system

Abstract

Provide a system. [Solution] A means for the user to enter necessary information and register; A means for recommending appropriate learning content based on the user's registration information; means for collecting and analyzing user learning progress data; means for generating an individualized optimized learning plan based on the analyzed data; means for providing the generated study plan to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, there are still regions where educational opportunities are unequal, resulting in the problem of many children being unable to receive an appropriate education. This widens educational disparities in some regions, increasing the risk of limiting future opportunities. Furthermore, existing online education systems are limited to general progress management and the provision of standardized learning plans, and are unable to fully address individual learning needs. Given this background, there is a need for the development of an education system that can provide high-quality, individually optimized learning plans to all children, regardless of region. [Means for solving the problem]

[0005] The present invention solves these problems with a system that includes a means for a user to input necessary information and register, a means for recommending appropriate learning content based on the user's registration information, a means for collecting and analyzing the user's learning progress data, a means for generating an individually optimized learning plan based on the analyzed data, and a means for providing the generated learning plan to the user. Specifically, by providing a means for a user to input learning progress and a means for generating and providing feedback messages based on the user's learning progress, learning progress can be managed in real time, providing an optimized learning experience for each individual user. Furthermore, by providing a means for providing learning content via streaming or download, wide access is possible regardless of region or infrastructure. Furthermore, by providing a means for securely storing and encrypting user information and learning data, data privacy and security are ensured. This contributes to equalizing education and making it possible to provide high-quality education to children living in any region.

[0006] "User registration information" refers to personal information and basic information about learning provided by users when they register with the system.

[0007] "Learning content" refers to educational materials and teaching materials provided for users to study.

[0008] "Study progress data" refers to data that indicates the progress and results a user has achieved through their learning activities.

[0009] An "individually optimized learning plan" is a learning schedule and recommended learning material plan customized to the user's individual learning needs.

[0010] "Streaming" is a method by which users can view learning content in real time over the Internet.

[0011] "Download" is a method by which users can save learning content to their devices and access it offline.

[0012] A "feedback message" is a message that indicates an evaluation or an area for improvement that is generated based on the user's learning progress.

[0013] The "database" is a data management system for safely storing user registration information, learning progress data, and the like.

[0014] "Encryption" is the art of transforming information to ensure data privacy and security.

[0015] An "AI engine" is an artificial intelligence technology that analyzes user data and generates individually optimized learning plans. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

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

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, learning content recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[0038] User Registration

[0039] 1. User Registration

[0040] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters their name, age, grade, location, email address, password, etc.

[0041] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[0042] Users: Receive a confirmation email and click the link provided to activate their account.

[0043] Educational content distribution

[0044] 2. Content Recommendation

[0045] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[0046] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[0047] 3. Content Access

[0048] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[0049] Device: Providing selected content for users to access in streaming or download format.

[0050] Learning progress management

[0051] 4. Collecting progress data

[0052] User: After studying, enter study progress data (which parts have been studied, test results, etc.) into the terminal.

[0053] Terminal: Sends the entered data to the server.

[0054] 5. Data analysis and feedback

[0055] Server: Analyzes the collected data and tracks progress. For example, if the percentage of correct answers to a particular math problem is low, it provides feedback on that task.

[0056] Terminal: Displays feedback messages sent by the server to the user.

[0057] Providing individually optimized learning plans

[0058] 6. Generate personalized learning plans

[0059] Server: Based on the collected progress data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[0060] Device: Displays the generated learning plan to the user.

[0061] 7. Implementing your learning plan

[0062] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[0063] Example: When a junior high school student uses the system

[0064] If the user is a junior high school student, the following is a specific example.

[0065] 1. User Registration

[0066] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, the user provides information such as 13 years old, living in Tokyo, and in the second year of junior high school.

[0067] Server: Stores the received information in a database and sends a confirmation email.

[0068] User: Receives a confirmation email and clicks on the link to complete registration.

[0069] 2. Content Recommendation

[0070] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[0071] Devices: Show recommended content to middle school students.

[0072] User: Selects and watches a mathematics geometry lesson from the recommended content.

[0073] 3. Collecting and analyzing progress data

[0074] User: After solving a geometry exercise, enter the result into the terminal.

[0075] Server: Analyzes the input data and detects low levels of understanding of specific problems.

[0076] Terminal: Feedback tells the user that additional geometry practice problems are needed.

[0077] 4. Providing personalized learning plans

[0078] Server: The AI ​​engine uses the user's progress data to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch.

[0079] Device: Display the new learning plan to the user.

[0080] Users: Follow the plan and solve geometry exercises every day to deepen their understanding.

[0081] In this way, the present invention achieves equalization and quality improvement of education by strictly managing users' learning progress and providing individually optimized educational experiences.

[0082] The processing flow will be explained below.

[0083] Step 1:

[0084] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​affiliation, email address, and password, and presses the submit button.

[0085] Step 2:

[0086] The terminal transmits the user's input information to the server.

[0087] Step 3:

[0088] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[0089] Step 4:

[0090] The user receives a confirmation email and clicks the link in the email to activate their account.

[0091] Step 5:

[0092] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[0093] Step 6:

[0094] The server generates a list of selected learning content and sends it to the terminal.

[0095] Step 7:

[0096] The terminal displays the received content list to the user, who can then click to select the content of interest.

[0097] Step 8:

[0098] The terminal provides the selected learning content in streaming or download format for the user to access.

[0099] Step 9:

[0100] After the user finishes the study session, they input information such as what they studied and test results into the terminal.

[0101] Step 10:

[0102] The terminal transmits the input learning progress data to the server.

[0103] Step 11:

[0104] The server analyzes the received learning progress data, for example determining if a particular task has not been understood and therefore requires special attention from the user.

[0105] Step 12:

[0106] Based on the analysis results, the server generates a feedback message for the user, pointing out the user's weaknesses and areas for improvement.

[0107] Step 13:

[0108] The terminal displays the feedback message sent from the server to the user.

[0109] Step 14:

[0110] The user periodically (e.g., on weekends) enters the study time and results into the terminal.

[0111] Step 15:

[0112] The terminal sends the input data to the server.

[0113] Step 16:

[0114] The server analyzes the accumulated data using an AI engine and generates a study plan optimized for the user (daily study time, tasks to focus on, study materials to use, etc.).

[0115] Step 17:

[0116] The server sends the generated learning plan to the device.

[0117] Step 18:

[0118] The device displays the new study plan to the user, who then follows the plan to study.

[0119] Step 19:

[0120] The user repeatedly inputs the learning progress into the terminal.

[0121] Step 20:

[0122] The server collects and analyzes data, and then adjusts feedback and learning plans to continuously optimize the user's learning experience.

[0123] In this way, the system manages the user's learning progress in real time and provides a personalized educational experience.

[0124] Example 1

[0125] 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."

[0126] In conventional online education systems, users are often provided with uniform learning content, making it difficult to provide an educational experience optimized for individual needs and progress. Furthermore, there is a lack of feedback based on the user's learning progress or the generation of individually optimized learning plans, which makes it difficult to provide effective learning support.

[0127] 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.

[0128] In this invention, the server includes a means for a user to input and register necessary information, a means for recommending appropriate educational materials based on the user's registration information, a means for collecting and analyzing the user's learning progress data, a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, and a means for creating prompt sentences when generating a learning plan from the user's progress data using a generative AI model. This enables a customized educational experience for each user, not only improving learning efficiency but also making it possible to provide an optimal learning plan based on each user's individual progress.

[0129] "User" refers to an individual who uses the online education system to learn.

[0130] "Information input" refers to the act of a user registering personal information such as name, age, grade, location, email address, and password into the system.

[0131] "Educational Materials" refers to educational content, such as teaching materials and video lessons, designed to enhance a user's learning.

[0132] "Recommendation" refers to the act of providing appropriate educational materials based on the user's registration information.

[0133] "Study progress data" refers to information indicating the user's learning progress, such as their learning status and test results.

[0134] "Analysis" refers to the act of analyzing collected learning progress data using data analysis tools and AI models.

[0135] "Individually optimized learning plan" refers to a learning plan that includes an optimal learning schedule and learning materials, which is generated based on each user's individual progress.

[0136] "Generation" refers to the act of creating a new learning plan based on collected data and analysis results.

[0137] "Providing" refers to the act of notifying or presenting the generated study plan to the user.

[0138] A "generative AI model" refers to an artificial intelligence model that automatically generates learning plans and feedback based on user progress data.

[0139] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[0140] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, educational material recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[0141] User Registration

[0142] Users access the system via a web browser or smartphone app and navigate to the registration page. They enter required information such as their name, age, grade, location, email address, and password. This data is sent to the server via their device and stored in a secure database. The server then sends a registration confirmation email to the user's email address, and the user activates their account by clicking the link in the email.

[0143] Recommend educational materials

[0144] The server retrieves the user's registration information from the database and uses a generative AI model to recommend appropriate educational materials. For example, it automatically selects geometry or chemistry video lessons based on the user's grade, location, and interests. The recommended educational materials are sent to the device, which displays the list to the user. The user can then select the content they want to study from the list.

[0145] Learning progress management

[0146] After using educational materials, users input their learning progress data (study content, test results, etc.) into their device. The device then sends this data to a server. The server collects the progress data and analyzes it using analytical tools and AI models. For example, if the accuracy rate for a particular math problem is low, a feedback message is generated based on the results.

[0147] The server sends the generated feedback message to the terminal, and the terminal displays the feedback to the user, so that the user can work on the next learning activity based on the feedback.

[0148] Providing individually optimized learning plans

[0149] The server uses a generative AI model to generate an individually optimized study plan based on the collected progress data. For example, it uses a prompt such as, "Based on the user's progress data, please generate a study plan for next week. Please include key topics, recommended learning materials, and study time." The generated study plan is stored in a database and sent to the user's device. The device displays the plan to the user, and the user proceeds with their studies according to the plan.

[0150] Specific examples

[0151] For example, when a second-year junior high school student uses the system, they follow the steps below. The user registers necessary information such as their name, age, grade, location, email address, and password. The server recommends educational materials for mathematics and science based on the registered information. From the recommended content, the user selects and watches a geometry video lesson.

[0152] After studying, the user enters progress data into the device and sends it to the server, which analyzes it and detects insufficient understanding of a particular problem, generating a feedback message to the user via the device indicating the need for additional geometry practice problems.

[0153] The server then uses an AI engine to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch, allowing users to take their next learning steps based on scientific evidence.

[0154] In this way, the system provides a customized educational experience for each user, improving learning efficiency.

[0155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0156] Step 1: Go to the user registration page

[0157] The user opens a web browser or smartphone app and accesses the system's user registration page.

[0158] The terminal receives the user's request and displays a registration page.

[0159] Input: User access request

[0160] Output: Display of registration page

[0161] What happens: The device interprets the URL and displays the appropriate registration page.

[0162] Step 2: Enter your information

[0163] The user enters necessary information such as name, age, grade, location, email address, and password.

[0164] The terminal temporarily stores the entered information in real time and displays it on the form.

[0165] Input: Personal information entered by the user

[0166] Output: Personal information displayed in the input form

[0167] Specific operation: The device receives user input and displays it in real time.

[0168] Step 3: Send information

[0169] The user clicks the send button to submit the information they have entered.

[0170] The terminal transmits the user's input information to the server.

[0171] Input: User submission requests and input information

[0172] Output: User information sent to the server

[0173] Specific operation: Create an HTTP request and send it to the server along with the input information.

[0174] Step 4: Receiving and storing information

[0175] The server checks the received information and stores it in a secure database, either an SQL database or a NoSQL database.

[0176] Input: User information sent from the device

[0177] Output: User information stored in the database

[0178] Specific operation: The server verifies the received information and stores it in a database using an SQL query, etc.

[0179] Step 5: Send a confirmation email

[0180] The server will send a registration confirmation email to the user's email address, which will contain an account activation link.

[0181] The user will receive a confirmation email and will need to click on the link to activate their account.

[0182] Input: User's email address

[0183] Output: A confirmation email sent to the user.

[0184] Specific operation: The server connects to the mail server and sends a confirmation email.

[0185] Step 6: Information Acquisition for Content Recommendation

[0186] The server retrieves the user's registration information from a database, often using an SQL query.

[0187] Input: User registration information

[0188] Output: Retrieved user information

[0189] What happens: The server executes an SQL query to get the required information from the database.

[0190] Step 7: Running the Content Recommendation Engine

[0191] Based on the registration information, the server runs a recommendation engine (e.g., a machine learning model) to select appropriate learning content, taking into account the user's grade, location, interests, etc.

[0192] Input: User registration information

[0193] Output: A list of recommended educational materials

[0194] What it does: It uses an algorithm to select the most relevant content for users.

[0195] Step 8: Submit your recommended content

[0196] The server transmits the recommended content list to the terminal.

[0197] Input: A list of recommended educational materials

[0198] Output: Content list sent to device

[0199] Specific operation: Create an HTTP response and send the content list to the terminal.

[0200] Step 9: Displaying Content

[0201] The device displays the received content list to the user, such as a list of "geometry video lessons" and "chemistry experiment videos."

[0202] Input: Content list sent from the server

[0203] Output: Content list displayed to the user

[0204] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[0205] Step 10: Input training data

[0206] After using the educational materials, the user inputs learning progress data (learning content, test results, etc.) into the terminal.

