system

Abstract

Providing an effective and efficient system to support comprehensive individualized education. [Solution] A system including: a means for receiving a user's basic information, learning situation, test results, and personality assessment results as input; a means for saving the received information in a database; a means for analyzing the user's level of understanding and ability based on the saved information; a means for automatically generating an individualized learning curriculum based on the analysis results; a means for providing the generated learning curriculum to the user; a means for monitoring the user's learning progress and sending the data to a server; a means for analyzing the sent data in real time and updating and providing feedback to the learning curriculum; and a means for providing feedback and the updated curriculum to the user.

Description

[Technical Field]

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

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

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

[0004] Despite their desire to learn, there are children who cannot attend school or cram schools due to unavoidable circumstances, such as physical, mental, or financial constraints. Providing these children with equal learning opportunities is a challenge. While detailed learning support tailored to each child's level of understanding, ability, and personality is required, the manpower and resources available to make this a reality are limited. Therefore, it is necessary to provide an effective and efficient system that supports a wide range of individualized education. [Means for solving the problem]

[0005] The present invention provides a system including means for receiving as input a user's basic information, learning situation, test results, and personality assessment results, means for saving the received information in a database, means for analyzing the user's level of understanding and ability based on the saved information, means for automatically generating an individualized learning curriculum based on the analysis results, means for providing the generated learning curriculum to the user, means for monitoring the user's learning progress and sending the data to a server, means for analyzing the sent data in real time and updating and providing feedback on the learning curriculum, and means for providing the feedback and updated curriculum to the user. This enables an environment in which users can receive optimized learning support at any time, regardless of location.

[0006] "Basic information" refers to personal information such as the user's name, age, grade, and areas of interest.

[0007] "Study status" refers to information related to learning, such as the subjects the user is currently studying, the content of their studies, and their progress.

[0008] "Test results" refers to the results of a test or exam taken by a user, such as grades, scores, and answer status.

[0009] "Personality diagnosis results" refers to the results of diagnosing the user's personality, behavioral characteristics, learning style, etc.

[0010] A "database" refers to an information storage system that stores and manages collected data and makes it available for retrieval when needed.

[0011] "Analysis" refers to the computational process of evaluating and determining the user's level of understanding and ability based on collected data.

[0012] "Individualized learning curriculum" refers to learning content and plans that are optimized to the individual characteristics and learning needs of each user.

[0013] "Generation" refers to the computational process of creating a new learning curriculum based on the analysis results.

[0014] "Monitoring" refers to the act of constantly monitoring a user's learning progress and behavior and collecting that data.

[0015] "Real-time analysis" refers to the process of analyzing collected data immediately and reflecting the results immediately.

[0016] "Feedback" refers to information about learning progress and areas for improvement provided to users based on the analysis results.

[0017] "Server" refers to a central computer that stores, processes, and transmits data.

[0018] "Terminal" refers to a device that is directly operated by a user to input or output data.

[0019] "User" refers to students or learners who use this system to study. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention relates to a system that uses a generative AI to provide a user with an optimized learning curriculum. Specific embodiments of the system are described below.

[0042] System Configuration

[0043] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[0044] Basic Features

[0045] 1. User Registration and Data Collection

[0046] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, grade, and areas of interest).

[0047] The user enters the necessary information through the input screen and presses the registration button.

[0048] The terminal transmits the input information to the server.

[0049] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0050] 2. Initial data collection

[0051] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[0052] The user answers the questionnaire and transmits the data to the terminal.

[0053] The terminal transmits the collected data to the server.

[0054] The server stores this data in a database and uses it as initial information.

[0055] 3. Curriculum Generation

[0056] The server uses generative AI to analyze the collected information.

[0057] The generative AI evaluates the user's level of understanding, ability, and personality, and generates a personalized learning curriculum.

[0058] The server transmits the generated curriculum to the terminal.

[0059] 4. Learning Progression and Monitoring

[0060] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0061] The device collects the user's learning progress and test results in real time and sends them to the server.

[0062] The server analyzes the collected data and updates the curriculum as needed.

[0063] 5. Providing Feedback

[0064] The server generates feedback based on the analysis results and sends it to the terminal.

[0065] The device provides the user with progress reports and next learning steps.

[0066] Specific examples

[0067] First time user

[0068] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[0069] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[0070] Inputting initial data and generating curriculum

[0071] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[0072] The user answers the questionnaire and transmits the answers to the terminal.

[0073] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[0074] Learning progress management and feedback

[0075] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[0076] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[0077] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[0078] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[0079] The processing flow will be explained below.

[0080] Step 1: User Registration

[0081] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[0082] The user enters the necessary information into the input screen and presses the registration button.

[0083] The terminal transmits the input information to the server.

[0084] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0085] Step 2: Initial data collection

[0086] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[0087] The user answers the questionnaire and enters the necessary data.

[0088] The terminal transmits the collected data to the server.

[0089] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[0090] Step 3: Generate the initial curriculum

[0091] The server performs analysis using generative AI based on the stored initial data.

[0092] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[0093] The server transmits the generated learning curriculum to the terminal.

[0094] Step 4: Start learning and monitor your progress

[0095] The terminal displays the learning curriculum provided to the user and presents the first task.

[0096] Users begin studying according to the curriculum and take assignments and tests.

[0097] The device records learning progress data and test results in real time and transmits them to the server.

[0098] Step 5: Analyze the training data

[0099] The server analyzes the received progress data and test results.

[0100] The generative AI evaluates the user's learning status in real time and adjusts the content and difficulty of the curriculum as needed.

[0101] Step 6: Feedback and curriculum updates

[0102] The server generates feedback based on the analysis results and transmits it to the terminal.

[0103] The device provides feedback and next learning steps to the user.

[0104] The server updates the learning curriculum based on the user's level of understanding and transmits the new curriculum to the terminal.

[0105] Step 7: Continuous monitoring

[0106] The terminal continuously monitors the user's learning progress and transmits progress data to the server as needed.

[0107] The server periodically reevaluates the learning curriculum based on this data and continues to provide optimal learning content.

[0108] This allows the system to respond to the user's learning needs and provide effective and personalized learning support.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] Conventional learning support systems have difficulty providing optimal learning curricula tailored to individual users' characteristics and learning progress, and tend to have uniform content. This leads to issues such as reduced learning efficiency and difficulty in sustaining motivation. Furthermore, it is difficult to provide real-time feedback or update the curriculum, resulting in a lack of flexibility in situations where immediate user response is essential.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes means for receiving the user's basic information, learning information, evaluation results, and diagnostic results as input, means for storing the received information in a data storage device, and means for analyzing the user's level of understanding and ability based on the stored information, thereby making it possible to automatically generate an individualized educational program, monitor the user's learning progress in real time, and provide feedback.

[0114] "User" refers to an individual who uses the system to learn.

[0115] "Basic information" refers to personal information such as the user's name, age, grade, and areas of interest.

[0116] "Learning information" refers to data such as a user's learning status, progress, and past test results.

[0117] "Assessment Results" refers to the scores or grades based on tests or assessments taken by a User.

[0118] "Diagnosis results" refers to data related to the user's personality diagnosis and ability evaluation.

[0119] "Means for receiving input" refers to the interface and functions for inputting basic information and learning information provided by users into the system.

[0120] "Data storage device" refers to a database or storage system for storing received information.

[0121] "Means for analyzing a user's level of understanding and ability based on stored information" refers to algorithms and analytical tools that analyze data in real time and evaluate a user's learning progress and ability.

[0122] "Individualized educational program" refers to a learning curriculum optimized for each user based on the analysis results.

[0123] "Means for automatic generation" refers to the function of automatically designing educational programs using a generative AI model based on collected data.

[0124] "Means for providing" refers to the interface and functions for presenting the generated educational program and feedback to the user.

[0125] "Means for monitoring and transmitting the data to an information processing device" refers to a function or device for acquiring the user's learning data in real time and sending it to a server.

[0126] "Means of analyzing in real time and updating and providing feedback to educational programs" refers to algorithms and functions that instantly analyze received data and generate new curricula and feedback.

[0127] "Means for providing feedback and updated educational programs" refers to interfaces and functions that notify users of analysis results and new curricula.

[0128] This invention is a system that uses generative AI to provide users with optimized educational programs. This system consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[0129] User registration and data collection

[0130] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, grade, and areas of interest). The user enters the necessary information through the input screen and presses the registration button. The terminal then sends the entered information to the server. The server stores the received information in a database (for example, MySQL (registered trademark) or PostgreSQL), generates a user ID, and returns it to the terminal.

[0131] Initial data collection

[0132] The device displays a questionnaire for learning status, past test results, and personality assessment. The user answers the questionnaire and sends the data to the device. The device then sends the collected data to the server. The server stores this data in a database and uses it as initial information.

[0133] Curriculum Generation

[0134] The server analyzes the collected information using a generation AI (e.g., GPT-3 (registered trademark) or ChatGPT (registered trademark)). The generation AI evaluates the user's level of understanding, ability, and personality, and generates a personalized educational program. The server then sends the generated educational program to the device.

[0135] Learning progression and monitoring

[0136] Users study according to the educational program provided through their device. The device collects the user's learning progress and test results in real time and sends them to the server. The server analyzes the collected data and updates the educational program as necessary.

[0137] Providing Feedback

[0138] The server generates feedback based on the analysis results and sends it to the device, which then presents the user with a progress report and next learning steps.

[0139] Specific examples

[0140] First time user

[0141] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button. The terminal then sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the terminal.

[0142] Inputting initial data and generating curriculum

[0143] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond. The user answers the questionnaire and sends the answers to the device. The device then sends the response data to a server, which stores it in a database. The server then uses generative AI to analyze this data and generate an educational program optimized for the user.

[0144] Learning progress management and feedback

[0145] As the user progresses through their studies, the device records their progress and test results in real time and sends them to the server. The server analyzes this data, adjusts the curriculum as needed, and generates feedback. The device then presents the feedback and updated curriculum information to the user and guides them on their next learning steps.

[0146] Examples of prompt statements

[0147] When registering as a first-time user:

[0148] Enter basic information about the user (name, age, grade, areas of interest).

[0149] When entering the initial data:

[0150] Please answer a questionnaire about your past learning history, test results, and personality assessment.

[0151] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] System processing steps

[0154] Step 1: Enter your user registration information

[0155] 1.1 The terminal displays the new user registration screen.

[0156] Input: User's basic information (name, age, grade, areas of interest)

[0157] Output: User input data

[0158] 1.2 The user enters basic information and presses the registration button.

[0159] Specific actions: form entry, button click

[0160] 1.3 The terminal sends the entered information to the server.

[0161] Input: User-entered data

[0162] Data processing: Convert to JSON format

[0163] Output: Send data to the server

[0164] 1.4 The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0165] Input: JSON data received from the terminal

[0166] Data calculation: saving to database, generating user ID

[0167] Output: User ID

[0168] Step 2: Initial data collection through a questionnaire

[0169] 2.1 The device displays a questionnaire about learning status, past test results, and personality assessment.

[0170] Input: None

[0171] Output: Survey screen

[0172] 2.2 The user answers the questionnaire and sends the data to the terminal.

[0173] Input: User response data

[0174] Specific actions: filling in a form, clicking the submit button

[0175] 2.3 The device sends the collected data to the server.

[0176] Input: User response data

[0177] Data processing: Convert to JSON format

[0178] Output: Send data to the server

[0179] 2.4 The server stores this data in a database.

[0180] Input: JSON data received from the terminal

[0181] Data calculation: Saving to database

[0182] Output: Save completion status

[0183] Step 3: Curriculum generation

[0184] 3.1 The server will use the generated AI to analyze the collected information.

[0185] Input: Collected user data

[0186] Data calculation: Analysis by generative AI (e.g., GPT-3)

[0187] Output: Analysis results

[0188] 3.2 Generative AI evaluates the user's level of understanding, ability, and personality and generates a personalized educational program.

[0189] Input: Analysis results

[0190] Data Computation: Generating Learning Curricula

[0191] Output: personalized education program

[0192] 3.3 The server sends the generated curriculum to the terminal.

[0193] Input: Educational Program Data

[0194] Data processing: Convert to JSON format

[0195] Output: Sending data to the terminal

[0196] Step 4: Progressing and monitoring your learning

[0197] 4.1 The user will study according to the educational program provided through the terminal.

[0198] Input: Educational Program

[0199] Specific behavior: Starting a learning activity

[0200] 4.2 The device records the user's learning progress and test results in real time and transmits them to the server.

[0201] Input: Learning progress data, test results

[0202] Data processing: Convert to JSON format

[0203] Output: Send data to the server

[0204] 4.3 The server will analyze the collected data and update the educational program as necessary.

[0205] Input: Learning progress data, test results

[0206] Data Calculation: Reanalysis by Generative AI

[0207] Output: Updated educational program

[0208] Step 5: Provide feedback

[0209] 5.1 The server generates feedback based on the analysis results and sends it to the device.

[0210] Input: Analysis results

[0211] Data Calculation: Feedback Generation

[0212] Output: Feedback data

[0213] 5.2 The device provides the user with progress reports and next learning steps.

[0214] Input: Feedback data, updated educational program

[0215] Specific operation: Screen display

[0216] Output: Providing feedback to the user

[0217] In this way, the system performs specific data processing and calculations at each processing step, providing users with an optimized educational program and feedback.

[0218] (Application example 1)

[0219] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0220] There is a need to maximize the effectiveness of training for robot operators in factories and provide an efficient curriculum tailored to each operator's skills and level of understanding. However, conventional training methods make it difficult to individually optimize training and are unable to reflect the different progress and proficiency levels of each operator. Real-time feedback and curriculum updates are also insufficient.

[0221] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0222] In this invention, the server includes means for receiving as input the user's basic information, learning status, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for applying the system to the training of robot operators in factories, means for progressing the learning using a smartphone or head-mounted display, and means for using a generative AI model to perform analysis based on prompt sentences. This makes it possible to generate an optimal training curriculum tailored to the skills and level of understanding of each robot operator and provide feedback in real time.

[0223] "Basic user information" refers to personal information such as the robot operator's name, age, job title, past experience, and areas of interest.

[0224] "Learning status" is information that indicates the current progress of the robot operator in learning.

[0225] "Test results" refers to the result data of skill tests and comprehension tests conducted by robot operators.

[0226] "Personality diagnosis results" are diagnostic results used to evaluate the personality and temperament of a robot operator.

[0227] A "database" is a digital data collection for storing collected information.

[0228] "Means for analyzing comprehension and performance" means a method or system used to evaluate a robot operator's comprehension and performance based on the collected information.

[0229] An "individualized learning curriculum" is a learning plan optimized for the individual characteristics and abilities of each robot operator.

[0230] "Monitoring learning progress" means constantly monitoring the robot operator's learning process and recording that progress.

[0231] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and generate optimal learning curricula.

[0232] A "prompt" is a document or question that is input into an AI model and forms the basis for the generated information.

[0233] "Feedback" means advice, comments, and guidance on next steps provided as learning progresses.

[0234] A "smartphone" is a mobile device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[0235] A "head-mounted display" is a device worn on the head that displays visual information directly in front of the eyes.

[0236] This invention is a system that utilizes a generative AI model to provide an individualized learning curriculum for robot operators in a factory. The system generates an optimal training curriculum based on the characteristics of a specific user and provides feedback. A specific embodiment of this system is described below.

[0237] System Configuration

[0238] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using a generative AI model. The terminal is a device that collects user input data and learning progress, specifically a smartphone or head-mounted display (HMD). The user is a robot operator in the factory.

[0239] Basic Features

[0240] 1. User Registration and Data Collection

[0241] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, job title, past experience, areas of interest, etc.).

[0242] The user enters the necessary information through the input screen and presses the registration button.

[0243] The terminal transmits the input information to the server.

[0244] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0245] 2. Initial data collection

[0246] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[0247] The user answers the questionnaire and transmits the data to the terminal.

[0248] The terminal transmits the collected data to the server.

[0249] The server stores this data in a database and uses it as initial information.

[0250] 3. Curriculum Generation

[0251] The server analyzes the collected information using a generative AI model.

[0252] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[0253] The server transmits the generated curriculum to the terminal.

[0254] 4. Learning Progression and Monitoring

[0255] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0256] The device collects the user's learning progress and test results in real time and sends them to the server.

[0257] The server analyzes the collected data and updates the curriculum as needed.

[0258] 5. Providing Feedback

[0259] The server generates feedback based on the analysis results and sends it to the terminal.

[0260] The device provides the user with progress reports and next learning steps.

[0261] Specific examples

[0262] First time user

[0263] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information: the user enters their name, age, job title, past experience, areas of interest, etc., and presses the submit button.

[0264] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[0265] Inputting initial data and generating curriculum

[0266] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[0267] The user answers the questionnaire and transmits the answers to the terminal.

[0268] The device sends the answer data to the server, which stores it in a database. The server then analyzes the data using a generative AI model to generate a learning curriculum optimized for the user.

[0269] Learning progress management and feedback

[0270] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[0271] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[0272] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[0273] Prompt sentences to input to the generative AI model

[0274] User Basic Information:

[0275] Name: XX

[0276] Age: 35

[0277] Position: Robot Operator

[0278] Previous experience: 3 years of factory operations

[0279] Areas of interest: Smart factory technology, safety protocols

[0280] Skills test result: Beginner

[0281] Use this information to generate the optimal robotics operations training curriculum.

[0282] This system will enable factory robot operators to receive individually optimized training, leading to efficient and effective skill development.

[0283] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0284] Step 1:

[0285] User registration and data collection

[0286] The terminal displays a new user registration screen and asks the user to enter information such as name, age, job title, past experience, and areas of interest.

[0287] The user enters the necessary information on the registration screen and presses the send button.

[0288] The terminal transmits the input data to the server.

[0289] The server stores the received data in a database, generates a user ID, and returns it to the terminal.

[0290] Input: User basic information

[0291] Output: User ID

[0292] Step 2:

[0293] Initial data collection

[0294] The device displays a questionnaire including learning status, past test results, and personality assessment.

[0295] The user answers the questionnaire and submits the data.

[0296] The terminal transmits the collected data to the server.

[0297] The server stores this data in a database and uses it as initial information.

[0298] Input: Study status, past test results, personality test answers

[0299] Output: Initial information

[0300] Step 3:

[0301] Curriculum Generation

[0302] The server uses a generative AI model to analyze the collected information.

[0303] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[0304] The server transmits the generated curriculum to the terminal.

[0305] Input: Basic information, learning status, past test results, personality test answers

[0306] Data processing: Analysis using generative AI models

[0307] Output: personalized learning curriculum

[0308] Step 4:

[0309] Learning progression and monitoring

[0310] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0311] The device collects the user's learning progress and test results in real time and sends them to the server.

[0312] The server analyzes the collected data and updates the curriculum as needed.

[0313] Input: Learning progress, test results

[0314] Data processing: Real-time analysis of progress data

[0315] Output: Updated learning curriculum

[0316] Step 5:

[0317] Providing Feedback

[0318] The server generates feedback based on the analysis results and sends it to the terminal.

[0319] The device provides the user with progress reports and next learning steps.

[0320] Input: Progress data, test results

[0321] Data processing: Generate feedback based on analysis results

[0322] Output: Feedback, progress report, next learning steps

[0323] As described above, the system inputs, processes, and outputs appropriate data at each step, providing users with the optimal learning curriculum and feedback.

[0324] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0325] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0326] System Configuration

[0327] The system mainly consists of a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[0328] Basic Features

[0329] 1. User Registration and Data Collection

[0330] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[0331] The user enters the necessary information into the input screen and presses the registration button.

[0332] The terminal transmits the input information to the server.

[0333] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0334] 2. Initial data collection

[0335] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[0336] The user answers the questionnaire and enters the necessary data.

[0337] The terminal transmits the collected data to the server.

[0338] The server stores the received data in a database and uses it as initial information.

[0339] 3. Curriculum Generation

[0340] The server performs analysis using generative AI based on the stored initial data.

