System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a user's information and creating a user account; means for generating and providing a questionnaire to the user regarding the user's learning style and interests; means for analyzing the results of the questionnaire and generating a customized learning plan for each user using an artificial intelligence engine; means for providing the customized learning plan and related content to the user; means for collecting the user's learning progress in real-time and recording it in a database; and means for recommending what to learn next based on the learning progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditional educational methods make it difficult to provide personalized education tailored to each student's learning progress and level of understanding. Uniform teaching materials and teaching methods are provided to all students, preventing them from maximizing each student's motivation to learn or their level of understanding. Furthermore, with the spread of online learning, there is a demand for interactive and engaging content that combines education and entertainment, but traditional systems have not been able to adequately meet these needs. Furthermore, the lack of real-time understanding of learning progress and the resulting feedback has led to a decline in learning effectiveness. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for receiving user information and creating a user account; means for generating and providing a questionnaire regarding the user's learning style and interests to the user; means for analyzing the questionnaire results using an artificial intelligence engine to generate a customized learning plan for each user; means for providing the customized learning plan and related content to the user; means for collecting the user's learning progress in real time and recording it in a database; and means for recommending next learning content based on the learning progress. This system can provide each user with a personalized learning experience, improving their motivation and comprehension. Furthermore, interactive quizzes and activities can reinforce the learning content and provide real-time feedback to maximize learning effectiveness.

[0006] "User" refers to an individual or learner who uses the system.

[0007] "Account" refers to a record on the system that contains identification information for managing a user's personal information and learning data.

[0008] A "survey" refers to a set of questions or survey items used to gather information about a user's learning style and interests.

[0009] "Artificial Intelligence Engine" refers to the AI ​​technology and algorithms used to analyze user data and generate personalized learning plans.

[0010] "Study Plan" means a plan that includes educational content and methods customized based on a user's individual learning needs and progress.

[0011] "Content" refers to information and materials, such as instructional materials, videos, quizzes, activities, etc., provided for educational purposes.

[0012] "Learning progress" refers to the achievement, understanding, and progress of a user as they progress through a learning activity.

[0013] "Database" refers to data storage for centrally managing user information, learning progress, content information, etc. collected within the learning system.

[0014] "Feedback" refers to evaluations, comments, and advice on the user's learning activities for the next learning content.

[0015] "Interactive quiz" refers to a test-style activity that includes interactive questions and multiple choice questions that allow users to actively participate and test their learning and deepen their understanding.

[0016] "Recommendation" refers to the act of suggesting the next content to learn or a customized learning plan based on the user's learning progress and level of understanding. [Brief explanation of the drawings]

[0017] [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 illustrating 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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[0039] 1. User Registration

[0040] Device:

[0041] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[0042] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[0043] 3. The terminal sends the entered information to the server.

[0044] server:

[0045] 1. Receive and verify user information received from the terminal.

[0046] 2. If the information is valid, create a user account in the database and save the information.

[0047] 3. Generate a registration completion confirmation message and send it to the device.

[0048] Device:

[0049] 1. Display the registration completion message received from the server.

[0050] 2. Personalization Settings

[0051] Device:

[0052] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[0053] 2. After logging in, you will be taken to the personalization settings screen.

[0054] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[0055] 4. The device sends the survey data to the server.

[0056] server:

[0057] 1. Collect survey data received from the device.

[0058] 2. Analyze the survey data using an artificial intelligence engine module.

[0059] 3. Based on the analysis results, generate the optimal learning plan for each user.

[0060] 4. Send a customized learning plan and related content list to your device.

[0061] Device:

[0062] 1. Display a customized learning plan and allow the user to confirm and begin learning.

[0063] 3. Learning progress management

[0064] Device:

[0065] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[0066] 2. Watch and take video learning materials and interactive quizzes.

[0067] 3. Send the learning progress and quiz results to the server.

[0068] server:

[0069] 1. Collect and store learning progress data and quiz results received from devices in real time.

[0070] 2. Evaluate the user's level of understanding and recommend the next piece of content to study.

[0071] 3. Send the recommendation to your device.

[0072] Device:

[0073] 1. The user continues learning based on the next content received from the server.

[0074] Specific examples

[0075] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[0076] 1. Device:

[0077] 1. The user selects the "Math" category and chooses the "Fractions" content.

[0078] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[0079] 2. Server:

[0080] 1. Receive quiz answer data and determine whether it is correct or incorrect.

[0081] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[0082] 3. Send the recommendation to your device.

[0083] 3. Terminal:

[0084] 1. The user sees the next recommended content and continues learning.

[0085] In this way, the present invention provides a system that provides a personalized learning experience to users and improves their motivation to learn and their level of understanding.

[0086] The processing flow will be explained below.

[0087] Program processing steps

[0088] 1. User Registration

[0089] Step 1:

[0090] User: Open the new registration screen.

[0091] Step 2:

[0092] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[0093] Step 3:

[0094] Terminal: Sends the entered information to the server.

[0095] Step 4:

[0096] Server: Verifies the user information received from the device.

[0097] Step 5:

[0098] Server: If validation is successful, create a user account in the database and save the information.

[0099] Step 6:

[0100] Server: Generates a registration completion confirmation message and sends it to the device.

[0101] Step 7:

[0102] Terminal: Display the registration completion message received from the server.

[0103] 2. Personalization Settings

[0104] Step 1:

[0105] User: Access the login screen and log in by entering your email address and password.

[0106] Step 2:

[0107] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[0108] Step 3:

[0109] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[0110] Step 4:

[0111] User: Answers survey questions and hits submit.

[0112] Step 5:

[0113] Terminal: Sends the response data to the server.

[0114] Step 6:

[0115] Server: Collects and stores the survey data received from the device.

[0116] Step 7:

[0117] Server: Analyzes the survey data using an artificial intelligence engine module.

[0118] Step 8:

[0119] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[0120] Step 9:

[0121] Server: Sends the created customized plan to the device.

[0122] Step 10:

[0123] On your device: The customized learning plan is displayed and reviewed by the user.

[0124] 3. Learning progress management

[0125] Step 1:

[0126] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[0127] Step 2:

[0128] Device: Requests the selected content from the server.

[0129] Step 3:

[0130] Server: Receives the request and sends the corresponding content (video learning materials or interactive quizzes) to the device.

[0131] Step 4:

[0132] Device: The transmitted content is displayed and the user begins learning.

[0133] Step 5:

[0134] User: Watches video instructional material and then takes an interactive quiz.

[0135] Step 6:

[0136] Device: Sends quiz answer data to the server.

[0137] Step 7:

[0138] Server: Receives the answer data and determines whether it is correct or not.

[0139] Step 8:

[0140] Server: Evaluates the user's understanding and stores their learning progress in a database.

[0141] Step 9:

[0142] Server: Generates recommendations for what to learn next and sends them to the device.

[0143] Step 10:

[0144] On your device: Shows the next recommended learning content so the user can continue learning.

[0145] This process step allows users to enjoy a personalized learning experience and receive real-time feedback to effectively progress their learning.

[0146] Example 1

[0147] 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."

[0148] Modern educational systems struggle to provide personalized learning experiences tailored to individual users' learning styles and progress. Many existing systems simply provide uniform learning materials, preventing variations in learning efficiency and outcomes for each user. Furthermore, they lack the ability to track users' progress and comprehension in real time and provide appropriate feedback, potentially delaying learning improvement.

[0149] 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.

[0150] In this invention, the server includes means for receiving user information and creating a user account, means for generating questions related to the user's learning style and interests and providing them to the user, means for analyzing the results of the questions using an artificial intelligence engine and generating a customized study plan for each user, means for providing the user with the customized study plan and related content, means for collecting the user's study progress in real time and recording it in a database, means for recommending next study content based on the study progress, means for the user to select specific content and provide a device for study, means for collecting the study progress and quiz results and evaluating comprehension, and means for recommending next study content based on the evaluation results. This makes it possible to provide a personalized learning experience for each user and receive appropriate feedback in real time according to each user's individual progress and comprehension.

[0151] "User information" refers to data about individual users registered in the system, including name, email address, password, age, learning style, etc.

[0152] A "user account" is account information required for a user to use the system, and is stored in a database based on the user information.

[0153] A "survey" is a data collection tool in the form of questions to gather information about a user's learning style and interests.

[0154] An "artificial intelligence engine" is a computer program that contains algorithms and models to analyze collected data and understand trends and patterns for each user.

[0155] A "customized study plan" is a study plan that is individually generated by an artificial intelligence engine based on the user's learning style and interests.

[0156] "Relevant content" refers to the specific materials and activities included in your customized learning plan.

[0157] "Study progress" is information about the progress of a user as they progress through their studies, and includes study time, progress of content, quiz results, and the like.

[0158] A "database" is a storage system for storing data such as user information, learning progress, and learning plans.

[0159] "Recommendation" is information generated by the server to indicate what the user should learn next based on their learning progress.

[0160] "Device" refers to the terminal (e.g., PC, tablet, smartphone) that a user uses to select and operate learning content.

[0161] "Feedback" means information about improvements or advice provided based on a user's understanding and learning progress.

[0162] The present invention is an educational system designed to provide users with a personalized learning experience. The system uses a server, a terminal, and an artificial intelligence engine to collect and analyze user information. Specific embodiments for implementing the present invention are described below.

[0163] 1. User Registration

[0164] Terminal

[0165] 1. When a user first accesses the system, they are presented with a registration screen. Examples include interfaces such as a web browser or a mobile application.

[0166] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[0167] 3. The terminal sends the entered information to the server.

[0168] server

[0169] 1. The server verifies the user information received from the device, specifically checking the format of the email address and the security of the password.

[0170] 2. If the validation is successful, the server creates a user account in the database and stores the entered information.

[0171] 3. The server generates a confirmation message confirming the completion of registration and sends it to the terminal.

[0172] Terminal

[0173] 1. The terminal displays the registration completion message received from the server.

[0174] 2. Personalization Settings

[0175] Terminal

[0176] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[0177] 2. After logging in, the user will be taken to the personalization settings screen.

[0178] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[0179] 4. The device sends the survey data to the server.

[0180] server

[0181] 1. The server collects the survey data received from the terminal.

[0182] 2. Analyze the survey data using an artificial intelligence engine and generate the optimal study plan for each user.

[0183] 3. Sending a customized learning plan and related content to your device.

[0184] Terminal

[0185] 1. The user checks the customized study plan on their device and begins studying.

[0186] 3. Learning progress management

[0187] Terminal

[0188] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[0189] 2. Users watch and take video tutorials and interactive quizzes.

[0190] 3. The device sends the learning progress and quiz results to the server.

[0191] server

[0192] 1. The server collects and stores learning progress data and quiz results received from the device in real time.

[0193] 2. The server evaluates the user's level of understanding and recommends what to study next.

[0194] 3. Send the recommendation to your device.

[0195] Terminal

[0196] 1. The device displays the next recommended content received from the server.

[0197] 2. The user reviews the recommended content and continues learning.

[0198] Specific examples

[0199] Below is a specific example of a fourth-grade elementary school student learning about "fractions in mathematics."

[0200] 1. Terminal

[0201] 1. The user selects the "Math" category and chooses the "Fractions" content.

[0202] 2. Users watch video instructional material and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?").

[0203] 2. Server

[0204] 1. The server receives the quiz answer data and determines whether it is correct or incorrect.

[0205] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[0206] 3. Send the recommendation to your device.

[0207] 3. Terminal

[0208] 1. The user sees the next recommended content and continues learning.

[0209] The invention personalizes the user's learning experience through generative AI models, assessing progress in real time and providing appropriate feedback and learning content.

[0210] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0211] Step 1:

[0212] Enter and send user information (terminal)

[0213] When a user accesses the system for the first time, a new registration screen is displayed. The user enters information such as name, email address, password, age, and learning style. After this information is entered, the terminal displays instructions to the user to press the "Register" button. When the user presses the "Register" button, the entered information is sent from the terminal to the server. The sent information is input data, and is transferred to the server in a structured format (for example, JSON format).

[0214] Step 2:

[0215] User information verification and storage (server)

[0216] The server receives the user information from the terminal. It validates the received information and checks the format of the email address and the security of the password. For example, it uses regular expression patterns to verify whether the email address is in the correct format. If the validation is successful, the server creates a new user account in the database and saves the entered information. The saved data is treated as new user account data. The server generates a confirmation message and sends it to the terminal.

[0217] Step 3:

[0218] Notification of registration completion (device)

[0219] The terminal displays the registration completion message received from the server. For example, a message such as "Registration completed" is displayed on the user's screen. This allows the user to confirm that the account registration has been completed successfully.

[0220] Step 4:

[0221] Perform login operation (terminal)

[0222] The user accesses the login screen and enters their registered email address and password. This input data is collected by the device, and an instruction to press the "Login" button is displayed. When the user presses the "Login" button, the input data is sent to the server as authentication data.

[0223] Step 5:

[0224] User authentication and transition to personalization setting screen (server)

[0225] The server verifies the login information received from the device, for example, by checking whether the entered email address and password match the information in the database. If authentication is successful, the server sends the data of the personalized settings screen to the device along with a message indicating that the user has successfully logged in.

[0226] Step 6:

[0227] Implementing and sending personalized settings (device)

[0228] The user moves to the personalized settings screen and answers the questions in the survey. For example, a question such as "What is your preferred method of learning?" is displayed. When the user enters their answer and presses the send button, the terminal sends the survey data to the server. This data is the user's learning style information.

[0229] Step 7:

[0230] Analysis of survey data and generation of learning plans (server)

[0231] The server collects the survey data received from the device. This data is analyzed using an artificial intelligence engine to generate an optimal learning plan for each user. For example, an algorithm analyzes learning patterns based on the user's responses and creates an individual learning plan. The generated learning plan and related content list are then sent from the server to the device.

[0232] Step 8:

[0233] Display customized plan (device)

[0234] The terminal displays the customized learning plan received from the server. The user confirms the plan and begins learning. The displayed content includes optimal learning content tailored to each individual user.

[0235] Step 9:

[0236] Selection and implementation of learning content (device)

[0237] The user selects specific content (e.g., "Math Fractions") from the learning start screen. The user then watches and completes video materials and interactive quizzes. Specific operations include, for example, playing videos and answering quizzes.

[0238] Step 10:

[0239] Sending learning progress data (device)

[0240] Learning progress and quiz results are collected continuously. The device sends this data to the server in real time. The data includes study time, correct / incorrect answers, and the user's progress.

[0241] Step 11:

[0242] Data collection and understanding assessment (server)

[0243] The server collects and stores learning progress data and quiz results received from the device in real time. Based on the collected data, the server uses an artificial intelligence engine to evaluate the user's level of understanding. For example, it analyzes which content the user is struggling with.

[0244] Step 12:

[0245] Recommending and sending next learning content (server)

[0246] Based on the comprehension assessment, the server recommends the next learning content. The generated recommendation is sent to the device. For example, if the user has not made much progress on a particular topic, the server will recommend supplementary learning on that topic.

[0247] Step 13:

[0248] Displaying recommended content and continuing learning (device)

[0249] The device displays the next recommended content received from the server. The user confirms the recommended content and starts the next learning session. A new learning loop begins based on the displayed content.

[0250] This series of processing steps effectively provides the user with a personalized learning experience.

[0251] (Application example 1)

[0252] 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."

[0253] Traditional educational systems struggle to provide an optimized learning plan for each user, often resulting in a decline in motivation to learn and a lack of understanding. Even in brick-and-mortar stores, it's difficult to provide appropriate products based on a user's learning style, resulting in a lack of support for users to select the learning materials that are best suited to them. Furthermore, there's no recommendation of the next learning material based on the progress or results of use after purchase, resulting in a lack of continuous learning support.

[0254] 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.

[0255] In this invention, the server includes means for receiving user attribute information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for providing the user with the customized learning plan and related content, means for collecting the user's learning progress in real time and recording it in a database, means for recommending the next content to study based on the learning progress, means for providing optimal product information based on the user's learning style in the store, and means for recording the progress and usage results of the learning materials purchased by the user and recommending the next learning material to purchase, thereby enabling learning support and product recommendations optimized for each user.

[0256] "User attribute information" is information specific to each individual user, such as the user's name, email address, age, learning style, etc.

[0257] The "means for creating a user account" is the process by which an individual user profile is generated on the server based on information provided by the user and stored in a database.

[0258] The "means for generating a questionnaire and providing it to the user" is a mechanism for creating a question form to understand the user's learning style and interests, and allowing the user to answer the question form.

[0259] An "artificial intelligence engine" is a software module that performs machine learning and data analysis, used to analyze large amounts of data and customize learning plans.

[0260] A "customized learning plan" is a set of learning content optimized for each individual user based on the results of a survey and user attribute information.

[0261] "Related content" refers to educational resources such as learning materials, videos, interactive quizzes and activities that are suggested based on the user's study plan.

[0262] "Means for collecting learning progress in real time and recording it in a database" refers to the process of collecting data generated in real time as the user progresses with their learning and managing that data in an integrated manner.

[0263] The "means for recommending the next learning content" is a mechanism that presents the user with the appropriate next learning content based on collected learning progress data.

[0264] "Means for providing optimal product information based on a user's learning style in a physical store" is a system for suggesting optimal learning materials and products in a physical store according to the user's learning style.

[0265] "Means for recording the progress and usage results of purchased learning materials and recommending the next learning material to purchase" refers to the process of tracking the usage and learning progress of the user's purchased learning materials and recommending the next learning material that will be required.

[0266] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[0267] System Configuration

[0268] 1. User Registration

[0269] Device:

[0270] When a user first accesses the system, a new registration screen is displayed.

[0271] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[0272] The terminal transmits the input information to the server.

[0273] server:

[0274] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[0275] A registration completion confirmation message is generated and sent to the terminal.

[0276] Device:

[0277] Displays the registration completion message received from the server.

[0278] 2. Personalization Settings

[0279] Device:

[0280] The user accesses the login screen and logs in by entering their registered email address and password.

[0281] After logging in, you will be taken to the personalization settings screen.

[0282] Answer the questions in the survey and send the answer data to the server.

[0283] server:

[0284] The received survey data is collected and analyzed using an artificial intelligence engine.

[0285] Based on the analysis results, a customized learning plan is generated for each user.

[0286] Send a customized study plan and related content list to your device.

[0287] Device:

[0288] The customized learning plan is displayed, and the user can confirm it and begin learning.

[0289] 3. Learning progress management

[0290] Device:

[0291] The user selects a particular piece of content (e.g., "math fractions").

[0292] View and take video tutorials and interactive quizzes.

[0293] Send learning progress and quiz results to the server.

[0294] server:

[0295] Collect and store incoming learning progress data and quiz results in real time.

[0296] Evaluate the user's level of understanding and recommend the next content to study.

[0297] The recommendation is sent to the device.

[0298] Device:

[0299] The user continues learning based on the next recommended content received from the server.

[0300] 4. Physical store applications

[0301] Device:

[0302] The app is used in physical stores to provide optimal product information based on the user's learning style.

[0303] For example, an education store might recommend learning materials based on a user's learning style (e.g., visual learner).

[0304] server:

[0305] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[0306] Recommendations are sent to devices in physical stores.

[0307] Device:

[0308] The user checks the next educational material to be purchased in the store and continues purchasing.

[0309] Hardware and software used

[0310] Hardware: smartphones, tablets, servers.

[0311] software:

[0312] Python: Used for application development.

[0313] Requests library: Sends HTTP requests and communicates with the API.

[0314] Flask / Django: A framework suitable for implementing server-side APIs.

[0315] AI Engine: An artificial intelligence module for learning style analysis and recommendations.

[0316] Specific examples

[0317] For example, when a 10-year-old elementary school student accesses the system for the first time, they register by entering basic information. They then answer a questionnaire to set their own learning style. They use the app in the store to find recommended learning materials and manage their learning progress with those materials within the app. The AI ​​engine then recommends the next learning material they need.

[0318] Prompt Sentence Examples

[0319] Example: User information

[0320] {name: "User name", email: "Email address", password: "Password", age: Age, learning_style: "Learning style"}

[0321]

[0322] Example: Survey response data

[0323] ["What is your favorite subject?": "Math", "What is your favorite way of learning?": "Visual aids"]

[0324] In this way, a personalized learning experience is provided for each user.

[0325] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0326] Step 1:

[0327] User Registration

[0328] Device:

[0329] When a user first accesses the system, a new registration screen is displayed.

[0330] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[0331] server:

[0332] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[0333] A registration completion confirmation message is generated and sent to the terminal.

[0334] Device:

[0335] Displays the registration completion message received from the server.

[0336] Input: User demographic information (name, email address, age, learning style)

[0337] Data processing: verification, account creation

[0338] Output: Registration complete message

[0339] Step 2:

[0340] Personalization Settings

[0341] Device:

[0342] The user accesses the login screen and logs in by entering their registered email address and password.

[0343] After logging in, you will be taken to the personalization settings screen.

[0344] Answer the questions in the survey and send the answer data to the server.

[0345] server:

[0346] The received survey data is collected and analyzed using an artificial intelligence engine.

[0347] Based on the analysis results, a customized learning plan is generated for each user.

[0348] Send a customized study plan and related content list to your device.

[0349] Device:

[0350] The customized learning plan is displayed, and the user can confirm it and begin learning.

[0351] Input: Survey response data

[0352] Data processing: AI analysis and learning plan generation

[0353] Output: Customized study plan, related content list

[0354] Step 3:

[0355] Learning progress management

[0356] Device:

[0357] The user selects a particular piece of content (e.g., "math fractions").