[0207] Input: User's learning progress data

[0208] Output: Progress data temporarily stored on the device

[0209] Specific behavior: The device receives user input and displays it in real time.

[0210] Step 11: Send data

[0211] The device sends the entered progress data to the server, often by POSTing the data using an API endpoint.

[0212] Input: User's learning progress data

[0213] Output: Progress data sent to the server

[0214] Specific behavior: Makes an API request and sends progress data to the server.

[0215] Step 12: Analyze progress data

[0216] The server analyzes the received data, such as test accuracy and study time, using analytical tools and AI models.

[0217] Input: Received learning progress data

[0218] Output: Analysis results

[0219] Specific actions: Analyze progress data using data analysis tools and AI models.

[0220] Step 13: Feedback Generation

[0221] The server generates feedback based on the analysis results. If the level of understanding of a particular problem is low, it generates a feedback message recommending additional study materials or practice problems.

[0222] Input: Analysis results

[0223] Output: Feedback message

[0224] Specific operation: Perform text processing to generate a feedback message based on the analysis results.

[0225] Step 14: Submit your feedback

[0226] The server sends a feedback message to the terminal.

[0227] Input: Feedback message

[0228] Output: Feedback message sent to the terminal

[0229] Specific operation: Creates an HTTP response and sends a feedback message to the terminal.

[0230] Step 15: Viewing feedback

[0231] The device displays the received feedback message to the user, including specific improvements and recommendations for further learning.

[0232] Input: Feedback message sent by the server

[0233] Output: Feedback that is displayed to the user

[0234] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[0235] Step 16: Run the AI ​​Engine

[0236] The server uses the collected progress data to run a generative AI model and create prompts to generate an individually optimized study plan. For example, the server uses the prompt, "Based on the user's progress data, please generate a study plan for next week. Please include key tasks, recommended learning materials, and study time."

[0237] Input: Progress data and prompt text

[0238] Output: Personalized learning plan

[0239] Specific operation: Call the AI ​​engine and obtain the generated learning plan.

[0240] Step 17: Save and submit your study plan

[0241] The server stores the generated study plan in a database and transmits it to the user's terminal.

[0242] Input: Generated lesson plan

[0243] Output: Study plan stored in the database and study plan sent to the device

[0244] Specific behavior: Saves a learning plan using an SQL query and creates and sends an HTTP response.

[0245] Step 18: View your learning plan

[0246] The device displays the generated study plan to the user, which includes a daily study schedule and the learning materials to be used.

[0247] Input: Study plan sent from the server

[0248] Output: The learning plan that is displayed to the user

[0249] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[0250] Step 19: Implementing your learning plan

[0251] The user progresses through the displayed study plan, for example by working through newly recommended math exercises.

[0252] Input: Study Plan

[0253] Output: User's learning progress

[0254] Specific actions: The user actually performs the learning activities according to the learning plan.

[0255] summary

[0256] This system aims to provide an individually optimized educational experience through collaboration between users, devices, and servers. By utilizing generative AI models and prompts, it is possible to generate and provide efficient and effective learning plans.

[0257] (Application example 1)

[0258] 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."

[0259] Conventional online education systems struggle to provide an individually optimized learning experience, and are unable to provide content tailored to each user's unique learning needs. As a result, learning effectiveness declines and user satisfaction declines. Furthermore, the inability to track learning progress in real time and provide appropriate feedback can impair learning efficiency.

[0260] 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.

[0261] In this invention, the server includes a means for the user to input necessary information and register, a means for recommending appropriate study content based on the user's registered information, and a means for collecting and analyzing the user's study progress data, thereby providing the user with an individually optimized learning experience and improving the effectiveness of their learning.

[0262] The server further includes a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, a means for having the user input information via an application installed on the smartphone terminal, and collecting and analyzing progress data, and a means for using the collected progress data to generate a feedback message using an AI model and notifying the user. This makes it possible to grasp learning progress in real time and provide effective feedback, thereby improving the user's learning efficiency.

[0263] "User" refers to an individual who uses the online education system.

[0264] "Required information" refers to specific data required for registration, such as name, age, grade, location, email address, and password.

[0265] "Means for Registration" refers to the process by which a user enters required information, saves that information in the system, and activates an account.

[0266] "Learning Content" means educational materials, such as instructional materials, video lessons, and exercises, that are provided to assist users in their learning.

[0267] "Recommendation means" refers to a method of selecting and presenting appropriate learning content based on the user's registration information.

[0268] "Study progress data" refers to data such as the progress, grades, and test results achieved by the user during their studies.

[0269] "Means for collecting and analyzing" refers to a method for collecting learning progress data from users and analyzing the data to evaluate the state of learning.

[0270] "Individually optimized learning plan" refers to a learning plan that is optimized based on each user's learning progress data.

[0271] "Means of generation" refers to the method of creating an individually optimized learning plan based on the collected data.

[0272] The "means for providing" refers to a means for displaying the generated learning plan to the user and encouraging them to carry it out.

[0273] A "smartphone terminal" refers to a mobile communication device that can connect to the Internet and on which applications can be installed and used.

[0274] "Application" refers to software that users install and use on their smartphone devices.

[0275] "Means for collecting and analyzing progress data" refers to a method in which a user inputs learning progress data through an application and the data is sent to a server for analysis.

[0276] An "AI model" refers to an artificial intelligence algorithm that performs inference and optimization based on collected data and generates feedback and learning plans.

[0277] "Feedback message" refers to advice and evaluation provided based on the user's learning progress data.

[0278] "Means for notifying" refers to a method for notifying the user of the generated feedback message.

[0279] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. The system is operated using an application installed on a user's smartphone terminal.

[0280] First, the user downloads and installs the online education application on their smartphone. The user enters the required information (e.g., name, age, grade, location, email address, password, etc.) to register. The server stores the user's registration information in a secure database and sends the user an email to confirm their registration. The user receives the confirmation email and clicks the link contained in it to activate their account.

[0281] After registration is complete, the server recommends appropriate learning content (such as teaching materials and video lessons) based on the user's registration information. This content is customized according to the user's grade level and interests. The recommended learning content is displayed to the user through an application on their smartphone, and the user can select from the content and begin watching.

[0282] As the user progresses with their studies, they input their learning progress data (such as which learning materials they have viewed and test results) into the application. The device then sends the input progress data to the server. The server analyzes the collected progress data and generates an individually optimized learning plan based on the analysis results. For example, if the user's understanding of a particular subject is insufficient, the plan will include additional learning content related to that subject.

[0283] Furthermore, the system uses an AI model to generate feedback messages based on the collected progress data and notifies the user of the generated feedback. The feedback messages include suggestions for additional practice in areas where understanding is insufficient and instructions on what to do next. This allows users to understand their own learning status in real time and study effectively.

[0284] As a concrete example, consider a middle school student watching a geometry video lesson and working on the exercises. The user opens the application and enters their progress data. For example, if they score 55 points on a geometry exercise, they enter that score. The server analyzes this score and uses an AI model to generate a feedback message to the user saying, "You need more practice in geometry. Please complete additional exercises."

[0285] Below are some examples of prompts used in this system:

[0286] "You are in eighth grade math class. Please solve a geometry problem. Enter the score you received for the problem."

[0287] In this way, the system of the present invention can closely manage the user's learning progress and provide a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[0288] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0289] Step 1:

[0290] A user downloads and installs an online education application on their smartphone. They register an account by entering required information such as their name, age, grade, location, email address, and password. The server receives the information, stores it in a secure database, and sends a confirmation email to the user. The user receives the confirmation email and clicks on the link included to activate their account.

[0291] Input: Registration information such as name, age, grade, location, email address, and password

[0292] Output: Verification email sent and account activated

[0293] Step 2:

[0294] The server recommends learning content (such as teaching materials and video lessons) based on the user's registration information. The recommended content is customized according to the user's grade and interests. The device displays the recommended learning content to the user, and the user selects the content they want to view from the displayed list.

[0295] Input: Registration information, grade, interests

[0296] Output: Display of customized learning content list

[0297] Step 3:

[0298] The user watches the selected learning content and proceeds with their learning. After studying, the user enters their learning progress data (viewed content, test results, etc.) into the device. The device then sends the entered progress data to the server.

[0299] Input: Learning progress data (content viewed, test results)

[0300] Output: Sending progress data

[0301] Step 4:

[0302] The server analyzes the collected progress data. For example, if a user scores low in a particular subject or topic, the server can identify the cause. Based on the analysis, the server generates a personalized, optimized learning plan, which includes focused learning content and additional practice questions.

[0303] Input: Progress data (content viewed, test results)

[0304] Output: Personalized learning plan

[0305] Step 5:

[0306] The server runs the generative AI model based on the collected progress data and generates feedback messages for the user. This feedback includes specific advice, such as "You need more practice on geometry. Please solve additional problems." The generated feedback messages are sent to the user's device.

[0307] Input: Progress data, execution results of generative AI model

[0308] Output: Feedback message

[0309] Step 6:

[0310] Users follow the feedback messages displayed on their device to progress through their learning according to an individually optimized learning plan. By using the device's application, they can improve their learning effectiveness by working on newly recommended content and practice problems.

[0311] Input: Feedback message, personalized learning plan

[0312] Output: Improved user learning progress

[0313] In this way, the system of the present invention closely manages the user's learning progress and provides a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[0314] 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.

[0315] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has functions for user registration, recommending learning content, collecting and analyzing learning progress data, and generating and providing personalized and optimized learning plans, as well as an emotion engine that recognizes the user's emotions.

[0316] User Registration

[0317] 1. User Registration

[0318] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters the required information such as name, age, grade, location, email address, and password, and presses the submit button.

[0319] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[0320] Users: Receive a confirmation email and click the link provided to activate their account.

[0321] Educational content distribution

[0322] 2. Content Recommendation

[0323] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[0324] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[0325] 3. Content Access

[0326] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[0327] Device: Providing selected content for user access in streaming or download format.

[0328] Learning progress management

[0329] 4. Collecting progress data

[0330] User: After learning, the user inputs learning progress data (which parts they have learned, test results, etc.) into the device. In addition, the emotion engine collects the user's emotion data from the facial recognition camera and sensors.

[0331] Terminal: Sends the input learning progress data and emotion data to the server.

[0332] 5. Data analysis and feedback

[0333] Server: Analyzes the collected data (learning progress data and emotion data) to understand the progress of the user. For example, if the correct answer rate for a particular math problem is low and the user is feeling confused or stressed, it determines that the user needs special attention.

[0334] Server: Based on the analysis results, it generates feedback messages for the user, pointing out their weaknesses and areas for improvement. Based on the emotional data, it also provides messages of encouragement and relaxation.

[0335] Terminal: Displays feedback messages sent by the server to the user.

[0336] Providing individually optimized learning plans

[0337] 6. Generate personalized learning plans

[0338] Server: Based on the collected progress and emotion data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[0339] Device: Displays the generated learning plan to the user.

[0340] 7. Implementing your learning plan

[0341] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[0342] Example: When a junior high school student uses the system

[0343] If the user is a junior high school student, the following is a specific example.

[0344] 1. User Registration

[0345] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, they provide information such as age 13, local resident, and second-year junior high school student.

[0346] Server: Stores the received information in a database and sends a confirmation email.

[0347] User: Receives a confirmation email and clicks on the link to complete registration.

[0348] 2. Content Recommendation

[0349] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[0350] Devices: Show recommended content to middle school students.

[0351] User: Selects and watches a mathematics geometry lesson from the recommended content.

[0352] 3. Collecting and analyzing progress and emotion data

[0353] User: After solving a geometry exercise, the user enters the results and emotions (e.g., stress or accomplishment) into the device.

[0354] Server: Analyzes the input data and detects the level of understanding and emotional state of the specific problem.

[0355] Terminal: Feedback informs the user that additional geometry practice problems are needed, and also displays encouraging messages to reduce stress.

[0356] 4. Providing personalized learning plans

[0357] Server: An AI engine uses progress and emotional data to generate a study plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks to relax.

[0358] Device: Display the new learning plan to the user.

[0359] Users: Follow a plan and solve geometry exercises every day to improve their understanding and manage stress.

[0360] In this way, the present invention manages the user's emotional state in real time along with their learning progress, providing a personalized and optimized educational experience, thereby enabling more effective learning.

[0361] The processing flow will be explained below.

[0362] Step 1:

[0363] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​residence, email address, and password, and presses the submit button.

[0364] Step 2:

[0365] The terminal transmits the user's input information to the server.

[0366] Step 3:

[0367] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[0368] Step 4:

[0369] The user receives a confirmation email and clicks the link in the email to activate their account.

[0370] Step 5:

[0371] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[0372] Step 6:

[0373] The server generates a list of selected learning content and sends it to the terminal.

[0374] Step 7:

[0375] The terminal displays the received content list to the user, who can then click to select the content of interest.

[0376] Step 8:

[0377] The terminal provides the selected learning content in streaming or download format for the user to access.

[0378] Step 9:

[0379] After completing a study session, the user inputs their learning progress and emotions into the device. Emotional data is collected using a facial recognition camera and sensors.

[0380] Step 10:

[0381] The terminal transmits the input learning progress data and emotion data to the server.

[0382] Step 11:

[0383] The server analyzes the received learning progress data. For example, if the percentage of correct answers to a particular math problem is low, it can be determined that the user is struggling with that subject.

[0384] Step 12:

[0385] The server uses an emotion engine to analyze the user's emotion data, for example, to detect if the user is feeling confused or stressed.