[0341] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[0342] The server transmits the generated learning curriculum to the terminal.

[0343] 4. Emotional Data Collection and Analysis

[0344] The terminal uses an emotion engine to recognize the user's emotions in real time.

[0345] Through the camera and microphone displayed while the user is learning, the emotion engine analyzes facial expressions and tone of voice to generate emotion data.

[0346] The terminal transmits the emotion data to the server.

[0347] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[0348] 5. Curriculum Adjustment and Feedback

[0349] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the entire curriculum.

[0350] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[0351] The server sends updated curriculum and feedback to the terminal.

[0352] 6. Learning Progression and Monitoring

[0353] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0354] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[0355] The server updates the curriculum and generates feedback based on the collected data.

[0356] Specific examples

[0357] First time user

[0358] When a user logs in to the system for the first time, the terminal displays a basic information entry screen. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[0359] The device sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[0360] Inputting initial data and generating curriculum

[0361] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[0362] The user answers the questionnaire and transmits the answers to the terminal.

[0363] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[0364] Emotion data collection and analysis

[0365] As the user progresses with their learning, the device uses an emotion engine to measure the user's facial expressions and tone of voice to obtain emotional data.

[0366] The terminal transmits the acquired emotion data to the server in real time.

[0367] The server analyzes the received data and assesses the user's emotional state.

[0368] Curriculum adjustment and feedback

[0369] The server integrates and analyzes the emotion data and the learning progress data, and adjusts the learning curriculum according to the user's current state.

[0370] The generative AI evaluates whether the current curriculum is appropriate based on the user's emotional state and automatically generates necessary changes.

[0371] The server sends the updated curriculum and feedback to the terminal.

[0372] This makes it possible for the present invention to provide an individually optimized learning environment that takes into account the user's emotional state.

[0373] The processing flow will be explained below.

[0374] Step 1: User Registration

[0375] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[0376] The user enters the necessary information into the input screen and presses the registration button.

[0377] The terminal transmits the input information to the server.

[0378] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0379] Step 2: Initial data collection

[0380] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[0381] The user answers the questionnaire and enters the necessary data.

[0382] The terminal transmits the collected data to the server.

[0383] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[0384] Step 3: Generate the initial curriculum

[0385] The server performs analysis using generative AI based on the stored initial data.

[0386] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[0387] The server transmits the generated learning curriculum to the terminal.

[0388] Step 4: Start learning and collect emotional data

[0389] The terminal displays the learning curriculum provided to the user and presents the first task.

[0390] Users begin studying according to the curriculum and take assignments and tests.

[0391] During learning, the device uses an emotion engine to analyze the user's facial expressions and voice in real time and obtain emotional data.

[0392] The terminal transmits the acquired emotion data to the server.

[0393] Step 5: Record your learning progress

[0394] The terminal records the user's learning progress data and test results and transmits them to the server.

[0395] The server stores the received data in a database for subsequent analysis.

[0396] Step 6: Analyze the sentiment data

[0397] The server analyzes the emotion data sent from the emotion engine.

[0398] The generative AI evaluates the user's current emotional state based on emotional data and estimates their learning load and stress level.

[0399] Step 7: Adjust the curriculum

[0400] The server integrates and analyzes the emotional data and learning progress data, and adjusts the learning curriculum as needed.

[0401] The generative AI updates the difficulty and content of the curriculum based on the user's emotions and learning situation.

[0402] The server transmits the updated curriculum to the terminal.

[0403] Step 8: Provide feedback

[0404] The server generates feedback based on the analysis results and transmits it to the terminal.

[0405] The device provides feedback and next learning steps to the user.

[0406] Step 9: Continuous monitoring

[0407] The device continuously monitors the user's learning progress and emotional data, and transmits the data to the server as needed.

[0408] The server periodically reevaluates the learning curriculum based on this data and makes any necessary updates.

[0409] The above processing steps enable the system to take emotional data into account and provide more personalized learning assistance.

[0410] Example 2

[0411] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0412] Current learning systems lack the ability to fully understand each user's learning progress and emotional state and automatically adjust the optimal learning curriculum accordingly. This increases the likelihood that users will experience stress or inappropriate learning load, which can reduce learning effectiveness. Therefore, a learning system that is personalized for each user and can adjust in real time is needed.

[0413] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0414] In this invention, the server includes means for receiving as input the user's basic information, learning situation, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for automatically generating an individualized learning curriculum based on the analysis results using a generation AI, means for providing the generated learning curriculum to the user, means for recognizing, recording, and transmitting the user's learning progress and emotional data in real time, means for analyzing the transmitted learning progress and emotional data in real time and updating and providing feedback on the learning curriculum, and means for providing the feedback and updated curriculum to the user. This makes it possible to provide an individual learning curriculum optimized for each user in real time, maximizing learning effectiveness.

[0415] "User" refers to an individual who uses the system to learn.

[0416] "Basic information" refers to information that identifies and locates an individual, such as the user's name, age, grade, and areas of interest.

[0417] "Study status" refers to information including a user's current learning progress, past learning records, and learning goals.

[0418] "Test results" refers to the scores and evaluation results of tests that a user has taken in the past.

[0419] "Personality diagnosis results" refers to the results of evaluating the user's personality traits and behavioral patterns.

[0420] "Database" refers to a collection used to store, manage, and retrieve received information.

[0421] "Analysis" refers to the act of assessing the user's level of understanding and ability based on the information received.

[0422] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to generate useful information from data for a specific task or purpose.

[0423] "Learning curriculum" refers to a systematic plan of the learning materials and learning steps necessary to achieve a user's learning goals.

[0424] "Feedback" refers to advice and suggestions for improvement provided at the appropriate time based on the results and emotional state of the user as they progress through their learning.

[0425] "Emotional Data" refers to information about a user's emotional state obtained through facial expressions, tone of voice, and other physiological responses.

[0426] "Real-time" refers to a situation where a response or processing occurs almost immediately.

[0427] "Analysis results" refers to the evaluation of the user's level of understanding and ability that is derived after the generating AI processes the information it receives.

[0428] "Monitoring" refers to the act of continuously observing a user's learning progress and emotional state and collecting data as necessary.

[0429] "Adjustment" refers to the act of changing the content or difficulty of the learning curriculum based on the analysis results and emotional data.

[0430] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0431] System Configuration

[0432] This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[0433] User registration and data collection

[0434] The terminal displays a new user registration screen and uses front-end technologies such as HTML, CSS, and JavaScript (registered trademark) to prompt the user to enter basic information such as name, age, grade, and areas of interest. The user enters this information and presses the registration button. For example, a user may enter "Yamada Taro," age "15 years old," grade "first year of high school," and area of ​​interest "mathematics." The terminal sends the entered information to the server via a POST request, and the server stores the received information in a database, then generates a unique user ID and returns it to the terminal.

[0435] Example prompt sentence:

[0436] "Please enter your name. Tell us your age. Also select your grade and areas of interest."

[0437] Initial data collection

[0438] Next, the device displays a questionnaire screen with information about the user's learning status, past test results, and personality assessment, and asks the user to answer. The user answers these questions, for example, entering "My past test results were 80 points" or "My personality assessment result is a cautious type." The device then sends this data to the server, which stores it in a database.

[0439] Example prompt sentence:

[0440] "Enter your past test results. Take the personality test."

[0441] Curriculum Generation

[0442] The server uses a generation AI to analyze the stored initial data. The generation AI evaluates the user's level of understanding, ability, and personality assessment results, and generates a personalized learning curriculum. For example, the generation AI determines that the user's level of understanding of mathematics is "intermediate," and generates a curriculum that includes "differential and integral calculus" as the next step. The generated curriculum is sent from the server to the device, providing the user with the next task to tackle.

[0443] Example prompt sentence:

[0444] "Generate an appropriate learning curriculum based on the user's initial data."

[0445] Emotion data collection and analysis

[0446] During training, the device uses an emotion engine to recognize the user's facial expressions and tone of voice in real time. For example, if the user shows a "tired" expression, the emotion engine will detect this and record it as data. The device then sends the emotion data to the server, which then evaluates the user's emotional state based on this data.

[0447] Example prompt sentence:

[0448] "Collect real-time user sentiment data."

[0449] Curriculum Adjustment and Feedback

[0450] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum. For example, if a user feels stressed, the generative AI will add a "break time" and suggest tasks that can be completed in a relaxed state. The adjusted curriculum is sent from the server to the device, and the latest learning plan is provided to the user.

[0451] Example prompt sentence:

[0452] "Adjust your curriculum based on user sentiment data."

[0453] Learning progression and monitoring

[0454] The user studies according to the learning curriculum provided through the device. The device records the user's learning progress and emotional data in real time and sends it to the server. The server analyzes this data, updates the curriculum as needed, and generates feedback. The feedback and updated curriculum are provided to the device, making it possible to maintain an optimal learning environment for the user.

[0455] Example prompt sentence:

[0456] "Monitor users' learning progress and provide feedback as needed."

[0457] The above is a specific embodiment of the system according to the present invention, which aims to grasp the user's emotional state and learning situation in real time and provide an optimal learning curriculum.

[0458] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0459] Step 1:

[0460] The terminal displays a new user registration screen.

[0461] Input: User registration form using HTML / CSS / JavaScript.

[0462] Details: The device displays a form that asks the user to enter their name, age, grade, and interests.

[0463] Output: Basic information entered by the user.

[0464] Specific operation: For example, a user inputs "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[0465] Step 2:

[0466] The user enters basic information and presses the registration button.

[0467] Input: User-entered name, age, grade, and interests.

[0468] Details: The user fills in the form with the required information and clicks the register button.

[0469] Output: Basic information received by the device.

[0470] Specific operation: When the user presses the registration button, the information displayed on the screen is sent from the device to the server.

[0471] Step 3:

[0472] The terminal transmits the input basic information to the server.

[0473] Input: Basic information about the user received by the device.

[0474] Details: The device sends input data to the server via an HTTP POST request.

[0475] Output: Basic information received by the server.

[0476] Specific operation: The request body sent by the device contains data such as name "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[0477] Step 4:

[0478] The server stores the received basic information in a database, generates a unique user ID, and returns it to the terminal.

[0479] Input: The user's basic information received by the server.

[0480] Details: The server stores the received data in a database and generates a unique user ID for the new user.

[0481] Output: A unique user ID and basic information stored in the database.

[0482] Specific operation: The server stores the information in a MySQL database, generates a user ID "12345", and returns it to the terminal.

[0483] Step 5:

[0484] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[0485] Input: User ID and basic information received from the server.

[0486] Details: The device displays a questionnaire screen where students can enter their learning status and past test results.

[0487] Output: User-entered learning status, past test results, and personality assessment results.

[0488] What it does: The device prompts you with a question, such as "Please enter your past math test scores."

[0489] Step 6:

[0490] The user answers the questionnaire and enters the required data.

[0491] Input: User-entered learning history, past test results, and personality assessment results.

[0492] Details: Users answer questions by entering text or by selecting multiple options.

[0493] Output: Survey results received by the device.

[0494] Specific behavior: The user enters "80 points" and selects "Conscientious" on the personality test.

[0495] Step 7:

[0496] The terminal transmits the collected questionnaire data to the server.

[0497] Input: Survey results received by the device.

[0498] Details: The device sends the survey data to the server via an HTTP POST request.

[0499] Output: Survey data received by the server.

[0500] Specific operation: The request sent by the device includes data such as learning status (80 points) and personality test result (cautious type).

[0501] Step 8:

[0502] The server stores the received survey data in a database and analyzes it using a generative AI model.

[0503] Input: Survey data received by the server.

[0504] Details: The server stores the received data in a database and analyzes it using a generative AI model.

[0505] Output: User's understanding and ability assessment as a result of the analysis.

[0506] Specific operation: The server stores the information in a database and passes the input data to the generation AI for analysis.

[0507] Step 9:

[0508] Generative AI generates an individualized learning curriculum based on the user's level of understanding, ability, and personality assessment results.

[0509] Input: Initial data and analysis results.

[0510] Details: Generative AI evaluates users' understanding, abilities, and personality assessment results to generate a personalized learning curriculum.

[0511] Output: A personalized learning curriculum.

[0512] Specific operation: The generation AI determines that the level of understanding is "intermediate" and generates a curriculum that includes "differential and integral calculus."

[0513] Step 10:

[0514] The server transmits the generated learning curriculum to the terminal.

[0515] Input: The generated learning curriculum.

[0516] Details: The server sends the generated curriculum to the device as an HTTP response.

[0517] Output: The learning curriculum sent to the device.

[0518] Specific operation: The device displays the curriculum details received from the generation AI and confirms the next assignment the user will work on.

[0519] Step 11:

[0520] The terminal uses an emotion engine to recognize the user's emotions in real time.

[0521] Input: The user's facial expression and tone of voice.

[0522] Details: The device senses the user's facial expressions and tone of voice while learning and analyzes them using an emotion engine.

[0523] Output: Recognized emotion data.

[0524] Specific operation: The device's camera and microphone detect the user's "tired" facial expression and "low-pitched voice."

[0525] Step 12:

[0526] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data.

[0527] Input: The user's facial expression and tone of voice.

[0528] Details: The emotion engine analyzes facial expressions as data and labels the emotions it detects in real time.

[0529] Output: Emotion data.

[0530] Specific behavior: The emotion engine generates emotion labels such as "fatigue" and "stress."

[0531] Step 13:

[0532] The terminal transmits the emotion data to the server.

[0533] Input: Emotion data generated by the emotion engine.

[0534] Details: The device sends the generated emotion data to the server.

[0535] Output: Emotion data received by the server.

[0536] Specific operation: The device sends the emotional data "fatigue" and "stress" generated to the server.

[0537] Step 14:

[0538] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[0539] Input: Received emotion data.

[0540] Details: The server analyzes the received data and evaluates the user's stress level and learning load.

[0541] Output: Assessment results of learning load and stress level.

[0542] Specific operation: The server analyzes the emotional data "fatigue" and "stress" and evaluates the user's stress level as "high."

[0543] Step 15:

[0544] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum.

[0545] Input: Learning progress data and emotion data.

[0546] Details: The server integrates and analyzes emotional data and learning progress data to regenerate an appropriate learning curriculum.

[0547] Output: A tailored learning curriculum.

[0548] Specific operation: The server informs the generated AI of the user's "high stress level" and adjusts the curriculum to "relaxing tasks that include breaks."

[0549] Step 16:

[0550] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[0551] Input: Data indicating the user's emotional state.

[0552] Details: Generative AI assesses the appropriateness of the curriculum based on the user's emotional state and makes necessary adjustments.

[0553] Output: Specific learning steps and break timings.

[0554] Specific behavior: The generative AI suggests to the user to "take a break for the next 30 minutes."

[0555] Step 17:

[0556] The server sends updated curriculum and feedback to the terminal.

[0557] Input: Updated learning curriculum and feedback.

[0558] Details: The server sends updated curriculum information to the device and provides feedback.

[0559] Output: Updated curriculum and feedback delivered to the device.

[0560] What happens: Materials containing the updated curriculum "Differential and Integral Calculus" and the feedback "Take a break for the next 30 minutes" are displayed on the device.

[0561] Step 18:

[0562] The user proceeds with the study according to the study curriculum provided through the terminal.

[0563] Input: The learning curriculum displayed on the device.

[0564] Details: The user progresses through the learning steps according to the curriculum displayed on the device.

[0565] Output: User's learning progress.

[0566] Specific action: The user works on a "differentiation exercise" while looking at the curriculum materials.

[0567] Step 19:

[0568] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[0569] Input: User's learning progress data and emotional data.

[0570] Details: The device records the user's progress and emotional data in real time and periodically transmits it to the server.

[0571] Output: Learning progress data and emotion data sent to the server.

[0572] Specific operation: The device sends the user's learning progress ("Differential task completed") and emotional data ("Concentration") to the server.

[0573] Step 20:

[0574] The server updates the curriculum based on the collected data and generates feedback.

[0575] Input: Collected learning progress data and emotion data.

[0576] Details: The server analyzes the collected data, updates the curriculum as needed, and generates new feedback.

[0577] Output: Updated curriculum and feedback.

[0578] Specific operation: The server generates feedback such as "Start the integration problem as the next step" and sends it to the device.

[0579] This allows the system to create an individually optimized learning environment that takes into account the user's emotions.

[0580] (Application example 2)

[0581] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0582] While conventional learning systems can provide an individualized curriculum based on the user's basic information and learning progress, it is difficult to flexibly adjust the curriculum taking into account the user's emotional state or to suggest break timing.In addition, in the work environment, there was a lack of means to evaluate work efficiency and fatigue level in real time and provide appropriate feedback accordingly, making it a challenge to reduce stress while maximizing worker efficiency.

[0583] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's basic information, learning situation, test results, and personality assessment results as input, means for storing the received information in a database, and means for analyzing the user's level of understanding and ability based on the stored information. This makes it possible to evaluate the user's emotional state and, based on that, propose a work curriculum and break timing.

[0584] "Basic information" refers to attribute information such as the user's name, age, grade, and areas of interest.

[0585] "Study status" is information that indicates the progress and level of understanding of the user's current learning.

[0586] "Test results" is information indicating the results of various tests and evaluations conducted in the past.

[0587] "Personality diagnosis results" are data about the user's personality obtained based on personality diagnosis tests and questionnaires.

[0588] A "database" is a system for storing and managing various information obtained from users.

[0589] "Means of analysis" is a function that evaluates the user's ability and level of understanding based on the information received.

[0590] An "individualized learning curriculum" is an optimal learning plan that is automatically generated to suit each user's level of understanding and ability.

[0591] "Emotion data" refers to information about emotions acquired from the user's facial expressions, tone of voice, etc.

[0592] A "generative AI model" is an algorithm that uses artificial intelligence to analyze data and suggest appropriate learning curricula and break timing.

[0593] "Feedback" refers to guidance and advice provided to users based on the analysis results.

[0594] "Work curriculum" refers to the user's work plan, which includes work content optimized based on the evaluation results.

[0595] "Rest timing" is information that indicates the optimal timing for the user to take an appropriate rest.

[0596] A "server" is a computer system that plays a central role in the system, such as analyzing data and running generative AI models.

[0597] The present invention combines a system that uses a generative AI model to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0598] System Configuration

[0599] The system consists of a server, a device, a user, and an emotion engine. The server analyzes data and runs the generative AI model. The device collects input data, learning progress, and emotion data from the user. The user interacts with the system as a learner. The emotion engine recognizes the user's emotions in real time and analyzes the data.

[0600] Overview of the embodiment

[0601] User registration and data collection

[0602] The device displays a new user registration screen, where the user enters basic information such as name, age, grade, and areas of interest. This information is sent from the device to the server and stored in a database. The server generates a user ID based on the received information and returns it to the device.

[0603] Initial data collection

[0604] Users answer questionnaires about their learning status, past test results, and personality assessments through their devices. The devices send the collected data to a server, which stores it in a database. The server analyzes the initial data it receives and generates a personalized learning curriculum.

[0605] Creating and providing learning curriculum

[0606] The generative AI model analyzes the user's level of understanding, ability, and personality assessment results to generate an optimized learning curriculum. The generated learning curriculum is sent from the server to the device and provided to the user. The user then follows the curriculum through their device to progress through their studies.

[0607] Emotion data collection and analysis

[0608] During the learning process, the device uses an emotion engine to recognize the user's emotions in real time. It analyzes the user's facial expressions and tone of voice through a camera and microphone to generate emotion data. This emotion data is sent from the device to a server, where it is analyzed.

[0609] Curriculum adjustment and feedback

[0610] The server integrates and analyzes emotional data and learning progress data to assess the user's learning load and stress level. Based on these results, the generative AI model adjusts the learning curriculum and suggests appropriate break times and refreshing activities. The updated curriculum and feedback are provided to the user via their device.

[0611] Hardware and software used

[0612] Server: A computer system that analyzes data and runs generative AI models.

[0613] Device: A device (e.g., smartphone, tablet) that collects data from users and provides learning curriculum.