[0358] View and take video tutorials and interactive quizzes.

[0359] server:

[0360] Collect and store learning progress data and quiz results received from devices in real time.

[0361] Evaluate the user's level of understanding and recommend the next content to study.

[0362] The recommendation is sent to the device.

[0363] Device:

[0364] The user continues learning based on the next recommended content received from the server.

[0365] Input: Learning progress data, quiz results

[0366] Data processing: Comprehension assessment, next learning content recommendation

[0367] Output: Recommended content

[0368] Step 4:

[0369] Application in physical stores

[0370] Device:

[0371] The app can be used in physical stores to provide optimal product information based on the user's learning style. For example, in a physical store that sells educational products, the app can recommend learning materials based on the user's learning style.

[0372] server:

[0373] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[0374] Recommendations are sent to devices in physical stores.

[0375] Device:

[0376] The user checks the next educational material to be purchased in the store and continues purchasing.

[0377] Input: Progress data of purchased teaching materials, usage results

[0378] Data processing: Recording results and recommending next learning materials

[0379] Output: Recommendations for next course material purchases

[0380] These steps make it possible to provide personalized learning support and product recommendations for each user.

[0381] 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.

[0382] This invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Specific embodiments of this system are shown below.

[0383] 1. User Registration

[0384] Device:

[0385] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[0386] 2. The user enters the required information (name, email address, password, age, learning style, etc.) and presses the "Register" button.

[0387] 3. The terminal sends the entered information to the server.

[0388] server:

[0389] 1. Verify the user information received from the terminal.

[0390] 2. If the validation is successful, create a user account in the database and save the information.

[0391] 3. Generate a registration completion confirmation message and send it to the device.

[0392] Device:

[0393] 1. Display the registration completion message received from the server.

[0394] 2. Personalization Settings

[0395] Device:

[0396] 1. The user accesses the login screen and logs in by entering their email address and password.

[0397] 2. After logging in, you will be taken to the personalization settings screen.

[0398] 3. Display the survey questions on the personalization settings screen and the user enters their answers.

[0399] 4. The user answers the survey questions and presses the submit button.

[0400] 5. The device sends the survey data to the server.

[0401] server:

[0402] 1. Collect and save survey data received from the device.

[0403] 2. Analyze the survey data using an artificial intelligence engine module.

[0404] 3. Generate a learning plan for each user based on the analysis results, creating a customized learning plan and related content list.

[0405] 4. Send the created customized plan to the device.

[0406] Device:

[0407] 1. The customized learning plan is displayed and confirmed by the user.

[0408] 3. Learning progress management and emotion recognition

[0409] Device:

[0410] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[0411] 2. Request the content from the server.

[0412] server:

[0413] 1. Receives a request and sends the corresponding content (video learning material or interactive quiz) to the device.

[0414] Device:

[0415] 1. The submitted content is displayed and the user begins learning.

[0416] 2. Use the emotion engine to analyze the user's facial expressions and tone of voice in real time while watching video materials or taking interactive quizzes.

[0417] Emotion Engine:

[0418] 1. Collect user emotional data (e.g., joy, confusion, concentration, etc.).

[0419] 2. Analyze the collected emotional data and evaluate the emotional state during learning.

[0420] server:

[0421] 1. Adjust learning progress and content difficulty in real time based on emotional data received from the emotion engine.

[0422] 2. Generate optimal feedback based on the user's emotional state and send it to the device.

[0423] 3. Furthermore, the user's level of understanding and progress are stored in a database.

[0424] Device:

[0425] 1. The user sees the feedback and recommended next learning content received from the server and confirms it.

[0426] Specific examples

[0427] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[0428] 1. Device:

[0429] 1. The user selects the "Math" category and chooses the "Fractions" content.

[0430] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[0431] 3. During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[0432] 2. Server:

[0433] 1. Receive quiz answer data and emotion data from the emotion engine.

[0434] 2. The answers are judged to be correct or incorrect, and the user's level of understanding and concentration is evaluated based on emotional data.

[0435] 3. Recommend the next learning content based on the evaluation results (e.g., learning from 1 / 3 to 1 / 6) and send it to the device.

[0436] 3. Terminal:

[0437] 1. The user sees the next recommended content and continues learning.

[0438] In this way, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[0439] The processing flow will be explained below.

[0440] Specific process steps for carrying out the invention

[0441] 1. User Registration

[0442] Step 1:

[0443] User: Open the new registration screen.

[0444] Step 2:

[0445] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[0446] Step 3:

[0447] Terminal: Sends the entered information to the server.

[0448] Step 4:

[0449] Server: Verifies the user information received from the device.

[0450] Step 5:

[0451] Server: If validation is successful, create a user account in the database and save the information.

[0452] Step 6:

[0453] Server: Generates a registration completion confirmation message and sends it to the device.

[0454] Step 7:

[0455] Terminal: Display the registration completion message received from the server.

[0456] 2. Personalization Settings

[0457] Step 1:

[0458] User: Access the login screen and log in by entering your email address and password.

[0459] Step 2:

[0460] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[0461] Step 3:

[0462] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[0463] Step 4:

[0464] User: Answers survey questions and hits submit.

[0465] Step 5:

[0466] Terminal: Sends the response data to the server.

[0467] Step 6:

[0468] Server: Collects and stores the survey data received from the device.

[0469] Step 7:

[0470] Server: Analyzes the survey data using an artificial intelligence engine module.

[0471] Step 8:

[0472] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[0473] Step 9:

[0474] Server: Sends the created customized plan to the device.

[0475] Step 10:

[0476] On your device: The customized learning plan is displayed and reviewed by the user.

[0477] 3. Learning progress management and emotion recognition

[0478] Step 1:

[0479] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[0480] Step 2:

[0481] Device: Sends a content request to the server.

[0482] Step 3:

[0483] Server: Receives the request and sends the corresponding content (video learning materials, interactive quizzes, etc.) to the device.

[0484] Step 4:

[0485] Device: The transmitted content is displayed and the user begins learning.

[0486] Step 5:

[0487] User: Watches video instructional material and then takes an interactive quiz.

[0488] Step 6:

[0489] Device: Sends quiz answer data to the server.

[0490] Step 7:

[0491] Emotion Engine: Analyzes the user's facial expressions and tone of voice to collect emotional data while watching videos and taking quizzes.

[0492] Step 8:

[0493] Server: Receives quiz answer data and emotion data.

[0494] Step 9:

[0495] Server: Determines whether the answers are correct or not, and evaluates the user's level of understanding and emotional state.

[0496] Step 10:

[0497] Server: Based on the level of comprehension and emotion data, generates the next learning content and feedback and sends it to the device.

[0498] Step 11:

[0499] On the device: The next recommended content and feedback received from the server are displayed and confirmed by the user.

[0500] Specific examples

[0501] For example, if a fourth grade user is learning "Fractions in Math":

[0502] Step 1:

[0503] User: Log in, select the "Math" category and choose the "Fractions" content.

[0504] Step 2:

[0505] Device: Sends a content request to the server.

[0506] Step 3:

[0507] Server: Sends video materials and quizzes to the device.

[0508] Step 4:

[0509] Device: Display the video material and start watching.

[0510] Step 5:

[0511] Emotion Engine: Analyzes the user's facial expressions and tone of voice in real time to collect emotional data.

[0512] Step 6:

[0513] Users: Watch a video and then answer a quiz (e.g., "1 / 2 + 1 / 4 = ?").

[0514] Step 7:

[0515] Device: Sends quiz answer data to the server.

[0516] Step 8:

[0517] Server: Determines whether the quiz is correct or incorrect and analyzes emotional data to evaluate the user's level of understanding and emotional state.

[0518] Step 9:

[0519] Server: Based on the evaluation results, it generates the next recommendation (e.g., "Study 1 / 3 - 1 / 6") and feedback.

[0520] Step 10:

[0521] On the device: The next content or feedback received from the server is displayed and confirmed by the user.

[0522] This process step allows users to enjoy a personalized learning experience and real-time feedback, making learning both efficient and engaging.

[0523] Example 2

[0524] 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."

[0525] Conventional learning systems have difficulty providing adaptive feedback based on individual users' emotions and level of understanding. As a result, the effectiveness of learning is limited, making it difficult to maintain user motivation. Furthermore, because it is not possible to grasp learning progress in real time or adjust the level of difficulty, there is a problem that the learning experience becomes uniform. A system that can solve these issues and provide a more personalized learning experience is needed.

[0526] 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.

[0527] In this invention, the server includes means for receiving user information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for collecting the user's learning progress in real time and recording it in a database, means for collecting emotion data using an emotion engine that analyzes the user's facial expressions and tone of voice while learning and adjusting the learning progress and difficulty level of the content in real time, and means for generating and providing feedback adapted to the user. This makes it possible to provide a detailed learning experience based on the emotions and level of understanding of each individual user.

[0528] A "user account" is a data set containing individual identification information for accessing a system.

[0529] "Learning style" is information that refers to the most effective learning method or tendency of a user.

[0530] A "survey" is a data collection tool in the form of questions that gather information about a user's learning style and interests.

[0531] An "artificial intelligence engine" is a computer program that analyzes a user's data and generates a customized study plan.

[0532] A "customized learning plan" is a learning plan that is tailored to a user's individual learning style and level of understanding.

[0533] "Related content" refers to study materials and reference materials provided based on the user's study plan.

[0534] "Study progress" is information indicating the progress and results of the user's learning activities.

[0535] A "database" is a data storage system for systematically storing and managing learning progress and user information.

[0536] The "emotion engine" is a computer program that analyzes the user's facial expressions and tone of voice to collect and evaluate emotional data.

[0537] "Emotional data" is digital information that indicates the user's emotional state during learning (e.g., joy, confusion, concentration, etc.).

[0538] "Feedback" refers to information that refers to responses or advice provided based on a user's learning activities and level of understanding.

[0539] The present invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Detailed embodiments of the system are described below.

[0540] User Registration Process

[0541] Device:

[0542] When a user accesses the system for the first time, a new registration screen is displayed. The user enters the required information (e.g., name, email address, password, age, learning style) and presses the "Register" button. The terminal then sends the entered information to the server using the HTTPS protocol.

[0543] server:

[0544] The server validates the received user information. For example, it checks whether the email address format and password length are appropriate. If validation is successful, it creates a new user account in a database (e.g., MySQL) and saves the information. It also generates a confirmation message confirming registration and sends it to the device.

[0545] Device:

[0546] A message received from the server confirming registration is displayed in a pop-up window or dialog box. The message "Registration completed" is displayed on the screen.

[0547] Personalization Setup Process

[0548] Device:

[0549] The user accesses the login screen, enters their email address and password, and presses the "Login" button. After logging in, they are taken to the personalization settings screen. On the personalization settings screen, a questionnaire about their learning style is displayed, and the user enters their answers to the questions. Once they have completed their answers, they press the "Send" button, and the device sends the survey data to the server.

[0550] server:

[0551] The server collects and stores the received survey data. It then uses an artificial intelligence engine (e.g., K-means clustering) to analyze the survey data and generate a personalized learning plan for each user. The generated customized plan and related content list are then sent to the device.

[0552] Device:

[0553] The device displays the customized study plan for the user to review. For example, the study plan may be displayed in a calendar or list format.

[0554] Learning progress management and emotion recognition process

[0555] Device:

[0556] The user selects a particular learning content (e.g., "math fractions") and the device requests the selected content from the server.

[0557] server:

[0558] The server receives the request and sends the corresponding content (video learning material or interactive quizzes) to the device.

[0559] Device:

[0560] The device displays the transmitted content and the user begins learning. During learning, the device uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to collect emotional data. For example, the device's camera and microphone are used for facial expression recognition and voice tone analysis.

[0561] Emotion Engine:

[0562] The emotion engine collects, analyzes, and evaluates the user's emotional data (e.g., joy, confusion, concentration, etc.), thereby understanding the user's emotional state in real time while learning.

[0563] server:

[0564] The server adjusts the learning progress and difficulty of the content in real time based on the emotional data received from the emotion engine. For example, dynamic adjustments are made, such as lowering the difficulty level if the level of concentration is declining. In addition, feedback adapted to the user is generated and provided to the device. The user's level of understanding and progress are also recorded in a database.

[0565] Device:

[0566] The device displays the feedback received from the server and the recommended next learning content for the user to confirm. For example, an adaptive feedback message and a link to the next recommended learning content are displayed on the learning screen.

[0567] Specific examples

[0568] For example, when a fourth-grade elementary school user is learning "fractions in mathematics," the learning process proceeds as follows:

[0569] Device:

[0570] A user selects the "Math" category and chooses "Fractions" content. They watch video lessons and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?"). During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[0571] server:

[0572] The system receives quiz answer data and emotion data from the emotion engine. It judges whether the answers are correct or incorrect, and evaluates the user's level of understanding and concentration based on the emotion data. It recommends the next study content based on the evaluation results (e.g., "Study 1 / 3 - 1 / 6") and sends it to the device.

[0573] Device:

[0574] The user sees the next recommended content and continues learning.

[0575] As described above, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[0576] Example prompts for a generative AI model based on concrete examples

[0577] "I want to create a program that explains recent scientific and technological developments to users. In the program, I want to implement a system that personalizes topics that are likely to interest users, collects emotional data as the program learns, and adjusts the content accordingly. Could you please tell me the specific steps of the program to maximize user interest?"

[0578] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0579] Step 1: User access and information entry

[0580] User:

[0581] When a user accesses the system for the first time, a new registration screen appears. The user enters information such as their name, email address, password, age, and learning style, and then presses the "Register" button.

[0582] input:

[0583] Name, email address, password, age, learning style, etc.

[0584] output:

[0585] Registration information will be sent

[0586] Specific behavior:

[0587] Open the URL in your browser and the registration screen will appear.

[0588] The user fills out the form and clicks the "Register" button

[0589] Step 2: Submit your input

[0590] Device:

[0591] The terminal sends the information entered by the user to the server using the HTTPS protocol.

[0592] input:

[0593] Various information entered by the user

[0594] output:

[0595] Data sent from the device to the server as an HTTPS request

[0596] Specific behavior:

[0597] The information you enter is encrypted and sent to the server as an HTTPS request.

[0598] Step 3: Verify your information and create a user account

[0599] server:

[0600] The server validates the received user information, for example by checking the format of the email address and the password length, and if validation is successful, creates a new user account in a database (e.g. MySQL) and stores the information.

[0601] input:

[0602] User registration information received by the server

[0603] output:

[0604] User accounts that pass validation and are saved in the database

[0605] Registration completion message

[0606] Specific behavior:

[0607] Validate email address format and password length

[0608] If the validation is successful, create a new record in the database to save the information and generate a confirmation message.

[0609] Step 4: Displaying the registration completion message

[0610] Device:

[0611] A confirmation message sent from the server confirming registration completion is displayed in a pop-up window or dialog box.

[0612] input:

[0613] Registration completion message sent from the server

[0614] output:

[0615] Pop-up windows or dialog boxes that appear on the terminal screen

[0616] Specific behavior:

[0617] The device will display the received confirmation message on the screen.

[0618] Step 5: Log in

[0619] User:

[0620] The user accesses the login screen, enters their email address and password, and clicks the "Login" button.

[0621] input:

[0622] Login information (email address, password)

[0623] output:

[0624] Logging requests sent to the server

[0625] Specific behavior:

[0626] Enter your email address and password on the login screen and click the login button.

[0627] Step 6: Conduct a survey

[0628] Device:

[0629] After logging in, a personalized settings screen appears, and a questionnaire about learning styles is provided to the user. The user answers the questions and the questionnaire data is sent to the server.

[0630] input:

[0631] Learning style survey data

[0632] output:

[0633] Survey data sent to the server

[0634] Specific behavior:

[0635] Survey questions are displayed on the personalization settings screen, and the user enters their answers.

[0636] Step 7: Analyze the survey data

[0637] server:

[0638] The server receives, collects, and stores the survey data. It then uses an AI engine to analyze the survey data and generate a learning plan for each user.

[0639] input:

[0640] Survey data sent from the device

[0641] output:

[0642] Customized learning plans based on stored survey data and analysis results

[0643] Specific behavior:

[0644] Survey data is stored in a database, and an artificial intelligence engine analyzes the data and generates a learning plan.

[0645] Step 8: Offer a customized plan

[0646] server:

[0647] The generated customized plan and related content list are transmitted to the terminal.

[0648] input:

[0649] Customized learning plans and related content

[0650] output:

[0651] Study plans and related content sent to your device

[0652] Specific behavior:

[0653] The generated learning plan and content list are encoded in JSON format or similar and sent to the device.

[0654] Step 9: View your learning plan

[0655] Device:

[0656] The customized learning plan is displayed and reviewed by the user.

[0657] input:

[0658] Learning plans and related content sent from the server

[0659] output:

[0660] On-screen lesson plans and related content

[0661] Specific behavior:

[0662] Displaying learning plans in a calendar or list format on the user interface

[0663] (Application example 2)

[0664] 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."

[0665] In factory workplaces, there is a lack of means to recognize workers' emotional states in real time and improve work efficiency and safety based on that information. In particular, there is a need for a system that can accurately detect when a worker is feeling stressed or fatigued and provide appropriate feedback or suggest breaks. Providing appropriate feedback can also maximize worker performance.

[0666] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0667] In this invention, the server includes: means for receiving user information and creating a user account; means for generating a questionnaire regarding the user's learning style and interests and providing it to the user; means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user; means for providing the customized learning plan and related content to the user; means for recognizing the worker's emotional state using an emotion recognition engine that analyzes the user's facial expressions and tone of voice in real time while the user is working; means for analyzing the emotional data collected by the emotion recognition engine and generating optimal feedback for the worker; means for providing the generated feedback to the worker; means for collecting the user's learning progress and work progress in real time and recording them in a database; and means for recommending the next learning content or optimal break timing based on the learning progress and work progress. This makes it possible to provide appropriate support and feedback according to the worker's emotional state, thereby improving work efficiency and safety.

[0668] "User Information" is personal data such as your name, email address, password, age, and learning style.

[0669] A "user account" is account information that is generated based on user information, identifies the user, and enables access to the system.

[0670] A "survey" is a survey in the form of questions that gather information about a user's learning style and interests.

[0671] An "artificial intelligence engine" is an algorithm and software that analyzes learning data and survey results to generate a customized learning plan for each user.

[0672] A "customized study plan" is a personalized study plan generated based on a user's learning style and interests.

[0673] "Related Content" refers to the specific learning materials and resources included in your customized learning plan.

[0674] An "emotion recognition engine" is a combination of software and hardware that analyzes a worker's facial expressions and tone of voice to recognize their emotional state in real time.

[0675] "Emotion data" is information about the worker's emotional state (e.g., joy, confusion, concentration, etc.) collected by an emotion recognition engine.

[0676] "Feedback" refers to messages of support and suggestions provided depending on the worker's emotional state and work progress.

[0677] "Study progress status" is data that indicates how far the user has progressed in their studies, and the level of understanding and progress of the learning content.

[0678] "Work progress status" is data that indicates how far a worker has progressed in the work and the progress of the work content.

[0679] "Rest timing" refers to the appropriate timing for suggesting a rest to a worker depending on the worker's level of fatigue and emotional state.

[0680] The system for implementing this invention is mainly configured through the interaction between a server, a terminal, and a user. Specific components of the system and the processing flow thereof will be described in detail below.

[0681] 1. User Registration

[0682] Device:

[0683] When a user accesses the system for the first time, a new registration screen appears, where the user enters information such as name, email address, password, age, and learning style, and presses the "Register" button. The terminal then sends the entered information to the server.

[0684] server:

[0685] The received user information is verified, and if the verification is successful, a user account is created in the database. A registration completion confirmation message is also generated and sent to the terminal.

[0686] Device:

[0687] Displays the registration completion message received from the server.

[0688] 2. Personalization Settings

[0689] Device:

[0690] When a user logs in by entering their email address and password on the login screen, they are taken to the personalization settings screen, where survey questions are displayed, and when the user enters their answers and presses the send button, the survey data is sent to the server.

[0691] server:

[0692] The system collects and stores the received survey data, analyzes it using an artificial intelligence engine, and generates a learning plan for each user based on the analysis results, which is then sent to the device along with a list of related content.

[0693] Device:

[0694] The customized learning plan is displayed and reviewed by the user.

[0695] 3. Emotion recognition during work

[0696] Device:

[0697] As the user (worker) performs a task, the robot's onboard camera and microphone capture the worker's facial expressions and tone of voice in real time. The hardware used is an Intel RealSense camera and a Shure MV5 microphone.

[0698] Emotion Recognition Engine:

[0699] The captured data is analyzed using "Affectiva SDK," software specialized in emotion recognition, and the emotional status (e.g., focused, tired, stressed) is output.

[0700] 4. Emotional Data Analysis and Feedback Generation

[0701] server:

[0702] The output emotional status is sent to a server and analyzed by an evaluation module, which uses a neural network model built with TensorFlow to generate optimal feedback based on the emotional data.

[0703] Device:

[0704] The generated feedback is displayed on the robot's screen and provided audibly through the speaker. The user interface is built using Flutter.

[0705] 5. Feedback examples and prompts

[0706] As a concrete example, the following prompt sentence will be used:

[0707] Feedback prompt when worker is "stressed":

[0708] "Provide relaxation advice to workers who experience stress while working."

[0709] Feedback prompt when worker is "fatigued":

[0710] "Provide prompts to workers to take short breaks when they are fatigued."