[0386] Step 13:

[0387] The server generates a feedback message based on the learning progress data and emotion data, such as "It seems you found this part difficult. Let's start with an easier problem next time."

[0388] Step 14:

[0389] The terminal displays the feedback message sent from the server to the user.

[0390] Step 15:

[0391] Users periodically (e.g., on weekends) enter their study time and results into the device, and emotional data is also collected.

[0392] Step 16:

[0393] The terminal sends the input data to the server.

[0394] Step 17:

[0395] The server analyzes the accumulated data using an AI engine and generates an optimized study plan for each user, including daily study time, tasks to focus on, and study materials to use.

[0396] Step 18:

[0397] The server sends the generated learning plan to the device.

[0398] Step 19:

[0399] The device displays the new study plan to the user, who then follows the plan to study.

[0400] Step 20:

[0401] The server continuously collects and analyzes learning progress and emotional data, and adjusts feedback and learning plans accordingly to continually optimize the user's learning experience.

[0402] In this way, the system manages the user's learning progress and emotional state in real time, providing a personalized educational experience.

[0403] Example 2

[0404] 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."

[0405] In conventional online education systems, it was difficult to grasp individual users' learning progress and emotions in real time and provide optimal learning plans. As a result, it was not possible to provide an effective and continuous learning experience for users, which could lead to a decline in learning effectiveness. In addition, there was a lack of means to provide feedback based on learning progress and changes in emotions, making it difficult to maintain users' motivation to learn.

[0406] 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.

[0407] In this invention, the server includes means for the user to input necessary information and register, means for recommending appropriate study content based on the user's registration information, means for collecting the user's study progress data and emotion data, means for analyzing the collected data and generating an individually optimized study plan, means for providing the generated study plan to the user, and means for displaying the provided feedback message to the user, thereby making it possible to provide the user with an individually optimized study plan and feedback messages based on their emotions.

[0408] "User" refers to an individual learner who uses the online education system.

[0409] "Information" or "Required Information" refers to personal information such as name, age, grade, location, email address, and password that a User provides to register with the System.

[0410] "Registration" refers to the process by which a User enters required information into the System to create and activate an account.

[0411] "Server" refers to a computer system that stores and processes user information, progress data, learning content, etc.

[0412] "Learning Content" refers to educational resources such as teaching materials, video lessons, and textbooks provided on the System.

[0413] "Recommendation" refers to the process of selecting and presenting the most appropriate learning content based on the user's registration information and learning history.

[0414] "Study progress data" refers to records of what parts a user has studied, the content of their studies, test results, and so on.

[0415] "Emotional data" refers to data on a user's emotional state based on facial expressions and physical reactions collected using facial recognition cameras and sensors.

[0416] "Analysis" refers to the process of analyzing the collected learning progress data and emotional data to evaluate the user's learning status and emotional state.

[0417] "Individually optimized learning plan" refers to a plan that includes a learning schedule and assignments that are optimal for a specific user, generated based on the user's learning progress data and emotional data.

[0418] "Feedback message" refers to a message of evaluation or encouragement that is generated based on the analysis results and provided to the user.

[0419] "Providing" refers to the process by which the server presents the generated learning plan and feedback messages to the user.

[0420] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. This system has the following functions: user registration, learning content recommendation, collection and analysis of learning progress data and emotion data, generation and provision of personalized and optimized learning plans, and provision of feedback messages.

[0421] 1. User Registration

[0422] Device: The user uses a device (such as a PC or smartphone) to open the system's registration page in a web browser or application. The user enters the required information (name, age, grade, location, email address, password, etc.) and clicks the "Register" button.

[0423] Server: Receives user information sent from the device and stores it in a secure database. The stored data is protected by encryption technology. A registration confirmation email is then automatically generated and sent to the user's email address.

[0424] User: The user receives a confirmation email and clicks the link in the email to activate their account.

[0425] 2. Educational content recommendations

[0426] Server: Based on the information registered by the user (age, grade, interests, etc.), the algorithm runs and automatically selects the most suitable learning content. The selected content is customized according to the user's grade, region, and interests. An AI-based recommendation system is used.

[0427] Device: Receives the selected content recommendation list and displays it to the user, who can then select the content they want to study from the list.

[0428] 3. Content Access

[0429] User: The user clicks to select the content they want to watch (e.g., a math video lesson) from the recommendations list.

[0430] Device: Providing selected content for user access in streaming or download format.

[0431] 4. Collecting learning progress data

[0432] User: After studying, the user enters progress data (which part they studied, what they learned, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[0433] Terminal: Sends the input learning progress data and emotion data to the server.

[0434] 5. Data analysis and feedback

[0435] Server: The AI ​​engine analyzes the collected data (learning progress data and emotional data) and evaluates the user's learning progress and emotional state. For example, if the user has a low success rate on a particular math problem and feels confused or stressed, the server will urge the user to pay special attention.

[0436] Server: Generates feedback messages based on the analysis results. The feedback messages include points out the user's weaknesses and areas for improvement, as well as messages of encouragement or relaxation based on emotional data.

[0437] Terminal: Receives feedback messages sent from the server and displays them to the user.

[0438] 6. Generate personalized learning plans

[0439] Server: The AI ​​engine generates an optimal study plan for each user based on the progress and emotion data collected. The plan includes daily study time, priority tasks, and study materials to use.

[0440] Device: Displays the generated learning plan to the user.

[0441] 7. Implementing your learning plan

[0442] User: The user follows the displayed study plan, for example, working on new suggested math exercises.

[0443] Specific examples

[0444] For example, if a user is a 13-year-old junior high school student, he or she accesses the system and registers by entering his or her name, age, grade, location, email address, password, etc. After receiving a confirmation email and completing registration, the server will recommend math and science learning content for eighth-grade students based on the user's information. The user can then select the video lesson they want to watch from the recommended list.

[0445] After learning, the user inputs their progress data and emotional state into the device. For example, they input the results of solving geometry exercises and the stress or sense of accomplishment they felt while solving the problems. The server analyzes this data and generates feedback to the user, such as the need for additional geometry exercises, or encouraging messages to reduce stress.

[0446] The generated feedback is displayed on the device, and the AI ​​engine generates a personalized learning plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks for relaxation. By following this plan, users can maximize their learning and reduce mental strain.

[0447] Prompt Sentence Examples

[0448] "Enter math progress data and emotional data to suggest new learning plans."

[0449] As described above, the present invention can achieve more effective learning by managing a user's learning progress and emotional state in real time and providing an individually optimized educational experience.

[0450] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0451] Step 1:

[0452] Fill in and submit the user registration form

[0453] Device: The user operates a device (PC or smartphone) to open a web browser or application, accesses the registration page, enters their name, age, grade, location, email address, and password in the form, and clicks the "Register" button.

[0454] Input: Personal information entered by the user (name, age, grade, location, email address, password)

[0455] Output: Registration request sent to the server

[0456] Specific operation: The user enters information into the form and presses the submit button. The device transfers the submitted data to the server.

[0457] Step 2:

[0458] Receiving and storing user information

[0459] Server: The server receives the user information sent from the device and stores it in a secure database using encryption technology.

[0460] Input: User information sent from the device

[0461] Output: User information stored in the database, preparation for sending a registration confirmation email

[0462] Specific operation: The server inserts and saves data into the database. After saving, a confirmation email is automatically generated.

[0463] Step 3:

[0464] Sending a confirmation email

[0465] Server: Automatically generates a registration confirmation email and sends it to the user's email address, which contains a link to activate the account.

[0466] Input: The user's email address stored in the database

[0467] Output: A confirmation email sent to the user's mailbox.

[0468] Specific operation: The server calls the email sending API and sends a confirmation email.

[0469] Step 4:

[0470] Activating your account

[0471] User: The user receives a confirmation email and activates their account by clicking the link in the email, which allows them to officially log in to the system.

[0472] Input: Activation link in confirmation email

[0473] Output: The user's account is enabled and they can log in.

[0474] Specific action: A user clicking on a link in an email.

[0475] Step 5:

[0476] Learning content recommendations

[0477] Server: Retrieves user registration information (age, grade, interests, etc.) from a database and uses an AI algorithm to select the most appropriate learning content.

[0478] Input: User registration information stored in the database

[0479] Output: A list of recommended learning content

[0480] How it works: The server executes a query to obtain user information and uses an AI model to generate recommended content.

[0481] Step 6:

[0482] Delivery and display of recommendation lists

[0483] Terminal: Receives the list of recommended learning content sent from the server and displays it to the user. The user can select the content they want to learn.

[0484] Input: Recommended learning content list sent from the server

[0485] Output: Content list displayed in the user interface

[0486] Specific operation: The device analyzes the data and displays it on the user interface.

[0487] Step 7:

[0488] Content Selection and Access

[0489] User: The user clicks to select the content they want to watch from the recommended list.

[0490] Device: Providing selected content for user access in streaming or download format.

[0491] Input: Information about content leaked or selected by the user

[0492] Output: Learning content delivered in streaming or download format

[0493] Specific operation: The user selects content, and the device communicates with the content server to retrieve and display the content in the appropriate format.

[0494] Step 8:

[0495] Collection of learning progress and emotion data

[0496] User: After studying, the user enters progress data (study content, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[0497] Terminal: Sends collected progress data and emotion data to the server.

[0498] Input: Progress data entered by the user, emotion data collected by the emotion engine

[0499] Output: Progress and emotion data sent to the server

[0500] Specific operation: The user inputs the learning results, cameras and sensors collect data, and the device sends the data to the server.

[0501] Step 9:

[0502] Data analysis

[0503] Server: Analyzes the collected data (learning progress data and emotional data) using an AI engine to evaluate the user's learning progress and emotional state.

[0504] Input: Learning progress data and emotion data stored on the server

[0505] Output: Analysis results and evaluation report

[0506] What it does: AI algorithms run, analyze the data, and generate reports.

[0507] Step 10:

[0508] Generating and providing feedback messages

[0509] Server: Based on the analysis results, it generates feedback messages that point out the user's weaknesses and areas for improvement, as well as encouragement and relaxation based on emotional data.

[0510] Input: Analysis results and evaluation report

[0511] Output: The generated feedback message

[0512] Terminal: Receives feedback messages sent from the server and displays them to the user.

[0513] Input: The generated feedback message

[0514] Output: Feedback message displayed in the user interface

[0515] Specific operation: The server generates and sends a message, and the terminal displays it.

[0516] Step 11:

[0517] Generate personalized learning plans

[0518] Server: The AI ​​engine uses progress and emotion data to generate a personalized learning plan for each user, including daily study time, focus points, and learning materials.

[0519] Input: Progress data and emotion data

[0520] Output: Generated personalized optimized learning plan

[0521] How it works: The AI ​​model analyzes the input data and generates a new learning plan.

[0522] Step 12:

[0523] Providing and implementing a learning plan

[0524] Terminal: Receives the generated learning plan and displays it on the user interface.

[0525] User: Follows the study plan, for example, works through new suggested math exercises.

[0526] Input: Generated lesson plan

[0527] Output: Learning plan displayed in the user interface, user's learning execution

[0528] Specific operation: The device displays the study plan and the user studies according to it.

[0529] (Application example 2)

[0530] 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."

[0531] Current online education systems are unable to fully address the individual needs of users, particularly in the real-time monitoring of learning progress and emotional state. They also struggle to provide effective learning plans for specific environments, often limiting learning effectiveness. Similar problems can occur in training factory workers, making it difficult for them to effectively acquire skills.

[0532] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to input and register necessary information, a means for recommending appropriate study content based on the user's registration information, a means for collecting and analyzing the user's study progress data and emotional data, a means for generating an individually optimized study plan based on the analyzed data, and a means for providing the generated study plan to the user. This makes it possible to grasp the user's study progress and emotional state in real time and provide an individually optimized study plan and feedback messages.

[0533] The "means for users to enter and register the necessary information" is an interface that allows users to enter necessary information such as name, age, job title, and work content, and register it in the system.

[0534] "Means for recommending appropriate learning content based on the user's registered information" refers to an algorithm and system for automatically selecting optimal learning content based on the user's registered information and recommending it to the user.

[0535] "Means for collecting and analyzing user learning progress data and emotional data" refers to a system for centrally collecting and analyzing learning progress information entered by users and emotional data collected using cameras and sensors.

[0536] The "means for generating an individually optimized learning plan based on the analyzed data" refers to an AI engine and system for analyzing a user's learning progress data and emotional data and generating an optimized learning plan for the user based on the results.

[0537] The "means for providing the generated study plan to the user" refers to an interface and system for presenting the generated individually optimized study plan to the user and allowing the user to study efficiently in accordance with the study plan.

[0538] The present invention provides a system for improving a user's learning environment and providing an individually optimized educational experience. Specific embodiments of the present invention will be described below.

[0539] 1. User Registration

[0540] The server provides a means for users to enter the required information and register. This means can be an interface, such as an HTML form, where users enter and submit information such as their name, age, job title, and job description. The registration information is securely stored in the server's database. The user then receives a confirmation email with a link to activate their account.

[0541] 2. Learning content recommendations

[0542] The server provides a means to recommend appropriate learning content based on the user's registered information. Specifically, it selects appropriate training content based on the user's work duties, job title, and past learning data using an algorithm. The selected content is optimized for the growth of factory workers and promotes efficient skill acquisition.