[0614] Emotion engine: Software for analyzing user emotions (e.g., EmotionEngine).

[0615] Generative AI model: An algorithm that analyzes data and generates an optimal curriculum (e.g., AIModule).

[0616] Specific examples

[0617] When a user logs in to the system for the first time, they are presented with a basic information entry screen. The user enters their name, age, grade, areas of interest, etc., and the information is sent from the device to the server. The server stores this information in a database and generates a user ID. Next, the user enters initial data (study status, past test results, personality assessment questionnaire, etc.), which is also sent to the server. A generative AI model generates a personalized learning curriculum and provides it to the device.

[0618] During learning, the device uses an emotion engine to analyze the user's facial expressions and tone of voice, acquiring emotional data and sending it to the server. The server then adjusts the curriculum and provides feedback to the user based on the emotional data and learning progress data.

[0619] An example prompt might be, "Assess the worker's fatigue level and emotional state and suggest optimal work schedules and break timings. Include the following data: work efficiency data, video feed, audio feed. If possible, suggest refreshment activities."

[0620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0621] Step 1:

[0622] The terminal displays a new user registration screen. The user enters basic information (name, age, grade, areas of interest) and presses the registration button. The input data is sent from the terminal to the server. The server stores the received basic information in a database, generates a new user ID, and returns it to the terminal. The input is basic information, and the output is the user ID.

[0623] Step 2:

[0624] The device displays learning status, past test results, and a personality assessment questionnaire, and the user answers each item. The device then sends the collected data to a server. The server stores the received data in a database and analyzes the data using a generative AI model. The input is learning status data, and the output is analyzed data.

[0625] Step 3:

[0626] The server uses a generative AI model to evaluate the user's level of understanding and ability, and generates an individualized learning curriculum. The generated curriculum is sent from the server to the terminal and provided to the user. The input is the evaluation data, and the output is the learning curriculum.

[0627] Step 4:

[0628] As the user continues learning, the device collects facial expressions and tone of voice through the built-in camera and microphone, and generates emotional data in real time using an emotion engine. The generated emotional data is sent from the device to a server. The input is the camera and microphone feed, and the output is emotional data.

[0629] Step 5:

[0630] The server integrates and analyzes the received emotional data and learning progress data to evaluate the user's learning load and stress level. Based on the analysis results, the generative AI model adjusts the learning curriculum and break timing. The input is emotional data and learning progress data, and the output is the adjusted curriculum.

[0631] Step 6:

[0632] The server sends the updated learning curriculum and feedback to the terminal and provides it to the user. The user proceeds with learning according to the adjusted curriculum through the terminal. The input is the adjusted curriculum, and the output is the user's learning progress.

[0633] Step 7:

[0634] The device continuously monitors the user's learning progress and emotional data and sends them to the server. The server analyzes this data at runtime and adjusts the learning curriculum and feedback as needed. The input is the continuously collected learning progress and emotional data, and the output is the adjusted learning curriculum and feedback.

[0635] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0636] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0637] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0638] [Second embodiment]

[0639] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0640] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0641] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0642] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0643] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0644] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0645] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0646] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0647] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0648] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0649] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0650] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0651] The present invention relates to a system that uses a generative AI to provide a user with an optimized learning curriculum. Specific embodiments of the system are described below.

[0652] System Configuration

[0653] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[0654] Basic Features

[0655] 1. User Registration and Data Collection

[0656] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, grade, and areas of interest).

[0657] The user enters the necessary information through the input screen and presses the registration button.

[0658] The terminal transmits the input information to the server.

[0659] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0660] 2. Initial data collection

[0661] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[0662] The user answers the questionnaire and transmits the data to the terminal.

[0663] The terminal transmits the collected data to the server.

[0664] The server stores this data in a database and uses it as initial information.

[0665] 3. Curriculum Generation

[0666] The server uses generative AI to analyze the collected information.

[0667] The generative AI evaluates the user's level of understanding, ability, and personality, and generates a personalized learning curriculum.

[0668] The server transmits the generated curriculum to the terminal.

[0669] 4. Learning Progression and Monitoring

[0670] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0671] The device collects the user's learning progress and test results in real time and sends them to the server.

[0672] The server analyzes the collected data and updates the curriculum as needed.

[0673] 5. Providing Feedback

[0674] The server generates feedback based on the analysis results and sends it to the terminal.

[0675] The device provides the user with progress reports and next learning steps.

[0676] Specific examples

[0677] First time user

[0678] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[0679] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[0680] Inputting initial data and generating curriculum

[0681] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[0682] The user answers the questionnaire and transmits the answers to the terminal.

[0683] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[0684] Learning progress management and feedback

[0685] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[0686] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[0687] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[0688] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[0689] The processing flow will be explained below.

[0690] Step 1: User Registration

[0691] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[0692] The user enters the necessary information into the input screen and presses the registration button.

[0693] The terminal transmits the input information to the server.

[0694] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0695] Step 2: Initial data collection

[0696] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[0697] The user answers the questionnaire and enters the necessary data.

[0698] The terminal transmits the collected data to the server.

[0699] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[0700] Step 3: Generate the initial curriculum

[0701] The server performs analysis using generative AI based on the stored initial data.

[0702] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[0703] The server transmits the generated learning curriculum to the terminal.

[0704] Step 4: Start learning and monitor your progress

[0705] The terminal displays the learning curriculum provided to the user and presents the first task.

[0706] Users begin studying according to the curriculum and take assignments and tests.

[0707] The device records learning progress data and test results in real time and transmits them to the server.

[0708] Step 5: Analyze the training data

[0709] The server analyzes the received progress data and test results.

[0710] The generative AI evaluates the user's learning status in real time and adjusts the content and difficulty of the curriculum as needed.

[0711] Step 6: Feedback and curriculum updates

[0712] The server generates feedback based on the analysis results and transmits it to the terminal.

[0713] The device provides feedback and next learning steps to the user.

[0714] The server updates the learning curriculum based on the user's level of understanding and transmits the new curriculum to the terminal.

[0715] Step 7: Continuous monitoring

[0716] The terminal continuously monitors the user's learning progress and transmits progress data to the server as needed.

[0717] The server periodically reevaluates the learning curriculum based on this data and continues to provide optimal learning content.

[0718] This allows the system to respond to the user's learning needs and provide effective and personalized learning support.

[0719] Example 1

[0720] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0721] Conventional learning support systems have difficulty providing optimal learning curricula tailored to individual users' characteristics and learning progress, and tend to have uniform content. This leads to issues such as reduced learning efficiency and difficulty in sustaining motivation. Furthermore, it is difficult to provide real-time feedback or update the curriculum, resulting in a lack of flexibility in situations where immediate user response is essential.

[0722] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0723] In this invention, the server includes means for receiving the user's basic information, learning information, evaluation results, and diagnostic results as input, means for storing the received information in a data storage device, and means for analyzing the user's level of understanding and ability based on the stored information, thereby making it possible to automatically generate an individualized educational program, monitor the user's learning progress in real time, and provide feedback.

[0724] "User" refers to an individual who uses the system to learn.

[0725] "Basic information" refers to personal information such as the user's name, age, grade, and areas of interest.

[0726] "Learning information" refers to data such as a user's learning status, progress, and past test results.

[0727] "Assessment Results" refers to the scores or grades based on tests or assessments taken by a User.

[0728] "Diagnosis results" refers to data related to the user's personality diagnosis and ability evaluation.

[0729] "Means for receiving input" refers to the interface and functions for inputting basic information and learning information provided by users into the system.

[0730] "Data storage device" refers to a database or storage system for storing received information.

[0731] "Means for analyzing a user's level of understanding and ability based on stored information" refers to algorithms and analytical tools that analyze data in real time and evaluate a user's learning progress and ability.

[0732] "Individualized educational program" refers to a learning curriculum optimized for each user based on the analysis results.

[0733] "Means for automatic generation" refers to the function of automatically designing educational programs using a generative AI model based on collected data.

[0734] "Means for providing" refers to the interface and functions for presenting the generated educational program and feedback to the user.

[0735] "Means for monitoring and transmitting the data to an information processing device" refers to a function or device for acquiring the user's learning data in real time and sending it to a server.

[0736] "Means of analyzing in real time and updating and providing feedback to educational programs" refers to algorithms and functions that instantly analyze received data and generate new curricula and feedback.

[0737] "Means for providing feedback and updated educational programs" refers to interfaces and functions that notify users of analysis results and new curricula.

[0738] This invention is a system that uses generative AI to provide users with optimized educational programs. This system consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[0739] User registration and data collection

[0740] The terminal displays a new user registration screen and asks the user to enter basic information (such as name, age, grade, and areas of interest). The user enters the necessary information through the input screen and presses the registration button. The terminal then sends the entered information to the server. The server stores the received information in a database (for example, MySQL or PostgreSQL), generates a user ID, and returns it to the terminal.

[0741] Initial data collection

[0742] The device displays a questionnaire for learning status, past test results, and personality assessment. The user answers the questionnaire and sends the data to the device. The device then sends the collected data to the server. The server stores this data in a database and uses it as initial information.

[0743] Curriculum Generation

[0744] The server analyzes the collected information using a generation AI (e.g., GPT-3 or ChatGPT). The generation AI evaluates the user's level of understanding, ability, and personality, and generates a personalized educational program. The server then sends the generated educational program to the device.

[0745] Learning progression and monitoring

[0746] Users study according to the educational program provided through their device. The device collects the user's learning progress and test results in real time and sends them to the server. The server analyzes the collected data and updates the educational program as necessary.

[0747] Providing Feedback

[0748] The server generates feedback based on the analysis results and sends it to the device, which then presents the user with a progress report and next learning steps.

[0749] Specific examples

[0750] First time user

[0751] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button. The terminal then sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the terminal.

[0752] Inputting initial data and generating curriculum

[0753] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond. The user answers the questionnaire and sends the answers to the device. The device then sends the response data to a server, which stores it in a database. The server then uses generative AI to analyze this data and generate an educational program optimized for the user.

[0754] Learning progress management and feedback

[0755] As the user progresses through their studies, the device records their progress and test results in real time and sends them to the server. The server analyzes this data, adjusts the curriculum as needed, and generates feedback. The device then presents the feedback and updated curriculum information to the user and guides them on their next learning steps.

[0756] Examples of prompt statements

[0757] When registering as a first-time user:

[0758] Enter basic information about the user (name, age, grade, areas of interest).

[0759] When entering the initial data:

[0760] Please answer a questionnaire about your past learning history, test results, and personality assessment.

[0761] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[0762] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0763] System processing steps

[0764] Step 1: Enter your user registration information

[0765] 1.1 The terminal displays the new user registration screen.

[0766] Input: User's basic information (name, age, grade, areas of interest)

[0767] Output: User input data

[0768] 1.2 The user enters basic information and presses the registration button.

[0769] Specific actions: form entry, button click

[0770] 1.3 The terminal sends the entered information to the server.

[0771] Input: User-entered data

[0772] Data processing: Convert to JSON format

[0773] Output: Send data to the server

[0774] 1.4 The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0775] Input: JSON data received from the terminal

[0776] Data calculation: saving to database, generating user ID

[0777] Output: User ID

[0778] Step 2: Initial data collection through a questionnaire

[0779] 2.1 The device displays a questionnaire about learning status, past test results, and personality assessment.

[0780] Input: None

[0781] Output: Survey screen

[0782] 2.2 The user answers the questionnaire and sends the data to the terminal.

[0783] Input: User response data

[0784] Specific actions: filling in a form, clicking the submit button

[0785] 2.3 The device sends the collected data to the server.

[0786] Input: User response data

[0787] Data processing: Convert to JSON format

[0788] Output: Send data to the server

[0789] 2.4 The server stores this data in a database.

[0790] Input: JSON data received from the terminal

[0791] Data calculation: Saving to database

[0792] Output: Save completion status

[0793] Step 3: Curriculum generation

[0794] 3.1 The server will use the generated AI to analyze the collected information.

[0795] Input: Collected user data

[0796] Data calculation: Analysis by generative AI (e.g., GPT-3)

[0797] Output: Analysis results

[0798] 3.2 Generative AI evaluates the user's level of understanding, ability, and personality and generates a personalized educational program.

[0799] Input: Analysis results

[0800] Data Computation: Generating Learning Curricula

[0801] Output: personalized education program

[0802] 3.3 The server sends the generated curriculum to the terminal.

[0803] Input: Educational Program Data

[0804] Data processing: Convert to JSON format

[0805] Output: Sending data to the terminal

[0806] Step 4: Progressing and monitoring your learning

[0807] 4.1 The user will study according to the educational program provided through the terminal.

[0808] Input: Educational Program

[0809] Specific behavior: Starting a learning activity

[0810] 4.2 The device records the user's learning progress and test results in real time and transmits them to the server.

[0811] Input: Learning progress data, test results

[0812] Data processing: Convert to JSON format

[0813] Output: Send data to the server

[0814] 4.3 The server will analyze the collected data and update the educational program as necessary.

[0815] Input: Learning progress data, test results

[0816] Data Calculation: Reanalysis by Generative AI

[0817] Output: Updated educational program

[0818] Step 5: Provide feedback

[0819] 5.1 The server generates feedback based on the analysis results and sends it to the device.

[0820] Input: Analysis results

[0821] Data Calculation: Feedback Generation

[0822] Output: Feedback data

[0823] 5.2 The device provides the user with progress reports and next learning steps.

[0824] Input: Feedback data, updated educational program

[0825] Specific operation: Screen display

[0826] Output: Providing feedback to the user

[0827] In this way, the system performs specific data processing and calculations at each processing step, providing users with an optimized educational program and feedback.

[0828] (Application example 1)

[0829] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0830] There is a need to maximize the effectiveness of training for robot operators in factories and provide an efficient curriculum tailored to each operator's skills and level of understanding. However, conventional training methods make it difficult to individually optimize training and are unable to reflect the different progress and proficiency levels of each operator. Real-time feedback and curriculum updates are also insufficient.

[0831] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0832] In this invention, the server includes means for receiving as input the user's basic information, learning status, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for applying the system to the training of robot operators in factories, means for progressing the learning using a smartphone or head-mounted display, and means for using a generative AI model to perform analysis based on prompt sentences. This makes it possible to generate an optimal training curriculum tailored to the skills and level of understanding of each robot operator and provide feedback in real time.

[0833] "Basic user information" refers to personal information such as the robot operator's name, age, job title, past experience, and areas of interest.

[0834] "Learning status" is information that indicates the current progress of the robot operator in learning.

[0835] "Test results" refers to the result data of skill tests and comprehension tests conducted by robot operators.

[0836] "Personality diagnosis results" are diagnostic results used to evaluate the personality and temperament of a robot operator.

[0837] A "database" is a digital data collection for storing collected information.

[0838] "Means for analyzing comprehension and performance" means a method or system used to evaluate a robot operator's comprehension and performance based on the collected information.

[0839] An "individualized learning curriculum" is a learning plan optimized for the individual characteristics and abilities of each robot operator.

[0840] "Monitoring learning progress" means constantly monitoring the robot operator's learning process and recording that progress.

[0841] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and generate optimal learning curricula.

[0842] A "prompt" is a document or question that is input into an AI model and forms the basis for the generated information.

[0843] "Feedback" means advice, comments, and guidance on next steps provided as learning progresses.

[0844] A "smartphone" is a mobile device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[0845] A "head-mounted display" is a device worn on the head that displays visual information directly in front of the eyes.

[0846] This invention is a system that utilizes a generative AI model to provide an individualized learning curriculum for robot operators in a factory. The system generates an optimal training curriculum based on the characteristics of a specific user and provides feedback. A specific embodiment of this system is described below.

[0847] System Configuration

[0848] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using a generative AI model. The terminal is a device that collects user input data and learning progress, specifically a smartphone or head-mounted display (HMD). The user is a robot operator in the factory.

[0849] Basic Features

[0850] 1. User Registration and Data Collection

[0851] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, job title, past experience, areas of interest, etc.).

[0852] The user enters the necessary information through the input screen and presses the registration button.

[0853] The terminal transmits the input information to the server.

[0854] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0855] 2. Initial data collection

[0856] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[0857] The user answers the questionnaire and transmits the data to the terminal.

[0858] The terminal transmits the collected data to the server.

[0859] The server stores this data in a database and uses it as initial information.

[0860] 3. Curriculum Generation

[0861] The server analyzes the collected information using a generative AI model.

[0862] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[0863] The server transmits the generated curriculum to the terminal.

[0864] 4. Learning Progression and Monitoring

[0865] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0866] The device collects the user's learning progress and test results in real time and sends them to the server.

[0867] The server analyzes the collected data and updates the curriculum as needed.

[0868] 5. Providing Feedback

[0869] The server generates feedback based on the analysis results and sends it to the terminal.

[0870] The device provides the user with progress reports and next learning steps.

[0871] Specific examples

[0872] First time user

[0873] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information: the user enters their name, age, job title, past experience, areas of interest, etc., and presses the submit button.

[0874] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[0875] Inputting initial data and generating curriculum

[0876] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[0877] The user answers the questionnaire and transmits the answers to the terminal.

[0878] The device sends the answer data to the server, which stores it in a database. The server then analyzes the data using a generative AI model to generate a learning curriculum optimized for the user.

[0879] Learning progress management and feedback

[0880] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[0881] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[0882] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[0883] Prompt sentences to input to the generative AI model

[0884] User Basic Information:

[0885] Name: XX

[0886] Age: 35

[0887] Position: Robot Operator

[0888] Previous experience: 3 years of factory operations

[0889] Areas of interest: Smart factory technology, safety protocols

[0890] Skills test result: Beginner

[0891] Use this information to generate the optimal robotics operations training curriculum.

[0892] This system will enable factory robot operators to receive individually optimized training, leading to efficient and effective skill development.

[0893] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0894] Step 1:

[0895] User registration and data collection

[0896] The terminal displays a new user registration screen and asks the user to enter information such as name, age, job title, past experience, and areas of interest.

[0897] The user enters the necessary information on the registration screen and presses the send button.

[0898] The terminal transmits the input data to the server.

[0899] The server stores the received data in a database, generates a user ID, and returns it to the terminal.

[0900] Input: User basic information

[0901] Output: User ID

[0902] Step 2:

[0903] Initial data collection

[0904] The device displays a questionnaire including learning status, past test results, and personality assessment.

[0905] The user answers the questionnaire and submits the data.

[0906] The terminal transmits the collected data to the server.

[0907] The server stores this data in a database and uses it as initial information.

[0908] Input: Study status, past test results, personality test answers

[0909] Output: Initial information

[0910] Step 3:

[0911] Curriculum Generation

[0912] The server uses a generative AI model to analyze the collected information.

[0913] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[0914] The server transmits the generated curriculum to the terminal.

[0915] Input: Basic information, learning status, past test results, personality test answers

[0916] Data processing: Analysis using generative AI models

[0917] Output: personalized learning curriculum

[0918] Step 4:

[0919] Learning progression and monitoring

[0920] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0921] The device collects the user's learning progress and test results in real time and sends them to the server.

[0922] The server analyzes the collected data and updates the curriculum as needed.

[0923] Input: Learning progress, test results

[0924] Data processing: Real-time analysis of progress data

[0925] Output: Updated learning curriculum

[0926] Step 5:

[0927] Providing Feedback

[0928] The server generates feedback based on the analysis results and sends it to the terminal.

[0929] The device provides the user with progress reports and next learning steps.

[0930] Input: Progress data, test results

[0931] Data processing: Generate feedback based on analysis results

[0932] Output: Feedback, progress report, next learning steps

[0933] As described above, the system inputs, processes, and outputs appropriate data at each step, providing users with the optimal learning curriculum and feedback.

[0934] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0935] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0936] System Configuration

[0937] The system mainly consists of a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[0938] Basic Features

[0939] 1. User Registration and Data Collection

[0940] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[0941] The user enters the necessary information into the input screen and presses the registration button.

[0942] The terminal transmits the input information to the server.