[0711] Feedback prompt when worker is "focused":

[0712] "Provide messages to workers who are concentrating on their work that praise their efforts."

[0713] As described above, the present invention provides personalized support and feedback according to the user's emotional state, contributing to improved work efficiency and worker safety.

[0714] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0715] Step 1:

[0716] When a user accesses the system for the first time, the terminal displays a new registration screen. The user enters information such as name, email address, password, age, and learning style, and then presses the "Register" button. This entered information is sent from the terminal to the server.

[0717] Step 2:

[0718] The server verifies the received user information and creates a new user account in the database. If the user account is successfully created, the server generates a registration completion confirmation message and sends it to the terminal. The terminal receives this and displays a registration completion message to the user.

[0719] Step 3:

[0720] The user accesses the login screen and logs in by entering their email address and password. The device then transitions to a personalization settings screen, which displays the survey questions. When the user answers the questions and presses the send button, the survey data is sent from the device to the server.

[0721] Step 4:

[0722] The server collects and stores the survey data received from the device and analyzes it using an artificial intelligence engine. Based on the analysis results, it generates a customized learning plan for each user. This generated learning plan is sent to the device along with a list of related content. The device receives this and displays the customized learning plan to the user.

[0723] Step 5:

[0724] When a user (worker) starts working, the device's camera and microphone capture the worker's facial expressions and tone of voice in real time, and the captured data is sent from the device to an emotion recognition engine.

[0725] Step 6:

[0726] The emotion recognition engine uses the captured data to analyze the worker's emotional state, using the Affectiva SDK to output an emotional status (e.g., focused, tired, stressed).

[0727] Step 7:

[0728] The emotional status is sent to a server, which receives the emotional data and analyzes it using a neural network model built with TensorFlow. Based on the results of this analysis, optimal feedback is generated for the worker.

[0729] Step 8:

[0730] The generated feedback is sent from the server to the device. The device receives it and displays it on the robot's screen. It also provides audio feedback through the speaker. The user interface is built using Flutter.

[0731] Step 9:

[0732] The user (worker) checks the feedback provided by the device and takes appropriate action, such as taking a break or continuing to work. The feedback may include advice on relaxation, prompts to take short breaks, or messages praising efforts.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] [Second embodiment]

[0737] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0738] 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.

[0739] 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).

[0740] 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.

[0741] 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.

[0742] 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).

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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."

[0749] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[0750] 1. User Registration

[0751] Device:

[0752] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[0753] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[0754] 3. The terminal sends the entered information to the server.

[0755] server:

[0756] 1. Receive and verify user information received from the terminal.

[0757] 2. If the information is valid, create a user account in the database and save the information.

[0758] 3. Generate a registration completion confirmation message and send it to the device.

[0759] Device:

[0760] 1. Display the registration completion message received from the server.

[0761] 2. Personalization Settings

[0762] Device:

[0763] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[0764] 2. After logging in, you will be taken to the personalization settings screen.

[0765] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[0766] 4. The device sends the survey data to the server.

[0767] server:

[0768] 1. Collect survey data received from the device.

[0769] 2. Analyze the survey data using an artificial intelligence engine module.

[0770] 3. Based on the analysis results, generate the optimal learning plan for each user.

[0771] 4. Send a customized learning plan and related content list to your device.

[0772] Device:

[0773] 1. Display a customized learning plan and allow the user to confirm and begin learning.

[0774] 3. Learning progress management

[0775] Device:

[0776] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[0777] 2. Watch and take video learning materials and interactive quizzes.

[0778] 3. Send the learning progress and quiz results to the server.

[0779] server:

[0780] 1. Collect and store learning progress data and quiz results received from devices in real time.

[0781] 2. Evaluate the user's level of understanding and recommend the next piece of content to study.

[0782] 3. Send the recommendation to your device.

[0783] Device:

[0784] 1. The user continues learning based on the next content received from the server.

[0785] Specific examples

[0786] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[0787] 1. Device:

[0788] 1. The user selects the "Math" category and chooses the "Fractions" content.

[0789] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[0790] 2. Server:

[0791] 1. Receive quiz answer data and determine whether it is correct or incorrect.

[0792] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[0793] 3. Send the recommendation to your device.

[0794] 3. Terminal:

[0795] 1. The user sees the next recommended content and continues learning.

[0796] In this way, the present invention provides a system that provides a personalized learning experience to users and improves their motivation to learn and their level of understanding.

[0797] The processing flow will be explained below.

[0798] Program processing steps

[0799] 1. User Registration

[0800] Step 1:

[0801] User: Open the new registration screen.

[0802] Step 2:

[0803] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[0804] Step 3:

[0805] Terminal: Sends the entered information to the server.

[0806] Step 4:

[0807] Server: Verifies the user information received from the device.

[0808] Step 5:

[0809] Server: If validation is successful, create a user account in the database and save the information.

[0810] Step 6:

[0811] Server: Generates a registration completion confirmation message and sends it to the device.

[0812] Step 7:

[0813] Terminal: Display the registration completion message received from the server.

[0814] 2. Personalization Settings

[0815] Step 1:

[0816] User: Access the login screen and log in by entering your email address and password.

[0817] Step 2:

[0818] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[0819] Step 3:

[0820] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[0821] Step 4:

[0822] User: Answers survey questions and hits submit.

[0823] Step 5:

[0824] Terminal: Sends the response data to the server.

[0825] Step 6:

[0826] Server: Collects and stores the survey data received from the device.

[0827] Step 7:

[0828] Server: Analyzes the survey data using an artificial intelligence engine module.

[0829] Step 8:

[0830] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[0831] Step 9:

[0832] Server: Sends the created customized plan to the device.

[0833] Step 10:

[0834] On your device: The customized learning plan is displayed and reviewed by the user.

[0835] 3. Learning progress management

[0836] Step 1:

[0837] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[0838] Step 2:

[0839] Device: Requests the selected content from the server.

[0840] Step 3:

[0841] Server: Receives the request and sends the corresponding content (video learning materials or interactive quizzes) to the device.

[0842] Step 4:

[0843] Device: The transmitted content is displayed and the user begins learning.

[0844] Step 5:

[0845] User: Watches video instructional material and then takes an interactive quiz.

[0846] Step 6:

[0847] Device: Sends quiz answer data to the server.

[0848] Step 7:

[0849] Server: Receives the answer data and determines whether it is correct or not.

[0850] Step 8:

[0851] Server: Evaluates the user's understanding and stores their learning progress in a database.

[0852] Step 9:

[0853] Server: Generates recommendations for what to learn next and sends them to the device.

[0854] Step 10:

[0855] On your device: Shows the next recommended learning content so the user can continue learning.

[0856] This process step allows users to enjoy a personalized learning experience and receive real-time feedback to effectively progress their learning.

[0857] Example 1

[0858] 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."

[0859] Modern educational systems struggle to provide personalized learning experiences tailored to individual users' learning styles and progress. Many existing systems simply provide uniform learning materials, preventing variations in learning efficiency and outcomes for each user. Furthermore, they lack the ability to track users' progress and comprehension in real time and provide appropriate feedback, potentially delaying learning improvement.

[0860] 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.

[0861] In this invention, the server includes means for receiving user information and creating a user account, means for generating questions related to the user's learning style and interests and providing them to the user, means for analyzing the results of the questions using an artificial intelligence engine and generating a customized study plan for each user, means for providing the user with the customized study plan and related content, means for collecting the user's study progress in real time and recording it in a database, means for recommending next study content based on the study progress, means for the user to select specific content and provide a device for study, means for collecting the study progress and quiz results and evaluating comprehension, and means for recommending next study content based on the evaluation results. This makes it possible to provide a personalized learning experience for each user and receive appropriate feedback in real time according to each user's individual progress and comprehension.

[0862] "User information" refers to data about individual users registered in the system, including name, email address, password, age, learning style, etc.

[0863] A "user account" is account information required for a user to use the system, and is stored in a database based on the user information.

[0864] A "survey" is a data collection tool in the form of questions to gather information about a user's learning style and interests.

[0865] An "artificial intelligence engine" is a computer program that contains algorithms and models to analyze collected data and understand trends and patterns for each user.

[0866] A "customized study plan" is a study plan that is individually generated by an artificial intelligence engine based on the user's learning style and interests.

[0867] "Relevant content" refers to the specific materials and activities included in your customized learning plan.

[0868] "Study progress" is information about the progress of a user as they progress through their studies, and includes study time, progress of content, quiz results, and the like.

[0869] A "database" is a storage system for storing data such as user information, learning progress, and learning plans.

[0870] "Recommendation" is information generated by the server to indicate what the user should learn next based on their learning progress.

[0871] "Device" refers to the terminal (e.g., PC, tablet, smartphone) that a user uses to select and operate learning content.

[0872] "Feedback" means information about improvements or advice provided based on a user's understanding and learning progress.

[0873] The present invention is an educational system designed to provide users with a personalized learning experience. The system uses a server, a terminal, and an artificial intelligence engine to collect and analyze user information. Specific embodiments for implementing the present invention are described below.

[0874] 1. User Registration

[0875] Terminal

[0876] 1. When a user first accesses the system, they are presented with a registration screen. Examples include interfaces such as a web browser or a mobile application.

[0877] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[0878] 3. The terminal sends the entered information to the server.

[0879] server

[0880] 1. The server verifies the user information received from the device, specifically checking the format of the email address and the security of the password.

[0881] 2. If the validation is successful, the server creates a user account in the database and stores the entered information.

[0882] 3. The server generates a confirmation message confirming the completion of registration and sends it to the terminal.

[0883] Terminal

[0884] 1. The terminal displays the registration completion message received from the server.

[0885] 2. Personalization Settings

[0886] Terminal

[0887] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[0888] 2. After logging in, the user will be taken to the personalization settings screen.

[0889] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[0890] 4. The device sends the survey data to the server.

[0891] server

[0892] 1. The server collects the survey data received from the terminal.

[0893] 2. Analyze the survey data using an artificial intelligence engine and generate the optimal study plan for each user.

[0894] 3. Sending a customized learning plan and related content to your device.

[0895] Terminal

[0896] 1. The user checks the customized study plan on their device and begins studying.

[0897] 3. Learning progress management

[0898] Terminal

[0899] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[0900] 2. Users watch and take video tutorials and interactive quizzes.

[0901] 3. The device sends the learning progress and quiz results to the server.

[0902] server

[0903] 1. The server collects and stores learning progress data and quiz results received from the device in real time.

[0904] 2. The server evaluates the user's level of understanding and recommends what to study next.

[0905] 3. Send the recommendation to your device.

[0906] Terminal

[0907] 1. The device displays the next recommended content received from the server.

[0908] 2. The user reviews the recommended content and continues learning.

[0909] Specific examples

[0910] Below is a specific example of a fourth-grade elementary school student learning about "fractions in mathematics."

[0911] 1. Terminal

[0912] 1. The user selects the "Math" category and chooses the "Fractions" content.

[0913] 2. Users watch video instructional material and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?").

[0914] 2. Server

[0915] 1. The server receives the quiz answer data and determines whether it is correct or incorrect.

[0916] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[0917] 3. Send the recommendation to your device.

[0918] 3. Terminal

[0919] 1. The user sees the next recommended content and continues learning.

[0920] The invention personalizes the user's learning experience through generative AI models, assessing progress in real time and providing appropriate feedback and learning content.

[0921] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0922] Step 1:

[0923] Enter and send user information (terminal)

[0924] When a user accesses the system for the first time, a new registration screen is displayed. The user enters information such as name, email address, password, age, and learning style. After this information is entered, the terminal displays instructions to the user to press the "Register" button. When the user presses the "Register" button, the entered information is sent from the terminal to the server. The sent information is input data, and is transferred to the server in a structured format (for example, JSON format).

[0925] Step 2:

[0926] User information verification and storage (server)

[0927] The server receives the user information from the terminal. It validates the received information and checks the format of the email address and the security of the password. For example, it uses regular expression patterns to verify whether the email address is in the correct format. If the validation is successful, the server creates a new user account in the database and saves the entered information. The saved data is treated as new user account data. The server generates a confirmation message and sends it to the terminal.

[0928] Step 3:

[0929] Notification of registration completion (device)

[0930] The terminal displays the registration completion message received from the server. For example, a message such as "Registration completed" is displayed on the user's screen. This allows the user to confirm that the account registration has been completed successfully.

[0931] Step 4:

[0932] Perform login operation (terminal)

[0933] The user accesses the login screen and enters their registered email address and password. This input data is collected by the device, and an instruction to press the "Login" button is displayed. When the user presses the "Login" button, the input data is sent to the server as authentication data.

[0934] Step 5:

[0935] User authentication and transition to personalization setting screen (server)

[0936] The server verifies the login information received from the device, for example, by checking whether the entered email address and password match the information in the database. If authentication is successful, the server sends the data of the personalized settings screen to the device along with a message indicating that the user has successfully logged in.

[0937] Step 6:

[0938] Implementing and sending personalized settings (device)

[0939] The user moves to the personalized settings screen and answers the questions in the survey. For example, a question such as "What is your preferred method of learning?" is displayed. When the user enters their answer and presses the send button, the terminal sends the survey data to the server. This data is the user's learning style information.

[0940] Step 7:

[0941] Analysis of survey data and generation of learning plans (server)

[0942] The server collects the survey data received from the device. This data is analyzed using an artificial intelligence engine to generate an optimal learning plan for each user. For example, an algorithm analyzes learning patterns based on the user's responses and creates an individual learning plan. The generated learning plan and related content list are then sent from the server to the device.

[0943] Step 8:

[0944] Display customized plan (device)

[0945] The terminal displays the customized learning plan received from the server. The user confirms the plan and begins learning. The displayed content includes optimal learning content tailored to each individual user.

[0946] Step 9:

[0947] Selection and implementation of learning content (device)

[0948] The user selects specific content (e.g., "Math Fractions") from the learning start screen. The user then watches and completes video materials and interactive quizzes. Specific operations include, for example, playing videos and answering quizzes.

[0949] Step 10:

[0950] Sending learning progress data (device)

[0951] Learning progress and quiz results are collected continuously. The device sends this data to the server in real time. The data includes study time, correct / incorrect answers, and the user's progress.

[0952] Step 11:

[0953] Data collection and understanding assessment (server)

[0954] The server collects and stores learning progress data and quiz results received from the device in real time. Based on the collected data, the server uses an artificial intelligence engine to evaluate the user's level of understanding. For example, it analyzes which content the user is struggling with.

[0955] Step 12:

[0956] Recommending and sending next learning content (server)

[0957] Based on the comprehension assessment, the server recommends the next learning content. The generated recommendation is sent to the device. For example, if the user has not made much progress on a particular topic, the server will recommend supplementary learning on that topic.

[0958] Step 13:

[0959] Displaying recommended content and continuing learning (device)

[0960] The device displays the next recommended content received from the server. The user confirms the recommended content and starts the next learning session. A new learning loop begins based on the displayed content.

[0961] This series of processing steps effectively provides the user with a personalized learning experience.

[0962] (Application example 1)

[0963] 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."

[0964] Traditional educational systems struggle to provide an optimized learning plan for each user, often resulting in a decline in motivation to learn and a lack of understanding. Even in brick-and-mortar stores, it's difficult to provide appropriate products based on a user's learning style, resulting in a lack of support for users to select the learning materials that are best suited to them. Furthermore, there's no recommendation of the next learning material based on the progress or results of use after purchase, resulting in a lack of continuous learning support.

[0965] 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.

[0966] In this invention, the server includes means for receiving user attribute information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for providing the user with the customized learning plan and related content, means for collecting the user's learning progress in real time and recording it in a database, means for recommending the next content to study based on the learning progress, means for providing optimal product information based on the user's learning style in the store, and means for recording the progress and usage results of the learning materials purchased by the user and recommending the next learning material to purchase, thereby enabling learning support and product recommendations optimized for each user.

[0967] "User attribute information" is information specific to each individual user, such as the user's name, email address, age, learning style, etc.

[0968] The "means for creating a user account" is the process by which an individual user profile is generated on the server based on information provided by the user and stored in a database.

[0969] The "means for generating a questionnaire and providing it to the user" is a mechanism for creating a question form to understand the user's learning style and interests, and allowing the user to answer the question form.

[0970] An "artificial intelligence engine" is a software module that performs machine learning and data analysis, used to analyze large amounts of data and customize learning plans.

[0971] A "customized learning plan" is a set of learning content optimized for each individual user based on the results of a survey and user attribute information.

[0972] "Related content" refers to educational resources such as learning materials, videos, interactive quizzes and activities that are suggested based on the user's study plan.

[0973] "Means for collecting learning progress in real time and recording it in a database" refers to the process of collecting data generated in real time as the user progresses with their learning and managing that data in an integrated manner.

[0974] The "means for recommending the next learning content" is a mechanism that presents the user with the appropriate next learning content based on collected learning progress data.

[0975] "Means for providing optimal product information based on a user's learning style in a physical store" is a system for suggesting optimal learning materials and products in a physical store according to the user's learning style.

[0976] "Means for recording the progress and usage results of purchased learning materials and recommending the next learning material to purchase" refers to the process of tracking the usage and learning progress of the user's purchased learning materials and recommending the next learning material that will be required.

[0977] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[0978] System Configuration

[0979] 1. User Registration

[0980] Device:

[0981] When a user first accesses the system, a new registration screen is displayed.

[0982] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[0983] The terminal transmits the input information to the server.

[0984] server:

[0985] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[0986] A registration completion confirmation message is generated and sent to the terminal.

[0987] Device:

[0988] Displays the registration completion message received from the server.

[0989] 2. Personalization Settings

[0990] Device:

[0991] The user accesses the login screen and logs in by entering their registered email address and password.

[0992] After logging in, you will be taken to the personalization settings screen.

[0993] Answer the questions in the survey and send the answer data to the server.

[0994] server:

[0995] The received survey data is collected and analyzed using an artificial intelligence engine.

[0996] Based on the analysis results, a customized learning plan is generated for each user.

[0997] Send a customized study plan and related content list to your device.

[0998] Device:

[0999] The customized learning plan is displayed, and the user can confirm it and begin learning.

[1000] 3. Learning progress management

[1001] Device:

[1002] The user selects a particular piece of content (e.g., "math fractions").

[1003] View and take video tutorials and interactive quizzes.

[1004] Send learning progress and quiz results to the server.

[1005] server:

[1006] Collect and store incoming learning progress data and quiz results in real time.

[1007] Evaluate the user's level of understanding and recommend the next content to study.

[1008] The recommendation is sent to the device.

[1009] Device:

[1010] The user continues learning based on the next recommended content received from the server.

[1011] 4. Physical store applications

[1012] Device:

[1013] The app is used in physical stores to provide optimal product information based on the user's learning style.

[1014] For example, an education store might recommend learning materials based on a user's learning style (e.g., visual learner).

[1015] server:

[1016] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[1017] Recommendations are sent to devices in physical stores.

[1018] Device:

[1019] The user checks the next educational material to be purchased in the store and continues purchasing.

[1020] Hardware and software used

[1021] Hardware: smartphones, tablets, servers.

[1022] software:

[1023] Python: Used for application development.

[1024] Requests library: Sends HTTP requests and communicates with the API.

[1025] Flask / Django: A framework suitable for implementing server-side APIs.

[1026] AI Engine: An artificial intelligence module for learning style analysis and recommendations.

[1027] Specific examples

[1028] For example, when a 10-year-old elementary school student accesses the system for the first time, they register by entering basic information. They then answer a questionnaire to set their own learning style. They use the app in the store to find recommended learning materials and manage their learning progress with those materials within the app. The AI ​​engine then recommends the next learning material they need.

[1029] Prompt Sentence Examples

[1030] Example: User information

[1031] {name: "User name", email: "Email address", password: "Password", age: Age, learning_style: "Learning style"}

[1032]

[1033] Example: Survey response data

[1034] ["What is your favorite subject?": "Math", "What is your favorite way of learning?": "Visual aids"]

[1035] In this way, a personalized learning experience is provided for each user.

[1036] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1037] Step 1:

[1038] User Registration

[1039] Device:

[1040] When a user first accesses the system, a new registration screen is displayed.

[1041] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[1042] server:

[1043] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[1044] A registration completion confirmation message is generated and sent to the terminal.

[1045] Device:

[1046] Displays the registration completion message received from the server.

[1047] Input: User demographic information (name, email address, age, learning style)

[1048] Data processing: verification, account creation

[1049] Output: Registration complete message

[1050] Step 2:

[1051] Personalization Settings

[1052] Device:

[1053] The user accesses the login screen and logs in by entering their registered email address and password.

[1054] After logging in, you will be taken to the personalization settings screen.

[1055] Answer the questions in the survey and send the answer data to the server.

[1056] server:

[1057] The received survey data is collected and analyzed using an artificial intelligence engine.

[1058] Based on the analysis results, a customized learning plan is generated for each user.

[1059] Send a customized study plan and related content list to your device.

[1060] Device:

[1061] The customized learning plan is displayed, and the user can confirm it and begin learning.

[1062] Input: Survey response data

[1063] Data processing: AI analysis and learning plan generation

[1064] Output: Customized study plan, related content list

[1065] Step 3:

[1066] Learning progress management

[1067] Device:

[1068] The user selects a particular piece of content (e.g., "math fractions").

[1069] View and take video tutorials and interactive quizzes.

[1070] server:

[1071] Collect and store learning progress data and quiz results received from devices in real time.