[0543] 3.Collection and analysis of learning progress and emotion data

[0544] The device provides a means to collect learning progress data and emotional data entered by the user. Emotional data is collected using an emotion recognition library (e.g., EmotionRecognizer) and analyzes the user's facial expressions using a camera sensor. The collected data is sent to a server and analyzed by an AI engine. This analysis provides a detailed understanding of the user's progress and emotional state.

[0545] 4.Generating an individualized learning plan

[0546] The server analyzes the user's learning progress data and emotional data and provides a means to generate an individually optimized learning plan. Specifically, an AI engine (e.g., AIEngine) analyzes the user's weaknesses and strengths and creates optimal training content and feedback. The generated learning plan includes daily training content, recommended learning materials, break times, etc.

[0547] 5. Providing study plans

[0548] The device has a means for providing the generated learning plan to the user, allowing the user to proceed with training according to the displayed plan. Feedback messages are provided according to the user's progress and emotional state, and may include encouraging messages such as "Your progress so far is great! Keep up the great work!"

[0549] Adding specific examples

[0550] For example, when a new factory worker is undergoing training on a certain process, if the emotion recognition engine detects that the worker is feeling stressed, the AI ​​engine will suggest encouraging messages or additional training content for the worker.

[0551] Prompt Sentence Examples

[0552] Analyze the training progress and sentiment data of your next worker to generate feedback and next steps:

[0553] User ID: 123

[0554] Progress data: { "Completion": 70, "Test result": 80}

[0555] Emotion data: { "Stress": High, "Concentration": Low}"

[0556] In this way, the system is able to grasp the user's learning progress and emotional state in real time, enabling it to provide individually optimized learning plans and feedback messages.

[0557] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0558] Step 1:

[0559] The user registers by entering the required information such as name, age, job title, and work details. The entered information is sent to the server through the device interface. The server securely stores the received information in a database and sends a registration confirmation email to the user's email address. The user activates their account by clicking the link in the confirmation email.

[0560] Step 2:

[0561] The server recommends appropriate learning content based on the user's registration information. It selects appropriate training content and generates a recommendation list based on the user's registration information (name, age, job title, and work content). The terminal displays this list to the user, who can then select training content from the provided options.

[0562] Step 3:

[0563] You watch or perform the training content you select. The device makes the selected content available to you in streaming or download format, and collects progress data (e.g., completion rate, test results) that occurs during the viewing or performance.

[0564] Step 4:

[0565] The device collects learning progress data and emotional data entered by the user. Emotional data is collected using a camera sensor and an emotion recognition library (e.g., EmotionRecognizer). Specifically, the camera captures a picture of the user's face, and the emotion recognition algorithm detects emotions from their facial expressions. This data is then sent to the server.

[0566] Step 5:

[0567] The server analyzes the user's learning progress data and emotional data. An AI engine (e.g., AIEngine) analyzes the input progress and emotional data. The analysis results reveal the user's progress and emotional state, and generates appropriate feedback messages based on that information.

[0568] Step 6:

[0569] The server then generates a personalized learning plan based on the analysis results. Based on the progress and emotional data, the AI ​​engine generates a learning plan that includes daily training content, specific learning materials, and break times. This plan is then sent to the device and displayed to the user.

[0570] Step 7:

[0571] Users progress through their training according to the individually optimized learning plan displayed on their device. The device has an interface that supports page transitions and training progress, and displays guides and reminders to help users progress through their studies efficiently. Continuous feedback messages are also provided to maintain motivation to study.

[0572] These are the specific processing steps of the system that realizes this application example. This makes it possible to grasp the user's learning progress and emotional state in real time and provide individually optimized learning plans and feedback messages.

[0573] 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.

[0574] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0575] 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.

[0576] [Second embodiment]

[0577] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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."

[0589] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, learning content recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[0590] User Registration

[0591] 1. User Registration

[0592] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters their name, age, grade, location, email address, password, etc.

[0593] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[0594] Users: Receive a confirmation email and click the link provided to activate their account.

[0595] Educational content distribution

[0596] 2. Content Recommendation

[0597] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[0598] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[0599] 3. Content Access

[0600] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[0601] Device: Providing selected content for users to access in streaming or download format.

[0602] Learning progress management

[0603] 4. Collecting progress data

[0604] User: After studying, enter study progress data (which parts have been studied, test results, etc.) into the terminal.

[0605] Terminal: Sends the entered data to the server.

[0606] 5. Data analysis and feedback

[0607] Server: Analyzes the collected data and tracks progress. For example, if the percentage of correct answers to a particular math problem is low, it provides feedback on that task.

[0608] Terminal: Displays feedback messages sent by the server to the user.

[0609] Providing individually optimized learning plans

[0610] 6. Generate personalized learning plans

[0611] Server: Based on the collected progress data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[0612] Device: Displays the generated learning plan to the user.

[0613] 7. Implementing your learning plan

[0614] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[0615] Example: When a junior high school student uses the system

[0616] If the user is a junior high school student, the following is a specific example.

[0617] 1. User Registration

[0618] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, the user provides information such as 13 years old, living in Tokyo, and in the second year of junior high school.

[0619] Server: Stores the received information in a database and sends a confirmation email.

[0620] User: Receives a confirmation email and clicks on the link to complete registration.

[0621] 2. Content Recommendation

[0622] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[0623] Devices: Show recommended content to middle school students.

[0624] User: Selects and watches a mathematics geometry lesson from the recommended content.

[0625] 3. Collecting and analyzing progress data

[0626] User: After solving a geometry exercise, enter the result into the terminal.

[0627] Server: Analyzes the input data and detects low levels of understanding of specific problems.

[0628] Terminal: Feedback tells the user that additional geometry practice problems are needed.

[0629] 4. Providing personalized learning plans

[0630] Server: The AI ​​engine uses the user's progress data to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch.

[0631] Device: Display the new learning plan to the user.

[0632] Users: Follow the plan and solve geometry exercises every day to deepen their understanding.

[0633] In this way, the present invention achieves equalization and quality improvement of education by strictly managing users' learning progress and providing individually optimized educational experiences.

[0634] The processing flow will be explained below.

[0635] Step 1:

[0636] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​affiliation, email address, and password, and presses the submit button.

[0637] Step 2:

[0638] The terminal transmits the user's input information to the server.

[0639] Step 3:

[0640] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[0641] Step 4:

[0642] The user receives a confirmation email and clicks the link in the email to activate their account.

[0643] Step 5:

[0644] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[0645] Step 6:

[0646] The server generates a list of selected learning content and sends it to the terminal.

[0647] Step 7:

[0648] The terminal displays the received content list to the user, who can then click to select the content of interest.

[0649] Step 8:

[0650] The terminal provides the selected learning content in streaming or download format for the user to access.

[0651] Step 9:

[0652] After the user finishes the study session, they input information such as what they studied and test results into the terminal.

[0653] Step 10:

[0654] The terminal transmits the input learning progress data to the server.

[0655] Step 11:

[0656] The server analyzes the received learning progress data, for example determining if a particular task has not been understood and therefore requires special attention from the user.

[0657] Step 12:

[0658] Based on the analysis results, the server generates a feedback message for the user, pointing out the user's weaknesses and areas for improvement.

[0659] Step 13:

[0660] The terminal displays the feedback message sent from the server to the user.

[0661] Step 14:

[0662] The user periodically (e.g., on weekends) enters the study time and results into the terminal.

[0663] Step 15:

[0664] The terminal sends the input data to the server.

[0665] Step 16:

[0666] The server analyzes the accumulated data using an AI engine and generates a study plan optimized for the user (daily study time, tasks to focus on, study materials to use, etc.).

[0667] Step 17:

[0668] The server sends the generated learning plan to the device.

[0669] Step 18:

[0670] The device displays the new study plan to the user, who then follows the plan to study.

[0671] Step 19:

[0672] The user repeatedly inputs the learning progress into the terminal.

[0673] Step 20:

[0674] The server collects and analyzes data, and then adjusts feedback and learning plans to continuously optimize the user's learning experience.

[0675] In this way, the system manages the user's learning progress in real time and provides a personalized educational experience.

[0676] Example 1

[0677] 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."

[0678] In conventional online education systems, users are often provided with uniform learning content, making it difficult to provide an educational experience optimized for individual needs and progress. Furthermore, there is a lack of feedback based on the user's learning progress or the generation of individually optimized learning plans, which makes it difficult to provide effective learning support.

[0679] 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.

[0680] In this invention, the server includes a means for a user to input and register necessary information, a means for recommending appropriate educational materials based on the user's registration information, a means for collecting and analyzing the user's learning progress data, a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, and a means for creating prompt sentences when generating a learning plan from the user's progress data using a generative AI model. This enables a customized educational experience for each user, not only improving learning efficiency but also making it possible to provide an optimal learning plan based on each user's individual progress.

[0681] "User" refers to an individual who uses the online education system to learn.

[0682] "Information input" refers to the act of a user registering personal information such as name, age, grade, location, email address, and password into the system.

[0683] "Educational Materials" refers to educational content, such as teaching materials and video lessons, designed to enhance a user's learning.

[0684] "Recommendation" refers to the act of providing appropriate educational materials based on the user's registration information.

[0685] "Study progress data" refers to information indicating the user's learning progress, such as their learning status and test results.

[0686] "Analysis" refers to the act of analyzing collected learning progress data using data analysis tools and AI models.

[0687] "Individually optimized learning plan" refers to a learning plan that includes an optimal learning schedule and learning materials, which is generated based on each user's individual progress.

[0688] "Generation" refers to the act of creating a new learning plan based on collected data and analysis results.

[0689] "Providing" refers to the act of notifying or presenting the generated study plan to the user.

[0690] A "generative AI model" refers to an artificial intelligence model that automatically generates learning plans and feedback based on user progress data.

[0691] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[0692] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, educational material recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[0693] User Registration

[0694] Users access the system via a web browser or smartphone app and navigate to the registration page. They enter required information such as their name, age, grade, location, email address, and password. This data is sent to the server via their device and stored in a secure database. The server then sends a registration confirmation email to the user's email address, and the user activates their account by clicking the link in the email.

[0695] Recommend educational materials

[0696] The server retrieves the user's registration information from the database and uses a generative AI model to recommend appropriate educational materials. For example, it automatically selects geometry or chemistry video lessons based on the user's grade, location, and interests. The recommended educational materials are sent to the device, which displays the list to the user. The user can then select the content they want to study from the list.

[0697] Learning progress management

[0698] After using educational materials, users input their learning progress data (study content, test results, etc.) into their device. The device then sends this data to a server. The server collects the progress data and analyzes it using analytical tools and AI models. For example, if the accuracy rate for a particular math problem is low, a feedback message is generated based on the results.

[0699] The server sends the generated feedback message to the terminal, and the terminal displays the feedback to the user, so that the user can work on the next learning activity based on the feedback.

[0700] Providing individually optimized learning plans

[0701] The server uses a generative AI model to generate an individually optimized study plan based on the collected progress data. For example, it uses a prompt such as, "Based on the user's progress data, please generate a study plan for next week. Please include key topics, recommended learning materials, and study time." The generated study plan is stored in a database and sent to the user's device. The device displays the plan to the user, and the user proceeds with their studies according to the plan.

[0702] Specific examples

[0703] For example, when a second-year junior high school student uses the system, they follow the steps below. The user registers necessary information such as their name, age, grade, location, email address, and password. The server recommends educational materials for mathematics and science based on the registered information. From the recommended content, the user selects and watches a geometry video lesson.

[0704] After studying, the user enters progress data into the device and sends it to the server, which analyzes it and detects insufficient understanding of a particular problem, generating a feedback message to the user via the device indicating the need for additional geometry practice problems.

[0705] The server then uses an AI engine to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch, allowing users to take their next learning steps based on scientific evidence.

[0706] In this way, the system provides a customized educational experience for each user, improving learning efficiency.

[0707] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0708] Step 1: Go to the user registration page

[0709] The user opens a web browser or smartphone app and accesses the system's user registration page.

[0710] The terminal receives the user's request and displays a registration page.

[0711] Input: User access request

[0712] Output: Display of registration page

[0713] What happens: The device interprets the URL and displays the appropriate registration page.

[0714] Step 2: Enter your information

[0715] The user enters necessary information such as name, age, grade, location, email address, and password.

[0716] The terminal temporarily stores the entered information in real time and displays it on the form.

[0717] Input: Personal information entered by the user

[0718] Output: Personal information displayed in the input form

[0719] Specific operation: The device receives user input and displays it in real time.

[0720] Step 3: Send information

[0721] The user clicks the send button to submit the information they have entered.

[0722] The terminal transmits the user's input information to the server.

[0723] Input: User submission requests and input information

[0724] Output: User information sent to the server

[0725] Specific operation: Create an HTTP request and send it to the server along with the input information.

[0726] Step 4: Receiving and storing information

[0727] The server checks the received information and stores it in a secure database, either an SQL database or a NoSQL database.

[0728] Input: User information sent from the device

[0729] Output: User information stored in the database

[0730] Specific operation: The server verifies the received information and stores it in a database using an SQL query, etc.

[0731] Step 5: Send a confirmation email

[0732] The server will send a registration confirmation email to the user's email address, which will contain an account activation link.

[0733] The user will receive a confirmation email and will need to click on the link to activate their account.

[0734] Input: User's email address

[0735] Output: A confirmation email sent to the user.