[0943] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0944] 2. Initial data collection

[0945] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[0946] The user answers the questionnaire and enters the necessary data.

[0947] The terminal transmits the collected data to the server.

[0948] The server stores the received data in a database and uses it as initial information.

[0949] 3. Curriculum Generation

[0950] The server performs analysis using generative AI based on the stored initial data.

[0951] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[0952] The server transmits the generated learning curriculum to the terminal.

[0953] 4. Emotional Data Collection and Analysis

[0954] The terminal uses an emotion engine to recognize the user's emotions in real time.

[0955] Through the camera and microphone displayed while the user is learning, the emotion engine analyzes facial expressions and tone of voice to generate emotion data.

[0956] The terminal transmits the emotion data to the server.

[0957] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[0958] 5. Curriculum Adjustment and Feedback

[0959] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the entire curriculum.

[0960] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[0961] The server sends updated curriculum and feedback to the terminal.

[0962] 6. Learning Progression and Monitoring

[0963] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[0964] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[0965] The server updates the curriculum and generates feedback based on the collected data.

[0966] Specific examples

[0967] First time user

[0968] When a user logs in to the system for the first time, the terminal displays a basic information entry screen. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[0969] The device sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[0970] Inputting initial data and generating curriculum

[0971] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[0972] The user answers the questionnaire and transmits the answers to the terminal.

[0973] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[0974] Emotion data collection and analysis

[0975] As the user progresses with their learning, the device uses an emotion engine to measure the user's facial expressions and tone of voice to obtain emotional data.

[0976] The terminal transmits the acquired emotion data to the server in real time.

[0977] The server analyzes the received data and assesses the user's emotional state.

[0978] Curriculum adjustment and feedback

[0979] The server integrates and analyzes the emotion data and the learning progress data, and adjusts the learning curriculum according to the user's current state.

[0980] The generative AI evaluates whether the current curriculum is appropriate based on the user's emotional state and automatically generates necessary changes.

[0981] The server sends the updated curriculum and feedback to the terminal.

[0982] This makes it possible for the present invention to provide an individually optimized learning environment that takes into account the user's emotional state.

[0983] The processing flow will be explained below.

[0984] Step 1: User Registration

[0985] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[0986] The user enters the necessary information into the input screen and presses the registration button.

[0987] The terminal transmits the input information to the server.

[0988] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[0989] Step 2: Initial data collection

[0990] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[0991] The user answers the questionnaire and enters the necessary data.

[0992] The terminal transmits the collected data to the server.

[0993] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[0994] Step 3: Generate the initial curriculum

[0995] The server performs analysis using generative AI based on the stored initial data.

[0996] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[0997] The server transmits the generated learning curriculum to the terminal.

[0998] Step 4: Start learning and collect emotional data

[0999] The terminal displays the learning curriculum provided to the user and presents the first task.

[1000] Users begin studying according to the curriculum and take assignments and tests.

[1001] During learning, the device uses an emotion engine to analyze the user's facial expressions and voice in real time and obtain emotional data.

[1002] The terminal transmits the acquired emotion data to the server.

[1003] Step 5: Record your learning progress

[1004] The terminal records the user's learning progress data and test results and transmits them to the server.

[1005] The server stores the received data in a database for subsequent analysis.

[1006] Step 6: Analyze the sentiment data

[1007] The server analyzes the emotion data sent from the emotion engine.

[1008] The generative AI evaluates the user's current emotional state based on emotional data and estimates their learning load and stress level.

[1009] Step 7: Adjust the curriculum

[1010] The server integrates and analyzes the emotional data and learning progress data, and adjusts the learning curriculum as needed.

[1011] The generative AI updates the difficulty and content of the curriculum based on the user's emotions and learning situation.

[1012] The server transmits the updated curriculum to the terminal.

[1013] Step 8: Provide feedback

[1014] The server generates feedback based on the analysis results and transmits it to the terminal.

[1015] The device provides feedback and next learning steps to the user.

[1016] Step 9: Continuous monitoring

[1017] The device continuously monitors the user's learning progress and emotional data, and transmits the data to the server as needed.

[1018] The server periodically reevaluates the learning curriculum based on this data and makes any necessary updates.

[1019] The above processing steps enable the system to take emotional data into account and provide more personalized learning assistance.

[1020] Example 2

[1021] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1022] Current learning systems lack the ability to fully understand each user's learning progress and emotional state and automatically adjust the optimal learning curriculum accordingly. This increases the likelihood that users will experience stress or inappropriate learning load, which can reduce learning effectiveness. Therefore, a learning system that is personalized for each user and can adjust in real time is needed.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1024] In this invention, the server includes means for receiving as input the user's basic information, learning situation, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for automatically generating an individualized learning curriculum based on the analysis results using a generation AI, means for providing the generated learning curriculum to the user, means for recognizing, recording, and transmitting the user's learning progress and emotional data in real time, means for analyzing the transmitted learning progress and emotional data in real time and updating and providing feedback on the learning curriculum, and means for providing the feedback and updated curriculum to the user. This makes it possible to provide an individual learning curriculum optimized for each user in real time, maximizing learning effectiveness.

[1025] "User" refers to an individual who uses the system to learn.

[1026] "Basic information" refers to information that identifies and locates an individual, such as the user's name, age, grade, and areas of interest.

[1027] "Study status" refers to information including a user's current learning progress, past learning records, and learning goals.

[1028] "Test results" refers to the scores and evaluation results of tests that a user has taken in the past.

[1029] "Personality diagnosis results" refers to the results of evaluating the user's personality traits and behavioral patterns.

[1030] "Database" refers to a collection used to store, manage, and retrieve received information.

[1031] "Analysis" refers to the act of assessing the user's level of understanding and ability based on the information received.

[1032] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to generate useful information from data for a specific task or purpose.

[1033] "Learning curriculum" refers to a systematic plan of the learning materials and learning steps necessary to achieve a user's learning goals.

[1034] "Feedback" refers to advice and suggestions for improvement provided at the appropriate time based on the results and emotional state of the user as they progress through their learning.

[1035] "Emotional Data" refers to information about a user's emotional state obtained through facial expressions, tone of voice, and other physiological responses.

[1036] "Real-time" refers to a situation where a response or processing occurs almost immediately.

[1037] "Analysis results" refers to the evaluation of the user's level of understanding and ability that is derived after the generating AI processes the information it receives.

[1038] "Monitoring" refers to the act of continuously observing a user's learning progress and emotional state and collecting data as necessary.

[1039] "Adjustment" refers to the act of changing the content or difficulty of the learning curriculum based on the analysis results and emotional data.

[1040] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[1041] System Configuration

[1042] This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[1043] User registration and data collection

[1044] The device displays a new user registration screen and uses front-end technologies such as HTML, CSS, and JavaScript to prompt the user to enter basic information such as name, age, grade, and areas of interest. The user enters this information and presses the registration button. For example, a user might enter "Yamada Taro," age "15," grade "first year of high school," and area of ​​interest "mathematics." The device sends the entered information to the server via a POST request, and the server stores the received information in a database, then generates a unique user ID and returns it to the device.

[1045] Example prompt sentence:

[1046] "Please enter your name. Tell us your age. Also select your grade and areas of interest."

[1047] Initial data collection

[1048] Next, the device displays a questionnaire screen with information about the user's learning status, past test results, and personality assessment, and asks the user to answer. The user answers these questions, for example, entering "My past test results were 80 points" or "My personality assessment result is a cautious type." The device then sends this data to the server, which stores it in a database.

[1049] Example prompt sentence:

[1050] "Enter your past test results. Take the personality test."

[1051] Curriculum Generation

[1052] The server uses a generation AI to analyze the stored initial data. The generation AI evaluates the user's level of understanding, ability, and personality assessment results, and generates a personalized learning curriculum. For example, the generation AI determines that the user's level of understanding of mathematics is "intermediate," and generates a curriculum that includes "differential and integral calculus" as the next step. The generated curriculum is sent from the server to the device, providing the user with the next task to tackle.

[1053] Example prompt sentence:

[1054] "Generate an appropriate learning curriculum based on the user's initial data."

[1055] Emotion data collection and analysis

[1056] During training, the device uses an emotion engine to recognize the user's facial expressions and tone of voice in real time. For example, if the user shows a "tired" expression, the emotion engine will detect this and record it as data. The device then sends the emotion data to the server, which then evaluates the user's emotional state based on this data.

[1057] Example prompt sentence:

[1058] "Collect real-time user sentiment data."

[1059] Curriculum Adjustment and Feedback

[1060] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum. For example, if a user feels stressed, the generative AI will add a "break time" and suggest tasks that can be completed in a relaxed state. The adjusted curriculum is sent from the server to the device, and the latest learning plan is provided to the user.

[1061] Example prompt sentence:

[1062] "Adjust your curriculum based on user sentiment data."

[1063] Learning progression and monitoring

[1064] The user studies according to the learning curriculum provided through the device. The device records the user's learning progress and emotional data in real time and sends it to the server. The server analyzes this data, updates the curriculum as needed, and generates feedback. The feedback and updated curriculum are provided to the device, making it possible to maintain an optimal learning environment for the user.

[1065] Example prompt sentence:

[1066] "Monitor users' learning progress and provide feedback as needed."

[1067] The above is a specific embodiment of the system according to the present invention, which aims to grasp the user's emotional state and learning situation in real time and provide an optimal learning curriculum.

[1068] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1069] Step 1:

[1070] The terminal displays a new user registration screen.

[1071] Input: User registration form using HTML / CSS / JavaScript.

[1072] Details: The device displays a form that asks the user to enter their name, age, grade, and interests.

[1073] Output: Basic information entered by the user.

[1074] Specific operation: For example, a user inputs "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[1075] Step 2:

[1076] The user enters basic information and presses the registration button.

[1077] Input: User-entered name, age, grade, and interests.

[1078] Details: The user fills in the form with the required information and clicks the register button.

[1079] Output: Basic information received by the device.

[1080] Specific operation: When the user presses the registration button, the information displayed on the screen is sent from the device to the server.

[1081] Step 3:

[1082] The terminal transmits the input basic information to the server.

[1083] Input: Basic information about the user received by the device.

[1084] Details: The device sends input data to the server via an HTTP POST request.

[1085] Output: Basic information received by the server.

[1086] Specific operation: The request body sent by the device contains data such as name "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[1087] Step 4:

[1088] The server stores the received basic information in a database, generates a unique user ID, and returns it to the terminal.

[1089] Input: The user's basic information received by the server.

[1090] Details: The server stores the received data in a database and generates a unique user ID for the new user.

[1091] Output: A unique user ID and basic information stored in the database.

[1092] Specific operation: The server stores the information in a MySQL database, generates a user ID "12345", and returns it to the terminal.

[1093] Step 5:

[1094] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[1095] Input: User ID and basic information received from the server.

[1096] Details: The device displays a questionnaire screen where students can enter their learning status and past test results.

[1097] Output: User-entered learning status, past test results, and personality assessment results.

[1098] What it does: The device prompts you with a question, such as "Please enter your past math test scores."

[1099] Step 6:

[1100] The user answers the questionnaire and enters the required data.

[1101] Input: User-entered learning history, past test results, and personality assessment results.

[1102] Details: Users answer questions by entering text or by selecting multiple options.

[1103] Output: Survey results received by the device.

[1104] Specific behavior: The user enters "80 points" and selects "Conscientious" on the personality test.

[1105] Step 7:

[1106] The terminal transmits the collected questionnaire data to the server.

[1107] Input: Survey results received by the device.

[1108] Details: The device sends the survey data to the server via an HTTP POST request.

[1109] Output: Survey data received by the server.

[1110] Specific operation: The request sent by the device includes data such as learning status (80 points) and personality test result (cautious type).

[1111] Step 8:

[1112] The server stores the received survey data in a database and analyzes it using a generative AI model.

[1113] Input: Survey data received by the server.

[1114] Details: The server stores the received data in a database and analyzes it using a generative AI model.

[1115] Output: User's understanding and ability assessment as a result of the analysis.

[1116] Specific operation: The server stores the information in a database and passes the input data to the generation AI for analysis.

[1117] Step 9:

[1118] Generative AI generates an individualized learning curriculum based on the user's level of understanding, ability, and personality assessment results.

[1119] Input: Initial data and analysis results.

[1120] Details: Generative AI evaluates users' understanding, abilities, and personality assessment results to generate a personalized learning curriculum.

[1121] Output: A personalized learning curriculum.

[1122] Specific operation: The generation AI determines that the level of understanding is "intermediate" and generates a curriculum that includes "differential and integral calculus."

[1123] Step 10:

[1124] The server transmits the generated learning curriculum to the terminal.

[1125] Input: The generated learning curriculum.

[1126] Details: The server sends the generated curriculum to the device as an HTTP response.

[1127] Output: The learning curriculum sent to the device.

[1128] Specific operation: The device displays the curriculum details received from the generation AI and confirms the next assignment the user will work on.

[1129] Step 11:

[1130] The terminal uses an emotion engine to recognize the user's emotions in real time.

[1131] Input: The user's facial expression and tone of voice.

[1132] Details: The device senses the user's facial expressions and tone of voice while learning and analyzes them using an emotion engine.

[1133] Output: Recognized emotion data.

[1134] Specific operation: The device's camera and microphone detect the user's "tired" facial expression and "low-pitched voice."

[1135] Step 12:

[1136] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data.

[1137] Input: The user's facial expression and tone of voice.

[1138] Details: The emotion engine analyzes facial expressions as data and labels the emotions it detects in real time.

[1139] Output: Emotion data.

[1140] Specific behavior: The emotion engine generates emotion labels such as "fatigue" and "stress."

[1141] Step 13:

[1142] The terminal transmits the emotion data to the server.

[1143] Input: Emotion data generated by the emotion engine.

[1144] Details: The device sends the generated emotion data to the server.

[1145] Output: Emotion data received by the server.

[1146] Specific operation: The device sends the emotional data "fatigue" and "stress" generated to the server.

[1147] Step 14:

[1148] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[1149] Input: Received emotion data.

[1150] Details: The server analyzes the received data and evaluates the user's stress level and learning load.

[1151] Output: Assessment results of learning load and stress level.

[1152] Specific operation: The server analyzes the emotional data "fatigue" and "stress" and evaluates the user's stress level as "high."

[1153] Step 15:

[1154] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum.

[1155] Input: Learning progress data and emotion data.

[1156] Details: The server integrates and analyzes emotional data and learning progress data to regenerate an appropriate learning curriculum.

[1157] Output: A tailored learning curriculum.

[1158] Specific operation: The server informs the generated AI of the user's "high stress level" and adjusts the curriculum to "relaxing tasks that include breaks."

[1159] Step 16:

[1160] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[1161] Input: Data indicating the user's emotional state.

[1162] Details: Generative AI assesses the appropriateness of the curriculum based on the user's emotional state and makes necessary adjustments.

[1163] Output: Specific learning steps and break timings.

[1164] Specific behavior: The generative AI suggests to the user to "take a break for the next 30 minutes."

[1165] Step 17:

[1166] The server sends updated curriculum and feedback to the terminal.

[1167] Input: Updated learning curriculum and feedback.

[1168] Details: The server sends updated curriculum information to the device and provides feedback.

[1169] Output: Updated curriculum and feedback delivered to the device.

[1170] What happens: Materials containing the updated curriculum "Differential and Integral Calculus" and the feedback "Take a break for the next 30 minutes" are displayed on the device.

[1171] Step 18:

[1172] The user proceeds with the study according to the study curriculum provided through the terminal.

[1173] Input: The learning curriculum displayed on the device.

[1174] Details: The user progresses through the learning steps according to the curriculum displayed on the device.

[1175] Output: User's learning progress.

[1176] Specific action: The user works on a "differentiation exercise" while looking at the curriculum materials.

[1177] Step 19:

[1178] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[1179] Input: User's learning progress data and emotional data.

[1180] Details: The device records the user's progress and emotional data in real time and periodically transmits it to the server.

[1181] Output: Learning progress data and emotion data sent to the server.

[1182] Specific operation: The device sends the user's learning progress ("Differential task completed") and emotional data ("Concentration") to the server.

[1183] Step 20:

[1184] The server updates the curriculum based on the collected data and generates feedback.

[1185] Input: Collected learning progress data and emotion data.

[1186] Details: The server analyzes the collected data, updates the curriculum as needed, and generates new feedback.

[1187] Output: Updated curriculum and feedback.

[1188] Specific operation: The server generates feedback such as "Start the integration problem as the next step" and sends it to the device.

[1189] This allows the system to create an individually optimized learning environment that takes into account the user's emotions.

[1190] (Application example 2)

[1191] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1192] While conventional learning systems can provide an individualized curriculum based on the user's basic information and learning progress, it is difficult to flexibly adjust the curriculum taking into account the user's emotional state or to suggest break timing.In addition, in the work environment, there was a lack of means to evaluate work efficiency and fatigue level in real time and provide appropriate feedback accordingly, making it a challenge to reduce stress while maximizing worker efficiency.

[1193] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's basic information, learning situation, test results, and personality assessment results as input, means for storing the received information in a database, and means for analyzing the user's level of understanding and ability based on the stored information. This makes it possible to evaluate the user's emotional state and, based on that, propose a work curriculum and break timing.

[1194] "Basic information" refers to attribute information such as the user's name, age, grade, and areas of interest.

[1195] "Study status" is information that indicates the progress and level of understanding of the user's current learning.

[1196] "Test results" is information indicating the results of various tests and evaluations conducted in the past.

[1197] "Personality diagnosis results" are data about the user's personality obtained based on personality diagnosis tests and questionnaires.

[1198] A "database" is a system for storing and managing various information obtained from users.

[1199] "Means of analysis" is a function that evaluates the user's ability and level of understanding based on the information received.

[1200] An "individualized learning curriculum" is an optimal learning plan that is automatically generated to suit each user's level of understanding and ability.

[1201] "Emotion data" refers to information about emotions acquired from the user's facial expressions, tone of voice, etc.

[1202] A "generative AI model" is an algorithm that uses artificial intelligence to analyze data and suggest appropriate learning curricula and break timing.

[1203] "Feedback" refers to guidance and advice provided to users based on the analysis results.

[1204] "Work curriculum" refers to the user's work plan, which includes work content optimized based on the evaluation results.

[1205] "Rest timing" is information that indicates the optimal timing for the user to take an appropriate rest.

[1206] A "server" is a computer system that plays a central role in the system, such as analyzing data and running generative AI models.

[1207] The present invention combines a system that uses a generative AI model to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1208] System Configuration

[1209] The system consists of a server, a device, a user, and an emotion engine. The server analyzes data and runs the generative AI model. The device collects input data, learning progress, and emotion data from the user. The user interacts with the system as a learner. The emotion engine recognizes the user's emotions in real time and analyzes the data.

[1210] Overview of the embodiment

[1211] User registration and data collection

[1212] The device displays a new user registration screen, where the user enters basic information such as name, age, grade, and areas of interest. This information is sent from the device to the server and stored in a database. The server generates a user ID based on the received information and returns it to the device.

[1213] Initial data collection

[1214] Users answer questionnaires about their learning status, past test results, and personality assessments through their devices. The devices send the collected data to a server, which stores it in a database. The server analyzes the initial data it receives and generates a personalized learning curriculum.

[1215] Creating and providing learning curriculum

[1216] The generative AI model analyzes the user's level of understanding, ability, and personality assessment results to generate an optimized learning curriculum. The generated learning curriculum is sent from the server to the device and provided to the user. The user then follows the curriculum through their device to progress through their studies.

[1217] Emotion data collection and analysis

[1218] During the learning process, the device uses an emotion engine to recognize the user's emotions in real time. It analyzes the user's facial expressions and tone of voice through a camera and microphone to generate emotion data. This emotion data is sent from the device to a server, where it is analyzed.

[1219] Curriculum adjustment and feedback

[1220] The server integrates and analyzes emotional data and learning progress data to assess the user's learning load and stress level. Based on these results, the generative AI model adjusts the learning curriculum and suggests appropriate break times and refreshing activities. The updated curriculum and feedback are provided to the user via their device.