[1072] Evaluate the user's level of understanding and recommend the next content to study.

[1073] The recommendation is sent to the device.

[1074] Device:

[1075] The user continues learning based on the next recommended content received from the server.

[1076] Input: Learning progress data, quiz results

[1077] Data processing: Comprehension assessment, next learning content recommendation

[1078] Output: Recommended content

[1079] Step 4:

[1080] Application in physical stores

[1081] Device:

[1082] The app can be used in physical stores to provide optimal product information based on the user's learning style. For example, in a physical store that sells educational products, the app can recommend learning materials based on the user's learning style.

[1083] server:

[1084] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[1085] Recommendations are sent to devices in physical stores.

[1086] Device:

[1087] The user checks the next educational material to be purchased in the store and continues purchasing.

[1088] Input: Progress data of purchased teaching materials, usage results

[1089] Data processing: Recording results and recommending next learning materials

[1090] Output: Recommendations for next course material purchases

[1091] These steps make it possible to provide personalized learning support and product recommendations for each user.

[1092] 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.

[1093] This invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Specific embodiments of this system are shown below.

[1094] 1. User Registration

[1095] Device:

[1096] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[1097] 2. The user enters the required information (name, email address, password, age, learning style, etc.) and presses the "Register" button.

[1098] 3. The terminal sends the entered information to the server.

[1099] server:

[1100] 1. Verify the user information received from the terminal.

[1101] 2. If the validation is successful, create a user account in the database and save the information.

[1102] 3. Generate a registration completion confirmation message and send it to the device.

[1103] Device:

[1104] 1. Display the registration completion message received from the server.

[1105] 2. Personalization Settings

[1106] Device:

[1107] 1. The user accesses the login screen and logs in by entering their email address and password.

[1108] 2. After logging in, you will be taken to the personalization settings screen.

[1109] 3. Display the survey questions on the personalization settings screen and the user enters their answers.

[1110] 4. The user answers the survey questions and presses the submit button.

[1111] 5. The device sends the survey data to the server.

[1112] server:

[1113] 1. Collect and save survey data received from the device.

[1114] 2. Analyze the survey data using an artificial intelligence engine module.

[1115] 3. Generate a learning plan for each user based on the analysis results, creating a customized learning plan and related content list.

[1116] 4. Send the created customized plan to the device.

[1117] Device:

[1118] 1. The customized learning plan is displayed and confirmed by the user.

[1119] 3. Learning progress management and emotion recognition

[1120] Device:

[1121] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[1122] 2. Request the content from the server.

[1123] server:

[1124] 1. Receives a request and sends the corresponding content (video learning material or interactive quiz) to the device.

[1125] Device:

[1126] 1. The submitted content is displayed and the user begins learning.

[1127] 2. Use the emotion engine to analyze the user's facial expressions and tone of voice in real time while watching video materials or taking interactive quizzes.

[1128] Emotion Engine:

[1129] 1. Collect user emotional data (e.g., joy, confusion, concentration, etc.).

[1130] 2. Analyze the collected emotional data and evaluate the emotional state during learning.

[1131] server:

[1132] 1. Adjust learning progress and content difficulty in real time based on emotional data received from the emotion engine.

[1133] 2. Generate optimal feedback based on the user's emotional state and send it to the device.

[1134] 3. Furthermore, the user's level of understanding and progress are stored in a database.

[1135] Device:

[1136] 1. The user sees the feedback and recommended next learning content received from the server and confirms it.

[1137] Specific examples

[1138] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[1139] 1. Device:

[1140] 1. The user selects the "Math" category and chooses the "Fractions" content.

[1141] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[1142] 3. During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[1143] 2. Server:

[1144] 1. Receive quiz answer data and emotion data from the emotion engine.

[1145] 2. The answers are judged to be correct or incorrect, and the user's level of understanding and concentration is evaluated based on emotional data.

[1146] 3. Recommend the next learning content based on the evaluation results (e.g., learning from 1 / 3 to 1 / 6) and send it to the device.

[1147] 3. Terminal:

[1148] 1. The user sees the next recommended content and continues learning.

[1149] In this way, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[1150] The processing flow will be explained below.

[1151] Specific process steps for carrying out the invention

[1152] 1. User Registration

[1153] Step 1:

[1154] User: Open the new registration screen.

[1155] Step 2:

[1156] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[1157] Step 3:

[1158] Terminal: Sends the entered information to the server.

[1159] Step 4:

[1160] Server: Verifies the user information received from the device.

[1161] Step 5:

[1162] Server: If validation is successful, create a user account in the database and save the information.

[1163] Step 6:

[1164] Server: Generates a registration completion confirmation message and sends it to the device.

[1165] Step 7:

[1166] Terminal: Display the registration completion message received from the server.

[1167] 2. Personalization Settings

[1168] Step 1:

[1169] User: Access the login screen and log in by entering your email address and password.

[1170] Step 2:

[1171] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[1172] Step 3:

[1173] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[1174] Step 4:

[1175] User: Answers survey questions and hits submit.

[1176] Step 5:

[1177] Terminal: Sends the response data to the server.

[1178] Step 6:

[1179] Server: Collects and stores the survey data received from the device.

[1180] Step 7:

[1181] Server: Analyzes the survey data using an artificial intelligence engine module.

[1182] Step 8:

[1183] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[1184] Step 9:

[1185] Server: Sends the created customized plan to the device.

[1186] Step 10:

[1187] On your device: The customized learning plan is displayed and reviewed by the user.

[1188] 3. Learning progress management and emotion recognition

[1189] Step 1:

[1190] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[1191] Step 2:

[1192] Device: Sends a content request to the server.

[1193] Step 3:

[1194] Server: Receives the request and sends the corresponding content (video learning materials, interactive quizzes, etc.) to the device.

[1195] Step 4:

[1196] Device: The transmitted content is displayed and the user begins learning.

[1197] Step 5:

[1198] User: Watches video instructional material and then takes an interactive quiz.

[1199] Step 6:

[1200] Device: Sends quiz answer data to the server.

[1201] Step 7:

[1202] Emotion Engine: Analyzes the user's facial expressions and tone of voice to collect emotional data while watching videos and taking quizzes.

[1203] Step 8:

[1204] Server: Receives quiz answer data and emotion data.

[1205] Step 9:

[1206] Server: Determines whether the answers are correct or not, and evaluates the user's level of understanding and emotional state.

[1207] Step 10:

[1208] Server: Based on the level of comprehension and emotion data, generates the next learning content and feedback and sends it to the device.

[1209] Step 11:

[1210] On the device: The next recommended content and feedback received from the server are displayed and confirmed by the user.

[1211] Specific examples

[1212] For example, if a fourth grade user is learning "Fractions in Math":

[1213] Step 1:

[1214] User: Log in, select the "Math" category and choose the "Fractions" content.

[1215] Step 2:

[1216] Device: Sends a content request to the server.

[1217] Step 3:

[1218] Server: Sends video materials and quizzes to the device.

[1219] Step 4:

[1220] Device: Display the video material and start watching.

[1221] Step 5:

[1222] Emotion Engine: Analyzes the user's facial expressions and tone of voice in real time to collect emotional data.

[1223] Step 6:

[1224] Users: Watch a video and then answer a quiz (e.g., "1 / 2 + 1 / 4 = ?").

[1225] Step 7:

[1226] Device: Sends quiz answer data to the server.

[1227] Step 8:

[1228] Server: Determines whether the quiz is correct or incorrect and analyzes emotional data to evaluate the user's level of understanding and emotional state.

[1229] Step 9:

[1230] Server: Based on the evaluation results, it generates the next recommendation (e.g., "Study 1 / 3 - 1 / 6") and feedback.

[1231] Step 10:

[1232] On the device: The next content or feedback received from the server is displayed and confirmed by the user.

[1233] This process step allows users to enjoy a personalized learning experience and real-time feedback, making learning both efficient and engaging.

[1234] Example 2

[1235] 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."

[1236] Conventional learning systems have difficulty providing adaptive feedback based on individual users' emotions and level of understanding. As a result, the effectiveness of learning is limited, making it difficult to maintain user motivation. Furthermore, because it is not possible to grasp learning progress in real time or adjust the level of difficulty, there is a problem that the learning experience becomes uniform. A system that can solve these issues and provide a more personalized learning experience is needed.

[1237] 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.

[1238] In this invention, the server includes means for receiving user information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for collecting the user's learning progress in real time and recording it in a database, means for collecting emotion data using an emotion engine that analyzes the user's facial expressions and tone of voice while learning and adjusting the learning progress and difficulty level of the content in real time, and means for generating and providing feedback adapted to the user. This makes it possible to provide a detailed learning experience based on the emotions and level of understanding of each individual user.

[1239] A "user account" is a data set containing individual identification information for accessing a system.

[1240] "Learning style" is information that refers to the most effective learning method or tendency of a user.

[1241] A "survey" is a data collection tool in the form of questions that gather information about a user's learning style and interests.

[1242] An "artificial intelligence engine" is a computer program that analyzes a user's data and generates a customized study plan.

[1243] A "customized learning plan" is a learning plan that is tailored to a user's individual learning style and level of understanding.

[1244] "Related content" refers to study materials and reference materials provided based on the user's study plan.

[1245] "Study progress" is information indicating the progress and results of the user's learning activities.

[1246] A "database" is a data storage system for systematically storing and managing learning progress and user information.

[1247] The "emotion engine" is a computer program that analyzes the user's facial expressions and tone of voice to collect and evaluate emotional data.

[1248] "Emotional data" is digital information that indicates the user's emotional state during learning (e.g., joy, confusion, concentration, etc.).

[1249] "Feedback" refers to information that refers to responses or advice provided based on a user's learning activities and level of understanding.

[1250] The present invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Detailed embodiments of the system are described below.

[1251] User Registration Process

[1252] Device:

[1253] When a user accesses the system for the first time, a new registration screen is displayed. The user enters the required information (e.g., name, email address, password, age, learning style) and presses the "Register" button. The terminal then sends the entered information to the server using the HTTPS protocol.

[1254] server:

[1255] The server validates the received user information. For example, it checks whether the email address format and password length are appropriate. If validation is successful, it creates a new user account in a database (e.g., MySQL) and saves the information. It also generates a confirmation message confirming registration and sends it to the device.

[1256] Device:

[1257] A message received from the server confirming registration is displayed in a pop-up window or dialog box. The message "Registration completed" is displayed on the screen.

[1258] Personalization Setup Process

[1259] Device:

[1260] The user accesses the login screen, enters their email address and password, and presses the "Login" button. After logging in, they are taken to the personalization settings screen. On the personalization settings screen, a questionnaire about their learning style is displayed, and the user enters their answers to the questions. Once they have completed their answers, they press the "Send" button, and the device sends the survey data to the server.

[1261] server:

[1262] The server collects and stores the received survey data. It then uses an artificial intelligence engine (e.g., K-means clustering) to analyze the survey data and generate a personalized learning plan for each user. The generated customized plan and related content list are then sent to the device.

[1263] Device:

[1264] The device displays the customized study plan for the user to review. For example, the study plan may be displayed in a calendar or list format.

[1265] Learning progress management and emotion recognition process

[1266] Device:

[1267] The user selects a particular learning content (e.g., "math fractions") and the device requests the selected content from the server.

[1268] server:

[1269] The server receives the request and sends the corresponding content (video learning material or interactive quizzes) to the device.

[1270] Device:

[1271] The device displays the transmitted content and the user begins learning. During learning, the device uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to collect emotional data. For example, the device's camera and microphone are used for facial expression recognition and voice tone analysis.

[1272] Emotion Engine:

[1273] The emotion engine collects, analyzes, and evaluates the user's emotional data (e.g., joy, confusion, concentration, etc.), thereby understanding the user's emotional state in real time while learning.

[1274] server:

[1275] The server adjusts the learning progress and difficulty of the content in real time based on the emotional data received from the emotion engine. For example, dynamic adjustments are made, such as lowering the difficulty level if the level of concentration is declining. In addition, feedback adapted to the user is generated and provided to the device. The user's level of understanding and progress are also recorded in a database.

[1276] Device:

[1277] The device displays the feedback received from the server and the recommended next learning content for the user to confirm. For example, an adaptive feedback message and a link to the next recommended learning content are displayed on the learning screen.

[1278] Specific examples

[1279] For example, when a fourth-grade elementary school user is learning "fractions in mathematics," the learning process proceeds as follows:

[1280] Device:

[1281] A user selects the "Math" category and chooses "Fractions" content. They watch video lessons and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?"). During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[1282] server:

[1283] The system receives quiz answer data and emotion data from the emotion engine. It judges whether the answers are correct or incorrect, and evaluates the user's level of understanding and concentration based on the emotion data. It recommends the next study content based on the evaluation results (e.g., "Study 1 / 3 - 1 / 6") and sends it to the device.

[1284] Device:

[1285] The user sees the next recommended content and continues learning.

[1286] As described above, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[1287] Example prompts for a generative AI model based on concrete examples

[1288] "I want to create a program that explains recent scientific and technological developments to users. In the program, I want to implement a system that personalizes topics that are likely to interest users, collects emotional data as the program learns, and adjusts the content accordingly. Could you please tell me the specific steps of the program to maximize user interest?"

[1289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1290] Step 1: User access and information entry

[1291] User:

[1292] When a user accesses the system for the first time, a new registration screen appears. The user enters information such as their name, email address, password, age, and learning style, and then presses the "Register" button.

[1293] input:

[1294] Name, email address, password, age, learning style, etc.

[1295] output:

[1296] Registration information will be sent

[1297] Specific behavior:

[1298] Open the URL in your browser and the registration screen will appear.

[1299] The user fills out the form and clicks the "Register" button

[1300] Step 2: Submit your input

[1301] Device:

[1302] The terminal sends the information entered by the user to the server using the HTTPS protocol.

[1303] input:

[1304] Various information entered by the user

[1305] output:

[1306] Data sent from the device to the server as an HTTPS request

[1307] Specific behavior:

[1308] The information you enter is encrypted and sent to the server as an HTTPS request.

[1309] Step 3: Verify your information and create a user account

[1310] server:

[1311] The server validates the received user information, for example by checking the format of the email address and the password length, and if validation is successful, creates a new user account in a database (e.g. MySQL) and stores the information.

[1312] input:

[1313] User registration information received by the server

[1314] output:

[1315] User accounts that pass validation and are saved in the database

[1316] Registration completion message

[1317] Specific behavior:

[1318] Validate email address format and password length

[1319] If the validation is successful, create a new record in the database to save the information and generate a confirmation message.

[1320] Step 4: Displaying the registration completion message

[1321] Device:

[1322] A confirmation message sent from the server confirming registration completion is displayed in a pop-up window or dialog box.

[1323] input:

[1324] Registration completion message sent from the server

[1325] output:

[1326] Pop-up windows or dialog boxes that appear on the terminal screen

[1327] Specific behavior:

[1328] The device will display the received confirmation message on the screen.

[1329] Step 5: Log in

[1330] User:

[1331] The user accesses the login screen, enters their email address and password, and clicks the "Login" button.

[1332] input:

[1333] Login information (email address, password)

[1334] output:

[1335] Logging requests sent to the server

[1336] Specific behavior:

[1337] Enter your email address and password on the login screen and click the login button.

[1338] Step 6: Conduct a survey

[1339] Device:

[1340] After logging in, a personalized settings screen appears, and a questionnaire about learning styles is provided to the user. The user answers the questions and the questionnaire data is sent to the server.

[1341] input:

[1342] Learning style survey data

[1343] output:

[1344] Survey data sent to the server

[1345] Specific behavior:

[1346] Survey questions are displayed on the personalization settings screen, and the user enters their answers.

[1347] Step 7: Analyze the survey data

[1348] server:

[1349] The server receives, collects, and stores the survey data. It then uses an AI engine to analyze the survey data and generate a learning plan for each user.

[1350] input:

[1351] Survey data sent from the device

[1352] output:

[1353] Customized learning plans based on stored survey data and analysis results

[1354] Specific behavior:

[1355] Survey data is stored in a database, and an artificial intelligence engine analyzes the data and generates a learning plan.

[1356] Step 8: Offer a customized plan

[1357] server:

[1358] The generated customized plan and related content list are transmitted to the terminal.

[1359] input:

[1360] Customized learning plans and related content

[1361] output:

[1362] Study plans and related content sent to your device

[1363] Specific behavior:

[1364] The generated learning plan and content list are encoded in JSON format or similar and sent to the device.

[1365] Step 9: View your learning plan

[1366] Device:

[1367] The customized learning plan is displayed and reviewed by the user.

[1368] input:

[1369] Learning plans and related content sent from the server

[1370] output:

[1371] On-screen lesson plans and related content

[1372] Specific behavior:

[1373] Displaying learning plans in a calendar or list format on the user interface

[1374] (Application example 2)

[1375] 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."

[1376] In factory workplaces, there is a lack of means to recognize workers' emotional states in real time and improve work efficiency and safety based on that information. In particular, there is a need for a system that can accurately detect when a worker is feeling stressed or fatigued and provide appropriate feedback or suggest breaks. Providing appropriate feedback can also maximize worker performance.

[1377] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1378] In this invention, the server includes: means for receiving user information and creating a user account; means for generating a questionnaire regarding the user's learning style and interests and providing it to the user; means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user; means for providing the customized learning plan and related content to the user; means for recognizing the worker's emotional state using an emotion recognition engine that analyzes the user's facial expressions and tone of voice in real time while the user is working; means for analyzing the emotional data collected by the emotion recognition engine and generating optimal feedback for the worker; means for providing the generated feedback to the worker; means for collecting the user's learning progress and work progress in real time and recording them in a database; and means for recommending the next learning content or optimal break timing based on the learning progress and work progress. This makes it possible to provide appropriate support and feedback according to the worker's emotional state, thereby improving work efficiency and safety.

[1379] "User Information" is personal data such as your name, email address, password, age, and learning style.

[1380] A "user account" is account information that is generated based on user information, identifies the user, and enables access to the system.

[1381] A "survey" is a survey in the form of questions that gather information about a user's learning style and interests.

[1382] An "artificial intelligence engine" is an algorithm and software that analyzes learning data and survey results to generate a customized learning plan for each user.

[1383] A "customized study plan" is a personalized study plan generated based on a user's learning style and interests.

[1384] "Related Content" refers to the specific learning materials and resources included in your customized learning plan.

[1385] An "emotion recognition engine" is a combination of software and hardware that analyzes a worker's facial expressions and tone of voice to recognize their emotional state in real time.

[1386] "Emotion data" is information about the worker's emotional state (e.g., joy, confusion, concentration, etc.) collected by an emotion recognition engine.

[1387] "Feedback" refers to messages of support and suggestions provided depending on the worker's emotional state and work progress.

[1388] "Study progress status" is data that indicates how far the user has progressed in their studies, and the level of understanding and progress of the learning content.

[1389] "Work progress status" is data that indicates how far a worker has progressed in the work and the progress of the work content.

[1390] "Rest timing" refers to the appropriate timing for suggesting a rest to a worker depending on the worker's level of fatigue and emotional state.

[1391] The system for implementing this invention is mainly configured through the interaction between a server, a terminal, and a user. Specific components of the system and the processing flow thereof will be described in detail below.

[1392] 1. User Registration

[1393] Device:

[1394] When a user accesses the system for the first time, a new registration screen appears, where the user enters information such as name, email address, password, age, and learning style, and presses the "Register" button. The terminal then sends the entered information to the server.

[1395] server:

[1396] The received user information is verified, and if the verification is successful, a user account is created in the database. A registration completion confirmation message is also generated and sent to the terminal.

[1397] Device:

[1398] Displays the registration completion message received from the server.

[1399] 2. Personalization Settings

[1400] Device:

[1401] When a user logs in by entering their email address and password on the login screen, they are taken to the personalization settings screen, where survey questions are displayed, and when the user enters their answers and presses the send button, the survey data is sent to the server.

[1402] server:

[1403] The system collects and stores the received survey data, analyzes it using an artificial intelligence engine, and generates a learning plan for each user based on the analysis results, which is then sent to the device along with a list of related content.

[1404] Device:

[1405] The customized learning plan is displayed and reviewed by the user.

[1406] 3. Emotion recognition during work

[1407] Device:

[1408] As the user (worker) performs a task, the robot's onboard camera and microphone capture the worker's facial expressions and tone of voice in real time. The hardware used is an Intel RealSense camera and a Shure MV5 microphone.

[1409] Emotion Recognition Engine:

[1410] The captured data is analyzed using "Affectiva SDK," software specialized in emotion recognition, and the emotional status (e.g., focused, tired, stressed) is output.

[1411] 4. Emotional Data Analysis and Feedback Generation

[1412] server:

[1413] The output emotional status is sent to a server and analyzed by an evaluation module, which uses a neural network model built with TensorFlow to generate optimal feedback based on the emotional data.

[1414] Device:

[1415] The generated feedback is displayed on the robot's screen and provided audibly through the speaker. The user interface is built using Flutter.

[1416] 5. Feedback examples and prompts

[1417] As a concrete example, the following prompt sentence will be used:

[1418] Feedback prompt when worker is "stressed":

[1419] "Provide relaxation advice to workers who experience stress while working."

[1420] Feedback prompt when worker is "fatigued":

[1421] "Provide prompts to workers to take short breaks when they are fatigued."

[1422] Feedback prompt when worker is "focused":

[1423] "Provide messages to workers who are concentrating on their work that praise their efforts."

[1424] As described above, the present invention provides personalized support and feedback according to the user's emotional state, contributing to improved work efficiency and worker safety.