[0736] Specific operation: The server connects to the mail server and sends a confirmation email.

[0737] Step 6: Information Acquisition for Content Recommendation

[0738] The server retrieves the user's registration information from a database, often using an SQL query.

[0739] Input: User registration information

[0740] Output: Retrieved user information

[0741] What happens: The server executes an SQL query to get the required information from the database.

[0742] Step 7: Running the Content Recommendation Engine

[0743] Based on the registration information, the server runs a recommendation engine (e.g., a machine learning model) to select appropriate learning content, taking into account the user's grade, location, interests, etc.

[0744] Input: User registration information

[0745] Output: A list of recommended educational materials

[0746] What it does: It uses an algorithm to select the most relevant content for users.

[0747] Step 8: Submit your recommended content

[0748] The server transmits the recommended content list to the terminal.

[0749] Input: A list of recommended educational materials

[0750] Output: Content list sent to device

[0751] Specific operation: Create an HTTP response and send the content list to the terminal.

[0752] Step 9: Displaying Content

[0753] The device displays the received content list to the user, such as a list of "geometry video lessons" and "chemistry experiment videos."

[0754] Input: Content list sent from the server

[0755] Output: Content list displayed to the user

[0756] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[0757] Step 10: Input training data

[0758] After using the educational materials, the user inputs learning progress data (learning content, test results, etc.) into the terminal.

[0759] Input: User's learning progress data

[0760] Output: Progress data temporarily stored on the device

[0761] Specific behavior: The device receives user input and displays it in real time.

[0762] Step 11: Send data

[0763] The device sends the entered progress data to the server, often by POSTing the data using an API endpoint.

[0764] Input: User's learning progress data

[0765] Output: Progress data sent to the server

[0766] Specific behavior: Makes an API request and sends progress data to the server.

[0767] Step 12: Analyze progress data

[0768] The server analyzes the received data, such as test accuracy and study time, using analytical tools and AI models.

[0769] Input: Received learning progress data

[0770] Output: Analysis results

[0771] Specific actions: Analyze progress data using data analysis tools and AI models.

[0772] Step 13: Feedback Generation

[0773] The server generates feedback based on the analysis results. If the level of understanding of a particular problem is low, it generates a feedback message recommending additional study materials or practice problems.

[0774] Input: Analysis results

[0775] Output: Feedback message

[0776] Specific operation: Perform text processing to generate a feedback message based on the analysis results.

[0777] Step 14: Submit your feedback

[0778] The server sends a feedback message to the terminal.

[0779] Input: Feedback message

[0780] Output: Feedback message sent to the terminal

[0781] Specific operation: Creates an HTTP response and sends a feedback message to the terminal.

[0782] Step 15: Viewing feedback

[0783] The device displays the received feedback message to the user, including specific improvements and recommendations for further learning.

[0784] Input: Feedback message sent by the server

[0785] Output: Feedback that is displayed to the user

[0786] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[0787] Step 16: Run the AI ​​Engine

[0788] The server uses the collected progress data to run a generative AI model and create prompts to generate an individually optimized study plan. For example, the server uses the prompt, "Based on the user's progress data, please generate a study plan for next week. Please include key tasks, recommended learning materials, and study time."

[0789] Input: Progress data and prompt text

[0790] Output: Personalized learning plan

[0791] Specific operation: Call the AI ​​engine and obtain the generated learning plan.

[0792] Step 17: Save and submit your study plan

[0793] The server stores the generated study plan in a database and transmits it to the user's terminal.

[0794] Input: Generated lesson plan

[0795] Output: Study plan stored in the database and study plan sent to the device

[0796] Specific behavior: Saves a learning plan using an SQL query and creates and sends an HTTP response.

[0797] Step 18: View your learning plan

[0798] The device displays the generated study plan to the user, which includes a daily study schedule and the learning materials to be used.

[0799] Input: Study plan sent from the server

[0800] Output: The learning plan that is displayed to the user

[0801] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[0802] Step 19: Implementing your learning plan

[0803] The user progresses through the displayed study plan, for example by working through newly recommended math exercises.

[0804] Input: Study Plan

[0805] Output: User's learning progress

[0806] Specific actions: The user actually performs the learning activities according to the learning plan.

[0807] summary

[0808] This system aims to provide an individually optimized educational experience through collaboration between users, devices, and servers. By utilizing generative AI models and prompts, it is possible to generate and provide efficient and effective learning plans.

[0809] (Application example 1)

[0810] 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."

[0811] Conventional online education systems struggle to provide an individually optimized learning experience, and are unable to provide content tailored to each user's unique learning needs. As a result, learning effectiveness declines and user satisfaction declines. Furthermore, the inability to track learning progress in real time and provide appropriate feedback can impair learning efficiency.

[0812] 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.

[0813] In this invention, the server includes a means for the user to input necessary information and register, a means for recommending appropriate study content based on the user's registered information, and a means for collecting and analyzing the user's study progress data, thereby providing the user with an individually optimized learning experience and improving the effectiveness of their learning.

[0814] The server further includes a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, a means for having the user input information via an application installed on the smartphone terminal, and collecting and analyzing progress data, and a means for using the collected progress data to generate a feedback message using an AI model and notifying the user. This makes it possible to grasp learning progress in real time and provide effective feedback, thereby improving the user's learning efficiency.

[0815] "User" refers to an individual who uses the online education system.

[0816] "Required information" refers to specific data required for registration, such as name, age, grade, location, email address, and password.

[0817] "Means for Registration" refers to the process by which a user enters required information, saves that information in the system, and activates an account.

[0818] "Learning Content" means educational materials, such as instructional materials, video lessons, and exercises, that are provided to assist users in their learning.

[0819] "Recommendation means" refers to a method of selecting and presenting appropriate learning content based on the user's registration information.

[0820] "Study progress data" refers to data such as the progress, grades, and test results achieved by the user during their studies.

[0821] "Means for collecting and analyzing" refers to a method for collecting learning progress data from users and analyzing the data to evaluate the state of learning.

[0822] "Individually optimized learning plan" refers to a learning plan that is optimized based on each user's learning progress data.

[0823] "Means of generation" refers to the method of creating an individually optimized learning plan based on the collected data.

[0824] The "means for providing" refers to a means for displaying the generated learning plan to the user and encouraging them to carry it out.

[0825] A "smartphone terminal" refers to a mobile communication device that can connect to the Internet and on which applications can be installed and used.

[0826] "Application" refers to software that users install and use on their smartphone devices.

[0827] "Means for collecting and analyzing progress data" refers to a method in which a user inputs learning progress data through an application and the data is sent to a server for analysis.

[0828] An "AI model" refers to an artificial intelligence algorithm that performs inference and optimization based on collected data and generates feedback and learning plans.

[0829] "Feedback message" refers to advice and evaluation provided based on the user's learning progress data.

[0830] "Means for notifying" refers to a method for notifying the user of the generated feedback message.

[0831] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. The system is operated using an application installed on a user's smartphone terminal.

[0832] First, the user downloads and installs the online education application on their smartphone. The user enters the required information (e.g., name, age, grade, location, email address, password, etc.) to register. The server stores the user's registration information in a secure database and sends the user an email to confirm their registration. The user receives the confirmation email and clicks the link contained in it to activate their account.

[0833] After registration is complete, the server recommends appropriate learning content (such as teaching materials and video lessons) based on the user's registration information. This content is customized according to the user's grade level and interests. The recommended learning content is displayed to the user through an application on their smartphone, and the user can select from the content and begin watching.

[0834] As the user progresses with their studies, they input their learning progress data (such as which learning materials they have viewed and test results) into the application. The device then sends the input progress data to the server. The server analyzes the collected progress data and generates an individually optimized learning plan based on the analysis results. For example, if the user's understanding of a particular subject is insufficient, the plan will include additional learning content related to that subject.

[0835] Furthermore, the system uses an AI model to generate feedback messages based on the collected progress data and notifies the user of the generated feedback. The feedback messages include suggestions for additional practice in areas where understanding is insufficient and instructions on what to do next. This allows users to understand their own learning status in real time and study effectively.

[0836] As a concrete example, consider a middle school student watching a geometry video lesson and working on the exercises. The user opens the application and enters their progress data. For example, if they score 55 points on a geometry exercise, they enter that score. The server analyzes this score and uses an AI model to generate a feedback message to the user saying, "You need more practice in geometry. Please complete additional exercises."

[0837] Below are some examples of prompts used in this system:

[0838] "You are in eighth grade math class. Please solve a geometry problem. Enter the score you received for the problem."

[0839] In this way, the system of the present invention can closely manage the user's learning progress and provide a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[0840] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0841] Step 1:

[0842] A user downloads and installs an online education application on their smartphone. They register an account by entering required information such as their name, age, grade, location, email address, and password. The server receives the information, stores it in a secure database, and sends a confirmation email to the user. The user receives the confirmation email and clicks on the link included to activate their account.

[0843] Input: Registration information such as name, age, grade, location, email address, and password

[0844] Output: Verification email sent and account activated

[0845] Step 2:

[0846] The server recommends learning content (such as teaching materials and video lessons) based on the user's registration information. The recommended content is customized according to the user's grade and interests. The device displays the recommended learning content to the user, and the user selects the content they want to view from the displayed list.

[0847] Input: Registration information, grade, interests

[0848] Output: Display of customized learning content list

[0849] Step 3:

[0850] The user watches the selected learning content and proceeds with their learning. After studying, the user enters their learning progress data (viewed content, test results, etc.) into the device. The device then sends the entered progress data to the server.

[0851] Input: Learning progress data (content viewed, test results)

[0852] Output: Sending progress data

[0853] Step 4:

[0854] The server analyzes the collected progress data. For example, if a user scores low in a particular subject or topic, the server can identify the cause. Based on the analysis, the server generates a personalized, optimized learning plan, which includes focused learning content and additional practice questions.

[0855] Input: Progress data (content viewed, test results)

[0856] Output: Personalized learning plan

[0857] Step 5:

[0858] The server runs the generative AI model based on the collected progress data and generates feedback messages for the user. This feedback includes specific advice, such as "You need more practice on geometry. Please solve additional problems." The generated feedback messages are sent to the user's device.

[0859] Input: Progress data, execution results of generative AI model

[0860] Output: Feedback message

[0861] Step 6:

[0862] Users follow the feedback messages displayed on their device to progress through their learning according to an individually optimized learning plan. By using the device's application, they can improve their learning effectiveness by working on newly recommended content and practice problems.

[0863] Input: Feedback message, personalized learning plan

[0864] Output: Improved user learning progress

[0865] In this way, the system of the present invention closely manages the user's learning progress and provides a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[0866] 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.

[0867] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has functions for user registration, recommending learning content, collecting and analyzing learning progress data, and generating and providing personalized and optimized learning plans, as well as an emotion engine that recognizes the user's emotions.

[0868] User Registration

[0869] 1. User Registration

[0870] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters the required information such as name, age, grade, location, email address, and password, and presses the submit button.

[0871] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[0872] Users: Receive a confirmation email and click the link provided to activate their account.

[0873] Educational content distribution

[0874] 2. Content Recommendation

[0875] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[0876] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[0877] 3. Content Access

[0878] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[0879] Device: Providing selected content for user access in streaming or download format.

[0880] Learning progress management

[0881] 4. Collecting progress data

[0882] User: After learning, the user inputs learning progress data (which parts they have learned, test results, etc.) into the device. In addition, the emotion engine collects the user's emotion data from the facial recognition camera and sensors.

[0883] Terminal: Sends the input learning progress data and emotion data to the server.

[0884] 5. Data analysis and feedback

[0885] Server: Analyzes the collected data (learning progress data and emotion data) to understand the progress of the user. For example, if the correct answer rate for a particular math problem is low and the user is feeling confused or stressed, it determines that the user needs special attention.

[0886] Server: Based on the analysis results, it generates feedback messages for the user, pointing out their weaknesses and areas for improvement. Based on the emotional data, it also provides messages of encouragement and relaxation.

[0887] Terminal: Displays feedback messages sent by the server to the user.

[0888] Providing individually optimized learning plans

[0889] 6. Generate personalized learning plans

[0890] Server: Based on the collected progress and emotion data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[0891] Device: Displays the generated learning plan to the user.

[0892] 7. Implementing your learning plan

[0893] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[0894] Example: When a junior high school student uses the system

[0895] If the user is a junior high school student, the following is a specific example.

[0896] 1. User Registration

[0897] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, they provide information such as age 13, local resident, and second-year junior high school student.

[0898] Server: Stores the received information in a database and sends a confirmation email.

[0899] User: Receives a confirmation email and clicks on the link to complete registration.

[0900] 2. Content Recommendation

[0901] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[0902] Devices: Show recommended content to middle school students.

[0903] User: Selects and watches a mathematics geometry lesson from the recommended content.

[0904] 3. Collecting and analyzing progress and emotion data

[0905] User: After solving a geometry exercise, the user enters the results and emotions (e.g., stress or accomplishment) into the device.

[0906] Server: Analyzes the input data and detects the level of understanding and emotional state of the specific problem.

[0907] Terminal: Feedback informs the user that additional geometry practice problems are needed, and also displays encouraging messages to reduce stress.

[0908] 4. Providing personalized learning plans

[0909] Server: An AI engine uses progress and emotional data to generate a study plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks to relax.

[0910] Device: Display the new learning plan to the user.