[1221] Hardware and software used

[1222] Server: A computer system that analyzes data and runs generative AI models.

[1223] Device: A device (e.g., smartphone, tablet) that collects data from users and provides learning curriculum.

[1224] Emotion engine: Software for analyzing user emotions (e.g., EmotionEngine).

[1225] Generative AI model: An algorithm that analyzes data and generates an optimal curriculum (e.g., AIModule).

[1226] Specific examples

[1227] When a user logs in to the system for the first time, they are presented with a basic information entry screen. The user enters their name, age, grade, areas of interest, etc., and the information is sent from the device to the server. The server stores this information in a database and generates a user ID. Next, the user enters initial data (study status, past test results, personality assessment questionnaire, etc.), which is also sent to the server. A generative AI model generates a personalized learning curriculum and provides it to the device.

[1228] During learning, the device uses an emotion engine to analyze the user's facial expressions and tone of voice, acquiring emotional data and sending it to the server. The server then adjusts the curriculum and provides feedback to the user based on the emotional data and learning progress data.

[1229] An example prompt might be, "Assess the worker's fatigue level and emotional state and suggest optimal work schedules and break timings. Include the following data: work efficiency data, video feed, audio feed. If possible, suggest refreshment activities."

[1230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1231] Step 1:

[1232] The terminal displays a new user registration screen. The user enters basic information (name, age, grade, areas of interest) and presses the registration button. The input data is sent from the terminal to the server. The server stores the received basic information in a database, generates a new user ID, and returns it to the terminal. The input is basic information, and the output is the user ID.

[1233] Step 2:

[1234] The device displays learning status, past test results, and a personality assessment questionnaire, and the user answers each item. The device then sends the collected data to a server. The server stores the received data in a database and analyzes the data using a generative AI model. The input is learning status data, and the output is analyzed data.

[1235] Step 3:

[1236] The server uses a generative AI model to evaluate the user's level of understanding and ability, and generates an individualized learning curriculum. The generated curriculum is sent from the server to the terminal and provided to the user. The input is the evaluation data, and the output is the learning curriculum.

[1237] Step 4:

[1238] As the user continues learning, the device collects facial expressions and tone of voice through the built-in camera and microphone, and generates emotional data in real time using an emotion engine. The generated emotional data is sent from the device to a server. The input is the camera and microphone feed, and the output is emotional data.

[1239] Step 5:

[1240] The server integrates and analyzes the received emotional data and learning progress data to evaluate the user's learning load and stress level. Based on the analysis results, the generative AI model adjusts the learning curriculum and break timing. The input is emotional data and learning progress data, and the output is the adjusted curriculum.

[1241] Step 6:

[1242] The server sends the updated learning curriculum and feedback to the terminal and provides it to the user. The user proceeds with learning according to the adjusted curriculum through the terminal. The input is the adjusted curriculum, and the output is the user's learning progress.

[1243] Step 7:

[1244] The device continuously monitors the user's learning progress and emotional data and sends them to the server. The server analyzes this data at runtime and adjusts the learning curriculum and feedback as needed. The input is the continuously collected learning progress and emotional data, and the output is the adjusted learning curriculum and feedback.

[1245] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1246] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1247] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1248] [Third embodiment]

[1249] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1250] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1251] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1252] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1253] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1254] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1255] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1256] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1257] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1258] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1259] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1260] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1261] The present invention relates to a system that uses a generative AI to provide a user with an optimized learning curriculum. Specific embodiments of the system are described below.

[1262] System Configuration

[1263] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[1264] Basic Features

[1265] 1. User Registration and Data Collection

[1266] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, grade, and areas of interest).

[1267] The user enters the necessary information through the input screen and presses the registration button.

[1268] The terminal transmits the input information to the server.

[1269] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1270] 2. Initial data collection

[1271] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[1272] The user answers the questionnaire and transmits the data to the terminal.

[1273] The terminal transmits the collected data to the server.

[1274] The server stores this data in a database and uses it as initial information.

[1275] 3. Curriculum Generation

[1276] The server uses generative AI to analyze the collected information.

[1277] The generative AI evaluates the user's level of understanding, ability, and personality, and generates a personalized learning curriculum.

[1278] The server transmits the generated curriculum to the terminal.

[1279] 4. Learning Progression and Monitoring

[1280] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[1281] The device collects the user's learning progress and test results in real time and sends them to the server.

[1282] The server analyzes the collected data and updates the curriculum as needed.

[1283] 5. Providing Feedback

[1284] The server generates feedback based on the analysis results and sends it to the terminal.

[1285] The device provides the user with progress reports and next learning steps.

[1286] Specific examples

[1287] First time user

[1288] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[1289] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[1290] Inputting initial data and generating curriculum

[1291] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[1292] The user answers the questionnaire and transmits the answers to the terminal.

[1293] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[1294] Learning progress management and feedback

[1295] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[1296] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[1297] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[1298] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[1299] The processing flow will be explained below.

[1300] Step 1: User Registration

[1301] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[1302] The user enters the necessary information into the input screen and presses the registration button.

[1303] The terminal transmits the input information to the server.

[1304] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1305] Step 2: Initial data collection

[1306] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[1307] The user answers the questionnaire and enters the necessary data.

[1308] The terminal transmits the collected data to the server.

[1309] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[1310] Step 3: Generate the initial curriculum

[1311] The server performs analysis using generative AI based on the stored initial data.

[1312] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[1313] The server transmits the generated learning curriculum to the terminal.

[1314] Step 4: Start learning and monitor your progress

[1315] The terminal displays the learning curriculum provided to the user and presents the first task.

[1316] Users begin studying according to the curriculum and take assignments and tests.

[1317] The device records learning progress data and test results in real time and transmits them to the server.

[1318] Step 5: Analyze the training data

[1319] The server analyzes the received progress data and test results.

[1320] The generative AI evaluates the user's learning status in real time and adjusts the content and difficulty of the curriculum as needed.

[1321] Step 6: Feedback and curriculum updates

[1322] The server generates feedback based on the analysis results and transmits it to the terminal.

[1323] The device provides feedback and next learning steps to the user.

[1324] The server updates the learning curriculum based on the user's level of understanding and transmits the new curriculum to the terminal.

[1325] Step 7: Continuous monitoring

[1326] The terminal continuously monitors the user's learning progress and transmits progress data to the server as needed.

[1327] The server periodically reevaluates the learning curriculum based on this data and continues to provide optimal learning content.

[1328] This allows the system to respond to the user's learning needs and provide effective and personalized learning support.

[1329] Example 1

[1330] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1331] Conventional learning support systems have difficulty providing optimal learning curricula tailored to individual users' characteristics and learning progress, and tend to have uniform content. This leads to issues such as reduced learning efficiency and difficulty in sustaining motivation. Furthermore, it is difficult to provide real-time feedback or update the curriculum, resulting in a lack of flexibility in situations where immediate user response is essential.

[1332] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1333] In this invention, the server includes means for receiving the user's basic information, learning information, evaluation results, and diagnostic results as input, means for storing the received information in a data storage device, and means for analyzing the user's level of understanding and ability based on the stored information, thereby making it possible to automatically generate an individualized educational program, monitor the user's learning progress in real time, and provide feedback.

[1334] "User" refers to an individual who uses the system to learn.

[1335] "Basic information" refers to personal information such as the user's name, age, grade, and areas of interest.

[1336] "Learning information" refers to data such as a user's learning status, progress, and past test results.

[1337] "Assessment Results" refers to the scores or grades based on tests or assessments taken by a User.

[1338] "Diagnosis results" refers to data related to the user's personality diagnosis and ability evaluation.

[1339] "Means for receiving input" refers to the interface and functions for inputting basic information and learning information provided by users into the system.

[1340] "Data storage device" refers to a database or storage system for storing received information.

[1341] "Means for analyzing a user's level of understanding and ability based on stored information" refers to algorithms and analytical tools that analyze data in real time and evaluate a user's learning progress and ability.

[1342] "Individualized educational program" refers to a learning curriculum optimized for each user based on the analysis results.

[1343] "Means for automatic generation" refers to the function of automatically designing educational programs using a generative AI model based on collected data.

[1344] "Means for providing" refers to the interface and functions for presenting the generated educational program and feedback to the user.

[1345] "Means for monitoring and transmitting the data to an information processing device" refers to a function or device for acquiring the user's learning data in real time and sending it to a server.

[1346] "Means of analyzing in real time and updating and providing feedback to educational programs" refers to algorithms and functions that instantly analyze received data and generate new curricula and feedback.

[1347] "Means for providing feedback and updated educational programs" refers to interfaces and functions that notify users of analysis results and new curricula.

[1348] This invention is a system that uses generative AI to provide users with optimized educational programs. This system consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[1349] User registration and data collection

[1350] The terminal displays a new user registration screen and asks the user to enter basic information (such as name, age, grade, and areas of interest). The user enters the necessary information through the input screen and presses the registration button. The terminal then sends the entered information to the server. The server stores the received information in a database (for example, MySQL or PostgreSQL), generates a user ID, and returns it to the terminal.

[1351] Initial data collection

[1352] The device displays a questionnaire for learning status, past test results, and personality assessment. The user answers the questionnaire and sends the data to the device. The device then sends the collected data to the server. The server stores this data in a database and uses it as initial information.

[1353] Curriculum Generation

[1354] The server analyzes the collected information using a generation AI (e.g., GPT-3 or ChatGPT). The generation AI evaluates the user's level of understanding, ability, and personality, and generates a personalized educational program. The server then sends the generated educational program to the device.

[1355] Learning progression and monitoring

[1356] Users study according to the educational program provided through their device. The device collects the user's learning progress and test results in real time and sends them to the server. The server analyzes the collected data and updates the educational program as necessary.

[1357] Providing Feedback

[1358] The server generates feedback based on the analysis results and sends it to the device, which then presents the user with a progress report and next learning steps.

[1359] Specific examples

[1360] First time user

[1361] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button. The terminal then sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the terminal.

[1362] Inputting initial data and generating curriculum

[1363] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond. The user answers the questionnaire and sends the answers to the device. The device then sends the response data to a server, which stores it in a database. The server then uses generative AI to analyze this data and generate an educational program optimized for the user.

[1364] Learning progress management and feedback

[1365] As the user progresses through their studies, the device records their progress and test results in real time and sends them to the server. The server analyzes this data, adjusts the curriculum as needed, and generates feedback. The device then presents the feedback and updated curriculum information to the user and guides them on their next learning steps.

[1366] Examples of prompt statements

[1367] When registering as a first-time user:

[1368] Enter basic information about the user (name, age, grade, areas of interest).

[1369] When entering the initial data:

[1370] Please answer a questionnaire about your past learning history, test results, and personality assessment.

[1371] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[1372] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1373] System processing steps

[1374] Step 1: Enter your user registration information

[1375] 1.1 The terminal displays the new user registration screen.

[1376] Input: User's basic information (name, age, grade, areas of interest)

[1377] Output: User input data

[1378] 1.2 The user enters basic information and presses the registration button.

[1379] Specific actions: form entry, button click

[1380] 1.3 The terminal sends the entered information to the server.

[1381] Input: User-entered data

[1382] Data processing: Convert to JSON format

[1383] Output: Send data to the server

[1384] 1.4 The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1385] Input: JSON data received from the terminal

[1386] Data calculation: saving to database, generating user ID

[1387] Output: User ID

[1388] Step 2: Initial data collection through a questionnaire

[1389] 2.1 The device displays a questionnaire about learning status, past test results, and personality assessment.

[1390] Input: None

[1391] Output: Survey screen

[1392] 2.2 The user answers the questionnaire and sends the data to the terminal.

[1393] Input: User response data

[1394] Specific actions: filling in a form, clicking the submit button

[1395] 2.3 The device sends the collected data to the server.

[1396] Input: User response data

[1397] Data processing: Convert to JSON format

[1398] Output: Send data to the server

[1399] 2.4 The server stores this data in a database.

[1400] Input: JSON data received from the terminal

[1401] Data calculation: Saving to database

[1402] Output: Save completion status

[1403] Step 3: Curriculum generation

[1404] 3.1 The server will use the generated AI to analyze the collected information.

[1405] Input: Collected user data

[1406] Data calculation: Analysis by generative AI (e.g., GPT-3)

[1407] Output: Analysis results

[1408] 3.2 Generative AI evaluates the user's level of understanding, ability, and personality and generates a personalized educational program.

[1409] Input: Analysis results

[1410] Data Computation: Generating Learning Curricula

[1411] Output: personalized education program

[1412] 3.3 The server sends the generated curriculum to the terminal.

[1413] Input: Educational Program Data

[1414] Data processing: Convert to JSON format

[1415] Output: Sending data to the terminal

[1416] Step 4: Progressing and monitoring your learning

[1417] 4.1 The user will study according to the educational program provided through the terminal.

[1418] Input: Educational Program

[1419] Specific behavior: Starting a learning activity

[1420] 4.2 The device records the user's learning progress and test results in real time and transmits them to the server.

[1421] Input: Learning progress data, test results

[1422] Data processing: Convert to JSON format

[1423] Output: Send data to the server

[1424] 4.3 The server will analyze the collected data and update the educational program as necessary.

[1425] Input: Learning progress data, test results

[1426] Data Calculation: Reanalysis by Generative AI

[1427] Output: Updated educational program

[1428] Step 5: Provide feedback

[1429] 5.1 The server generates feedback based on the analysis results and sends it to the device.

[1430] Input: Analysis results

[1431] Data Calculation: Feedback Generation

[1432] Output: Feedback data

[1433] 5.2 The device provides the user with progress reports and next learning steps.

[1434] Input: Feedback data, updated educational program

[1435] Specific operation: Screen display

[1436] Output: Providing feedback to the user

[1437] In this way, the system performs specific data processing and calculations at each processing step, providing users with an optimized educational program and feedback.

[1438] (Application example 1)

[1439] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1440] There is a need to maximize the effectiveness of training for robot operators in factories and provide an efficient curriculum tailored to each operator's skills and level of understanding. However, conventional training methods make it difficult to individually optimize training and are unable to reflect the different progress and proficiency levels of each operator. Real-time feedback and curriculum updates are also insufficient.

[1441] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1442] In this invention, the server includes means for receiving as input the user's basic information, learning status, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for applying the system to the training of robot operators in factories, means for progressing the learning using a smartphone or head-mounted display, and means for using a generative AI model to perform analysis based on prompt sentences. This makes it possible to generate an optimal training curriculum tailored to the skills and level of understanding of each robot operator and provide feedback in real time.

[1443] "Basic user information" refers to personal information such as the robot operator's name, age, job title, past experience, and areas of interest.

[1444] "Learning status" is information that indicates the current progress of the robot operator in learning.

[1445] "Test results" refers to the result data of skill tests and comprehension tests conducted by robot operators.

[1446] "Personality diagnosis results" are diagnostic results used to evaluate the personality and temperament of a robot operator.

[1447] A "database" is a digital data collection for storing collected information.

[1448] "Means for analyzing comprehension and performance" means a method or system used to evaluate a robot operator's comprehension and performance based on the collected information.

[1449] An "individualized learning curriculum" is a learning plan optimized for the individual characteristics and abilities of each robot operator.

[1450] "Monitoring learning progress" means constantly monitoring the robot operator's learning process and recording that progress.

[1451] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and generate optimal learning curricula.

[1452] A "prompt" is a document or question that is input into an AI model and forms the basis for the generated information.

[1453] "Feedback" means advice, comments, and guidance on next steps provided as learning progresses.

[1454] A "smartphone" is a mobile device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[1455] A "head-mounted display" is a device worn on the head that displays visual information directly in front of the eyes.

[1456] This invention is a system that utilizes a generative AI model to provide an individualized learning curriculum for robot operators in a factory. The system generates an optimal training curriculum based on the characteristics of a specific user and provides feedback. A specific embodiment of this system is described below.

[1457] System Configuration

[1458] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using a generative AI model. The terminal is a device that collects user input data and learning progress, specifically a smartphone or head-mounted display (HMD). The user is a robot operator in the factory.

[1459] Basic Features

[1460] 1. User Registration and Data Collection

[1461] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, job title, past experience, areas of interest, etc.).

[1462] The user enters the necessary information through the input screen and presses the registration button.

[1463] The terminal transmits the input information to the server.

[1464] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1465] 2. Initial data collection

[1466] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[1467] The user answers the questionnaire and transmits the data to the terminal.

[1468] The terminal transmits the collected data to the server.

[1469] The server stores this data in a database and uses it as initial information.

[1470] 3. Curriculum Generation

[1471] The server analyzes the collected information using a generative AI model.

[1472] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[1473] The server transmits the generated curriculum to the terminal.

[1474] 4. Learning Progression and Monitoring

[1475] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[1476] The device collects the user's learning progress and test results in real time and sends them to the server.

[1477] The server analyzes the collected data and updates the curriculum as needed.

[1478] 5. Providing Feedback

[1479] The server generates feedback based on the analysis results and sends it to the terminal.

[1480] The device provides the user with progress reports and next learning steps.

[1481] Specific examples

[1482] First time user

[1483] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information: the user enters their name, age, job title, past experience, areas of interest, etc., and presses the submit button.

[1484] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[1485] Inputting initial data and generating curriculum

[1486] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[1487] The user answers the questionnaire and transmits the answers to the terminal.

[1488] The device sends the answer data to the server, which stores it in a database. The server then analyzes the data using a generative AI model to generate a learning curriculum optimized for the user.

[1489] Learning progress management and feedback

[1490] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[1491] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[1492] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[1493] Prompt sentences to input to the generative AI model

[1494] User Basic Information:

[1495] Name: XX

[1496] Age: 35

[1497] Position: Robot Operator

[1498] Previous experience: 3 years of factory operations

[1499] Areas of interest: Smart factory technology, safety protocols

[1500] Skills test result: Beginner

[1501] Use this information to generate the optimal robotics operations training curriculum.

[1502] This system will enable factory robot operators to receive individually optimized training, leading to efficient and effective skill development.

[1503] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1504] Step 1:

[1505] User registration and data collection

[1506] The terminal displays a new user registration screen and asks the user to enter information such as name, age, job title, past experience, and areas of interest.

[1507] The user enters the necessary information on the registration screen and presses the send button.

[1508] The terminal transmits the input data to the server.

[1509] The server stores the received data in a database, generates a user ID, and returns it to the terminal.

[1510] Input: User basic information

[1511] Output: User ID

[1512] Step 2:

[1513] Initial data collection

[1514] The device displays a questionnaire including learning status, past test results, and personality assessment.

[1515] The user answers the questionnaire and submits the data.

[1516] The terminal transmits the collected data to the server.

[1517] The server stores this data in a database and uses it as initial information.

[1518] Input: Study status, past test results, personality test answers

[1519] Output: Initial information

[1520] Step 3:

[1521] Curriculum Generation

[1522] The server uses a generative AI model to analyze the collected information.

[1523] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[1524] The server transmits the generated curriculum to the terminal.

[1525] Input: Basic information, learning status, past test results, personality test answers

[1526] Data processing: Analysis using generative AI models

[1527] Output: personalized learning curriculum

[1528] Step 4:

[1529] Learning progression and monitoring

[1530] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[1531] The device collects the user's learning progress and test results in real time and sends them to the server.

[1532] The server analyzes the collected data and updates the curriculum as needed.

[1533] Input: Learning progress, test results

[1534] Data processing: Real-time analysis of progress data

[1535] Output: Updated learning curriculum

[1536] Step 5:

[1537] Providing Feedback

[1538] The server generates feedback based on the analysis results and sends it to the terminal.

[1539] The device provides the user with progress reports and next learning steps.

[1540] Input: Progress data, test results

[1541] Data processing: Generate feedback based on analysis results

[1542] Output: Feedback, progress report, next learning steps

[1543] As described above, the system inputs, processes, and outputs appropriate data at each step, providing users with the optimal learning curriculum and feedback.

