] If the operator feels anxious while operating the robotic arm, generate and display feedback that provides relaxation techniques and specific operating procedures.

[1609] The above describes the specific implementation of the system of the present invention. This system makes it possible to provide each operator with an optimal learning experience, and as a result, both improved efficiency in skill acquisition and a sense of psychological security can be expected.

[1610] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1611] Step 1:

[1612] The server collects students' learning history and test results from a database. As input, it retrieves students' past learning data and test scores. As output, the collected data is prepared for analysis.

[1613] Step 2:

[1614] The server analyzes the collected data and evaluates each student's level of understanding. It uses learning history and test score data as input. For data processing, it uses machine learning algorithms to evaluate students' understanding. The output is the understanding evaluation result for each student.

[1615] Step 3:

[1616] The server generates individual learning materials based on evaluation results. It uses student comprehension assessment results as input. As data processing, it applies a material generation algorithm to create materials appropriate to the student's level of comprehension. Individual learning materials are generated as output.

[1617] Step 4:

[1618] The server distributes the generated learning materials to the students' devices. It handles the individually generated learning materials as input. The learning materials are sent to the students' devices as output.

[1619] Step 5:

[1620] The device monitors data in real time as the user engages in learning activities. It captures user learning behavior data (e.g., answer speed, correct / incorrect answers, facial expressions, voice) as input. For data processing, it collects and processes this behavioral data at high frequency. The output is the monitoring data.

[1621] Step 6:

[1622] The device sends monitoring data to the emotion engine, which generates emotion data. Facial expression and voice data are used as input. As data processing, the emotion engine analyzes this data to estimate the real-time emotional state. Emotion data is obtained as output.

[1623] Step 7:

[1624] The server generates real-time feedback based on monitoring data and sentiment data. Sentiment data and learned behavior data are used as input. A feedback generation algorithm is applied as data processing to create appropriate feedback. Feedback data is obtained as output.

[1625] Step 8:

[1626] The device provides the generated feedback to the student. It uses feedback data as input. The feedback message is displayed on the device's screen as output.

[1627] Step 9:

[1628] The server periodically analyzes students' learning progress data and generates an optimal learning plan. It uses learning history and progress data as input. As data processing, it applies a progress analysis algorithm to create the learning plan. The output is the optimal learning plan.

[1629] Step 10:

[1630] The server provides the generated study plan to the student's device. The generated study plan is used as input. The study plan is sent to the student's device as output.

[1631] Step 11:

[1632] The device analyzes the user's facial expressions and voice in real time and generates emotion data. It uses student facial expression and voice data as input. The emotion engine analyzes this data for processing. The output is emotion data.

[1633] Step 12:

[1634] The device generates feedback to reduce stress and anxiety based on emotional data. Emotional data is used as input. A feedback generation algorithm creates feedback tailored to the emotional state as data processing. A feedback message is obtained as output.

[1635] Step 13:

[1636] The terminal displays the generated feedback on the smart device's display. It uses a feedback message as input. The feedback is displayed in the user's field of view as output.

[1637] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1638] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1639] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1640] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1641] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1642] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1643] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1644] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1645] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1646] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1647] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1648] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1649] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1650] 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.

[1651] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1652] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1653] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1654] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1655] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1656] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1657] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1658] The following is further disclosed regarding the embodiments described above.

[1659] (Claim 1)

[1660] Means for collecting students' learning history and test results,

[1661] A method for analyzing collected data and evaluating each student's level of understanding,

[1662] A means for generating individual learning materials based on evaluation results,

[1663] A means of distributing the generated learning materials to students' devices,

[1664] We monitor data in real time as students engage in learning activities.

[1665] A means for generating real-time feedback based on monitoring data,

[1666] A means of providing the generated feedback to the student's device,

[1667] A means for analyzing students' learning progress data and generating an optimal learning plan,

[1668] A system that includes means for providing the generated learning plan to the student's device.

[1669] (Claim 2)

[1670] The system according to claim 1, comprising means for providing students with links to necessary content based on data collected in real time.

[1671] (Claim 3)

[1672] The system according to claim 1, wherein the aforementioned learning plan includes means for including the materials to be studied next, the content to be reviewed, and the allocation of learning time.

[1673]

[1674] "Example 1"

[1675] (Claim 1)

[1676] Means for collecting students' learning history and test results,

[1677] A method for analyzing collected data and evaluating each student's level of understanding,

[1678] A means for generating individual learning materials based on evaluation results,

[1679] A means of generating individual learning materials using a generative AI model,

[1680] A means of dist...

Claims

1. Means for collecting students' learning history and test results, A method for analyzing collected data and evaluating each student's level of understanding, A means for generating individual learning materials based on evaluation results, A means of distributing the generated learning materials to students' devices, We monitor data in real time as students engage in learning activities. A means for generating real-time feedback based on monitoring data, A means of providing the generated feedback to the student's device, A means for analyzing students' learning progress data and generating an optimal learning plan, A system that includes means for providing the generated learning plan to the student's device.

2. The system according to claim 1, which includes means for providing students with links to necessary content based on data collected in real time.

3. The system according to claim 1, wherein the aforementioned learning plan includes means for specifying the next learning materials, the content to be reviewed, and the allocation of learning time.

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