[0911] Users: Follow a plan and solve geometry exercises every day to improve their understanding and manage stress.

[0912] In this way, the present invention manages the user's emotional state in real time along with their learning progress, providing a personalized and optimized educational experience, thereby enabling more effective learning.

[0913] The processing flow will be explained below.

[0914] Step 1:

[0915] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​residence, email address, and password, and presses the submit button.

[0916] Step 2:

[0917] The terminal transmits the user's input information to the server.

[0918] Step 3:

[0919] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[0920] Step 4:

[0921] The user receives a confirmation email and clicks the link in the email to activate their account.

[0922] Step 5:

[0923] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[0924] Step 6:

[0925] The server generates a list of selected learning content and sends it to the terminal.

[0926] Step 7:

[0927] The terminal displays the received content list to the user, who can then click to select the content of interest.

[0928] Step 8:

[0929] The terminal provides the selected learning content in streaming or download format for the user to access.

[0930] Step 9:

[0931] After completing a study session, the user inputs their learning progress and emotions into the device. Emotional data is collected using a facial recognition camera and sensors.

[0932] Step 10:

[0933] The terminal transmits the input learning progress data and emotion data to the server.

[0934] Step 11:

[0935] The server analyzes the received learning progress data. For example, if the percentage of correct answers to a particular math problem is low, it can be determined that the user is struggling with that subject.

[0936] Step 12:

[0937] The server uses an emotion engine to analyze the user's emotion data, for example, to detect if the user is feeling confused or stressed.

[0938] Step 13:

[0939] The server generates a feedback message based on the learning progress data and emotion data, such as "It seems you found this part difficult. Let's start with an easier problem next time."

[0940] Step 14:

[0941] The terminal displays the feedback message sent from the server to the user.

[0942] Step 15:

[0943] Users periodically (e.g., on weekends) enter their study time and results into the device, and emotional data is also collected.

[0944] Step 16:

[0945] The terminal sends the input data to the server.

[0946] Step 17:

[0947] The server analyzes the accumulated data using an AI engine and generates an optimized study plan for each user, including daily study time, tasks to focus on, and study materials to use.

[0948] Step 18:

[0949] The server sends the generated learning plan to the device.

[0950] Step 19:

[0951] The device displays the new study plan to the user, who then follows the plan to study.

[0952] Step 20:

[0953] The server continuously collects and analyzes learning progress and emotional data, and adjusts feedback and learning plans accordingly to continually optimize the user's learning experience.

[0954] In this way, the system manages the user's learning progress and emotional state in real time, providing a personalized educational experience.

[0955] Example 2

[0956] 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."

[0957] In conventional online education systems, it was difficult to grasp individual users' learning progress and emotions in real time and provide optimal learning plans. As a result, it was not possible to provide an effective and continuous learning experience for users, which could lead to a decline in learning effectiveness. In addition, there was a lack of means to provide feedback based on learning progress and changes in emotions, making it difficult to maintain users' motivation to learn.

[0958] 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.

[0959] In this invention, the server includes means for the user to input necessary information and register, means for recommending appropriate study content based on the user's registration information, means for collecting the user's study progress data and emotion data, means for analyzing the collected data and generating an individually optimized study plan, means for providing the generated study plan to the user, and means for displaying the provided feedback message to the user, thereby making it possible to provide the user with an individually optimized study plan and feedback messages based on their emotions.

[0960] "User" refers to an individual learner who uses the online education system.

[0961] "Information" or "Required Information" refers to personal information such as name, age, grade, location, email address, and password that a User provides to register with the System.

[0962] "Registration" refers to the process by which a User enters required information into the System to create and activate an account.

[0963] "Server" refers to a computer system that stores and processes user information, progress data, learning content, etc.

[0964] "Learning Content" refers to educational resources such as teaching materials, video lessons, and textbooks provided on the System.

[0965] "Recommendation" refers to the process of selecting and presenting the most appropriate learning content based on the user's registration information and learning history.

[0966] "Study progress data" refers to records of what parts a user has studied, the content of their studies, test results, and so on.

[0967] "Emotional data" refers to data on a user's emotional state based on facial expressions and physical reactions collected using facial recognition cameras and sensors.

[0968] "Analysis" refers to the process of analyzing the collected learning progress data and emotional data to evaluate the user's learning status and emotional state.

[0969] "Individually optimized learning plan" refers to a plan that includes a learning schedule and assignments that are optimal for a specific user, generated based on the user's learning progress data and emotional data.

[0970] "Feedback message" refers to a message of evaluation or encouragement that is generated based on the analysis results and provided to the user.

[0971] "Providing" refers to the process by which the server presents the generated learning plan and feedback messages to the user.

[0972] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. This system has the following functions: user registration, learning content recommendation, collection and analysis of learning progress data and emotion data, generation and provision of personalized and optimized learning plans, and provision of feedback messages.

[0973] 1. User Registration

[0974] Device: The user uses a device (such as a PC or smartphone) to open the system's registration page in a web browser or application. The user enters the required information (name, age, grade, location, email address, password, etc.) and clicks the "Register" button.

[0975] Server: Receives user information sent from the device and stores it in a secure database. The stored data is protected by encryption technology. A registration confirmation email is then automatically generated and sent to the user's email address.

[0976] User: The user receives a confirmation email and clicks the link in the email to activate their account.

[0977] 2. Educational content recommendations

[0978] Server: Based on the information registered by the user (age, grade, interests, etc.), the algorithm runs and automatically selects the most suitable learning content. The selected content is customized according to the user's grade, region, and interests. An AI-based recommendation system is used.

[0979] Device: Receives the selected content recommendation list and displays it to the user, who can then select the content they want to study from the list.

[0980] 3. Content Access

[0981] User: The user clicks to select the content they want to watch (e.g., a math video lesson) from the recommendations list.

[0982] Device: Providing selected content for user access in streaming or download format.

[0983] 4. Collecting learning progress data

[0984] User: After studying, the user enters progress data (which part they studied, what they learned, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[0985] Terminal: Sends the input learning progress data and emotion data to the server.

[0986] 5. Data analysis and feedback

[0987] Server: The AI ​​engine analyzes the collected data (learning progress data and emotional data) and evaluates the user's learning progress and emotional state. For example, if the user has a low success rate on a particular math problem and feels confused or stressed, the server will urge the user to pay special attention.

[0988] Server: Generates feedback messages based on the analysis results. The feedback messages include points out the user's weaknesses and areas for improvement, as well as messages of encouragement or relaxation based on emotional data.

[0989] Terminal: Receives feedback messages sent from the server and displays them to the user.

[0990] 6. Generate personalized learning plans

[0991] Server: The AI ​​engine generates an optimal study plan for each user based on the progress and emotion data collected. The plan includes daily study time, priority tasks, and study materials to use.

[0992] Device: Displays the generated learning plan to the user.

[0993] 7. Implementing your learning plan

[0994] User: The user follows the displayed study plan, for example, working on new suggested math exercises.

[0995] Specific examples

[0996] For example, if a user is a 13-year-old junior high school student, he or she accesses the system and registers by entering his or her name, age, grade, location, email address, password, etc. After receiving a confirmation email and completing registration, the server will recommend math and science learning content for eighth-grade students based on the user's information. The user can then select the video lesson they want to watch from the recommended list.

[0997] After learning, the user inputs their progress data and emotional state into the device. For example, they input the results of solving geometry exercises and the stress or sense of accomplishment they felt while solving the problems. The server analyzes this data and generates feedback to the user, such as the need for additional geometry exercises, or encouraging messages to reduce stress.

[0998] The generated feedback is displayed on the device, and the AI ​​engine generates a personalized learning plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks for relaxation. By following this plan, users can maximize their learning and reduce mental strain.

[0999] Prompt Sentence Examples

[1000] "Enter math progress data and emotional data to suggest new learning plans."

[1001] As described above, the present invention can achieve more effective learning by managing a user's learning progress and emotional state in real time and providing an individually optimized educational experience.

[1002] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1003] Step 1:

[1004] Fill in and submit the user registration form

[1005] Device: The user operates a device (PC or smartphone) to open a web browser or application, accesses the registration page, enters their name, age, grade, location, email address, and password in the form, and clicks the "Register" button.

[1006] Input: Personal information entered by the user (name, age, grade, location, email address, password)

[1007] Output: Registration request sent to the server

[1008] Specific operation: The user enters information into the form and presses the submit button. The device transfers the submitted data to the server.

[1009] Step 2:

[1010] Receiving and storing user information

[1011] Server: The server receives the user information sent from the device and stores it in a secure database using encryption technology.

[1012] Input: User information sent from the device

[1013] Output: User information stored in the database, preparation for sending a registration confirmation email

[1014] Specific operation: The server inserts and saves data into the database. After saving, a confirmation email is automatically generated.

[1015] Step 3:

[1016] Sending a confirmation email

[1017] Server: Automatically generates a registration confirmation email and sends it to the user's email address, which contains a link to activate the account.

[1018] Input: The user's email address stored in the database

[1019] Output: A confirmation email sent to the user's mailbox.

[1020] Specific operation: The server calls the email sending API and sends a confirmation email.

[1021] Step 4:

[1022] Activating your account

[1023] User: The user receives a confirmation email and activates their account by clicking the link in the email, which allows them to officially log in to the system.

[1024] Input: Activation link in confirmation email

[1025] Output: The user's account is enabled and they can log in.

[1026] Specific action: A user clicking on a link in an email.

[1027] Step 5:

[1028] Learning content recommendations

[1029] Server: Retrieves user registration information (age, grade, interests, etc.) from a database and uses an AI algorithm to select the most appropriate learning content.

[1030] Input: User registration information stored in the database

[1031] Output: A list of recommended learning content

[1032] How it works: The server executes a query to obtain user information and uses an AI model to generate recommended content.

[1033] Step 6:

[1034] Delivery and display of recommendation lists

[1035] Terminal: Receives the list of recommended learning content sent from the server and displays it to the user. The user can select the content they want to learn.

[1036] Input: Recommended learning content list sent from the server

[1037] Output: Content list displayed in the user interface

[1038] Specific operation: The device analyzes the data and displays it on the user interface.

[1039] Step 7:

[1040] Content Selection and Access

[1041] User: The user clicks to select the content they want to watch from the recommended list.

[1042] Device: Providing selected content for user access in streaming or download format.

[1043] Input: Information about content leaked or selected by the user

[1044] Output: Learning content delivered in streaming or download format

[1045] Specific operation: The user selects content, and the device communicates with the content server to retrieve and display the content in the appropriate format.

[1046] Step 8:

[1047] Collection of learning progress and emotion data

[1048] User: After studying, the user enters progress data (study content, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[1049] Terminal: Sends collected progress data and emotion data to the server.

[1050] Input: Progress data entered by the user, emotion data collected by the emotion engine

[1051] Output: Progress and emotion data sent to the server

[1052] Specific operation: The user inputs the learning results, cameras and sensors collect data, and the device sends the data to the server.

[1053] Step 9:

[1054] Data analysis

[1055] Server: Analyzes the collected data (learning progress data and emotional data) using an AI engine to evaluate the user's learning progress and emotional state.

[1056] Input: Learning progress data and emotion data stored on the server

[1057] Output: Analysis results and evaluation report

[1058] What it does: AI algorithms run, analyze the data, and generate reports.

[1059] Step 10:

[1060] Generating and providing feedback messages

[1061] Server: Based on the analysis results, it generates feedback messages that point out the user's weaknesses and areas for improvement, as well as encouragement and relaxation based on emotional data.

[1062] Input: Analysis results and evaluation report

[1063] Output: The generated feedback message

[1064] Terminal: Receives feedback messages sent from the server and displays them to the user.

[1065] Input: The generated feedback message

[1066] Output: Feedback message displayed in the user interface

[1067] Specific operation: The server generates and sends a message, and the terminal displays it.

[1068] Step 11:

[1069] Generate personalized learning plans

[1070] Server: The AI ​​engine uses progress and emotion data to generate a personalized learning plan for each user, including daily study time, focus points, and learning materials.

[1071] Input: Progress data and emotion data

[1072] Output: Generated personalized optimized learning plan

[1073] How it works: The AI ​​model analyzes the input data and generates a new learning plan.

[1074] Step 12:

[1075] Providing and implementing a learning plan

[1076] Terminal: Receives the generated learning plan and displays it on the user interface.

[1077] User: Follows the study plan, for example, works through new suggested math exercises.

[1078] Input: Generated lesson plan

[1079] Output: Learning plan displayed in the user interface, user's learning execution

[1080] Specific operation: The device displays the study plan and the user studies according to it.

[1081] (Application example 2)

[1082] 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."

[1083] Current online education systems are unable to fully address the individual needs of users, particularly in the real-time monitoring of learning progress and emotional state. They also struggle to provide effective learning plans for specific environments, often limiting learning effectiveness. Similar problems can occur in training factory workers, making it difficult for them to effectively acquire skills.

[1084] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to input and register necessary information, a means for recommending appropriate study content based on the user's registration information, a means for collecting and analyzing the user's study progress data and emotional data, a means for generating an individually optimized study plan based on the analyzed data, and a means for providing the generated study plan to the user. This makes it possible to grasp the user's study progress and emotional state in real time and provide an individually optimized study plan and feedback messages.

[1085] The "means for users to enter and register the necessary information" is an interface that allows users to enter necessary information such as name, age, job title, and work content, and register it in the system.