[1544] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1545] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1546] System Configuration

[1547] The system mainly consists of a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[1548] Basic Features

[1549] 1. User Registration and Data Collection

[1550] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[1551] The user enters the necessary information into the input screen and presses the registration button.

[1552] The terminal transmits the input information to the server.

[1553] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1554] 2. Initial data collection

[1555] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[1556] The user answers the questionnaire and enters the necessary data.

[1557] The terminal transmits the collected data to the server.

[1558] The server stores the received data in a database and uses it as initial information.

[1559] 3. Curriculum Generation

[1560] The server performs analysis using generative AI based on the stored initial data.

[1561] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[1562] The server transmits the generated learning curriculum to the terminal.

[1563] 4. Emotional Data Collection and Analysis

[1564] The terminal uses an emotion engine to recognize the user's emotions in real time.

[1565] Through the camera and microphone displayed while the user is learning, the emotion engine analyzes facial expressions and tone of voice to generate emotion data.

[1566] The terminal transmits the emotion data to the server.

[1567] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[1568] 5. Curriculum Adjustment and Feedback

[1569] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the entire curriculum.

[1570] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[1571] The server sends updated curriculum and feedback to the terminal.

[1572] 6. Learning Progression and Monitoring

[1573] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[1574] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[1575] The server updates the curriculum and generates feedback based on the collected data.

[1576] Specific examples

[1577] First time user

[1578] When a user logs in to the system for the first time, the terminal displays a basic information entry screen. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[1579] The device sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[1580] Inputting initial data and generating curriculum

[1581] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[1582] The user answers the questionnaire and transmits the answers to the terminal.

[1583] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[1584] Emotion data collection and analysis

[1585] As the user progresses with their learning, the device uses an emotion engine to measure the user's facial expressions and tone of voice to obtain emotional data.

[1586] The terminal transmits the acquired emotion data to the server in real time.

[1587] The server analyzes the received data and assesses the user's emotional state.

[1588] Curriculum adjustment and feedback

[1589] The server integrates and analyzes the emotion data and the learning progress data, and adjusts the learning curriculum according to the user's current state.

[1590] The generative AI evaluates whether the current curriculum is appropriate based on the user's emotional state and automatically generates necessary changes.

[1591] The server sends the updated curriculum and feedback to the terminal.

[1592] This makes it possible for the present invention to provide an individually optimized learning environment that takes into account the user's emotional state.

[1593] The processing flow will be explained below.

[1594] Step 1: User Registration

[1595] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[1596] The user enters the necessary information into the input screen and presses the registration button.

[1597] The terminal transmits the input information to the server.

[1598] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1599] Step 2: Initial data collection

[1600] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[1601] The user answers the questionnaire and enters the necessary data.

[1602] The terminal transmits the collected data to the server.

[1603] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[1604] Step 3: Generate the initial curriculum

[1605] The server performs analysis using generative AI based on the stored initial data.

[1606] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[1607] The server transmits the generated learning curriculum to the terminal.

[1608] Step 4: Start learning and collect emotional data

[1609] The terminal displays the learning curriculum provided to the user and presents the first task.

[1610] Users begin studying according to the curriculum and take assignments and tests.

[1611] During learning, the device uses an emotion engine to analyze the user's facial expressions and voice in real time and obtain emotional data.

[1612] The terminal transmits the acquired emotion data to the server.

[1613] Step 5: Record your learning progress

[1614] The terminal records the user's learning progress data and test results and transmits them to the server.

[1615] The server stores the received data in a database for subsequent analysis.

[1616] Step 6: Analyze the sentiment data

[1617] The server analyzes the emotion data sent from the emotion engine.

[1618] The generative AI evaluates the user's current emotional state based on emotional data and estimates their learning load and stress level.

[1619] Step 7: Adjust the curriculum

[1620] The server integrates and analyzes the emotional data and learning progress data, and adjusts the learning curriculum as needed.

[1621] The generative AI updates the difficulty and content of the curriculum based on the user's emotions and learning situation.

[1622] The server transmits the updated curriculum to the terminal.

[1623] Step 8: Provide feedback

[1624] The server generates feedback based on the analysis results and transmits it to the terminal.

[1625] The device provides feedback and next learning steps to the user.

[1626] Step 9: Continuous monitoring

[1627] The device continuously monitors the user's learning progress and emotional data, and transmits the data to the server as needed.

[1628] The server periodically reevaluates the learning curriculum based on this data and makes any necessary updates.

[1629] The above processing steps enable the system to take emotional data into account and provide more personalized learning assistance.

[1630] Example 2

[1631] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1632] Current learning systems lack the ability to fully understand each user's learning progress and emotional state and automatically adjust the optimal learning curriculum accordingly. This increases the likelihood that users will experience stress or inappropriate learning load, which can reduce learning effectiveness. Therefore, a learning system that is personalized for each user and can adjust in real time is needed.

[1633] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1634] In this invention, the server includes means for receiving as input the user's basic information, learning situation, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for automatically generating an individualized learning curriculum based on the analysis results using a generation AI, means for providing the generated learning curriculum to the user, means for recognizing, recording, and transmitting the user's learning progress and emotional data in real time, means for analyzing the transmitted learning progress and emotional data in real time and updating and providing feedback on the learning curriculum, and means for providing the feedback and updated curriculum to the user. This makes it possible to provide an individual learning curriculum optimized for each user in real time, maximizing learning effectiveness.

[1635] "User" refers to an individual who uses the system to learn.

[1636] "Basic information" refers to information that identifies and locates an individual, such as the user's name, age, grade, and areas of interest.

[1637] "Study status" refers to information including a user's current learning progress, past learning records, and learning goals.

[1638] "Test results" refers to the scores and evaluation results of tests that a user has taken in the past.

[1639] "Personality diagnosis results" refers to the results of evaluating the user's personality traits and behavioral patterns.

[1640] "Database" refers to a collection used to store, manage, and retrieve received information.

[1641] "Analysis" refers to the act of assessing the user's level of understanding and ability based on the information received.

[1642] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to generate useful information from data for a specific task or purpose.

[1643] "Learning curriculum" refers to a systematic plan of the learning materials and learning steps necessary to achieve a user's learning goals.

[1644] "Feedback" refers to advice and suggestions for improvement provided at the appropriate time based on the results and emotional state of the user as they progress through their learning.

[1645] "Emotional Data" refers to information about a user's emotional state obtained through facial expressions, tone of voice, and other physiological responses.

[1646] "Real-time" refers to a situation where a response or processing occurs almost immediately.

[1647] "Analysis results" refers to the evaluation of the user's level of understanding and ability that is derived after the generating AI processes the information it receives.

[1648] "Monitoring" refers to the act of continuously observing a user's learning progress and emotional state and collecting data as necessary.

[1649] "Adjustment" refers to the act of changing the content or difficulty of the learning curriculum based on the analysis results and emotional data.

[1650] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[1651] System Configuration

[1652] This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[1653] User registration and data collection

[1654] The device displays a new user registration screen and uses front-end technologies such as HTML, CSS, and JavaScript to prompt the user to enter basic information such as name, age, grade, and areas of interest. The user enters this information and presses the registration button. For example, a user might enter "Yamada Taro," age "15," grade "first year of high school," and area of ​​interest "mathematics." The device sends the entered information to the server via a POST request, and the server stores the received information in a database, then generates a unique user ID and returns it to the device.

[1655] Example prompt sentence:

[1656] "Please enter your name. Tell us your age. Also select your grade and areas of interest."

[1657] Initial data collection

[1658] Next, the device displays a questionnaire screen with information about the user's learning status, past test results, and personality assessment, and asks the user to answer. The user answers these questions, for example, entering "My past test results were 80 points" or "My personality assessment result is a cautious type." The device then sends this data to the server, which stores it in a database.

[1659] Example prompt sentence:

[1660] "Enter your past test results. Take the personality test."

[1661] Curriculum Generation

[1662] The server uses a generation AI to analyze the stored initial data. The generation AI evaluates the user's level of understanding, ability, and personality assessment results, and generates a personalized learning curriculum. For example, the generation AI determines that the user's level of understanding of mathematics is "intermediate," and generates a curriculum that includes "differential and integral calculus" as the next step. The generated curriculum is sent from the server to the device, providing the user with the next task to tackle.

[1663] Example prompt sentence:

[1664] "Generate an appropriate learning curriculum based on the user's initial data."

[1665] Emotion data collection and analysis

[1666] During training, the device uses an emotion engine to recognize the user's facial expressions and tone of voice in real time. For example, if the user shows a "tired" expression, the emotion engine will detect this and record it as data. The device then sends the emotion data to the server, which then evaluates the user's emotional state based on this data.

[1667] Example prompt sentence:

[1668] "Collect real-time user sentiment data."

[1669] Curriculum Adjustment and Feedback

[1670] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum. For example, if a user feels stressed, the generative AI will add a "break time" and suggest tasks that can be completed in a relaxed state. The adjusted curriculum is sent from the server to the device, and the latest learning plan is provided to the user.

[1671] Example prompt sentence:

[1672] "Adjust your curriculum based on user sentiment data."

[1673] Learning progression and monitoring

[1674] The user studies according to the learning curriculum provided through the device. The device records the user's learning progress and emotional data in real time and sends it to the server. The server analyzes this data, updates the curriculum as needed, and generates feedback. The feedback and updated curriculum are provided to the device, making it possible to maintain an optimal learning environment for the user.

[1675] Example prompt sentence:

[1676] "Monitor users' learning progress and provide feedback as needed."

[1677] The above is a specific embodiment of the system according to the present invention, which aims to grasp the user's emotional state and learning situation in real time and provide an optimal learning curriculum.

[1678] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1679] Step 1:

[1680] The terminal displays a new user registration screen.

[1681] Input: User registration form using HTML / CSS / JavaScript.

[1682] Details: The device displays a form that asks the user to enter their name, age, grade, and interests.

[1683] Output: Basic information entered by the user.

[1684] Specific operation: For example, a user inputs "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[1685] Step 2:

[1686] The user enters basic information and presses the registration button.

[1687] Input: User-entered name, age, grade, and interests.

[1688] Details: The user fills in the form with the required information and clicks the register button.

[1689] Output: Basic information received by the device.

[1690] Specific operation: When the user presses the registration button, the information displayed on the screen is sent from the device to the server.

[1691] Step 3:

[1692] The terminal transmits the input basic information to the server.

[1693] Input: Basic information about the user received by the device.

[1694] Details: The device sends input data to the server via an HTTP POST request.

[1695] Output: Basic information received by the server.

[1696] Specific operation: The request body sent by the device contains data such as name "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[1697] Step 4:

[1698] The server stores the received basic information in a database, generates a unique user ID, and returns it to the terminal.

[1699] Input: The user's basic information received by the server.

[1700] Details: The server stores the received data in a database and generates a unique user ID for the new user.

[1701] Output: A unique user ID and basic information stored in the database.

[1702] Specific operation: The server stores the information in a MySQL database, generates a user ID "12345", and returns it to the terminal.

[1703] Step 5:

[1704] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[1705] Input: User ID and basic information received from the server.

[1706] Details: The device displays a questionnaire screen where students can enter their learning status and past test results.

[1707] Output: User-entered learning status, past test results, and personality assessment results.

[1708] What it does: The device prompts you with a question, such as "Please enter your past math test scores."

[1709] Step 6:

[1710] The user answers the questionnaire and enters the required data.

[1711] Input: User-entered learning history, past test results, and personality assessment results.

[1712] Details: Users answer questions by entering text or by selecting multiple options.

[1713] Output: Survey results received by the device.

[1714] Specific behavior: The user enters "80 points" and selects "Conscientious" on the personality test.

[1715] Step 7:

[1716] The terminal transmits the collected questionnaire data to the server.

[1717] Input: Survey results received by the device.

[1718] Details: The device sends the survey data to the server via an HTTP POST request.

[1719] Output: Survey data received by the server.

[1720] Specific operation: The request sent by the device includes data such as learning status (80 points) and personality test result (cautious type).

[1721] Step 8:

[1722] The server stores the received survey data in a database and analyzes it using a generative AI model.

[1723] Input: Survey data received by the server.

[1724] Details: The server stores the received data in a database and analyzes it using a generative AI model.

[1725] Output: User's understanding and ability assessment as a result of the analysis.

[1726] Specific operation: The server stores the information in a database and passes the input data to the generation AI for analysis.

[1727] Step 9:

[1728] Generative AI generates an individualized learning curriculum based on the user's level of understanding, ability, and personality assessment results.

[1729] Input: Initial data and analysis results.

[1730] Details: Generative AI evaluates users' understanding, abilities, and personality assessment results to generate a personalized learning curriculum.

[1731] Output: A personalized learning curriculum.

[1732] Specific operation: The generation AI determines that the level of understanding is "intermediate" and generates a curriculum that includes "differential and integral calculus."

[1733] Step 10:

[1734] The server transmits the generated learning curriculum to the terminal.

[1735] Input: The generated learning curriculum.

[1736] Details: The server sends the generated curriculum to the device as an HTTP response.

[1737] Output: The learning curriculum sent to the device.

[1738] Specific operation: The device displays the curriculum details received from the generation AI and confirms the next assignment the user will work on.

[1739] Step 11:

[1740] The terminal uses an emotion engine to recognize the user's emotions in real time.

[1741] Input: The user's facial expression and tone of voice.

[1742] Details: The device senses the user's facial expressions and tone of voice while learning and analyzes them using an emotion engine.

[1743] Output: Recognized emotion data.

[1744] Specific operation: The device's camera and microphone detect the user's "tired" facial expression and "low-pitched voice."

[1745] Step 12:

[1746] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data.

[1747] Input: The user's facial expression and tone of voice.

[1748] Details: The emotion engine analyzes facial expressions as data and labels the emotions it detects in real time.

[1749] Output: Emotion data.

[1750] Specific behavior: The emotion engine generates emotion labels such as "fatigue" and "stress."

[1751] Step 13:

[1752] The terminal transmits the emotion data to the server.

[1753] Input: Emotion data generated by the emotion engine.

[1754] Details: The device sends the generated emotion data to the server.

[1755] Output: Emotion data received by the server.

[1756] Specific operation: The device sends the emotional data "fatigue" and "stress" generated to the server.

[1757] Step 14:

[1758] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[1759] Input: Received emotion data.

[1760] Details: The server analyzes the received data and evaluates the user's stress level and learning load.

[1761] Output: Assessment results of learning load and stress level.

[1762] Specific operation: The server analyzes the emotional data "fatigue" and "stress" and evaluates the user's stress level as "high."

[1763] Step 15:

[1764] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum.

[1765] Input: Learning progress data and emotion data.

[1766] Details: The server integrates and analyzes emotional data and learning progress data to regenerate an appropriate learning curriculum.

[1767] Output: A tailored learning curriculum.

[1768] Specific operation: The server informs the generated AI of the user's "high stress level" and adjusts the curriculum to "relaxing tasks that include breaks."

[1769] Step 16:

[1770] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[1771] Input: Data indicating the user's emotional state.

[1772] Details: Generative AI assesses the appropriateness of the curriculum based on the user's emotional state and makes necessary adjustments.

[1773] Output: Specific learning steps and break timings.

[1774] Specific behavior: The generative AI suggests to the user to "take a break for the next 30 minutes."

[1775] Step 17:

[1776] The server sends updated curriculum and feedback to the terminal.

[1777] Input: Updated learning curriculum and feedback.

[1778] Details: The server sends updated curriculum information to the device and provides feedback.

[1779] Output: Updated curriculum and feedback delivered to the device.

[1780] What happens: Materials containing the updated curriculum "Differential and Integral Calculus" and the feedback "Take a break for the next 30 minutes" are displayed on the device.

[1781] Step 18:

[1782] The user proceeds with the study according to the study curriculum provided through the terminal.

[1783] Input: The learning curriculum displayed on the device.

[1784] Details: The user progresses through the learning steps according to the curriculum displayed on the device.

[1785] Output: User's learning progress.

[1786] Specific action: The user works on a "differentiation exercise" while looking at the curriculum materials.

[1787] Step 19:

[1788] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[1789] Input: User's learning progress data and emotional data.

[1790] Details: The device records the user's progress and emotional data in real time and periodically transmits it to the server.

[1791] Output: Learning progress data and emotion data sent to the server.

[1792] Specific operation: The device sends the user's learning progress ("Differential task completed") and emotional data ("Concentration") to the server.

[1793] Step 20:

[1794] The server updates the curriculum based on the collected data and generates feedback.

[1795] Input: Collected learning progress data and emotion data.

[1796] Details: The server analyzes the collected data, updates the curriculum as needed, and generates new feedback.

[1797] Output: Updated curriculum and feedback.

[1798] Specific operation: The server generates feedback such as "Start the integration problem as the next step" and sends it to the device.

[1799] This allows the system to create an individually optimized learning environment that takes into account the user's emotions.

[1800] (Application example 2)

[1801] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1802] While conventional learning systems can provide an individualized curriculum based on the user's basic information and learning progress, it is difficult to flexibly adjust the curriculum taking into account the user's emotional state or to suggest break timing.In addition, in the work environment, there was a lack of means to evaluate work efficiency and fatigue level in real time and provide appropriate feedback accordingly, making it a challenge to reduce stress while maximizing worker efficiency.

[1803] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's basic information, learning situation, test results, and personality assessment results as input, means for storing the received information in a database, and means for analyzing the user's level of understanding and ability based on the stored information. This makes it possible to evaluate the user's emotional state and, based on that, propose a work curriculum and break timing.

[1804] "Basic information" refers to attribute information such as the user's name, age, grade, and areas of interest.

[1805] "Study status" is information that indicates the progress and level of understanding of the user's current learning.

[1806] "Test results" is information indicating the results of various tests and evaluations conducted in the past.

[1807] "Personality diagnosis results" are data about the user's personality obtained based on personality diagnosis tests and questionnaires.

[1808] A "database" is a system for storing and managing various information obtained from users.

[1809] "Means of analysis" is a function that evaluates the user's ability and level of understanding based on the information received.

[1810] An "individualized learning curriculum" is an optimal learning plan that is automatically generated to suit each user's level of understanding and ability.

[1811] "Emotion data" refers to information about emotions acquired from the user's facial expressions, tone of voice, etc.

[1812] A "generative AI model" is an algorithm that uses artificial intelligence to analyze data and suggest appropriate learning curricula and break timing.

[1813] "Feedback" refers to guidance and advice provided to users based on the analysis results.

[1814] "Work curriculum" refers to the user's work plan, which includes work content optimized based on the evaluation results.

[1815] "Rest timing" is information that indicates the optimal timing for the user to take an appropriate rest.

[1816] A "server" is a computer system that plays a central role in the system, such as analyzing data and running generative AI models.

[1817] The present invention combines a system that uses a generative AI model to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1818] System Configuration

[1819] The system consists of a server, a device, a user, and an emotion engine. The server analyzes data and runs the generative AI model. The device collects input data, learning progress, and emotion data from the user. The user interacts with the system as a learner. The emotion engine recognizes the user's emotions in real time and analyzes the data.

[1820] Overview of the embodiment

[1821] User registration and data collection

[1822] The device displays a new user registration screen, where the user enters basic information such as name, age, grade, and areas of interest. This information is sent from the device to the server and stored in a database. The server generates a user ID based on the received information and returns it to the device.

[1823] Initial data collection

[1824] Users answer questionnaires about their learning status, past test results, and personality assessments through their devices. The devices send the collected data to a server, which stores it in a database. The server analyzes the initial data it receives and generates a personalized learning curriculum.

[1825] Creating and providing learning curriculum

[1826] The generative AI model analyzes the user's level of understanding, ability, and personality assessment results to generate an optimized learning curriculum. The generated learning curriculum is sent from the server to the device and provided to the user. The user then follows the curriculum through their device to progress through their studies.

[1827] Emotion data collection and analysis

[1828] During the learning process, the device uses an emotion engine to recognize the user's emotions in real time. It analyzes the user's facial expressions and tone of voice through a camera and microphone to generate emotion data. This emotion data is sent from the device to a server, where it is analyzed.