[1425] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1426] Step 1:

[1427] When a user accesses the system for the first time, the terminal displays a new registration screen. The user enters information such as name, email address, password, age, and learning style, and then presses the "Register" button. This entered information is sent from the terminal to the server.

[1428] Step 2:

[1429] The server verifies the received user information and creates a new user account in the database. If the user account is successfully created, the server generates a registration completion confirmation message and sends it to the terminal. The terminal receives this and displays a registration completion message to the user.

[1430] Step 3:

[1431] The user accesses the login screen and logs in by entering their email address and password. The device then transitions to a personalization settings screen, which displays the survey questions. When the user answers the questions and presses the send button, the survey data is sent from the device to the server.

[1432] Step 4:

[1433] The server collects and stores the survey data received from the device and analyzes it using an artificial intelligence engine. Based on the analysis results, it generates a customized learning plan for each user. This generated learning plan is sent to the device along with a list of related content. The device receives this and displays the customized learning plan to the user.

[1434] Step 5:

[1435] When a user (worker) starts working, the device's camera and microphone capture the worker's facial expressions and tone of voice in real time, and the captured data is sent from the device to an emotion recognition engine.

[1436] Step 6:

[1437] The emotion recognition engine uses the captured data to analyze the worker's emotional state, using the Affectiva SDK to output an emotional status (e.g., focused, tired, stressed).

[1438] Step 7:

[1439] The emotional status is sent to a server, which receives the emotional data and analyzes it using a neural network model built with TensorFlow. Based on the results of this analysis, optimal feedback is generated for the worker.

[1440] Step 8:

[1441] The generated feedback is sent from the server to the device. The device receives it and displays it on the robot's screen. It also provides audio feedback through the speaker. The user interface is built using Flutter.

[1442] Step 9:

[1443] The user (worker) checks the feedback provided by the device and takes appropriate action, such as taking a break or continuing to work. The feedback may include advice on relaxation, prompts to take short breaks, or messages praising efforts.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] [Third embodiment]

[1448] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1449] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1450] 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).

[1451] 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.

[1452] 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.

[1453] 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).

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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.

[1459] 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."

[1460] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[1461] 1. User Registration

[1462] Device:

[1463] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[1464] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[1465] 3. The terminal sends the entered information to the server.

[1466] server:

[1467] 1. Receive and verify user information received from the terminal.

[1468] 2. If the information is valid, create a user account in the database and save the information.

[1469] 3. Generate a registration completion confirmation message and send it to the device.

[1470] Device:

[1471] 1. Display the registration completion message received from the server.

[1472] 2. Personalization Settings

[1473] Device:

[1474] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[1475] 2. After logging in, you will be taken to the personalization settings screen.

[1476] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[1477] 4. The device sends the survey data to the server.

[1478] server:

[1479] 1. Collect survey data received from the device.

[1480] 2. Analyze the survey data using an artificial intelligence engine module.

[1481] 3. Based on the analysis results, generate the optimal learning plan for each user.

[1482] 4. Send a customized learning plan and related content list to your device.

[1483] Device:

[1484] 1. Display a customized learning plan and allow the user to confirm and begin learning.

[1485] 3. Learning progress management

[1486] Device:

[1487] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[1488] 2. Watch and take video learning materials and interactive quizzes.

[1489] 3. Send the learning progress and quiz results to the server.

[1490] server:

[1491] 1. Collect and store learning progress data and quiz results received from devices in real time.

[1492] 2. Evaluate the user's level of understanding and recommend the next piece of content to study.

[1493] 3. Send the recommendation to your device.

[1494] Device:

[1495] 1. The user continues learning based on the next content received from the server.

[1496] Specific examples

[1497] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[1498] 1. Device:

[1499] 1. The user selects the "Math" category and chooses the "Fractions" content.

[1500] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[1501] 2. Server:

[1502] 1. Receive quiz answer data and determine whether it is correct or incorrect.

[1503] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[1504] 3. Send the recommendation to your device.

[1505] 3. Terminal:

[1506] 1. The user sees the next recommended content and continues learning.

[1507] In this way, the present invention provides a system that provides a personalized learning experience to users and improves their motivation to learn and their level of understanding.

[1508] The processing flow will be explained below.

[1509] Program processing steps

[1510] 1. User Registration

[1511] Step 1:

[1512] User: Open the new registration screen.

[1513] Step 2:

[1514] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[1515] Step 3:

[1516] Terminal: Sends the entered information to the server.

[1517] Step 4:

[1518] Server: Verifies the user information received from the device.

[1519] Step 5:

[1520] Server: If validation is successful, create a user account in the database and save the information.

[1521] Step 6:

[1522] Server: Generates a registration completion confirmation message and sends it to the device.

[1523] Step 7:

[1524] Terminal: Display the registration completion message received from the server.

[1525] 2. Personalization Settings

[1526] Step 1:

[1527] User: Access the login screen and log in by entering your email address and password.

[1528] Step 2:

[1529] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[1530] Step 3:

[1531] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[1532] Step 4:

[1533] User: Answers survey questions and hits submit.

[1534] Step 5:

[1535] Terminal: Sends the response data to the server.

[1536] Step 6:

[1537] Server: Collects and stores the survey data received from the device.

[1538] Step 7:

[1539] Server: Analyzes the survey data using an artificial intelligence engine module.

[1540] Step 8:

[1541] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[1542] Step 9:

[1543] Server: Sends the created customized plan to the device.

[1544] Step 10:

[1545] On your device: The customized learning plan is displayed and reviewed by the user.

[1546] 3. Learning progress management

[1547] Step 1:

[1548] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[1549] Step 2:

[1550] Device: Requests the selected content from the server.

[1551] Step 3:

[1552] Server: Receives the request and sends the corresponding content (video learning materials or interactive quizzes) to the device.

[1553] Step 4:

[1554] Device: The transmitted content is displayed and the user begins learning.

[1555] Step 5:

[1556] User: Watches video instructional material and then takes an interactive quiz.

[1557] Step 6:

[1558] Device: Sends quiz answer data to the server.

[1559] Step 7:

[1560] Server: Receives the answer data and determines whether it is correct or not.

[1561] Step 8:

[1562] Server: Evaluates the user's understanding and stores their learning progress in a database.

[1563] Step 9:

[1564] Server: Generates recommendations for what to learn next and sends them to the device.

[1565] Step 10:

[1566] On your device: Shows the next recommended learning content so the user can continue learning.

[1567] This process step allows users to enjoy a personalized learning experience and receive real-time feedback to effectively progress their learning.

[1568] Example 1

[1569] 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."

[1570] Modern educational systems struggle to provide personalized learning experiences tailored to individual users' learning styles and progress. Many existing systems simply provide uniform learning materials, preventing variations in learning efficiency and outcomes for each user. Furthermore, they lack the ability to track users' progress and comprehension in real time and provide appropriate feedback, potentially delaying learning improvement.

[1571] 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.

[1572] In this invention, the server includes means for receiving user information and creating a user account, means for generating questions related to the user's learning style and interests and providing them to the user, means for analyzing the results of the questions using an artificial intelligence engine and generating a customized study plan for each user, means for providing the user with the customized study plan and related content, means for collecting the user's study progress in real time and recording it in a database, means for recommending next study content based on the study progress, means for the user to select specific content and provide a device for study, means for collecting the study progress and quiz results and evaluating comprehension, and means for recommending next study content based on the evaluation results. This makes it possible to provide a personalized learning experience for each user and receive appropriate feedback in real time according to each user's individual progress and comprehension.

[1573] "User information" refers to data about individual users registered in the system, including name, email address, password, age, learning style, etc.

[1574] A "user account" is account information required for a user to use the system, and is stored in a database based on the user information.

[1575] A "survey" is a data collection tool in the form of questions to gather information about a user's learning style and interests.

[1576] An "artificial intelligence engine" is a computer program that contains algorithms and models to analyze collected data and understand trends and patterns for each user.

[1577] A "customized study plan" is a study plan that is individually generated by an artificial intelligence engine based on the user's learning style and interests.

[1578] "Relevant content" refers to the specific materials and activities included in your customized learning plan.

[1579] "Study progress" is information about the progress of a user as they progress through their studies, and includes study time, progress of content, quiz results, and the like.

[1580] A "database" is a storage system for storing data such as user information, learning progress, and learning plans.

[1581] "Recommendation" is information generated by the server to indicate what the user should learn next based on their learning progress.

[1582] "Device" refers to the terminal (e.g., PC, tablet, smartphone) that a user uses to select and operate learning content.

[1583] "Feedback" means information about improvements or advice provided based on a user's understanding and learning progress.

[1584] The present invention is an educational system designed to provide users with a personalized learning experience. The system uses a server, a terminal, and an artificial intelligence engine to collect and analyze user information. Specific embodiments for implementing the present invention are described below.

[1585] 1. User Registration

[1586] Terminal

[1587] 1. When a user first accesses the system, they are presented with a registration screen. Examples include interfaces such as a web browser or a mobile application.

[1588] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[1589] 3. The terminal sends the entered information to the server.

[1590] server

[1591] 1. The server verifies the user information received from the device, specifically checking the format of the email address and the security of the password.

[1592] 2. If the validation is successful, the server creates a user account in the database and stores the entered information.

[1593] 3. The server generates a confirmation message confirming the completion of registration and sends it to the terminal.

[1594] Terminal

[1595] 1. The terminal displays the registration completion message received from the server.

[1596] 2. Personalization Settings

[1597] Terminal

[1598] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[1599] 2. After logging in, the user will be taken to the personalization settings screen.

[1600] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[1601] 4. The device sends the survey data to the server.

[1602] server

[1603] 1. The server collects the survey data received from the terminal.

[1604] 2. Analyze the survey data using an artificial intelligence engine and generate the optimal study plan for each user.

[1605] 3. Sending a customized learning plan and related content to your device.

[1606] Terminal

[1607] 1. The user checks the customized study plan on their device and begins studying.

[1608] 3. Learning progress management

[1609] Terminal

[1610] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[1611] 2. Users watch and take video tutorials and interactive quizzes.

[1612] 3. The device sends the learning progress and quiz results to the server.

[1613] server

[1614] 1. The server collects and stores learning progress data and quiz results received from the device in real time.

[1615] 2. The server evaluates the user's level of understanding and recommends what to study next.

[1616] 3. Send the recommendation to your device.

[1617] Terminal

[1618] 1. The device displays the next recommended content received from the server.

[1619] 2. The user reviews the recommended content and continues learning.

[1620] Specific examples

[1621] Below is a specific example of a fourth-grade elementary school student learning about "fractions in mathematics."

[1622] 1. Terminal

[1623] 1. The user selects the "Math" category and chooses the "Fractions" content.

[1624] 2. Users watch video instructional material and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?").

[1625] 2. Server

[1626] 1. The server receives the quiz answer data and determines whether it is correct or incorrect.

[1627] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[1628] 3. Send the recommendation to your device.

[1629] 3. Terminal

[1630] 1. The user sees the next recommended content and continues learning.

[1631] The invention personalizes the user's learning experience through generative AI models, assessing progress in real time and providing appropriate feedback and learning content.

[1632] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1633] Step 1:

[1634] Enter and send user information (terminal)

[1635] When a user accesses the system for the first time, a new registration screen is displayed. The user enters information such as name, email address, password, age, and learning style. After this information is entered, the terminal displays instructions to the user to press the "Register" button. When the user presses the "Register" button, the entered information is sent from the terminal to the server. The sent information is input data, and is transferred to the server in a structured format (for example, JSON format).

[1636] Step 2:

[1637] User information verification and storage (server)

[1638] The server receives the user information from the terminal. It validates the received information and checks the format of the email address and the security of the password. For example, it uses regular expression patterns to verify whether the email address is in the correct format. If the validation is successful, the server creates a new user account in the database and saves the entered information. The saved data is treated as new user account data. The server generates a confirmation message and sends it to the terminal.

[1639] Step 3:

[1640] Notification of registration completion (device)

[1641] The terminal displays the registration completion message received from the server. For example, a message such as "Registration completed" is displayed on the user's screen. This allows the user to confirm that the account registration has been completed successfully.

[1642] Step 4:

[1643] Perform login operation (terminal)

[1644] The user accesses the login screen and enters their registered email address and password. This input data is collected by the device, and an instruction to press the "Login" button is displayed. When the user presses the "Login" button, the input data is sent to the server as authentication data.

[1645] Step 5:

[1646] User authentication and transition to personalization setting screen (server)

[1647] The server verifies the login information received from the device, for example, by checking whether the entered email address and password match the information in the database. If authentication is successful, the server sends the data of the personalized settings screen to the device along with a message indicating that the user has successfully logged in.

[1648] Step 6:

[1649] Implementing and sending personalized settings (device)

[1650] The user moves to the personalized settings screen and answers the questions in the survey. For example, a question such as "What is your preferred method of learning?" is displayed. When the user enters their answer and presses the send button, the terminal sends the survey data to the server. This data is the user's learning style information.

[1651] Step 7:

[1652] Analysis of survey data and generation of learning plans (server)

[1653] The server collects the survey data received from the device. This data is analyzed using an artificial intelligence engine to generate an optimal learning plan for each user. For example, an algorithm analyzes learning patterns based on the user's responses and creates an individual learning plan. The generated learning plan and related content list are then sent from the server to the device.

[1654] Step 8:

[1655] Display customized plan (device)

[1656] The terminal displays the customized learning plan received from the server. The user confirms the plan and begins learning. The displayed content includes optimal learning content tailored to each individual user.

[1657] Step 9:

[1658] Selection and implementation of learning content (device)

[1659] The user selects specific content (e.g., "Math Fractions") from the learning start screen. The user then watches and completes video materials and interactive quizzes. Specific operations include, for example, playing videos and answering quizzes.

[1660] Step 10:

[1661] Sending learning progress data (device)

[1662] Learning progress and quiz results are collected continuously. The device sends this data to the server in real time. The data includes study time, correct / incorrect answers, and the user's progress.

[1663] Step 11:

[1664] Data collection and understanding assessment (server)

[1665] The server collects and stores learning progress data and quiz results received from the device in real time. Based on the collected data, the server uses an artificial intelligence engine to evaluate the user's level of understanding. For example, it analyzes which content the user is struggling with.

[1666] Step 12:

[1667] Recommending and sending next learning content (server)

[1668] Based on the comprehension assessment, the server recommends the next learning content. The generated recommendation is sent to the device. For example, if the user has not made much progress on a particular topic, the server will recommend supplementary learning on that topic.

[1669] Step 13:

[1670] Displaying recommended content and continuing learning (device)

[1671] The device displays the next recommended content received from the server. The user confirms the recommended content and starts the next learning session. A new learning loop begins based on the displayed content.

[1672] This series of processing steps effectively provides the user with a personalized learning experience.

[1673] (Application example 1)

[1674] 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."

[1675] Traditional educational systems struggle to provide an optimized learning plan for each user, often resulting in a decline in motivation to learn and a lack of understanding. Even in brick-and-mortar stores, it's difficult to provide appropriate products based on a user's learning style, resulting in a lack of support for users to select the learning materials that are best suited to them. Furthermore, there's no recommendation of the next learning material based on the progress or results of use after purchase, resulting in a lack of continuous learning support.

[1676] 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.

[1677] In this invention, the server includes means for receiving user attribute information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for providing the user with the customized learning plan and related content, means for collecting the user's learning progress in real time and recording it in a database, means for recommending the next content to study based on the learning progress, means for providing optimal product information based on the user's learning style in the store, and means for recording the progress and usage results of the learning materials purchased by the user and recommending the next learning material to purchase, thereby enabling learning support and product recommendations optimized for each user.

[1678] "User attribute information" is information specific to each individual user, such as the user's name, email address, age, learning style, etc.

[1679] The "means for creating a user account" is the process by which an individual user profile is generated on the server based on information provided by the user and stored in a database.

[1680] The "means for generating a questionnaire and providing it to the user" is a mechanism for creating a question form to understand the user's learning style and interests, and allowing the user to answer the question form.

[1681] An "artificial intelligence engine" is a software module that performs machine learning and data analysis, used to analyze large amounts of data and customize learning plans.

[1682] A "customized learning plan" is a set of learning content optimized for each individual user based on the results of a survey and user attribute information.

[1683] "Related content" refers to educational resources such as learning materials, videos, interactive quizzes and activities that are suggested based on the user's study plan.

[1684] "Means for collecting learning progress in real time and recording it in a database" refers to the process of collecting data generated in real time as the user progresses with their learning and managing that data in an integrated manner.

[1685] The "means for recommending the next learning content" is a mechanism that presents the user with the appropriate next learning content based on collected learning progress data.

[1686] "Means for providing optimal product information based on a user's learning style in a physical store" is a system for suggesting optimal learning materials and products in a physical store according to the user's learning style.

[1687] "Means for recording the progress and usage results of purchased learning materials and recommending the next learning material to purchase" refers to the process of tracking the usage and learning progress of the user's purchased learning materials and recommending the next learning material that will be required.

[1688] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[1689] System Configuration

[1690] 1. User Registration

[1691] Device:

[1692] When a user first accesses the system, a new registration screen is displayed.

[1693] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[1694] The terminal transmits the input information to the server.

[1695] server:

[1696] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[1697] A registration completion confirmation message is generated and sent to the terminal.

[1698] Device:

[1699] Displays the registration completion message received from the server.

[1700] 2. Personalization Settings

[1701] Device:

[1702] The user accesses the login screen and logs in by entering their registered email address and password.

[1703] After logging in, you will be taken to the personalization settings screen.

[1704] Answer the questions in the survey and send the answer data to the server.

[1705] server:

[1706] The received survey data is collected and analyzed using an artificial intelligence engine.

[1707] Based on the analysis results, a customized learning plan is generated for each user.

[1708] Send a customized study plan and related content list to your device.

[1709] Device:

[1710] The customized learning plan is displayed, and the user can confirm it and begin learning.

[1711] 3. Learning progress management

[1712] Device:

[1713] The user selects a particular piece of content (e.g., "math fractions").

[1714] View and take video tutorials and interactive quizzes.

[1715] Send learning progress and quiz results to the server.

[1716] server:

[1717] Collect and store incoming learning progress data and quiz results in real time.

[1718] Evaluate the user's level of understanding and recommend the next content to study.

[1719] The recommendation is sent to the device.

[1720] Device:

[1721] The user continues learning based on the next recommended content received from the server.

[1722] 4. Physical store applications

[1723] Device:

[1724] The app is used in physical stores to provide optimal product information based on the user's learning style.

[1725] For example, an education store might recommend learning materials based on a user's learning style (e.g., visual learner).

[1726] server:

[1727] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[1728] Recommendations are sent to devices in physical stores.

[1729] Device:

[1730] The user checks the next educational material to be purchased in the store and continues purchasing.

[1731] Hardware and software used

[1732] Hardware: smartphones, tablets, servers.

[1733] software:

[1734] Python: Used for application development.

[1735] Requests library: Sends HTTP requests and communicates with the API.

[1736] Flask / Django: A framework suitable for implementing server-side APIs.

[1737] AI Engine: An artificial intelligence module for learning style analysis and recommendations.

[1738] Specific examples

[1739] For example, when a 10-year-old elementary school student accesses the system for the first time, they register by entering basic information. They then answer a questionnaire to set their own learning style. They use the app in the store to find recommended learning materials and manage their learning progress with those materials within the app. The AI ​​engine then recommends the next learning material they need.

[1740] Prompt Sentence Examples

[1741] Example: User information

[1742] {name: "User name", email: "Email address", password: "Password", age: Age, learning_style: "Learning style"}

[1743]

[1744] Example: Survey response data

[1745] ["What is your favorite subject?": "Math", "What is your favorite way of learning?": "Visual aids"]

[1746] In this way, a personalized learning experience is provided for each user.

[1747] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1748] Step 1:

[1749] User Registration

[1750] Device:

[1751] When a user first accesses the system, a new registration screen is displayed.

[1752] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[1753] server:

[1754] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[1755] A registration completion confirmation message is generated and sent to the terminal.

[1756] Device:

[1757] Displays the registration completion message received from the server.

[1758] Input: User demographic information (name, email address, age, learning style)

[1759] Data processing: verification, account creation

[1760] Output: Registration complete message

[1761] Step 2:

[1762] Personalization Settings

[1763] Device:

[1764] The user accesses the login screen and logs in by entering their registered email address and password.

[1765] After logging in, you will be taken to the personalization settings screen.

[1766] Answer the questions in the survey and send the answer data to the server.

[1767] server:

[1768] The received survey data is collected and analyzed using an artificial intelligence engine.

[1769] Based on the analysis results, a customized learning plan is generated for each user.

[1770] Send a customized study plan and related content list to your device.

[1771] Device:

[1772] The customized learning plan is displayed, and the user can confirm it and begin learning.

[1773] Input: Survey response data

[1774] Data processing: AI analysis and learning plan generation

[1775] Output: Customized study plan, related content list

[1776] Step 3:

[1777] Learning progress management

[1778] Device:

[1779] The user selects a particular piece of content (e.g., "math fractions").

[1780] View and take video tutorials and interactive quizzes.

[1781] server:

[1782] Collect and store learning progress data and quiz results received from devices in real time.

[1783] Evaluate the user's level of understanding and recommend the next content to study.

[1784] The recommendation is sent to the device.

[1785] Device:

[1786] The user continues learning based on the next recommended content received from the server.