[1086] "Means for recommending appropriate learning content based on the user's registered information" refers to an algorithm and system for automatically selecting optimal learning content based on the user's registered information and recommending it to the user.

[1087] "Means for collecting and analyzing user learning progress data and emotional data" refers to a system for centrally collecting and analyzing learning progress information entered by users and emotional data collected using cameras and sensors.

[1088] The "means for generating an individually optimized learning plan based on the analyzed data" refers to an AI engine and system for analyzing a user's learning progress data and emotional data and generating an optimized learning plan for the user based on the results.

[1089] The "means for providing the generated study plan to the user" refers to an interface and system for presenting the generated individually optimized study plan to the user and allowing the user to study efficiently in accordance with the study plan.

[1090] The present invention provides a system for improving a user's learning environment and providing an individually optimized educational experience. Specific embodiments of the present invention will be described below.

[1091] 1. User Registration

[1092] The server provides a means for users to enter the required information and register. This means can be an interface, such as an HTML form, where users enter and submit information such as their name, age, job title, and job description. The registration information is securely stored in the server's database. The user then receives a confirmation email with a link to activate their account.

[1093] 2. Learning content recommendations

[1094] The server provides a means to recommend appropriate learning content based on the user's registered information. Specifically, it selects appropriate training content based on the user's work duties, job title, and past learning data using an algorithm. The selected content is optimized for the growth of factory workers and promotes efficient skill acquisition.

[1095] 3.Collection and analysis of learning progress and emotion data

[1096] The device provides a means to collect learning progress data and emotional data entered by the user. Emotional data is collected using an emotion recognition library (e.g., EmotionRecognizer) and analyzes the user's facial expressions using a camera sensor. The collected data is sent to a server and analyzed by an AI engine. This analysis provides a detailed understanding of the user's progress and emotional state.

[1097] 4.Generating an individualized learning plan

[1098] The server analyzes the user's learning progress data and emotional data and provides a means to generate an individually optimized learning plan. Specifically, an AI engine (e.g., AIEngine) analyzes the user's weaknesses and strengths and creates optimal training content and feedback. The generated learning plan includes daily training content, recommended learning materials, break times, etc.

[1099] 5. Providing study plans

[1100] The device has a means for providing the generated learning plan to the user, allowing the user to proceed with training according to the displayed plan. Feedback messages are provided according to the user's progress and emotional state, and may include encouraging messages such as "Your progress so far is great! Keep up the great work!"

[1101] Adding specific examples

[1102] For example, when a new factory worker is undergoing training on a certain process, if the emotion recognition engine detects that the worker is feeling stressed, the AI ​​engine will suggest encouraging messages or additional training content for the worker.

[1103] Prompt Sentence Examples

[1104] Analyze the training progress and sentiment data of your next worker to generate feedback and next steps:

[1105] User ID: 123

[1106] Progress data: { "Completion": 70, "Test result": 80}

[1107] Emotion data: { "Stress": High, "Concentration": Low}"

[1108] In this way, the system is able to grasp the user's learning progress and emotional state in real time, enabling it to provide individually optimized learning plans and feedback messages.

[1109] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1110] Step 1:

[1111] The user registers by entering the required information such as name, age, job title, and work details. The entered information is sent to the server through the device interface. The server securely stores the received information in a database and sends a registration confirmation email to the user's email address. The user activates their account by clicking the link in the confirmation email.

[1112] Step 2:

[1113] The server recommends appropriate learning content based on the user's registration information. It selects appropriate training content and generates a recommendation list based on the user's registration information (name, age, job title, and work content). The terminal displays this list to the user, who can then select training content from the provided options.

[1114] Step 3:

[1115] You watch or perform the training content you select. The device makes the selected content available to you in streaming or download format, and collects progress data (e.g., completion rate, test results) that occurs during the viewing or performance.

[1116] Step 4:

[1117] The device collects learning progress data and emotional data entered by the user. Emotional data is collected using a camera sensor and an emotion recognition library (e.g., EmotionRecognizer). Specifically, the camera captures a picture of the user's face, and the emotion recognition algorithm detects emotions from their facial expressions. This data is then sent to the server.

[1118] Step 5:

[1119] The server analyzes the user's learning progress data and emotional data. An AI engine (e.g., AIEngine) analyzes the input progress and emotional data. The analysis results reveal the user's progress and emotional state, and generates appropriate feedback messages based on that information.

[1120] Step 6:

[1121] The server then generates a personalized learning plan based on the analysis results. Based on the progress and emotional data, the AI ​​engine generates a learning plan that includes daily training content, specific learning materials, and break times. This plan is then sent to the device and displayed to the user.

[1122] Step 7:

[1123] Users progress through their training according to the individually optimized learning plan displayed on their device. The device has an interface that supports page transitions and training progress, and displays guides and reminders to help users progress through their studies efficiently. Continuous feedback messages are also provided to maintain motivation to study.

[1124] These are the specific processing steps of the system that realizes this application example. This makes it possible to grasp the user's learning progress and emotional state in real time and provide individually optimized learning plans and feedback messages.

[1125] 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.

[1126] 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.

[1127] 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.

[1128] [Third embodiment]

[1129] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1130] 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.

[1131] 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).

[1132] 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.

[1133] 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.

[1134] 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).

[1135] 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.

[1136] 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.

[1137] 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.

[1138] 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.

[1139] 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.

[1140] 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."

[1141] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, learning content recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[1142] User Registration

[1143] 1. User Registration

[1144] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters their name, age, grade, location, email address, password, etc.

[1145] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[1146] Users: Receive a confirmation email and click the link provided to activate their account.

[1147] Educational content distribution

[1148] 2. Content Recommendation

[1149] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[1150] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[1151] 3. Content Access

[1152] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[1153] Device: Providing selected content for users to access in streaming or download format.

[1154] Learning progress management

[1155] 4. Collecting progress data

[1156] User: After studying, enter study progress data (which parts have been studied, test results, etc.) into the terminal.

[1157] Terminal: Sends the entered data to the server.

[1158] 5. Data analysis and feedback

[1159] Server: Analyzes the collected data and tracks progress. For example, if the percentage of correct answers to a particular math problem is low, it provides feedback on that task.

[1160] Terminal: Displays feedback messages sent by the server to the user.

[1161] Providing individually optimized learning plans

[1162] 6. Generate personalized learning plans

[1163] Server: Based on the collected progress data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[1164] Device: Displays the generated learning plan to the user.

[1165] 7. Implementing your learning plan

[1166] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[1167] Example: When a junior high school student uses the system

[1168] If the user is a junior high school student, the following is a specific example.

[1169] 1. User Registration

[1170] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, the user provides information such as 13 years old, living in Tokyo, and in the second year of junior high school.

[1171] Server: Stores the received information in a database and sends a confirmation email.

[1172] User: Receives a confirmation email and clicks on the link to complete registration.

[1173] 2. Content Recommendation

[1174] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[1175] Devices: Show recommended content to middle school students.

[1176] User: Selects and watches a mathematics geometry lesson from the recommended content.

[1177] 3. Collecting and analyzing progress data

[1178] User: After solving a geometry exercise, enter the result into the terminal.

[1179] Server: Analyzes the input data and detects low levels of understanding of specific problems.

[1180] Terminal: Feedback tells the user that additional geometry practice problems are needed.

[1181] 4. Providing personalized learning plans

[1182] Server: The AI ​​engine uses the user's progress data to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch.

[1183] Device: Display the new learning plan to the user.

[1184] Users: Follow the plan and solve geometry exercises every day to deepen their understanding.

[1185] In this way, the present invention achieves equalization and quality improvement of education by strictly managing users' learning progress and providing individually optimized educational experiences.

[1186] The processing flow will be explained below.

[1187] Step 1:

[1188] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​affiliation, email address, and password, and presses the submit button.

[1189] Step 2:

[1190] The terminal transmits the user's input information to the server.

[1191] Step 3:

[1192] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[1193] Step 4:

[1194] The user receives a confirmation email and clicks the link in the email to activate their account.

[1195] Step 5:

[1196] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[1197] Step 6:

[1198] The server generates a list of selected learning content and sends it to the terminal.

[1199] Step 7:

[1200] The terminal displays the received content list to the user, who can then click to select the content of interest.

[1201] Step 8:

[1202] The terminal provides the selected learning content in streaming or download format for the user to access.

[1203] Step 9:

[1204] After the user finishes the study session, they input information such as what they studied and test results into the terminal.

[1205] Step 10:

[1206] The terminal transmits the input learning progress data to the server.

[1207] Step 11:

[1208] The server analyzes the received learning progress data, for example determining if a particular task has not been understood and therefore requires special attention from the user.

[1209] Step 12:

[1210] Based on the analysis results, the server generates a feedback message for the user, pointing out the user's weaknesses and areas for improvement.

[1211] Step 13:

[1212] The terminal displays the feedback message sent from the server to the user.

[1213] Step 14:

[1214] The user periodically (e.g., on weekends) enters the study time and results into the terminal.

[1215] Step 15:

[1216] The terminal sends the input data to the server.

[1217] Step 16:

[1218] The server analyzes the accumulated data using an AI engine and generates a study plan optimized for the user (daily study time, tasks to focus on, study materials to use, etc.).

[1219] Step 17:

[1220] The server sends the generated learning plan to the device.

[1221] Step 18:

[1222] The device displays the new study plan to the user, who then follows the plan to study.

[1223] Step 19:

[1224] The user repeatedly inputs the learning progress into the terminal.

[1225] Step 20:

[1226] The server collects and analyzes data, and then adjusts feedback and learning plans to continuously optimize the user's learning experience.

[1227] In this way, the system manages the user's learning progress in real time and provides a personalized educational experience.

[1228] Example 1

[1229] 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."

[1230] In conventional online education systems, users are often provided with uniform learning content, making it difficult to provide an educational experience optimized for individual needs and progress. Furthermore, there is a lack of feedback based on the user's learning progress or the generation of individually optimized learning plans, which makes it difficult to provide effective learning support.

[1231] 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.

[1232] In this invention, the server includes a means for a user to input and register necessary information, a means for recommending appropriate educational materials based on the user's registration information, a means for collecting and analyzing the user's learning progress data, a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, and a means for creating prompt sentences when generating a learning plan from the user's progress data using a generative AI model. This enables a customized educational experience for each user, not only improving learning efficiency but also making it possible to provide an optimal learning plan based on each user's individual progress.

[1233] "User" refers to an individual who uses the online education system to learn.

[1234] "Information input" refers to the act of a user registering personal information such as name, age, grade, location, email address, and password into the system.

[1235] "Educational Materials" refers to educational content, such as teaching materials and video lessons, designed to enhance a user's learning.

[1236] "Recommendation" refers to the act of providing appropriate educational materials based on the user's registration information.

[1237] "Study progress data" refers to information indicating the user's learning progress, such as their learning status and test results.

[1238] "Analysis" refers to the act of analyzing collected learning progress data using data analysis tools and AI models.

[1239] "Individually optimized learning plan" refers to a learning plan that includes an optimal learning schedule and learning materials, which is generated based on each user's individual progress.

[1240] "Generation" refers to the act of creating a new learning plan based on collected data and analysis results.

[1241] "Providing" refers to the act of notifying or presenting the generated study plan to the user.

[1242] A "generative AI model" refers to an artificial intelligence model that automatically generates learning plans and feedback based on user progress data.

[1243] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[1244] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, educational material recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[1245] User Registration

[1246] Users access the system via a web browser or smartphone app and navigate to the registration page. They enter required information such as their name, age, grade, location, email address, and password. This data is sent to the server via their device and stored in a secure database. The server then sends a registration confirmation email to the user's email address, and the user activates their account by clicking the link in the email.

[1247] Recommend educational materials

[1248] The server retrieves the user's registration information from the database and uses a generative AI model to recommend appropriate educational materials. For example, it automatically selects geometry or chemistry video lessons based on the user's grade, location, and interests. The recommended educational materials are sent to the device, which displays the list to the user. The user can then select the content they want to study from the list.

[1249] Learning progress management

[1250] After using educational materials, users input their learning progress data (study content, test results, etc.) into their device. The device then sends this data to a server. The server collects the progress data and analyzes it using analytical tools and AI models. For example, if the accuracy rate for a particular math problem is low, a feedback message is generated based on the results.

[1251] The server sends the generated feedback message to the terminal, and the terminal displays the feedback to the user, so that the user can work on the next learning activity based on the feedback.

[1252] Providing individually optimized learning plans

[1253] The server uses a generative AI model to generate an individually optimized study plan based on the collected progress data. For example, it uses a prompt such as, "Based on the user's progress data, please generate a study plan for next week. Please include key topics, recommended learning materials, and study time." The generated study plan is stored in a database and sent to the user's device. The device displays the plan to the user, and the user proceeds with their studies according to the plan.

[1254] Specific examples

[1255] For example, when a second-year junior high school student uses the system, they follow the steps below. The user registers necessary information such as their name, age, grade, location, email address, and password. The server recommends educational materials for mathematics and science based on the registered information. From the recommended content, the user selects and watches a geometry video lesson.

[1256] After studying, the user enters progress data into the device and sends it to the server, which analyzes it and detects insufficient understanding of a particular problem, generating a feedback message to the user via the device indicating the need for additional geometry practice problems.

[1257] The server then uses an AI engine to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch, allowing users to take their next learning steps based on scientific evidence.