[1829] Curriculum adjustment and feedback

[1830] The server integrates and analyzes emotional data and learning progress data to assess the user's learning load and stress level. Based on these results, the generative AI model adjusts the learning curriculum and suggests appropriate break times and refreshing activities. The updated curriculum and feedback are provided to the user via their device.

[1831] Hardware and software used

[1832] Server: A computer system that analyzes data and runs generative AI models.

[1833] Device: A device (e.g., smartphone, tablet) that collects data from users and provides learning curriculum.

[1834] Emotion engine: Software for analyzing user emotions (e.g., EmotionEngine).

[1835] Generative AI model: An algorithm that analyzes data and generates an optimal curriculum (e.g., AIModule).

[1836] Specific examples

[1837] When a user logs in to the system for the first time, they are presented with a basic information entry screen. The user enters their name, age, grade, areas of interest, etc., and the information is sent from the device to the server. The server stores this information in a database and generates a user ID. Next, the user enters initial data (study status, past test results, personality assessment questionnaire, etc.), which is also sent to the server. A generative AI model generates a personalized learning curriculum and provides it to the device.

[1838] During learning, the device uses an emotion engine to analyze the user's facial expressions and tone of voice, acquiring emotional data and sending it to the server. The server then adjusts the curriculum and provides feedback to the user based on the emotional data and learning progress data.

[1839] An example prompt might be, "Assess the worker's fatigue level and emotional state and suggest optimal work schedules and break timings. Include the following data: work efficiency data, video feed, audio feed. If possible, suggest refreshment activities."

[1840] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1841] Step 1:

[1842] The terminal displays a new user registration screen. The user enters basic information (name, age, grade, areas of interest) and presses the registration button. The input data is sent from the terminal to the server. The server stores the received basic information in a database, generates a new user ID, and returns it to the terminal. The input is basic information, and the output is the user ID.

[1843] Step 2:

[1844] The device displays learning status, past test results, and a personality assessment questionnaire, and the user answers each item. The device then sends the collected data to a server. The server stores the received data in a database and analyzes the data using a generative AI model. The input is learning status data, and the output is analyzed data.

[1845] Step 3:

[1846] The server uses a generative AI model to evaluate the user's level of understanding and ability, and generates an individualized learning curriculum. The generated curriculum is sent from the server to the terminal and provided to the user. The input is the evaluation data, and the output is the learning curriculum.

[1847] Step 4:

[1848] As the user continues learning, the device collects facial expressions and tone of voice through the built-in camera and microphone, and generates emotional data in real time using an emotion engine. The generated emotional data is sent from the device to a server. The input is the camera and microphone feed, and the output is emotional data.

[1849] Step 5:

[1850] The server integrates and analyzes the received emotional data and learning progress data to evaluate the user's learning load and stress level. Based on the analysis results, the generative AI model adjusts the learning curriculum and break timing. The input is emotional data and learning progress data, and the output is the adjusted curriculum.

[1851] Step 6:

[1852] The server sends the updated learning curriculum and feedback to the terminal and provides it to the user. The user proceeds with learning according to the adjusted curriculum through the terminal. The input is the adjusted curriculum, and the output is the user's learning progress.

[1853] Step 7:

[1854] The device continuously monitors the user's learning progress and emotional data and sends them to the server. The server analyzes this data at runtime and adjusts the learning curriculum and feedback as needed. The input is the continuously collected learning progress and emotional data, and the output is the adjusted learning curriculum and feedback.

[1855] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1856] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1857] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1858] [Fourth embodiment]

[1859] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1860] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1861] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1862] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1863] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1864] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1865] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1866] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1867] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1868] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1869] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1870] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1871] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1872] The present invention relates to a system that uses a generative AI to provide a user with an optimized learning curriculum. Specific embodiments of the system are described below.

[1873] System Configuration

[1874] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[1875] Basic Features

[1876] 1. User Registration and Data Collection

[1877] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, grade, and areas of interest).

[1878] The user enters the necessary information through the input screen and presses the registration button.

[1879] The terminal transmits the input information to the server.

[1880] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1881] 2. Initial data collection

[1882] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[1883] The user answers the questionnaire and transmits the data to the terminal.

[1884] The terminal transmits the collected data to the server.

[1885] The server stores this data in a database and uses it as initial information.

[1886] 3. Curriculum Generation

[1887] The server uses generative AI to analyze the collected information.

[1888] The generative AI evaluates the user's level of understanding, ability, and personality, and generates a personalized learning curriculum.

[1889] The server transmits the generated curriculum to the terminal.

[1890] 4. Learning Progression and Monitoring

[1891] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[1892] The device collects the user's learning progress and test results in real time and sends them to the server.

[1893] The server analyzes the collected data and updates the curriculum as needed.

[1894] 5. Providing Feedback

[1895] The server generates feedback based on the analysis results and sends it to the terminal.

[1896] The device provides the user with progress reports and next learning steps.

[1897] Specific examples

[1898] First time user

[1899] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[1900] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[1901] Inputting initial data and generating curriculum

[1902] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[1903] The user answers the questionnaire and transmits the answers to the terminal.

[1904] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[1905] Learning progress management and feedback

[1906] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[1907] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[1908] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[1909] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[1910] The processing flow will be explained below.

[1911] Step 1: User Registration

[1912] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[1913] The user enters the necessary information into the input screen and presses the registration button.

[1914] The terminal transmits the input information to the server.

[1915] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1916] Step 2: Initial data collection

[1917] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[1918] The user answers the questionnaire and enters the necessary data.

[1919] The terminal transmits the collected data to the server.

[1920] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[1921] Step 3: Generate the initial curriculum

[1922] The server performs analysis using generative AI based on the stored initial data.

[1923] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[1924] The server transmits the generated learning curriculum to the terminal.

[1925] Step 4: Start learning and monitor your progress

[1926] The terminal displays the learning curriculum provided to the user and presents the first task.

[1927] Users begin studying according to the curriculum and take assignments and tests.

[1928] The device records learning progress data and test results in real time and transmits them to the server.

[1929] Step 5: Analyze the training data

[1930] The server analyzes the received progress data and test results.

[1931] The generative AI evaluates the user's learning status in real time and adjusts the content and difficulty of the curriculum as needed.

[1932] Step 6: Feedback and curriculum updates

[1933] The server generates feedback based on the analysis results and transmits it to the terminal.

[1934] The device provides feedback and next learning steps to the user.

[1935] The server updates the learning curriculum based on the user's level of understanding and transmits the new curriculum to the terminal.

[1936] Step 7: Continuous monitoring

[1937] The terminal continuously monitors the user's learning progress and transmits progress data to the server as needed.

[1938] The server periodically reevaluates the learning curriculum based on this data and continues to provide optimal learning content.

[1939] This allows the system to respond to the user's learning needs and provide effective and personalized learning support.

[1940] Example 1

[1941] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1942] Conventional learning support systems have difficulty providing optimal learning curricula tailored to individual users' characteristics and learning progress, and tend to have uniform content. This leads to issues such as reduced learning efficiency and difficulty in sustaining motivation. Furthermore, it is difficult to provide real-time feedback or update the curriculum, resulting in a lack of flexibility in situations where immediate user response is essential.

[1943] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1944] In this invention, the server includes means for receiving the user's basic information, learning information, evaluation results, and diagnostic results as input, means for storing the received information in a data storage device, and means for analyzing the user's level of understanding and ability based on the stored information, thereby making it possible to automatically generate an individualized educational program, monitor the user's learning progress in real time, and provide feedback.

[1945] "User" refers to an individual who uses the system to learn.

[1946] "Basic information" refers to personal information such as the user's name, age, grade, and areas of interest.

[1947] "Learning information" refers to data such as a user's learning status, progress, and past test results.

[1948] "Assessment Results" refers to the scores or grades based on tests or assessments taken by a User.

[1949] "Diagnosis results" refers to data related to the user's personality diagnosis and ability evaluation.

[1950] "Means for receiving input" refers to the interface and functions for inputting basic information and learning information provided by users into the system.

[1951] "Data storage device" refers to a database or storage system for storing received information.

[1952] "Means for analyzing a user's level of understanding and ability based on stored information" refers to algorithms and analytical tools that analyze data in real time and evaluate a user's learning progress and ability.

[1953] "Individualized educational program" refers to a learning curriculum optimized for each user based on the analysis results.

[1954] "Means for automatic generation" refers to the function of automatically designing educational programs using a generative AI model based on collected data.

[1955] "Means for providing" refers to the interface and functions for presenting the generated educational program and feedback to the user.

[1956] "Means for monitoring and transmitting the data to an information processing device" refers to a function or device for acquiring the user's learning data in real time and sending it to a server.

[1957] "Means of analyzing in real time and updating and providing feedback to educational programs" refers to algorithms and functions that instantly analyze received data and generate new curricula and feedback.

[1958] "Means for providing feedback and updated educational programs" refers to interfaces and functions that notify users of analysis results and new curricula.

[1959] This invention is a system that uses generative AI to provide users with optimized educational programs. This system consists of a server, a terminal, and a user. The server processes data and performs analysis using generative AI, and the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner.

[1960] User registration and data collection

[1961] The terminal displays a new user registration screen and asks the user to enter basic information (such as name, age, grade, and areas of interest). The user enters the necessary information through the input screen and presses the registration button. The terminal then sends the entered information to the server. The server stores the received information in a database (for example, MySQL or PostgreSQL), generates a user ID, and returns it to the terminal.

[1962] Initial data collection

[1963] The device displays a questionnaire for learning status, past test results, and personality assessment. The user answers the questionnaire and sends the data to the device. The device then sends the collected data to the server. The server stores this data in a database and uses it as initial information.

[1964] Curriculum Generation

[1965] The server analyzes the collected information using a generation AI (e.g., GPT-3 or ChatGPT). The generation AI evaluates the user's level of understanding, ability, and personality, and generates a personalized educational program. The server then sends the generated educational program to the device.

[1966] Learning progression and monitoring

[1967] Users study according to the educational program provided through their device. The device collects the user's learning progress and test results in real time and sends them to the server. The server analyzes the collected data and updates the educational program as necessary.

[1968] Providing Feedback

[1969] The server generates feedback based on the analysis results and sends it to the device, which then presents the user with a progress report and next learning steps.

[1970] Specific examples

[1971] First time user

[1972] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information. The user enters their name, age, grade, areas of interest, etc., and presses the submit button. The terminal then sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the terminal.

[1973] Inputting initial data and generating curriculum

[1974] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond. The user answers the questionnaire and sends the answers to the device. The device then sends the response data to a server, which stores it in a database. The server then uses generative AI to analyze this data and generate an educational program optimized for the user.

[1975] Learning progress management and feedback

[1976] As the user progresses through their studies, the device records their progress and test results in real time and sends them to the server. The server analyzes this data, adjusts the curriculum as needed, and generates feedback. The device then presents the feedback and updated curriculum information to the user and guides them on their next learning steps.

[1977] Examples of prompt statements

[1978] When registering as a first-time user:

[1979] Enter basic information about the user (name, age, grade, areas of interest).

[1980] When entering the initial data:

[1981] Please answer a questionnaire about your past learning history, test results, and personality assessment.

[1982] In this way, the system of the present invention can provide an optimal learning environment that meets the individual needs of each user.

[1983] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1984] System processing steps

[1985] Step 1: Enter your user registration information

[1986] 1.1 The terminal displays the new user registration screen.

[1987] Input: User's basic information (name, age, grade, areas of interest)

[1988] Output: User input data

[1989] 1.2 The user enters basic information and presses the registration button.

[1990] Specific actions: form entry, button click

[1991] 1.3 The terminal sends the entered information to the server.

[1992] Input: User-entered data

[1993] Data processing: Convert to JSON format

[1994] Output: Send data to the server

[1995] 1.4 The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[1996] Input: JSON data received from the terminal

[1997] Data calculation: saving to database, generating user ID

[1998] Output: User ID

[1999] Step 2: Initial data collection through a questionnaire

[2000] 2.1 The device displays a questionnaire about learning status, past test results, and personality assessment.

[2001] Input: None

[2002] Output: Survey screen

[2003] 2.2 The user answers the questionnaire and sends the data to the terminal.

[2004] Input: User response data

[2005] Specific actions: filling in a form, clicking the submit button

[2006] 2.3 The device sends the collected data to the server.

[2007] Input: User response data

[2008] Data processing: Convert to JSON format

[2009] Output: Send data to the server

[2010] 2.4 The server stores this data in a database.

[2011] Input: JSON data received from the terminal

[2012] Data calculation: Saving to database

[2013] Output: Save completion status

[2014] Step 3: Curriculum generation

[2015] 3.1 The server will use the generated AI to analyze the collected information.

[2016] Input: Collected user data

[2017] Data calculation: Analysis by generative AI (e.g., GPT-3)

[2018] Output: Analysis results

[2019] 3.2 Generative AI evaluates the user's level of understanding, ability, and personality and generates a personalized educational program.

[2020] Input: Analysis results

[2021] Data Computation: Generating Learning Curricula

[2022] Output: personalized education program

[2023] 3.3 The server sends the generated curriculum to the terminal.

[2024] Input: Educational Program Data

[2025] Data processing: Convert to JSON format

[2026] Output: Sending data to the terminal

[2027] Step 4: Progressing and monitoring your learning

[2028] 4.1 The user will study according to the educational program provided through the terminal.

[2029] Input: Educational Program

[2030] Specific behavior: Starting a learning activity

[2031] 4.2 The device records the user's learning progress and test results in real time and transmits them to the server.

[2032] Input: Learning progress data, test results

[2033] Data processing: Convert to JSON format

[2034] Output: Send data to the server

[2035] 4.3 The server will analyze the collected data and update the educational program as necessary.

[2036] Input: Learning progress data, test results

[2037] Data Calculation: Reanalysis by Generative AI

[2038] Output: Updated educational program

[2039] Step 5: Provide feedback

[2040] 5.1 The server generates feedback based on the analysis results and sends it to the device.

[2041] Input: Analysis results

[2042] Data Calculation: Feedback Generation

[2043] Output: Feedback data

[2044] 5.2 The device provides the user with progress reports and next learning steps.

[2045] Input: Feedback data, updated educational program

[2046] Specific operation: Screen display

[2047] Output: Providing feedback to the user

[2048] In this way, the system performs specific data processing and calculations at each processing step, providing users with an optimized educational program and feedback.

[2049] (Application example 1)

[2050] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2051] There is a need to maximize the effectiveness of training for robot operators in factories and provide an efficient curriculum tailored to each operator's skills and level of understanding. However, conventional training methods make it difficult to individually optimize training and are unable to reflect the different progress and proficiency levels of each operator. Real-time feedback and curriculum updates are also insufficient.

[2052] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2053] In this invention, the server includes means for receiving as input the user's basic information, learning status, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for applying the system to the training of robot operators in factories, means for progressing the learning using a smartphone or head-mounted display, and means for using a generative AI model to perform analysis based on prompt sentences. This makes it possible to generate an optimal training curriculum tailored to the skills and level of understanding of each robot operator and provide feedback in real time.

[2054] "Basic user information" refers to personal information such as the robot operator's name, age, job title, past experience, and areas of interest.

[2055] "Learning status" is information that indicates the current progress of the robot operator in learning.

[2056] "Test results" refers to the result data of skill tests and comprehension tests conducted by robot operators.

[2057] "Personality diagnosis results" are diagnostic results used to evaluate the personality and temperament of a robot operator.

[2058] A "database" is a digital data collection for storing collected information.

[2059] "Means for analyzing comprehension and performance" means a method or system used to evaluate a robot operator's comprehension and performance based on the collected information.

[2060] An "individualized learning curriculum" is a learning plan optimized for the individual characteristics and abilities of each robot operator.

[2061] "Monitoring learning progress" means constantly monitoring the robot operator's learning process and recording that progress.

[2062] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and generate optimal learning curricula.

[2063] A "prompt" is a document or question that is input into an AI model and forms the basis for the generated information.

[2064] "Feedback" means advice, comments, and guidance on next steps provided as learning progresses.

[2065] A "smartphone" is a mobile device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[2066] A "head-mounted display" is a device worn on the head that displays visual information directly in front of the eyes.

[2067] This invention is a system that utilizes a generative AI model to provide an individualized learning curriculum for robot operators in a factory. The system generates an optimal training curriculum based on the characteristics of a specific user and provides feedback. A specific embodiment of this system is described below.

[2068] System Configuration

[2069] The system mainly consists of a server, a terminal, and a user. The server processes data and performs analysis using a generative AI model. The terminal is a device that collects user input data and learning progress, specifically a smartphone or head-mounted display (HMD). The user is a robot operator in the factory.

[2070] Basic Features

[2071] 1. User Registration and Data Collection

[2072] The terminal displays a new user registration screen and prompts the user to enter basic information (such as name, age, job title, past experience, areas of interest, etc.).

[2073] The user enters the necessary information through the input screen and presses the registration button.

[2074] The terminal transmits the input information to the server.

[2075] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[2076] 2. Initial data collection

[2077] The device displays learning progress, past test results, and a questionnaire for personality assessment.

[2078] The user answers the questionnaire and transmits the data to the terminal.

[2079] The terminal transmits the collected data to the server.

[2080] The server stores this data in a database and uses it as initial information.

[2081] 3. Curriculum Generation

[2082] The server analyzes the collected information using a generative AI model.

[2083] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[2084] The server transmits the generated curriculum to the terminal.

[2085] 4. Learning Progression and Monitoring

[2086] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[2087] The device collects the user's learning progress and test results in real time and sends them to the server.

[2088] The server analyzes the collected data and updates the curriculum as needed.

[2089] 5. Providing Feedback

[2090] The server generates feedback based on the analysis results and sends it to the terminal.

[2091] The device provides the user with progress reports and next learning steps.

[2092] Specific examples

[2093] First time user

[2094] When a user logs in to the system for the first time, the terminal displays a screen for entering basic information: the user enters their name, age, job title, past experience, areas of interest, etc., and presses the submit button.

[2095] The device sends this information to the server, which stores it in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[2096] Inputting initial data and generating curriculum

[2097] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[2098] The user answers the questionnaire and transmits the answers to the terminal.

[2099] The device sends the answer data to the server, which stores it in a database. The server then analyzes the data using a generative AI model to generate a learning curriculum optimized for the user.

[2100] Learning progress management and feedback

[2101] As the user progresses with their studies, the device records the user's progress data and test results in real time and transmits them to the server.

[2102] The server analyzes this data, adjusts the curriculum as needed, and generates feedback.

[2103] The terminal provides the user with feedback from the server and updated curriculum information, and guides them on their next learning steps.

[2104] Prompt sentences to input to the generative AI model

[2105] User Basic Information:

[2106] Name: XX

[2107] Age: 35

[2108] Position: Robot Operator

[2109] Previous experience: 3 years of factory operations

[2110] Areas of interest: Smart factory technology, safety protocols

[2111] Skills test result: Beginner

[2112] Use this information to generate the optimal robotics operations training curriculum.

[2113] This system will enable factory robot operators to receive individually optimized training, leading to efficient and effective skill development.

[2114] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2115] Step 1:

[2116] User registration and data collection

[2117] The terminal displays a new user registration screen and asks the user to enter information such as name, age, job title, past experience, and areas of interest.

[2118] The user enters the necessary information on the registration screen and presses the send button.

[2119] The terminal transmits the input data to the server.

[2120] The server stores the received data in a database, generates a user ID, and returns it to the terminal.

[2121] Input: User basic information

[2122] Output: User ID

[2123] Step 2:

[2124] Initial data collection

[2125] The device displays a questionnaire including learning status, past test results, and personality assessment.

[2126] The user answers the questionnaire and submits the data.

[2127] The terminal transmits the collected data to the server.

[2128] The server stores this data in a database and uses it as initial information.