[1787] Input: Learning progress data, quiz results

[1788] Data processing: Comprehension assessment, next learning content recommendation

[1789] Output: Recommended content

[1790] Step 4:

[1791] Application in physical stores

[1792] Device:

[1793] The app can be used in physical stores to provide optimal product information based on the user's learning style. For example, in a physical store that sells educational products, the app can recommend learning materials based on the user's learning style.

[1794] server:

[1795] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[1796] Recommendations are sent to devices in physical stores.

[1797] Device:

[1798] The user checks the next educational material to be purchased in the store and continues purchasing.

[1799] Input: Progress data of purchased teaching materials, usage results

[1800] Data processing: Recording results and recommending next learning materials

[1801] Output: Recommendations for next course material purchases

[1802] These steps make it possible to provide personalized learning support and product recommendations for each user.

[1803] 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.

[1804] This invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Specific embodiments of this system are shown below.

[1805] 1. User Registration

[1806] Device:

[1807] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[1808] 2. The user enters the required information (name, email address, password, age, learning style, etc.) and presses the "Register" button.

[1809] 3. The terminal sends the entered information to the server.

[1810] server:

[1811] 1. Verify the user information received from the terminal.

[1812] 2. If the validation is successful, create a user account in the database and save the information.

[1813] 3. Generate a registration completion confirmation message and send it to the device.

[1814] Device:

[1815] 1. Display the registration completion message received from the server.

[1816] 2. Personalization Settings

[1817] Device:

[1818] 1. The user accesses the login screen and logs in by entering their email address and password.

[1819] 2. After logging in, you will be taken to the personalization settings screen.

[1820] 3. Display the survey questions on the personalization settings screen and the user enters their answers.

[1821] 4. The user answers the survey questions and presses the submit button.

[1822] 5. The device sends the survey data to the server.

[1823] server:

[1824] 1. Collect and save survey data received from the device.

[1825] 2. Analyze the survey data using an artificial intelligence engine module.

[1826] 3. Generate a learning plan for each user based on the analysis results, creating a customized learning plan and related content list.

[1827] 4. Send the created customized plan to the device.

[1828] Device:

[1829] 1. The customized learning plan is displayed and confirmed by the user.

[1830] 3. Learning progress management and emotion recognition

[1831] Device:

[1832] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[1833] 2. Request the content from the server.

[1834] server:

[1835] 1. Receives a request and sends the corresponding content (video learning material or interactive quiz) to the device.

[1836] Device:

[1837] 1. The submitted content is displayed and the user begins learning.

[1838] 2. Use the emotion engine to analyze the user's facial expressions and tone of voice in real time while watching video materials or taking interactive quizzes.

[1839] Emotion Engine:

[1840] 1. Collect user emotional data (e.g., joy, confusion, concentration, etc.).

[1841] 2. Analyze the collected emotional data and evaluate the emotional state during learning.

[1842] server:

[1843] 1. Adjust learning progress and content difficulty in real time based on emotional data received from the emotion engine.

[1844] 2. Generate optimal feedback based on the user's emotional state and send it to the device.

[1845] 3. Furthermore, the user's level of understanding and progress are stored in a database.

[1846] Device:

[1847] 1. The user sees the feedback and recommended next learning content received from the server and confirms it.

[1848] Specific examples

[1849] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[1850] 1. Device:

[1851] 1. The user selects the "Math" category and chooses the "Fractions" content.

[1852] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[1853] 3. During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[1854] 2. Server:

[1855] 1. Receive quiz answer data and emotion data from the emotion engine.

[1856] 2. The answers are judged to be correct or incorrect, and the user's level of understanding and concentration is evaluated based on emotional data.

[1857] 3. Recommend the next learning content based on the evaluation results (e.g., learning from 1 / 3 to 1 / 6) and send it to the device.

[1858] 3. Terminal:

[1859] 1. The user sees the next recommended content and continues learning.

[1860] In this way, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[1861] The processing flow will be explained below.

[1862] Specific process steps for carrying out the invention

[1863] 1. User Registration

[1864] Step 1:

[1865] User: Open the new registration screen.

[1866] Step 2:

[1867] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[1868] Step 3:

[1869] Terminal: Sends the entered information to the server.

[1870] Step 4:

[1871] Server: Verifies the user information received from the device.

[1872] Step 5:

[1873] Server: If validation is successful, create a user account in the database and save the information.

[1874] Step 6:

[1875] Server: Generates a registration completion confirmation message and sends it to the device.

[1876] Step 7:

[1877] Terminal: Display the registration completion message received from the server.

[1878] 2. Personalization Settings

[1879] Step 1:

[1880] User: Access the login screen and log in by entering your email address and password.

[1881] Step 2:

[1882] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[1883] Step 3:

[1884] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[1885] Step 4:

[1886] User: Answers survey questions and hits submit.

[1887] Step 5:

[1888] Terminal: Sends the response data to the server.

[1889] Step 6:

[1890] Server: Collects and stores the survey data received from the device.

[1891] Step 7:

[1892] Server: Analyzes the survey data using an artificial intelligence engine module.

[1893] Step 8:

[1894] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[1895] Step 9:

[1896] Server: Sends the created customized plan to the device.

[1897] Step 10:

[1898] On your device: The customized learning plan is displayed and reviewed by the user.

[1899] 3. Learning progress management and emotion recognition

[1900] Step 1:

[1901] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[1902] Step 2:

[1903] Device: Sends a content request to the server.

[1904] Step 3:

[1905] Server: Receives the request and sends the corresponding content (video learning materials, interactive quizzes, etc.) to the device.

[1906] Step 4:

[1907] Device: The transmitted content is displayed and the user begins learning.

[1908] Step 5:

[1909] User: Watches video instructional material and then takes an interactive quiz.

[1910] Step 6:

[1911] Device: Sends quiz answer data to the server.

[1912] Step 7:

[1913] Emotion Engine: Analyzes the user's facial expressions and tone of voice to collect emotional data while watching videos and taking quizzes.

[1914] Step 8:

[1915] Server: Receives quiz answer data and emotion data.

[1916] Step 9:

[1917] Server: Determines whether the answers are correct or not, and evaluates the user's level of understanding and emotional state.

[1918] Step 10:

[1919] Server: Based on the level of comprehension and emotion data, generates the next learning content and feedback and sends it to the device.

[1920] Step 11:

[1921] On the device: The next recommended content and feedback received from the server are displayed and confirmed by the user.

[1922] Specific examples

[1923] For example, if a fourth grade user is learning "Fractions in Math":

[1924] Step 1:

[1925] User: Log in, select the "Math" category and choose the "Fractions" content.

[1926] Step 2:

[1927] Device: Sends a content request to the server.

[1928] Step 3:

[1929] Server: Sends video materials and quizzes to the device.

[1930] Step 4:

[1931] Device: Display the video material and start watching.

[1932] Step 5:

[1933] Emotion Engine: Analyzes the user's facial expressions and tone of voice in real time to collect emotional data.

[1934] Step 6:

[1935] Users: Watch a video and then answer a quiz (e.g., "1 / 2 + 1 / 4 = ?").

[1936] Step 7:

[1937] Device: Sends quiz answer data to the server.

[1938] Step 8:

[1939] Server: Determines whether the quiz is correct or incorrect and analyzes emotional data to evaluate the user's level of understanding and emotional state.

[1940] Step 9:

[1941] Server: Based on the evaluation results, it generates the next recommendation (e.g., "Study 1 / 3 - 1 / 6") and feedback.

[1942] Step 10:

[1943] On the device: The next content or feedback received from the server is displayed and confirmed by the user.

[1944] This process step allows users to enjoy a personalized learning experience and real-time feedback, making learning both efficient and engaging.

[1945] Example 2

[1946] 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."

[1947] Conventional learning systems have difficulty providing adaptive feedback based on individual users' emotions and level of understanding. As a result, the effectiveness of learning is limited, making it difficult to maintain user motivation. Furthermore, because it is not possible to grasp learning progress in real time or adjust the level of difficulty, there is a problem that the learning experience becomes uniform. A system that can solve these issues and provide a more personalized learning experience is needed.

[1948] 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.

[1949] In this invention, the server includes means for receiving user information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for collecting the user's learning progress in real time and recording it in a database, means for collecting emotion data using an emotion engine that analyzes the user's facial expressions and tone of voice while learning and adjusting the learning progress and difficulty level of the content in real time, and means for generating and providing feedback adapted to the user. This makes it possible to provide a detailed learning experience based on the emotions and level of understanding of each individual user.

[1950] A "user account" is a data set containing individual identification information for accessing a system.

[1951] "Learning style" is information that refers to the most effective learning method or tendency of a user.

[1952] A "survey" is a data collection tool in the form of questions that gather information about a user's learning style and interests.

[1953] An "artificial intelligence engine" is a computer program that analyzes a user's data and generates a customized study plan.

[1954] A "customized learning plan" is a learning plan that is tailored to a user's individual learning style and level of understanding.

[1955] "Related content" refers to study materials and reference materials provided based on the user's study plan.

[1956] "Study progress" is information indicating the progress and results of the user's learning activities.

[1957] A "database" is a data storage system for systematically storing and managing learning progress and user information.

[1958] The "emotion engine" is a computer program that analyzes the user's facial expressions and tone of voice to collect and evaluate emotional data.

[1959] "Emotional data" is digital information that indicates the user's emotional state during learning (e.g., joy, confusion, concentration, etc.).

[1960] "Feedback" refers to information that refers to responses or advice provided based on a user's learning activities and level of understanding.

[1961] The present invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Detailed embodiments of the system are described below.

[1962] User Registration Process

[1963] Device:

[1964] When a user accesses the system for the first time, a new registration screen is displayed. The user enters the required information (e.g., name, email address, password, age, learning style) and presses the "Register" button. The terminal then sends the entered information to the server using the HTTPS protocol.

[1965] server:

[1966] The server validates the received user information. For example, it checks whether the email address format and password length are appropriate. If validation is successful, it creates a new user account in a database (e.g., MySQL) and saves the information. It also generates a confirmation message confirming registration and sends it to the device.

[1967] Device:

[1968] A message received from the server confirming registration is displayed in a pop-up window or dialog box. The message "Registration completed" is displayed on the screen.

[1969] Personalization Setup Process

[1970] Device:

[1971] The user accesses the login screen, enters their email address and password, and presses the "Login" button. After logging in, they are taken to the personalization settings screen. On the personalization settings screen, a questionnaire about their learning style is displayed, and the user enters their answers to the questions. Once they have completed their answers, they press the "Send" button, and the device sends the survey data to the server.

[1972] server:

[1973] The server collects and stores the received survey data. It then uses an artificial intelligence engine (e.g., K-means clustering) to analyze the survey data and generate a personalized learning plan for each user. The generated customized plan and related content list are then sent to the device.

[1974] Device:

[1975] The device displays the customized study plan for the user to review. For example, the study plan may be displayed in a calendar or list format.

[1976] Learning progress management and emotion recognition process

[1977] Device:

[1978] The user selects a particular learning content (e.g., "math fractions") and the device requests the selected content from the server.

[1979] server:

[1980] The server receives the request and sends the corresponding content (video learning material or interactive quizzes) to the device.

[1981] Device:

[1982] The device displays the transmitted content and the user begins learning. During learning, the device uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to collect emotional data. For example, the device's camera and microphone are used for facial expression recognition and voice tone analysis.

[1983] Emotion Engine:

[1984] The emotion engine collects, analyzes, and evaluates the user's emotional data (e.g., joy, confusion, concentration, etc.), thereby understanding the user's emotional state in real time while learning.

[1985] server:

[1986] The server adjusts the learning progress and difficulty of the content in real time based on the emotional data received from the emotion engine. For example, dynamic adjustments are made, such as lowering the difficulty level if the level of concentration is declining. In addition, feedback adapted to the user is generated and provided to the device. The user's level of understanding and progress are also recorded in a database.

[1987] Device:

[1988] The device displays the feedback received from the server and the recommended next learning content for the user to confirm. For example, an adaptive feedback message and a link to the next recommended learning content are displayed on the learning screen.

[1989] Specific examples

[1990] For example, when a fourth-grade elementary school user is learning "fractions in mathematics," the learning process proceeds as follows:

[1991] Device:

[1992] A user selects the "Math" category and chooses "Fractions" content. They watch video lessons and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?"). During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[1993] server:

[1994] The system receives quiz answer data and emotion data from the emotion engine. It judges whether the answers are correct or incorrect, and evaluates the user's level of understanding and concentration based on the emotion data. It recommends the next study content based on the evaluation results (e.g., "Study 1 / 3 - 1 / 6") and sends it to the device.

[1995] Device:

[1996] The user sees the next recommended content and continues learning.

[1997] As described above, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[1998] Example prompts for a generative AI model based on concrete examples

[1999] "I want to create a program that explains recent scientific and technological developments to users. In the program, I want to implement a system that personalizes topics that are likely to interest users, collects emotional data as the program learns, and adjusts the content accordingly. Could you please tell me the specific steps of the program to maximize user interest?"

[2000] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2001] Step 1: User access and information entry

[2002] User:

[2003] When a user accesses the system for the first time, a new registration screen appears. The user enters information such as their name, email address, password, age, and learning style, and then presses the "Register" button.

[2004] input:

[2005] Name, email address, password, age, learning style, etc.

[2006] output:

[2007] Registration information will be sent

[2008] Specific behavior:

[2009] Open the URL in your browser and the registration screen will appear.

[2010] The user fills out the form and clicks the "Register" button

[2011] Step 2: Submit your input

[2012] Device:

[2013] The terminal sends the information entered by the user to the server using the HTTPS protocol.

[2014] input:

[2015] Various information entered by the user

[2016] output:

[2017] Data sent from the device to the server as an HTTPS request

[2018] Specific behavior:

[2019] The information you enter is encrypted and sent to the server as an HTTPS request.

[2020] Step 3: Verify your information and create a user account

[2021] server:

[2022] The server validates the received user information, for example by checking the format of the email address and the password length, and if validation is successful, creates a new user account in a database (e.g. MySQL) and stores the information.

[2023] input:

[2024] User registration information received by the server

[2025] output:

[2026] User accounts that pass validation and are saved in the database

[2027] Registration completion message

[2028] Specific behavior:

[2029] Validate email address format and password length

[2030] If the validation is successful, create a new record in the database to save the information and generate a confirmation message.

[2031] Step 4: Displaying the registration completion message

[2032] Device:

[2033] A confirmation message sent from the server confirming registration completion is displayed in a pop-up window or dialog box.

[2034] input:

[2035] Registration completion message sent from the server

[2036] output:

[2037] Pop-up windows or dialog boxes that appear on the terminal screen

[2038] Specific behavior:

[2039] The device will display the received confirmation message on the screen.

[2040] Step 5: Log in

[2041] User:

[2042] The user accesses the login screen, enters their email address and password, and clicks the "Login" button.

[2043] input:

[2044] Login information (email address, password)

[2045] output:

[2046] Logging requests sent to the server

[2047] Specific behavior:

[2048] Enter your email address and password on the login screen and click the login button.

[2049] Step 6: Conduct a survey

[2050] Device:

[2051] After logging in, a personalized settings screen appears, and a questionnaire about learning styles is provided to the user. The user answers the questions and the questionnaire data is sent to the server.

[2052] input:

[2053] Learning style survey data

[2054] output:

[2055] Survey data sent to the server

[2056] Specific behavior:

[2057] Survey questions are displayed on the personalization settings screen, and the user enters their answers.

[2058] Step 7: Analyze the survey data

[2059] server:

[2060] The server receives, collects, and stores the survey data. It then uses an AI engine to analyze the survey data and generate a learning plan for each user.

[2061] input:

[2062] Survey data sent from the device

[2063] output:

[2064] Customized learning plans based on stored survey data and analysis results

[2065] Specific behavior:

[2066] Survey data is stored in a database, and an artificial intelligence engine analyzes the data and generates a learning plan.

[2067] Step 8: Offer a customized plan

[2068] server:

[2069] The generated customized plan and related content list are transmitted to the terminal.

[2070] input:

[2071] Customized learning plans and related content

[2072] output:

[2073] Study plans and related content sent to your device

[2074] Specific behavior:

[2075] The generated learning plan and content list are encoded in JSON format or similar and sent to the device.

[2076] Step 9: View your learning plan

[2077] Device:

[2078] The customized learning plan is displayed and reviewed by the user.

[2079] input:

[2080] Learning plans and related content sent from the server

[2081] output:

[2082] On-screen lesson plans and related content

[2083] Specific behavior:

[2084] Displaying learning plans in a calendar or list format on the user interface

[2085] (Application example 2)

[2086] 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."

[2087] In factory workplaces, there is a lack of means to recognize workers' emotional states in real time and improve work efficiency and safety based on that information. In particular, there is a need for a system that can accurately detect when a worker is feeling stressed or fatigued and provide appropriate feedback or suggest breaks. Providing appropriate feedback can also maximize worker performance.

[2088] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2089] In this invention, the server includes: means for receiving user information and creating a user account; means for generating a questionnaire regarding the user's learning style and interests and providing it to the user; means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user; means for providing the customized learning plan and related content to the user; means for recognizing the worker's emotional state using an emotion recognition engine that analyzes the user's facial expressions and tone of voice in real time while the user is working; means for analyzing the emotional data collected by the emotion recognition engine and generating optimal feedback for the worker; means for providing the generated feedback to the worker; means for collecting the user's learning progress and work progress in real time and recording them in a database; and means for recommending the next learning content or optimal break timing based on the learning progress and work progress. This makes it possible to provide appropriate support and feedback according to the worker's emotional state, thereby improving work efficiency and safety.

[2090] "User Information" is personal data such as your name, email address, password, age, and learning style.

[2091] A "user account" is account information that is generated based on user information, identifies the user, and enables access to the system.

[2092] A "survey" is a survey in the form of questions that gather information about a user's learning style and interests.

[2093] An "artificial intelligence engine" is an algorithm and software that analyzes learning data and survey results to generate a customized learning plan for each user.

[2094] A "customized study plan" is a personalized study plan generated based on a user's learning style and interests.

[2095] "Related Content" refers to the specific learning materials and resources included in your customized learning plan.

[2096] An "emotion recognition engine" is a combination of software and hardware that analyzes a worker's facial expressions and tone of voice to recognize their emotional state in real time.

[2097] "Emotion data" is information about the worker's emotional state (e.g., joy, confusion, concentration, etc.) collected by an emotion recognition engine.

[2098] "Feedback" refers to messages of support and suggestions provided depending on the worker's emotional state and work progress.

[2099] "Study progress status" is data that indicates how far the user has progressed in their studies, and the level of understanding and progress of the learning content.

[2100] "Work progress status" is data that indicates how far a worker has progressed in the work and the progress of the work content.

[2101] "Rest timing" refers to the appropriate timing for suggesting a rest to a worker depending on the worker's level of fatigue and emotional state.

[2102] The system for implementing this invention is mainly configured through the interaction between a server, a terminal, and a user. Specific components of the system and the processing flow thereof will be described in detail below.

[2103] 1. User Registration

[2104] Device:

[2105] When a user accesses the system for the first time, a new registration screen appears, where the user enters information such as name, email address, password, age, and learning style, and presses the "Register" button. The terminal then sends the entered information to the server.

[2106] server:

[2107] The received user information is verified, and if the verification is successful, a user account is created in the database. A registration completion confirmation message is also generated and sent to the terminal.

[2108] Device:

[2109] Displays the registration completion message received from the server.

[2110] 2. Personalization Settings

[2111] Device:

[2112] When a user logs in by entering their email address and password on the login screen, they are taken to the personalization settings screen, where survey questions are displayed, and when the user enters their answers and presses the send button, the survey data is sent to the server.

[2113] server:

[2114] The system collects and stores the received survey data, analyzes it using an artificial intelligence engine, and generates a learning plan for each user based on the analysis results, which is then sent to the device along with a list of related content.

[2115] Device:

[2116] The customized learning plan is displayed and reviewed by the user.

[2117] 3. Emotion recognition during work

[2118] Device:

[2119] As the user (worker) performs a task, the robot's onboard camera and microphone capture the worker's facial expressions and tone of voice in real time. The hardware used is an Intel RealSense camera and a Shure MV5 microphone.

[2120] Emotion Recognition Engine:

[2121] The captured data is analyzed using "Affectiva SDK," software specialized in emotion recognition, and the emotional status (e.g., focused, tired, stressed) is output.

[2122] 4. Emotional Data Analysis and Feedback Generation

[2123] server:

[2124] The output emotional status is sent to a server and analyzed by an evaluation module, which uses a neural network model built with TensorFlow to generate optimal feedback based on the emotional data.

[2125] Device:

[2126] The generated feedback is displayed on the robot's screen and provided audibly through the speaker. The user interface is built using Flutter.

[2127] 5. Feedback examples and prompts

[2128] As a concrete example, the following prompt sentence will be used:

[2129] Feedback prompt when worker is "stressed":

[2130] "Provide relaxation advice to workers who experience stress while working."

[2131] Feedback prompt when worker is "fatigued":

[2132] "Provide prompts to workers to take short breaks when they are fatigued."

[2133] Feedback prompt when worker is "focused":

[2134] "Provide messages to workers who are concentrating on their work that praise their efforts."

[2135] As described above, the present invention provides personalized support and feedback according to the user's emotional state, contributing to improved work efficiency and worker safety.

[2136] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2137] Step 1:

[2138] When a user accesses the system for the first time, the terminal displays a new registration screen. The user enters information such as name, email address, password, age, and learning style, and then presses the "Register" button. This entered information is sent from the terminal to the server.

[2139] Step 2:

[2140] The server verifies the received user information and creates a new user account in the database. If the user account is successfully created, the server generates a registration completion confirmation message and sends it to the terminal. The terminal receives this and displays a registration completion message to the user.

[2141] Step 3:

[2142] The user accesses the login screen and logs in by entering their email address and password. The device then transitions to a personalization settings screen, which displays the survey questions. When the user answers the questions and presses the send button, the survey data is sent from the device to the server.

[2143] Step 4:

[2144] The server collects and stores the survey data received from the device and analyzes it using an artificial intelligence engine. Based on the analysis results, it generates a customized learning plan for each user. This generated learning plan is sent to the device along with a list of related content. The device receives this and displays the customized learning plan to the user.

[2145] Step 5:

[2146] When a user (worker) starts working, the device's camera and microphone capture the worker's facial expressions and tone of voice in real time, and the captured data is sent from the device to an emotion recognition engine.

[2147] Step 6:

[2148] The emotion recognition engine uses the captured data to analyze the worker's emotional state, using the Affectiva SDK to output an emotional status (e.g., focused, tired, stressed).

[2149] Step 7:

[2150] The emotional status is sent to a server, which receives the emotional data and analyzes it using a neural network model built with TensorFlow. Based on the results of this analysis, optimal feedback is generated for the worker.

[2151] Step 8:

[2152] The generated feedback is sent from the server to the device. The device receives it and displays it on the robot's screen. It also provides audio feedback through the speaker. The user interface is built using Flutter.

[2153] Step 9:

[2154] The user (worker) checks the feedback provided by the device and takes appropriate action, such as taking a break or continuing to work. The feedback may include advice on relaxation, prompts to take short breaks, or messages praising efforts.

[2155] 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.

[2156] 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.

[2157] 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.

[2158] [Fourth embodiment]

[2159] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2160] 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.

[2161] 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).

[2162] 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.

[2163] 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.

[2164] 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).

[2165] 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.

[2166] 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.

[2167] 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.

[2168] 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.

[2169] 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.

[2170] 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.

[2171] 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."

[2172] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[2173] 1. User Registration

[2174] Device:

[2175] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[2176] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[2177] 3. The terminal sends the entered information to the server.

[2178] server:

[2179] 1. Receive and verify user information received from the terminal.

[2180] 2. If the information is valid, create a user account in the database and save the information.

[2181] 3. Generate a registration completion confirmation message and send it to the device.

[2182] Device:

[2183] 1. Display the registration completion message received from the server.

[2184] 2. Personalization Settings

[2185] Device:

[2186] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[2187] 2. After logging in, you will be taken to the personalization settings screen.

[2188] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[2189] 4. The device sends the survey data to the server.

[2190] server:

[2191] 1. Collect survey data received from the device.

[2192] 2. Analyze the survey data using an artificial intelligence engine module.

[2193] 3. Based on the analysis results, generate the optimal learning plan for each user.

[2194] 4. Send a customized learning plan and related content list to your device.

[2195] Device:

[2196] 1. Display a customized learning plan and allow the user to confirm and begin learning.

[2197] 3. Learning progress management

[2198] Device:

[2199] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[2200] 2. Watch and take video learning materials and interactive quizzes.

[2201] 3. Send the learning progress and quiz results to the server.

[2202] server:

[2203] 1. Collect and store learning progress data and quiz results received from devices in real time.

[2204] 2. Evaluate the user's level of understanding and recommend the next piece of content to study.

[2205] 3. Send the recommendation to your device.

[2206] Device:

[2207] 1. The user continues learning based on the next content received from the server.

[2208] Specific examples

[2209] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[2210] 1. Device:

[2211] 1. The user selects the "Math" category and chooses the "Fractions" content.

[2212] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[2213] 2. Server:

[2214] 1. Receive quiz answer data and determine whether it is correct or incorrect.

[2215] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[2216] 3. Send the recommendation to your device.

[2217] 3. Terminal:

[2218] 1. The user sees the next recommended content and continues learning.

[2219] In this way, the present invention provides a system that provides a personalized learning experience to users and improves their motivation to learn and their level of understanding.

[2220] The processing flow will be explained below.

[2221] Program processing steps

[2222] 1. User Registration

[2223] Step 1:

[2224] User: Open the new registration screen.

[2225] Step 2:

[2226] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[2227] Step 3:

[2228] Terminal: Sends the entered information to the server.

[2229] Step 4:

[2230] Server: Verifies the user information received from the device.

[2231] Step 5:

[2232] Server: If validation is successful, create a user account in the database and save the information.

[2233] Step 6:

[2234] Server: Generates a registration completion confirmation message and sends it to the device.

[2235] Step 7:

[2236] Terminal: Display the registration completion message received from the server.

[2237] 2. Personalization Settings

[2238] Step 1:

[2239] User: Access the login screen and log in by entering your email address and password.

[2240] Step 2:

[2241] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[2242] Step 3:

[2243] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[2244] Step 4:

[2245] User: Answers survey questions and hits submit.

[2246] Step 5:

[2247] Terminal: Sends the response data to the server.

[2248] Step 6:

[2249] Server: Collects and stores the survey data received from the device.

[2250] Step 7:

[2251] Server: Analyzes the survey data using an artificial intelligence engine module.

[2252] Step 8:

[2253] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[2254] Step 9:

[2255] Server: Sends the created customized plan to the device.

[2256] Step 10:

[2257] On your device: The customized learning plan is displayed and reviewed by the user.

[2258] 3. Learning progress management

[2259] Step 1:

[2260] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[2261] Step 2:

[2262] Device: Requests the selected content from the server.

[2263] Step 3:

[2264] Server: Receives the request and sends the corresponding content (video learning materials or interactive quizzes) to the device.

[2265] Step 4:

[2266] Device: The transmitted content is displayed and the user begins learning.

[2267] Step 5:

[2268] User: Watches video instructional material and then takes an interactive quiz.

[2269] Step 6:

[2270] Device: Sends quiz answer data to the server.

[2271] Step 7:

[2272] Server: Receives the answer data and determines whether it is correct or not.

[2273] Step 8:

[2274] Server: Evaluates the user's understanding and stores their learning progress in a database.

[2275] Step 9:

[2276] Server: Generates recommendations for what to learn next and sends them to the device.

[2277] Step 10:

[2278] On your device: Shows the next recommended learning content so the user can continue learning.

[2279] This process step allows users to enjoy a personalized learning experience and receive real-time feedback to effectively progress their learning.

[2280] Example 1

[2281] 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."

[2282] Modern educational systems struggle to provide personalized learning experiences tailored to individual users' learning styles and progress. Many existing systems simply provide uniform learning materials, preventing variations in learning efficiency and outcomes for each user. Furthermore, they lack the ability to track users' progress and comprehension in real time and provide appropriate feedback, potentially delaying learning improvement.

[2283] 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.

[2284] In this invention, the server includes means for receiving user information and creating a user account, means for generating questions related to the user's learning style and interests and providing them to the user, means for analyzing the results of the questions using an artificial intelligence engine and generating a customized study plan for each user, means for providing the user with the customized study plan and related content, means for collecting the user's study progress in real time and recording it in a database, means for recommending next study content based on the study progress, means for the user to select specific content and provide a device for study, means for collecting the study progress and quiz results and evaluating comprehension, and means for recommending next study content based on the evaluation results. This makes it possible to provide a personalized learning experience for each user and receive appropriate feedback in real time according to each user's individual progress and comprehension.

[2285] "User information" refers to data about individual users registered in the system, including name, email address, password, age, learning style, etc.

[2286] A "user account" is account information required for a user to use the system, and is stored in a database based on the user information.

[2287] A "survey" is a data collection tool in the form of questions to gather information about a user's learning style and interests.

[2288] An "artificial intelligence engine" is a computer program that contains algorithms and models to analyze collected data and understand trends and patterns for each user.

[2289] A "customized study plan" is a study plan that is individually generated by an artificial intelligence engine based on the user's learning style and interests.

[2290] "Relevant content" refers to the specific materials and activities included in your customized learning plan.

[2291] "Study progress" is information about the progress of a user as they progress through their studies, and includes study time, progress of content, quiz results, and the like.

[2292] A "database" is a storage system for storing data such as user information, learning progress, and learning plans.

[2293] "Recommendation" is information generated by the server to indicate what the user should learn next based on their learning progress.

[2294] "Device" refers to the terminal (e.g., PC, tablet, smartphone) that a user uses to select and operate learning content.

[2295] "Feedback" means information about improvements or advice provided based on a user's understanding and learning progress.

[2296] The present invention is an educational system designed to provide users with a personalized learning experience. The system uses a server, a terminal, and an artificial intelligence engine to collect and analyze user information. Specific embodiments for implementing the present invention are described below.

[2297] 1. User Registration

[2298] Terminal

[2299] 1. When a user first accesses the system, they are presented with a registration screen. Examples include interfaces such as a web browser or a mobile application.

[2300] 2. The user enters information such as name, email address, password, age, learning style, etc. and presses the "Register" button.

[2301] 3. The terminal sends the entered information to the server.

[2302] server

[2303] 1. The server verifies the user information received from the device, specifically checking the format of the email address and the security of the password.

[2304] 2. If the validation is successful, the server creates a user account in the database and stores the entered information.

[2305] 3. The server generates a confirmation message confirming the completion of registration and sends it to the terminal.

[2306] Terminal

[2307] 1. The terminal displays the registration completion message received from the server.

[2308] 2. Personalization Settings

[2309] Terminal

[2310] 1. The user accesses the login screen and logs in by entering their registered email address and password.

[2311] 2. After logging in, the user will be taken to the personalization settings screen.

[2312] 3. Answer the survey questions on the personalization settings screen and press the submit button.

[2313] 4. The device sends the survey data to the server.

[2314] server

[2315] 1. The server collects the survey data received from the terminal.

[2316] 2. Analyze the survey data using an artificial intelligence engine and generate the optimal study plan for each user.

[2317] 3. Sending a customized learning plan and related content to your device.

[2318] Terminal

[2319] 1. The user checks the customized study plan on their device and begins studying.

[2320] 3. Learning progress management

[2321] Terminal

[2322] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[2323] 2. Users watch and take video tutorials and interactive quizzes.

[2324] 3. The device sends the learning progress and quiz results to the server.

[2325] server

[2326] 1. The server collects and stores learning progress data and quiz results received from the device in real time.

[2327] 2. The server evaluates the user's level of understanding and recommends what to study next.

[2328] 3. Send the recommendation to your device.

[2329] Terminal

[2330] 1. The device displays the next recommended content received from the server.

[2331] 2. The user reviews the recommended content and continues learning.

[2332] Specific examples

[2333] Below is a specific example of a fourth-grade elementary school student learning about "fractions in mathematics."

[2334] 1. Terminal

[2335] 1. The user selects the "Math" category and chooses the "Fractions" content.

[2336] 2. Users watch video instructional material and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?").

[2337] 2. Server

[2338] 1. The server receives the quiz answer data and determines whether it is correct or incorrect.

[2339] 2. Evaluate the user's level of understanding and recommend what to learn next (e.g., learning 1 / 3 - 1 / 6).

[2340] 3. Send the recommendation to your device.

[2341] 3. Terminal

[2342] 1. The user sees the next recommended content and continues learning.

[2343] The invention personalizes the user's learning experience through generative AI models, assessing progress in real time and providing appropriate feedback and learning content.

[2344] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2345] Step 1:

[2346] Enter and send user information (terminal)

[2347] When a user accesses the system for the first time, a new registration screen is displayed. The user enters information such as name, email address, password, age, and learning style. After this information is entered, the terminal displays instructions to the user to press the "Register" button. When the user presses the "Register" button, the entered information is sent from the terminal to the server. The sent information is input data, and is transferred to the server in a structured format (for example, JSON format).

[2348] Step 2:

[2349] User information verification and storage (server)

[2350] The server receives the user information from the terminal. It validates the received information and checks the format of the email address and the security of the password. For example, it uses regular expression patterns to verify whether the email address is in the correct format. If the validation is successful, the server creates a new user account in the database and saves the entered information. The saved data is treated as new user account data. The server generates a confirmation message and sends it to the terminal.

[2351] Step 3:

[2352] Notification of registration completion (device)

[2353] The terminal displays the registration completion message received from the server. For example, a message such as "Registration completed" is displayed on the user's screen. This allows the user to confirm that the account registration has been completed successfully.

[2354] Step 4:

[2355] Perform login operation (terminal)

[2356] The user accesses the login screen and enters their registered email address and password. This input data is collected by the device, and an instruction to press the "Login" button is displayed. When the user presses the "Login" button, the input data is sent to the server as authentication data.

[2357] Step 5:

[2358] User authentication and transition to personalization setting screen (server)

[2359] The server verifies the login information received from the device, for example, by checking whether the entered email address and password match the information in the database. If authentication is successful, the server sends the data of the personalized settings screen to the device along with a message indicating that the user has successfully logged in.

[2360] Step 6:

[2361] Implementing and sending personalized settings (device)

[2362] The user moves to the personalized settings screen and answers the questions in the survey. For example, a question such as "What is your preferred method of learning?" is displayed. When the user enters their answer and presses the send button, the terminal sends the survey data to the server. This data is the user's learning style information.

[2363] Step 7:

[2364] Analysis of survey data and generation of learning plans (server)

[2365] The server collects the survey data received from the device. This data is analyzed using an artificial intelligence engine to generate an optimal learning plan for each user. For example, an algorithm analyzes learning patterns based on the user's responses and creates an individual learning plan. The generated learning plan and related content list are then sent from the server to the device.

[2366] Step 8:

[2367] Display customized plan (device)

[2368] The terminal displays the customized learning plan received from the server. The user confirms the plan and begins learning. The displayed content includes optimal learning content tailored to each individual user.

[2369] Step 9:

[2370] Selection and implementation of learning content (device)

[2371] The user selects specific content (e.g., "Math Fractions") from the learning start screen. The user then watches and completes video materials and interactive quizzes. Specific operations include, for example, playing videos and answering quizzes.

[2372] Step 10:

[2373] Sending learning progress data (device)

[2374] Learning progress and quiz results are collected continuously. The device sends this data to the server in real time. The data includes study time, correct / incorrect answers, and the user's progress.

[2375] Step 11:

[2376] Data collection and understanding assessment (server)

[2377] The server collects and stores learning progress data and quiz results received from the device in real time. Based on the collected data, the server uses an artificial intelligence engine to evaluate the user's level of understanding. For example, it analyzes which content the user is struggling with.

[2378] Step 12:

[2379] Recommending and sending next learning content (server)

[2380] Based on the comprehension assessment, the server recommends the next learning content. The generated recommendation is sent to the device. For example, if the user has not made much progress on a particular topic, the server will recommend supplementary learning on that topic.

[2381] Step 13:

[2382] Displaying recommended content and continuing learning (device)

[2383] The device displays the next recommended content received from the server. The user confirms the recommended content and starts the next learning session. A new learning loop begins based on the displayed content.

[2384] This series of processing steps effectively provides the user with a personalized learning experience.

[2385] (Application example 1)

[2386] 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."

[2387] Traditional educational systems struggle to provide an optimized learning plan for each user, often resulting in a decline in motivation to learn and a lack of understanding. Even in brick-and-mortar stores, it's difficult to provide appropriate products based on a user's learning style, resulting in a lack of support for users to select the learning materials that are best suited to them. Furthermore, there's no recommendation of the next learning material based on the progress or results of use after purchase, resulting in a lack of continuous learning support.

[2388] 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.

[2389] In this invention, the server includes means for receiving user attribute information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for providing the user with the customized learning plan and related content, means for collecting the user's learning progress in real time and recording it in a database, means for recommending the next content to study based on the learning progress, means for providing optimal product information based on the user's learning style in the store, and means for recording the progress and usage results of the learning materials purchased by the user and recommending the next learning material to purchase, thereby enabling learning support and product recommendations optimized for each user.

[2390] "User attribute information" is information specific to each individual user, such as the user's name, email address, age, learning style, etc.

[2391] The "means for creating a user account" is the process by which an individual user profile is generated on the server based on information provided by the user and stored in a database.

[2392] The "means for generating a questionnaire and providing it to the user" is a mechanism for creating a question form to understand the user's learning style and interests, and allowing the user to answer the question form.

[2393] An "artificial intelligence engine" is a software module that performs machine learning and data analysis, used to analyze large amounts of data and customize learning plans.

[2394] A "customized learning plan" is a set of learning content optimized for each individual user based on the results of a survey and user attribute information.

[2395] "Related content" refers to educational resources such as learning materials, videos, interactive quizzes and activities that are suggested based on the user's study plan.

[2396] "Means for collecting learning progress in real time and recording it in a database" refers to the process of collecting data generated in real time as the user progresses with their learning and managing that data in an integrated manner.

[2397] The "means for recommending the next learning content" is a mechanism that presents the user with the appropriate next learning content based on collected learning progress data.

[2398] "Means for providing optimal product information based on a user's learning style in a physical store" is a system for suggesting optimal learning materials and products in a physical store according to the user's learning style.

[2399] "Means for recording the progress and usage results of purchased learning materials and recommending the next learning material to purchase" refers to the process of tracking the usage and learning progress of the user's purchased learning materials and recommending the next learning material that will be required.

[2400] The present invention relates to a system for providing a user with a personalized learning experience. Specific embodiments of the system are described below.

[2401] System Configuration

[2402] 1. User Registration

[2403] Device:

[2404] When a user first accesses the system, a new registration screen is displayed.

[2405] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[2406] The terminal transmits the input information to the server.

[2407] server:

[2408] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[2409] A registration completion confirmation message is generated and sent to the terminal.

[2410] Device:

[2411] Displays the registration completion message received from the server.

[2412] 2. Personalization Settings

[2413] Device:

[2414] The user accesses the login screen and logs in by entering their registered email address and password.

[2415] After logging in, you will be taken to the personalization settings screen.

[2416] Answer the questions in the survey and send the answer data to the server.

[2417] server:

[2418] The received survey data is collected and analyzed using an artificial intelligence engine.

[2419] Based on the analysis results, a customized learning plan is generated for each user.

[2420] Send a customized study plan and related content list to your device.

[2421] Device:

[2422] The customized learning plan is displayed, and the user can confirm it and begin learning.

[2423] 3. Learning progress management

[2424] Device:

[2425] The user selects a particular piece of content (e.g., "math fractions").

[2426] View and take video tutorials and interactive quizzes.

[2427] Send learning progress and quiz results to the server.

[2428] server:

[2429] Collect and store incoming learning progress data and quiz results in real time.

[2430] Evaluate the user's level of understanding and recommend the next content to study.

[2431] The recommendation is sent to the device.

[2432] Device:

[2433] The user continues learning based on the next recommended content received from the server.

[2434] 4. Physical store applications

[2435] Device:

[2436] The app is used in physical stores to provide optimal product information based on the user's learning style.

[2437] For example, an education store might recommend learning materials based on a user's learning style (e.g., visual learner).

[2438] server:

[2439] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[2440] Recommendations are sent to devices in physical stores.

[2441] Device:

[2442] The user checks the next educational material to be purchased in the store and continues purchasing.

[2443] Hardware and software used

[2444] Hardware: smartphones, tablets, servers.

[2445] software:

[2446] Python: Used for application development.

[2447] Requests library: Sends HTTP requests and communicates with the API.

[2448] Flask / Django: A framework suitable for implementing server-side APIs.

[2449] AI Engine: An artificial intelligence module for learning style analysis and recommendations.

[2450] Specific examples

[2451] For example, when a 10-year-old elementary school student accesses the system for the first time, they register by entering basic information. They then answer a questionnaire to set their own learning style. They use the app in the store to find recommended learning materials and manage their learning progress with those materials within the app. The AI ​​engine then recommends the next learning material they need.

[2452] Prompt Sentence Examples

[2453] Example: User information

[2454] {name: "User name", email: "Email address", password: "Password", age: Age, learning_style: "Learning style"}

[2455]

[2456] Example: Survey response data

[2457] ["What is your favorite subject?": "Math", "What is your favorite way of learning?": "Visual aids"]

[2458] In this way, a personalized learning experience is provided for each user.

[2459] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2460] Step 1:

[2461] User Registration

[2462] Device:

[2463] When a user first accesses the system, a new registration screen is displayed.

[2464] The user enters attribute information such as name, email address, age, learning style, etc., and presses the "Register" button.

[2465] server:

[2466] The user attribute information received from the terminal is verified, and a user account is created in the database and the information is saved.

[2467] A registration completion confirmation message is generated and sent to the terminal.

[2468] Device:

[2469] Displays the registration completion message received from the server.

[2470] Input: User demographic information (name, email address, age, learning style)

[2471] Data processing: verification, account creation

[2472] Output: Registration complete message

[2473] Step 2:

[2474] Personalization Settings

[2475] Device:

[2476] The user accesses the login screen and logs in by entering their registered email address and password.

[2477] After logging in, you will be taken to the personalization settings screen.

[2478] Answer the questions in the survey and send the answer data to the server.

[2479] server:

[2480] The received survey data is collected and analyzed using an artificial intelligence engine.

[2481] Based on the analysis results, a customized learning plan is generated for each user.

[2482] Send a customized study plan and related content list to your device.

[2483] Device:

[2484] The customized learning plan is displayed, and the user can confirm it and begin learning.