[2129] Input: Study status, past test results, personality test answers

[2130] Output: Initial information

[2131] Step 3:

[2132] Curriculum Generation

[2133] The server uses a generative AI model to analyze the collected information.

[2134] The generative AI model uses prompts to assess the user's comprehension, ability, and personality, and generates a personalized learning curriculum.

[2135] The server transmits the generated curriculum to the terminal.

[2136] Input: Basic information, learning status, past test results, personality test answers

[2137] Data processing: Analysis using generative AI models

[2138] Output: personalized learning curriculum

[2139] Step 4:

[2140] Learning progression and monitoring

[2141] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[2142] The device collects the user's learning progress and test results in real time and sends them to the server.

[2143] The server analyzes the collected data and updates the curriculum as needed.

[2144] Input: Learning progress, test results

[2145] Data processing: Real-time analysis of progress data

[2146] Output: Updated learning curriculum

[2147] Step 5:

[2148] Providing Feedback

[2149] The server generates feedback based on the analysis results and sends it to the terminal.

[2150] The device provides the user with progress reports and next learning steps.

[2151] Input: Progress data, test results

[2152] Data processing: Generate feedback based on analysis results

[2153] Output: Feedback, progress report, next learning steps

[2154] As described above, the system inputs, processes, and outputs appropriate data at each step, providing users with the optimal learning curriculum and feedback.

[2155] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2156] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[2157] System Configuration

[2158] The system mainly consists of a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[2159] Basic Features

[2160] 1. User Registration and Data Collection

[2161] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[2162] The user enters the necessary information into the input screen and presses the registration button.

[2163] The terminal transmits the input information to the server.

[2164] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[2165] 2. Initial data collection

[2166] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[2167] The user answers the questionnaire and enters the necessary data.

[2168] The terminal transmits the collected data to the server.

[2169] The server stores the received data in a database and uses it as initial information.

[2170] 3. Curriculum Generation

[2171] The server performs analysis using generative AI based on the stored initial data.

[2172] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[2173] The server transmits the generated learning curriculum to the terminal.

[2174] 4. Emotional Data Collection and Analysis

[2175] The terminal uses an emotion engine to recognize the user's emotions in real time.

[2176] Through the camera and microphone displayed while the user is learning, the emotion engine analyzes facial expressions and tone of voice to generate emotion data.

[2177] The terminal transmits the emotion data to the server.

[2178] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[2179] 5. Curriculum Adjustment and Feedback

[2180] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the entire curriculum.

[2181] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[2182] The server sends updated curriculum and feedback to the terminal.

[2183] 6. Learning Progression and Monitoring

[2184] The user proceeds with his / her studies according to the learning curriculum provided through the terminal.

[2185] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[2186] The server updates the curriculum and generates feedback based on the collected data.

[2187] Specific examples

[2188] First time user

[2189] When a user logs in to the system for the first time, the terminal displays a basic information entry screen. The user enters their name, age, grade, areas of interest, etc., and presses the submit button.

[2190] The device sends this information to the server, which stores the received information in a database. Based on the basic information, the server generates a user ID and sends it back to the device.

[2191] Inputting initial data and generating curriculum

[2192] The device displays a questionnaire about learning status, past test results, and personality assessment, and asks the user to respond.

[2193] The user answers the questionnaire and transmits the answers to the terminal.

[2194] The device sends the answer data to the server, which stores it in a database. The server then uses generative AI to analyze the data and generate a learning curriculum optimized for the user.

[2195] Emotion data collection and analysis

[2196] As the user progresses with their learning, the device uses an emotion engine to measure the user's facial expressions and tone of voice to obtain emotional data.

[2197] The terminal transmits the acquired emotion data to the server in real time.

[2198] The server analyzes the received data and assesses the user's emotional state.

[2199] Curriculum adjustment and feedback

[2200] The server integrates and analyzes the emotion data and the learning progress data, and adjusts the learning curriculum according to the user's current state.

[2201] The generative AI evaluates whether the current curriculum is appropriate based on the user's emotional state and automatically generates necessary changes.

[2202] The server sends the updated curriculum and feedback to the terminal.

[2203] This makes it possible for the present invention to provide an individually optimized learning environment that takes into account the user's emotional state.

[2204] The processing flow will be explained below.

[2205] Step 1: User Registration

[2206] The terminal displays a new user registration screen and prompts the user to enter basic information such as name, age, grade, and areas of interest.

[2207] The user enters the necessary information into the input screen and presses the registration button.

[2208] The terminal transmits the input information to the server.

[2209] The server stores the received information in a database, generates a user ID, and returns it to the terminal.

[2210] Step 2: Initial data collection

[2211] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[2212] The user answers the questionnaire and enters the necessary data.

[2213] The terminal transmits the collected data to the server.

[2214] The server stores the received data in a database and uses it as initial data for the generative AI to learn.

[2215] Step 3: Generate the initial curriculum

[2216] The server performs analysis using generative AI based on the stored initial data.

[2217] The generative AI evaluates the user's level of understanding, ability, and personality assessment results, and generates an individualized learning curriculum.

[2218] The server transmits the generated learning curriculum to the terminal.

[2219] Step 4: Start learning and collect emotional data

[2220] The terminal displays the learning curriculum provided to the user and presents the first task.

[2221] Users begin studying according to the curriculum and take assignments and tests.

[2222] During learning, the device uses an emotion engine to analyze the user's facial expressions and voice in real time and obtain emotional data.

[2223] The terminal transmits the acquired emotion data to the server.

[2224] Step 5: Record your learning progress

[2225] The terminal records the user's learning progress data and test results and transmits them to the server.

[2226] The server stores the received data in a database for subsequent analysis.

[2227] Step 6: Analyze the sentiment data

[2228] The server analyzes the emotion data sent from the emotion engine.

[2229] The generative AI evaluates the user's current emotional state based on emotional data and estimates their learning load and stress level.

[2230] Step 7: Adjust the curriculum

[2231] The server integrates and analyzes the emotional data and learning progress data, and adjusts the learning curriculum as needed.

[2232] The generative AI updates the difficulty and content of the curriculum based on the user's emotions and learning situation.

[2233] The server transmits the updated curriculum to the terminal.

[2234] Step 8: Provide feedback

[2235] The server generates feedback based on the analysis results and transmits it to the terminal.

[2236] The device provides feedback and next learning steps to the user.

[2237] Step 9: Continuous monitoring

[2238] The device continuously monitors the user's learning progress and emotional data, and transmits the data to the server as needed.

[2239] The server periodically reevaluates the learning curriculum based on this data and makes any necessary updates.

[2240] The above processing steps enable the system to take emotional data into account and provide more personalized learning assistance.

[2241] Example 2

[2242] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2243] Current learning systems lack the ability to fully understand each user's learning progress and emotional state and automatically adjust the optimal learning curriculum accordingly. This increases the likelihood that users will experience stress or inappropriate learning load, which can reduce learning effectiveness. Therefore, a learning system that is personalized for each user and can adjust in real time is needed.

[2244] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2245] In this invention, the server includes means for receiving as input the user's basic information, learning situation, test results, and personality assessment results, means for storing the received information in a database, means for analyzing the user's level of understanding and ability based on the stored information, means for automatically generating an individualized learning curriculum based on the analysis results using a generation AI, means for providing the generated learning curriculum to the user, means for recognizing, recording, and transmitting the user's learning progress and emotional data in real time, means for analyzing the transmitted learning progress and emotional data in real time and updating and providing feedback on the learning curriculum, and means for providing the feedback and updated curriculum to the user. This makes it possible to provide an individual learning curriculum optimized for each user in real time, maximizing learning effectiveness.

[2246] "User" refers to an individual who uses the system to learn.

[2247] "Basic information" refers to information that identifies and locates an individual, such as the user's name, age, grade, and areas of interest.

[2248] "Study status" refers to information including a user's current learning progress, past learning records, and learning goals.

[2249] "Test results" refers to the scores and evaluation results of tests that a user has taken in the past.

[2250] "Personality diagnosis results" refers to the results of evaluating the user's personality traits and behavioral patterns.

[2251] "Database" refers to a collection used to store, manage, and retrieve received information.

[2252] "Analysis" refers to the act of assessing the user's level of understanding and ability based on the information received.

[2253] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to generate useful information from data for a specific task or purpose.

[2254] "Learning curriculum" refers to a systematic plan of the learning materials and learning steps necessary to achieve a user's learning goals.

[2255] "Feedback" refers to advice and suggestions for improvement provided at the appropriate time based on the results and emotional state of the user as they progress through their learning.

[2256] "Emotional Data" refers to information about a user's emotional state obtained through facial expressions, tone of voice, and other physiological responses.

[2257] "Real-time" refers to a situation where a response or processing occurs almost immediately.

[2258] "Analysis results" refers to the evaluation of the user's level of understanding and ability that is derived after the generating AI processes the information it receives.

[2259] "Monitoring" refers to the act of continuously observing a user's learning progress and emotional state and collecting data as necessary.

[2260] "Adjustment" refers to the act of changing the content or difficulty of the learning curriculum based on the analysis results and emotional data.

[2261] The present invention combines a system that uses generative AI to provide users with an optimized learning curriculum, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[2262] System Configuration

[2263] This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The server processes data and performs analysis using generative AI, while the terminal is a device that collects user input data and learning progress. The user interacts with the system as a learner, and the emotion engine is responsible for recognizing the user's emotions in real time.

[2264] User registration and data collection

[2265] The device displays a new user registration screen and uses front-end technologies such as HTML, CSS, and JavaScript to prompt the user to enter basic information such as name, age, grade, and areas of interest. The user enters this information and presses the registration button. For example, a user might enter "Yamada Taro," age "15," grade "first year of high school," and area of ​​interest "mathematics." The device sends the entered information to the server via a POST request, and the server stores the received information in a database, then generates a unique user ID and returns it to the device.

[2266] Example prompt sentence:

[2267] "Please enter your name. Tell us your age. Also select your grade and areas of interest."

[2268] Initial data collection

[2269] Next, the device displays a questionnaire screen with information about the user's learning status, past test results, and personality assessment, and asks the user to answer. The user answers these questions, for example, entering "My past test results were 80 points" or "My personality assessment result is a cautious type." The device then sends this data to the server, which stores it in a database.

[2270] Example prompt sentence:

[2271] "Enter your past test results. Take the personality test."

[2272] Curriculum Generation

[2273] The server uses a generation AI to analyze the stored initial data. The generation AI evaluates the user's level of understanding, ability, and personality assessment results, and generates a personalized learning curriculum. For example, the generation AI determines that the user's level of understanding of mathematics is "intermediate," and generates a curriculum that includes "differential and integral calculus" as the next step. The generated curriculum is sent from the server to the device, providing the user with the next task to tackle.

[2274] Example prompt sentence:

[2275] "Generate an appropriate learning curriculum based on the user's initial data."

[2276] Emotion data collection and analysis

[2277] During training, the device uses an emotion engine to recognize the user's facial expressions and tone of voice in real time. For example, if the user shows a "tired" expression, the emotion engine will detect this and record it as data. The device then sends the emotion data to the server, which then evaluates the user's emotional state based on this data.

[2278] Example prompt sentence:

[2279] "Collect real-time user sentiment data."

[2280] Curriculum Adjustment and Feedback

[2281] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum. For example, if a user feels stressed, the generative AI will add a "break time" and suggest tasks that can be completed in a relaxed state. The adjusted curriculum is sent from the server to the device, and the latest learning plan is provided to the user.

[2282] Example prompt sentence:

[2283] "Adjust your curriculum based on user sentiment data."

[2284] Learning progression and monitoring

[2285] The user studies according to the learning curriculum provided through the device. The device records the user's learning progress and emotional data in real time and sends it to the server. The server analyzes this data, updates the curriculum as needed, and generates feedback. The feedback and updated curriculum are provided to the device, making it possible to maintain an optimal learning environment for the user.

[2286] Example prompt sentence:

[2287] "Monitor users' learning progress and provide feedback as needed."

[2288] The above is a specific embodiment of the system according to the present invention, which aims to grasp the user's emotional state and learning situation in real time and provide an optimal learning curriculum.

[2289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2290] Step 1:

[2291] The terminal displays a new user registration screen.

[2292] Input: User registration form using HTML / CSS / JavaScript.

[2293] Details: The device displays a form that asks the user to enter their name, age, grade, and interests.

[2294] Output: Basic information entered by the user.

[2295] Specific operation: For example, a user inputs "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[2296] Step 2:

[2297] The user enters basic information and presses the registration button.

[2298] Input: User-entered name, age, grade, and interests.

[2299] Details: The user fills in the form with the required information and clicks the register button.

[2300] Output: Basic information received by the device.

[2301] Specific operation: When the user presses the registration button, the information displayed on the screen is sent from the device to the server.

[2302] Step 3:

[2303] The terminal transmits the input basic information to the server.

[2304] Input: Basic information about the user received by the device.

[2305] Details: The device sends input data to the server via an HTTP POST request.

[2306] Output: Basic information received by the server.

[2307] Specific operation: The request body sent by the device contains data such as name "Yamada Taro", age "15 years old", grade "first year of high school", and field of interest "mathematics".

[2308] Step 4:

[2309] The server stores the received basic information in a database, generates a unique user ID, and returns it to the terminal.

[2310] Input: The user's basic information received by the server.

[2311] Details: The server stores the received data in a database and generates a unique user ID for the new user.

[2312] Output: A unique user ID and basic information stored in the database.

[2313] Specific operation: The server stores the information in a MySQL database, generates a user ID "12345", and returns it to the terminal.

[2314] Step 5:

[2315] The device displays a questionnaire screen with information on learning status, past test results, and personality assessment.

[2316] Input: User ID and basic information received from the server.

[2317] Details: The device displays a questionnaire screen where students can enter their learning status and past test results.

[2318] Output: User-entered learning status, past test results, and personality assessment results.

[2319] What it does: The device prompts you with a question, such as "Please enter your past math test scores."

[2320] Step 6:

[2321] The user answers the questionnaire and enters the required data.

[2322] Input: User-entered learning history, past test results, and personality assessment results.

[2323] Details: Users answer questions by entering text or by selecting multiple options.

[2324] Output: Survey results received by the device.

[2325] Specific behavior: The user enters "80 points" and selects "Conscientious" on the personality test.

[2326] Step 7:

[2327] The terminal transmits the collected questionnaire data to the server.

[2328] Input: Survey results received by the device.

[2329] Details: The device sends the survey data to the server via an HTTP POST request.

[2330] Output: Survey data received by the server.

[2331] Specific operation: The request sent by the device includes data such as learning status (80 points) and personality test result (cautious type).

[2332] Step 8:

[2333] The server stores the received survey data in a database and analyzes it using a generative AI model.

[2334] Input: Survey data received by the server.

[2335] Details: The server stores the received data in a database and analyzes it using a generative AI model.

[2336] Output: User's understanding and ability assessment as a result of the analysis.

[2337] Specific operation: The server stores the information in a database and passes the input data to the generation AI for analysis.

[2338] Step 9:

[2339] Generative AI generates an individualized learning curriculum based on the user's level of understanding, ability, and personality assessment results.

[2340] Input: Initial data and analysis results.

[2341] Details: Generative AI evaluates users' understanding, abilities, and personality assessment results to generate a personalized learning curriculum.

[2342] Output: A personalized learning curriculum.

[2343] Specific operation: The generation AI determines that the level of understanding is "intermediate" and generates a curriculum that includes "differential and integral calculus."

[2344] Step 10:

[2345] The server transmits the generated learning curriculum to the terminal.

[2346] Input: The generated learning curriculum.

[2347] Details: The server sends the generated curriculum to the device as an HTTP response.

[2348] Output: The learning curriculum sent to the device.

[2349] Specific operation: The device displays the curriculum details received from the generation AI and confirms the next assignment the user will work on.

[2350] Step 11:

[2351] The terminal uses an emotion engine to recognize the user's emotions in real time.

[2352] Input: The user's facial expression and tone of voice.

[2353] Details: The device senses the user's facial expressions and tone of voice while learning and analyzes them using an emotion engine.

[2354] Output: Recognized emotion data.

[2355] Specific operation: The device's camera and microphone detect the user's "tired" facial expression and "low-pitched voice."

[2356] Step 12:

[2357] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data.

[2358] Input: The user's facial expression and tone of voice.

[2359] Details: The emotion engine analyzes facial expressions as data and labels the emotions it detects in real time.

[2360] Output: Emotion data.

[2361] Specific behavior: The emotion engine generates emotion labels such as "fatigue" and "stress."

[2362] Step 13:

[2363] The terminal transmits the emotion data to the server.

[2364] Input: Emotion data generated by the emotion engine.

[2365] Details: The device sends the generated emotion data to the server.

[2366] Output: Emotion data received by the server.

[2367] Specific operation: The device sends the emotional data "fatigue" and "stress" generated to the server.

[2368] Step 14:

[2369] The server then performs further analysis based on the emotional data to assess the user's learning load and stress level.

[2370] Input: Received emotion data.

[2371] Details: The server analyzes the received data and evaluates the user's stress level and learning load.

[2372] Output: Assessment results of learning load and stress level.

[2373] Specific operation: The server analyzes the emotional data "fatigue" and "stress" and evaluates the user's stress level as "high."

[2374] Step 15:

[2375] The server integrates and analyzes emotional data and learning progress data to adjust the difficulty and content of the learning curriculum.

[2376] Input: Learning progress data and emotion data.

[2377] Details: The server integrates and analyzes emotional data and learning progress data to regenerate an appropriate learning curriculum.

[2378] Output: A tailored learning curriculum.

[2379] Specific operation: The server informs the generated AI of the user's "high stress level" and adjusts the curriculum to "relaxing tasks that include breaks."

[2380] Step 16:

[2381] The generative AI suggests specific learning steps and break times based on the user's emotional state.

[2382] Input: Data indicating the user's emotional state.

[2383] Details: Generative AI assesses the appropriateness of the curriculum based on the user's emotional state and makes necessary adjustments.

[2384] Output: Specific learning steps and break timings.

[2385] Specific behavior: The generative AI suggests to the user to "take a break for the next 30 minutes."

[2386] Step 17:

[2387] The server sends updated curriculum and feedback to the terminal.

[2388] Input: Updated learning curriculum and feedback.

[2389] Details: The server sends updated curriculum information to the device and provides feedback.

[2390] Output: Updated curriculum and feedback delivered to the device.

[2391] What happens: Materials containing the updated curriculum "Differential and Integral Calculus" and the feedback "Take a break for the next 30 minutes" are displayed on the device.

[2392] Step 18:

[2393] The user proceeds with the study according to the study curriculum provided through the terminal.

[2394] Input: The learning curriculum displayed on the device.

[2395] Details: The user progresses through the learning steps according to the curriculum displayed on the device.

[2396] Output: User's learning progress.

[2397] Specific action: The user works on a "differentiation exercise" while looking at the curriculum materials.

[2398] Step 19:

[2399] The terminal records the user's learning progress and emotional data in real time and transmits it to the server.

[2400] Input: User's learning progress data and emotional data.

[2401] Details: The device records the user's progress and emotional data in real time and periodically transmits it to the server.

[2402] Output: Learning progress data and emotion data sent to the server.

[2403] Specific operation: The device sends the user's learning progress ("Differential task completed") and emotional data ("Concentration") to the server.

[2404] Step 20:

[2405] The server updates the curriculum...

Claims

1. A means for receiving basic information, learning status, test results, and personality assessment results of a user as input; a means for storing the received information in a database; and A means for analyzing the user's understanding and ability based on the stored information; A means for automatically generating an individualized learning curriculum based on the analysis results; A means for providing the generated learning curriculum to a user; a means for monitoring the user's learning progress and transmitting the data to a server; A means to analyze the transmitted data in real time and update the learning curriculum and provide feedback. a means of providing feedback and updated curriculum to users; A system including:

2. The system according to claim 1 , further comprising means for adjusting the difficulty level of the learning content based on the analysis result.

3. 2. The system according to claim 1, further comprising means for suggesting the next most suitable study content according to the user's learning stage.

Images