[2485] Input: Survey response data

[2486] Data processing: AI analysis and learning plan generation

[2487] Output: Customized study plan, related content list

[2488] Step 3:

[2489] Learning progress management

[2490] Device:

[2491] The user selects a particular piece of content (e.g., "math fractions").

[2492] View and take video tutorials and interactive quizzes.

[2493] server:

[2494] Collect and store learning progress data and quiz results received from devices in real time.

[2495] Evaluate the user's level of understanding and recommend the next content to study.

[2496] The recommendation is sent to the device.

[2497] Device:

[2498] The user continues learning based on the next recommended content received from the server.

[2499] Input: Learning progress data, quiz results

[2500] Data processing: Comprehension assessment, next learning content recommendation

[2501] Output: Recommended content

[2502] Step 4:

[2503] Application in physical stores

[2504] Device:

[2505] The app can be used in physical stores to provide optimal product information based on the user's learning style. For example, in a physical store that sells educational products, the app can recommend learning materials based on the user's learning style.

[2506] server:

[2507] It records the progress and results of the user's purchased learning materials and recommends the next learning material to purchase.

[2508] Recommendations are sent to devices in physical stores.

[2509] Device:

[2510] The user checks the next educational material to be purchased in the store and continues purchasing.

[2511] Input: Progress data of purchased teaching materials, usage results

[2512] Data processing: Recording results and recommending next learning materials

[2513] Output: Recommendations for next course material purchases

[2514] These steps make it possible to provide personalized learning support and product recommendations for each user.

[2515] 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.

[2516] This invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Specific embodiments of this system are shown below.

[2517] 1. User Registration

[2518] Device:

[2519] 1. When a user accesses the system for the first time, a new registration screen will be displayed.

[2520] 2. The user enters the required information (name, email address, password, age, learning style, etc.) and presses the "Register" button.

[2521] 3. The terminal sends the entered information to the server.

[2522] server:

[2523] 1. Verify the user information received from the terminal.

[2524] 2. If the validation is successful, create a user account in the database and save the information.

[2525] 3. Generate a registration completion confirmation message and send it to the device.

[2526] Device:

[2527] 1. Display the registration completion message received from the server.

[2528] 2. Personalization Settings

[2529] Device:

[2530] 1. The user accesses the login screen and logs in by entering their email address and password.

[2531] 2. After logging in, you will be taken to the personalization settings screen.

[2532] 3. Display the survey questions on the personalization settings screen and the user enters their answers.

[2533] 4. The user answers the survey questions and presses the submit button.

[2534] 5. The device sends the survey data to the server.

[2535] server:

[2536] 1. Collect and save survey data received from the device.

[2537] 2. Analyze the survey data using an artificial intelligence engine module.

[2538] 3. Generate a learning plan for each user based on the analysis results, creating a customized learning plan and related content list.

[2539] 4. Send the created customized plan to the device.

[2540] Device:

[2541] 1. The customized learning plan is displayed and confirmed by the user.

[2542] 3. Learning progress management and emotion recognition

[2543] Device:

[2544] 1. The user selects a specific piece of content (e.g., "Fractions in Math") from the start screen.

[2545] 2. Request the content from the server.

[2546] server:

[2547] 1. Receives a request and sends the corresponding content (video learning material or interactive quiz) to the device.

[2548] Device:

[2549] 1. The submitted content is displayed and the user begins learning.

[2550] 2. Use the emotion engine to analyze the user's facial expressions and tone of voice in real time while watching video materials or taking interactive quizzes.

[2551] Emotion Engine:

[2552] 1. Collect user emotional data (e.g., joy, confusion, concentration, etc.).

[2553] 2. Analyze the collected emotional data and evaluate the emotional state during learning.

[2554] server:

[2555] 1. Adjust learning progress and content difficulty in real time based on emotional data received from the emotion engine.

[2556] 2. Generate optimal feedback based on the user's emotional state and send it to the device.

[2557] 3. Furthermore, the user's level of understanding and progress are stored in a database.

[2558] Device:

[2559] 1. The user sees the feedback and recommended next learning content received from the server and confirms it.

[2560] Specific examples

[2561] For example, if a fourth-grade user is learning "fractions in math," they might proceed as follows:

[2562] 1. Device:

[2563] 1. The user selects the "Math" category and chooses the "Fractions" content.

[2564] 2. Watch the video material and then answer an interactive quiz (e.g., "1 / 2 + 1 / 4 = ?").

[2565] 3. During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[2566] 2. Server:

[2567] 1. Receive quiz answer data and emotion data from the emotion engine.

[2568] 2. The answers are judged to be correct or incorrect, and the user's level of understanding and concentration is evaluated based on emotional data.

[2569] 3. Recommend the next learning content based on the evaluation results (e.g., learning from 1 / 3 to 1 / 6) and send it to the device.

[2570] 3. Terminal:

[2571] 1. The user sees the next recommended content and continues learning.

[2572] In this way, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[2573] The processing flow will be explained below.

[2574] Specific process steps for carrying out the invention

[2575] 1. User Registration

[2576] Step 1:

[2577] User: Open the new registration screen.

[2578] Step 2:

[2579] User: Enter information such as name, email address, password, age, learning style, etc. and press the "Register" button.

[2580] Step 3:

[2581] Terminal: Sends the entered information to the server.

[2582] Step 4:

[2583] Server: Verifies the user information received from the device.

[2584] Step 5:

[2585] Server: If validation is successful, create a user account in the database and save the information.

[2586] Step 6:

[2587] Server: Generates a registration completion confirmation message and sends it to the device.

[2588] Step 7:

[2589] Terminal: Display the registration completion message received from the server.

[2590] 2. Personalization Settings

[2591] Step 1:

[2592] User: Access the login screen and log in by entering your email address and password.

[2593] Step 2:

[2594] Device: Login authentication is performed, and if authentication is successful, you will be taken to the personalization settings screen.

[2595] Step 3:

[2596] Device: Display survey questions on the personalization settings screen and have the user enter their answers.

[2597] Step 4:

[2598] User: Answers survey questions and hits submit.

[2599] Step 5:

[2600] Terminal: Sends the response data to the server.

[2601] Step 6:

[2602] Server: Collects and stores the survey data received from the device.

[2603] Step 7:

[2604] Server: Analyzes the survey data using an artificial intelligence engine module.

[2605] Step 8:

[2606] Server: Generates a learning plan for each user based on the analysis results, and creates a customized learning plan and related content list.

[2607] Step 9:

[2608] Server: Sends the created customized plan to the device.

[2609] Step 10:

[2610] On your device: The customized learning plan is displayed and reviewed by the user.

[2611] 3. Learning progress management and emotion recognition

[2612] Step 1:

[2613] User: Selects specific content (e.g., "Fractions in Math") from the start screen.

[2614] Step 2:

[2615] Device: Sends a content request to the server.

[2616] Step 3:

[2617] Server: Receives the request and sends the corresponding content (video learning materials, interactive quizzes, etc.) to the device.

[2618] Step 4:

[2619] Device: The transmitted content is displayed and the user begins learning.

[2620] Step 5:

[2621] User: Watches video instructional material and then takes an interactive quiz.

[2622] Step 6:

[2623] Device: Sends quiz answer data to the server.

[2624] Step 7:

[2625] Emotion Engine: Analyzes the user's facial expressions and tone of voice to collect emotional data while watching videos and taking quizzes.

[2626] Step 8:

[2627] Server: Receives quiz answer data and emotion data.

[2628] Step 9:

[2629] Server: Determines whether the answers are correct or not, and evaluates the user's level of understanding and emotional state.

[2630] Step 10:

[2631] Server: Based on the level of comprehension and emotion data, generates the next learning content and feedback and sends it to the device.

[2632] Step 11:

[2633] On the device: The next recommended content and feedback received from the server are displayed and confirmed by the user.

[2634] Specific examples

[2635] For example, if a fourth grade user is learning "Fractions in Math":

[2636] Step 1:

[2637] User: Log in, select the "Math" category and choose the "Fractions" content.

[2638] Step 2:

[2639] Device: Sends a content request to the server.

[2640] Step 3:

[2641] Server: Sends video materials and quizzes to the device.

[2642] Step 4:

[2643] Device: Display the video material and start watching.

[2644] Step 5:

[2645] Emotion Engine: Analyzes the user's facial expressions and tone of voice in real time to collect emotional data.

[2646] Step 6:

[2647] Users: Watch a video and then answer a quiz (e.g., "1 / 2 + 1 / 4 = ?").

[2648] Step 7:

[2649] Device: Sends quiz answer data to the server.

[2650] Step 8:

[2651] Server: Determines whether the quiz is correct or incorrect and analyzes emotional data to evaluate the user's level of understanding and emotional state.

[2652] Step 9:

[2653] Server: Based on the evaluation results, it generates the next recommendation (e.g., "Study 1 / 3 - 1 / 6") and feedback.

[2654] Step 10:

[2655] On the device: The next content or feedback received from the server is displayed and confirmed by the user.

[2656] This process step allows users to enjoy a personalized learning experience and real-time feedback, making learning both efficient and engaging.

[2657] Example 2

[2658] 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."

[2659] Conventional learning systems have difficulty providing adaptive feedback based on individual users' emotions and level of understanding. As a result, the effectiveness of learning is limited, making it difficult to maintain user motivation. Furthermore, because it is not possible to grasp learning progress in real time or adjust the level of difficulty, there is a problem that the learning experience becomes uniform. A system that can solve these issues and provide a more personalized learning experience is needed.

[2660] 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.

[2661] In this invention, the server includes means for receiving user information and creating a user account, means for generating a questionnaire regarding the user's learning style and interests and providing it to the user, means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user, means for collecting the user's learning progress in real time and recording it in a database, means for collecting emotion data using an emotion engine that analyzes the user's facial expressions and tone of voice while learning and adjusting the learning progress and difficulty level of the content in real time, and means for generating and providing feedback adapted to the user. This makes it possible to provide a detailed learning experience based on the emotions and level of understanding of each individual user.

[2662] A "user account" is a data set containing individual identification information for accessing a system.

[2663] "Learning style" is information that refers to the most effective learning method or tendency of a user.

[2664] A "survey" is a data collection tool in the form of questions that gather information about a user's learning style and interests.

[2665] An "artificial intelligence engine" is a computer program that analyzes a user's data and generates a customized study plan.

[2666] A "customized learning plan" is a learning plan that is tailored to a user's individual learning style and level of understanding.

[2667] "Related content" refers to study materials and reference materials provided based on the user's study plan.

[2668] "Study progress" is information indicating the progress and results of the user's learning activities.

[2669] A "database" is a data storage system for systematically storing and managing learning progress and user information.

[2670] The "emotion engine" is a computer program that analyzes the user's facial expressions and tone of voice to collect and evaluate emotional data.

[2671] "Emotional data" is digital information that indicates the user's emotional state during learning (e.g., joy, confusion, concentration, etc.).

[2672] "Feedback" refers to information that refers to responses or advice provided based on a user's learning activities and level of understanding.

[2673] The present invention relates to a system that provides a more personalized learning experience by incorporating an emotion engine that recognizes the user's emotions. Detailed embodiments of the system are described below.

[2674] User Registration Process

[2675] Device:

[2676] When a user accesses the system for the first time, a new registration screen is displayed. The user enters the required information (e.g., name, email address, password, age, learning style) and presses the "Register" button. The terminal then sends the entered information to the server using the HTTPS protocol.

[2677] server:

[2678] The server validates the received user information. For example, it checks whether the email address format and password length are appropriate. If validation is successful, it creates a new user account in a database (e.g., MySQL) and saves the information. It also generates a confirmation message confirming registration and sends it to the device.

[2679] Device:

[2680] A message received from the server confirming registration is displayed in a pop-up window or dialog box. The message "Registration completed" is displayed on the screen.

[2681] Personalization Setup Process

[2682] Device:

[2683] The user accesses the login screen, enters their email address and password, and presses the "Login" button. After logging in, they are taken to the personalization settings screen. On the personalization settings screen, a questionnaire about their learning style is displayed, and the user enters their answers to the questions. Once they have completed their answers, they press the "Send" button, and the device sends the survey data to the server.

[2684] server:

[2685] The server collects and stores the received survey data. It then uses an artificial intelligence engine (e.g., K-means clustering) to analyze the survey data and generate a personalized learning plan for each user. The generated customized plan and related content list are then sent to the device.

[2686] Device:

[2687] The device displays the customized study plan for the user to review. For example, the study plan may be displayed in a calendar or list format.

[2688] Learning progress management and emotion recognition process

[2689] Device:

[2690] The user selects a particular learning content (e.g., "math fractions") and the device requests the selected content from the server.

[2691] server:

[2692] The server receives the request and sends the corresponding content (video learning material or interactive quizzes) to the device.

[2693] Device:

[2694] The device displays the transmitted content and the user begins learning. During learning, the device uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to collect emotional data. For example, the device's camera and microphone are used for facial expression recognition and voice tone analysis.

[2695] Emotion Engine:

[2696] The emotion engine collects, analyzes, and evaluates the user's emotional data (e.g., joy, confusion, concentration, etc.), thereby understanding the user's emotional state in real time while learning.

[2697] server:

[2698] The server adjusts the learning progress and difficulty of the content in real time based on the emotional data received from the emotion engine. For example, dynamic adjustments are made, such as lowering the difficulty level if the level of concentration is declining. In addition, feedback adapted to the user is generated and provided to the device. The user's level of understanding and progress are also recorded in a database.

[2699] Device:

[2700] The device displays the feedback received from the server and the recommended next learning content for the user to confirm. For example, an adaptive feedback message and a link to the next recommended learning content are displayed on the learning screen.

[2701] Specific examples

[2702] For example, when a fourth-grade elementary school user is learning "fractions in mathematics," the learning process proceeds as follows:

[2703] Device:

[2704] A user selects the "Math" category and chooses "Fractions" content. They watch video lessons and then answer interactive quizzes (e.g., "1 / 2 + 1 / 4 = ?"). During the learning process, the emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[2705] server:

[2706] The system receives quiz answer data and emotion data from the emotion engine. It judges whether the answers are correct or incorrect, and evaluates the user's level of understanding and concentration based on the emotion data. It recommends the next study content based on the evaluation results (e.g., "Study 1 / 3 - 1 / 6") and sends it to the device.

[2707] Device:

[2708] The user sees the next recommended content and continues learning.

[2709] As described above, the present invention realizes a system that recognizes a user's emotions in real time and further personalizes the learning experience accordingly, thereby providing a more effective and engaging learning environment.

[2710] Example prompts for a generative AI model based on concrete examples

[2711] "I want to create a program that explains recent scientific and technological developments to users. In the program, I want to implement a system that personalizes topics that are likely to interest users, collects emotional data as the program learns, and adjusts the content accordingly. Could you please tell me the specific steps of the program to maximize user interest?"

[2712] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2713] Step 1: User access and information entry

[2714] User:

[2715] When a user accesses the system for the first time, a new registration screen appears. The user enters information such as their name, email address, password, age, and learning style, and then presses the "Register" button.

[2716] input:

[2717] Name, email address, password, age, learning style, etc.

[2718] output:

[2719] Registration information will be sent

[2720] Specific behavior:

[2721] Open the URL in your browser and the registration screen will appear.

[2722] The user fills out the form and clicks the "Register" button

[2723] Step 2: Submit your input

[2724] Device:

[2725] The terminal sends the information entered by the user to the server using the HTTPS protocol.

[2726] input:

[2727] Various information entered by the user

[2728] output:

[2729] Data sent from the device to the server as an HTTPS request

[2730] Specific behavior:

[2731] The information you enter is encrypted and sent to the server as an HTTPS request.

[2732] Step 3: Verify your information and create a user account

[2733] server:

[2734] The server validates the received user information, for example by checking the format of the email address and the password length, and if validation is successful, creates a new user account in a database (e.g. MySQL) and stores the information.

[2735] input:

[2736] User registration information received by the server

[2737] output:

[2738] User accounts that pass validation and are saved in the database

[2739] Registration completion message

[2740] Specific behavior:

[2741] Validate email address format and password length

[2742] If the validation is successful, create a new record in the database to save the information and generate a confirmation message.

[2743] Step 4: Displaying the registration completion message

[2744] Device:

[2745] A confirmation message sent from the server confirming registration completion is displayed in a pop-up window or dialog box.

[2746] input:

[2747] Registration completion message sent from the server

[2748] output:

[2749] Pop-up windows or dialog boxes that appear on the terminal screen

[2750] Specific behavior:

[2751] The device will display the received confirmation message on the screen.

[2752] Step 5: Log in

[2753] User:

[2754] The user accesses the login screen, enters their email address and password, and clicks the "Login" button.

[2755] input:

[2756] Login information (email address, password)

[2757] output:

[2758] Logging requests sent to the server

[2759] Specific behavior:

[2760] Enter your email address and password on the login screen and click the login button.

[2761] Step 6: Conduct a survey

[2762] Device:

[2763] After logging in, a personalized settings screen appears, and a questionnaire about learning styles is provided to the user. The user answers the questions and the questionnaire data is sent to the server.

[2764] input:

[2765] Learning style survey data

[2766] output:

[2767] Survey data sent to the server

[2768] Specific behavior:

[2769] Survey questions are displayed on the personalization settings screen, and the user enters their answers.

[2770] Step 7: Analyze the survey data

[2771] server:

[2772] The server receives, collects, and stores the survey data. It then uses an AI engine to analyze the survey data and generate a learning plan for each user.

[2773] input:

[2774] Survey data sent from the device

[2775] output:

[2776] Customized learning plans based on stored survey data and analysis results

[2777] Specific behavior:

[2778] Survey data is stored in a database, and an artificial intelligence engine analyzes the data and generates a learning plan.

[2779] Step 8: Offer a customized plan

[2780] server:

[2781] The generated customized plan and related content list are transmitted to the terminal.

[2782] input:

[2783] Customized learning plans and related content

[2784] output:

[2785] Study plans and related content sent to your device

[2786] Specific behavior:

[2787] The generated learning plan and content list are encoded in JSON format or similar and sent to the device.

[2788] Step 9: View your learning plan

[2789] Device:

[2790] The customized learning plan is displayed and reviewed by the user.

[2791] input:

[2792] Learning plans and related content sent from the server

[2793] output:

[2794] On-screen lesson plans and related content

[2795] Specific behavior:

[2796] Displaying learning plans in a calendar or list format on the user interface

[2797] (Application example 2)

[2798] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2799] In factory workplaces, there is a lack of means to recognize workers' emotional states in real time and improve work efficiency and safety based on that information. In particular, there is a need for a system that can accurately detect when a worker is feeling stressed or fatigued and provide appropriate feedback or suggest breaks. Providing appropriate feedback can also maximize worker performance.

[2800] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2801] In this invention, the server includes: means for receiving user information and creating a user account; means for generating a questionnaire regarding the user's learning style and interests and providing it to the user; means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user; means for providing the customized learning plan and related content to the user; means for recognizing the worker's emotional state using an emotion recognition engine that analyzes the user's facial expressions and tone of voice in real time while the user is working; means for analyzing the emotional data collected by the emotion recognition engine and generating optimal feedback for the worker; means for providing the generated feedback to the worker; means for collecting the user's learning progress and work progress in real time and recording them in a database; and means for recommending the next learning content or optimal break timing based on the learning progress and work progress. This makes it possible to provide appropriate support and feedback according to the worker's emotional state, thereby improving work efficiency and safety.

[2802] "User Information" is personal data such as your name, email address, password, age, and learning style.

[2803] A "user account" is account information that is generated based on user information, identifies the user, and enables access to the system.

[2804] A "survey" is a survey in the form of questions that gather information about a user's learning style and interests.

[2805] An "artificial intelligence engine" is an algorithm and software that analyzes learning data and survey results to generate a customized learning plan for each user.

[2806] A "customized study plan" is a personalized study plan generated based on a user's learning style and interests.

[2807] "Related Content" refers to the specific learning materials and resources included in your customized learning plan.

[2808] An "emotion recognition engine" is a combination of software and hardware that analyzes a worker's facial expressions and tone of voice to recognize their emotional state in real time.

[2809] "Emotion data" is information about the worker's emotional state (e.g., joy, confusion, concentration, etc.) collected by an emotion recognition engine.

[2810] "Feedback" refers to messages of support and suggestions provided depending on the worker's emotional state and work progress.

[2811] "Study progress status" is data that indicates how far the user has progressed in their studies, and the level of understanding and progress of the learning content.

[2812] "Work progress status" is data that indicates how far a worker has progressed in the work and the progress of the work content.

[2813] "Rest timing" refers to the appropriate timing for suggesting a rest to a worker depending on the worker's level of fatigue and emotional state.

[2814] The system for implementing this invention is mainly configured through the interaction between a server, a terminal, and a user. Specific components of the system and the processing flow thereof will be described in detail below.

[2815] 1. User Registration

[2816] Device:

[2817] When a user accesses the system for the first time, a new registration screen appears, where the user enters information such as name, email address, p...

Claims

1. means for receiving user information and creating a user account; means for generating and providing to the user a questionnaire regarding the user's learning style and interests; a means for analyzing the results of the questionnaire using an artificial intelligence engine and generating a customized learning plan for each user; means for providing the customized learning plan and related content to the user; A means for collecting the user's learning progress in real time and recording it in a database; means for recommending content to be studied next based on the learning progress; A system including:

2. The system according to claim 1 , further comprising means for evaluating the user's level of understanding based on the user's learning progress and providing feedback according to the level of understanding.

3. The system of claim 1 , wherein the customized study plan and associated content includes interactive quizzes and activities.

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