[1258] In this way, the system provides a customized educational experience for each user, improving learning efficiency.

[1259] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1260] Step 1: Go to the user registration page

[1261] The user opens a web browser or smartphone app and accesses the system's user registration page.

[1262] The terminal receives the user's request and displays a registration page.

[1263] Input: User access request

[1264] Output: Display of registration page

[1265] What happens: The device interprets the URL and displays the appropriate registration page.

[1266] Step 2: Enter your information

[1267] The user enters necessary information such as name, age, grade, location, email address, and password.

[1268] The terminal temporarily stores the entered information in real time and displays it on the form.

[1269] Input: Personal information entered by the user

[1270] Output: Personal information displayed in the input form

[1271] Specific operation: The device receives user input and displays it in real time.

[1272] Step 3: Send information

[1273] The user clicks the send button to submit the information they have entered.

[1274] The terminal transmits the user's input information to the server.

[1275] Input: User submission requests and input information

[1276] Output: User information sent to the server

[1277] Specific operation: Create an HTTP request and send it to the server along with the input information.

[1278] Step 4: Receiving and storing information

[1279] The server checks the received information and stores it in a secure database, either an SQL database or a NoSQL database.

[1280] Input: User information sent from the device

[1281] Output: User information stored in the database

[1282] Specific operation: The server verifies the received information and stores it in a database using an SQL query, etc.

[1283] Step 5: Send a confirmation email

[1284] The server will send a registration confirmation email to the user's email address, which will contain an account activation link.

[1285] The user will receive a confirmation email and will need to click on the link to activate their account.

[1286] Input: User's email address

[1287] Output: A confirmation email sent to the user.

[1288] Specific operation: The server connects to the mail server and sends a confirmation email.

[1289] Step 6: Information Acquisition for Content Recommendation

[1290] The server retrieves the user's registration information from a database, often using an SQL query.

[1291] Input: User registration information

[1292] Output: Retrieved user information

[1293] What happens: The server executes an SQL query to get the required information from the database.

[1294] Step 7: Running the Content Recommendation Engine

[1295] Based on the registration information, the server runs a recommendation engine (e.g., a machine learning model) to select appropriate learning content, taking into account the user's grade, location, interests, etc.

[1296] Input: User registration information

[1297] Output: A list of recommended educational materials

[1298] What it does: It uses an algorithm to select the most relevant content for users.

[1299] Step 8: Submit your recommended content

[1300] The server transmits the recommended content list to the terminal.

[1301] Input: A list of recommended educational materials

[1302] Output: Content list sent to device

[1303] Specific operation: Create an HTTP response and send the content list to the terminal.

[1304] Step 9: Displaying Content

[1305] The device displays the received content list to the user, such as a list of "geometry video lessons" and "chemistry experiment videos."

[1306] Input: Content list sent from the server

[1307] Output: Content list displayed to the user

[1308] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[1309] Step 10: Input training data

[1310] After using the educational materials, the user inputs learning progress data (learning content, test results, etc.) into the terminal.

[1311] Input: User's learning progress data

[1312] Output: Progress data temporarily stored on the device

[1313] Specific behavior: The device receives user input and displays it in real time.

[1314] Step 11: Send data

[1315] The device sends the entered progress data to the server, often by POSTing the data using an API endpoint.

[1316] Input: User's learning progress data

[1317] Output: Progress data sent to the server

[1318] Specific behavior: Makes an API request and sends progress data to the server.

[1319] Step 12: Analyze progress data

[1320] The server analyzes the received data, such as test accuracy and study time, using analytical tools and AI models.

[1321] Input: Received learning progress data

[1322] Output: Analysis results

[1323] Specific actions: Analyze progress data using data analysis tools and AI models.

[1324] Step 13: Feedback Generation

[1325] The server generates feedback based on the analysis results. If the level of understanding of a particular problem is low, it generates a feedback message recommending additional study materials or practice problems.

[1326] Input: Analysis results

[1327] Output: Feedback message

[1328] Specific operation: Perform text processing to generate a feedback message based on the analysis results.

[1329] Step 14: Submit your feedback

[1330] The server sends a feedback message to the terminal.

[1331] Input: Feedback message

[1332] Output: Feedback message sent to the terminal

[1333] Specific operation: Creates an HTTP response and sends a feedback message to the terminal.

[1334] Step 15: Viewing feedback

[1335] The device displays the received feedback message to the user, including specific improvements and recommendations for further learning.

[1336] Input: Feedback message sent by the server

[1337] Output: Feedback that is displayed to the user

[1338] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[1339] Step 16: Run the AI ​​Engine

[1340] The server uses the collected progress data to run a generative AI model and create prompts to generate an individually optimized study plan. For example, the server uses the prompt, "Based on the user's progress data, please generate a study plan for next week. Please include key tasks, recommended learning materials, and study time."

[1341] Input: Progress data and prompt text

[1342] Output: Personalized learning plan

[1343] Specific operation: Call the AI ​​engine and obtain the generated learning plan.

[1344] Step 17: Save and submit your study plan

[1345] The server stores the generated study plan in a database and transmits it to the user's terminal.

[1346] Input: Generated lesson plan

[1347] Output: Study plan stored in the database and study plan sent to the device

[1348] Specific behavior: Saves a learning plan using an SQL query and creates and sends an HTTP response.

[1349] Step 18: View your learning plan

[1350] The device displays the generated study plan to the user, which includes a daily study schedule and the learning materials to be used.

[1351] Input: Study plan sent from the server

[1352] Output: The learning plan that is displayed to the user

[1353] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[1354] Step 19: Implementing your learning plan

[1355] The user progresses through the displayed study plan, for example by working through newly recommended math exercises.

[1356] Input: Study Plan

[1357] Output: User's learning progress

[1358] Specific actions: The user actually performs the learning activities according to the learning plan.

[1359] summary

[1360] This system aims to provide an individually optimized educational experience through collaboration between users, devices, and servers. By utilizing generative AI models and prompts, it is possible to generate and provide efficient and effective learning plans.

[1361] (Application example 1)

[1362] 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."

[1363] Conventional online education systems struggle to provide an individually optimized learning experience, and are unable to provide content tailored to each user's unique learning needs. As a result, learning effectiveness declines and user satisfaction declines. Furthermore, the inability to track learning progress in real time and provide appropriate feedback can impair learning efficiency.

[1364] 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.

[1365] In this invention, the server includes a means for the user to input necessary information and register, a means for recommending appropriate study content based on the user's registered information, and a means for collecting and analyzing the user's study progress data, thereby providing the user with an individually optimized learning experience and improving the effectiveness of their learning.

[1366] The server further includes a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, a means for having the user input information via an application installed on the smartphone terminal, and collecting and analyzing progress data, and a means for using the collected progress data to generate a feedback message using an AI model and notifying the user. This makes it possible to grasp learning progress in real time and provide effective feedback, thereby improving the user's learning efficiency.

[1367] "User" refers to an individual who uses the online education system.

[1368] "Required information" refers to specific data required for registration, such as name, age, grade, location, email address, and password.

[1369] "Means for Registration" refers to the process by which a user enters required information, saves that information in the system, and activates an account.

[1370] "Learning Content" means educational materials, such as instructional materials, video lessons, and exercises, that are provided to assist users in their learning.

[1371] "Recommendation means" refers to a method of selecting and presenting appropriate learning content based on the user's registration information.

[1372] "Study progress data" refers to data such as the progress, grades, and test results achieved by the user during their studies.

[1373] "Means for collecting and analyzing" refers to a method for collecting learning progress data from users and analyzing the data to evaluate the state of learning.

[1374] "Individually optimized learning plan" refers to a learning plan that is optimized based on each user's learning progress data.

[1375] "Means of generation" refers to the method of creating an individually optimized learning plan based on the collected data.

[1376] The "means for providing" refers to a means for displaying the generated learning plan to the user and encouraging them to carry it out.

[1377] A "smartphone terminal" refers to a mobile communication device that can connect to the Internet and on which applications can be installed and used.

[1378] "Application" refers to software that users install and use on their smartphone devices.

[1379] "Means for collecting and analyzing progress data" refers to a method in which a user inputs learning progress data through an application and the data is sent to a server for analysis.

[1380] An "AI model" refers to an artificial intelligence algorithm that performs inference and optimization based on collected data and generates feedback and learning plans.

[1381] "Feedback message" refers to advice and evaluation provided based on the user's learning progress data.

[1382] "Means for notifying" refers to a method for notifying the user of the generated feedback message.

[1383] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. The system is operated using an application installed on a user's smartphone terminal.

[1384] First, the user downloads and installs the online education application on their smartphone. The user enters the required information (e.g., name, age, grade, location, email address, password, etc.) to register. The server stores the user's registration information in a secure database and sends the user an email to confirm their registration. The user receives the confirmation email and clicks the link contained in it to activate their account.

[1385] After registration is complete, the server recommends appropriate learning content (such as teaching materials and video lessons) based on the user's registration information. This content is customized according to the user's grade level and interests. The recommended learning content is displayed to the user through an application on their smartphone, and the user can select from the content and begin watching.

[1386] As the user progresses with their studies, they input their learning progress data (such as which learning materials they have viewed and test results) into the application. The device then sends the input progress data to the server. The server analyzes the collected progress data and generates an individually optimized learning plan based on the analysis results. For example, if the user's understanding of a particular subject is insufficient, the plan will include additional learning content related to that subject.

[1387] Furthermore, the system uses an AI model to generate feedback messages based on the collected progress data and notifies the user of the generated feedback. The feedback messages include suggestions for additional practice in areas where understanding is insufficient and instructions on what to do next. This allows users to understand their own learning status in real time and study effectively.

[1388] As a concrete example, consider a middle school student watching a geometry video lesson and working on the exercises. The user opens the application and enters their progress data. For example, if they score 55 points on a geometry exercise, they enter that score. The server analyzes this score and uses an AI model to generate a feedback message to the user saying, "You need more practice in geometry. Please complete additional exercises."

[1389] Below are some examples of prompts used in this system:

[1390] "You are in eighth grade math class. Please solve a geometry problem. Enter the score you received for the problem."

[1391] In this way, the system of the present invention can closely manage the user's learning progress and provide a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[1392] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1393] Step 1:

[1394] A user downloads and installs an online education application on their smartphone. They register an account by entering required information such as their name, age, grade, location, email address, and password. The server receives the information, stores it in a secure database, and sends a confirmation email to the user. The user receives the confirmation email and clicks on the link included to activate their account.

[1395] Input: Registration information such as name, age, grade, location, email address, and password

[1396] Output: Verification email sent and account activated

[1397] Step 2:

[1398] The server recommends learning content (such as teaching materials and video lessons) based on the user's registration information. The recommended content is customized according to the user's grade and interests. The device displays the recommended learning content to the user, and the user selects the content they want to view from the displayed list.

[1399] Input: Registration information, grade, interests

[1400] Output: Display of customized learning content list

[1401] Step 3:

[1402] The user watches the selected learning content and proceeds with their learning. After studying, the user enters their learning progress data (viewed content, test results, etc.) into the device. The device then sends the entered progress data to the server.

[1403] Input: Learning progress data (content viewed, test results)

[1404] Output: Sending progress data

[1405] Step 4:

[1406] The server analyzes the collected progress data. For example, if a user scores low in a particular subject or topic, the server can identify the cause. Based on the analysis, the server generates a personalized, optimized learning plan, which includes focused learning content and additional practice questions.

[1407] Input: Progress data (content viewed, test results)

[1408] Output: Personalized learning plan

[1409] Step 5:

[1410] The server runs the generative AI model based on the collected progress data and generates feedback messages for the user. This feedback includes specific advice, such as "You need more practice on geometry. Please solve additional problems." The generated feedback messages are sent to the user's device.

[1411] Input: Progress data, execution results of generative AI model

[1412] Output: Feedback message

[1413] Step 6:

[1414] Users follow the feedback messages displayed on their device to progress through their learning according to an individually optimized learning plan. By using the device's application, they can improve their learning effectiveness by working on newly recommended content and practice problems.

[1415] Input: Feedback message, personalized learning plan

[1416] Output: Improved user learning progress

[1417] In this way, the system of the present invention closely manages the user's learning progress and provides a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[1418] 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.

[1419] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has functions for user registration, recommending learning content, collecting and analyzing learning progress data, and generating and providing personalized and optimized learning plans, as well as an emotion engine that recognizes the user's emotions.

[1420] User Registration

[1421] 1. User Registration

[1422] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters the required information such as name, age, grade, location, email address, and password, and presses the submit button.

[1423] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[1424] Users: Receive a confirmation email and click the link provided to activate their account.

[1425] Educational content distribution

[1426] 2. Content Recommendation

[1427] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[1428] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[1429] 3. Content Access

[1430] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[1431] Device: Providing selected content for user access in streaming or download format.

[1432] Learning progress management

[1433] 4. Collecting progress data

[1434] User: After learning, the user inputs learning progress data (which parts they have learned, test results, etc.) into the device. In addition, the emotion engine collects the user's emotion data from the facial recognition camera and sensors.

[1435] Terminal: Sends the input learning progress data and emotion data to the server.

[1436] 5. Data analysis and feedback

[1437] Server: Analyzes the collected data (learning progress data and emotion data) to understand the progress of the user. For example, if the correct answer rate for a particular math problem is low and the user is feeling confused or stressed, it determines that the user needs special attention.

[1438] Server: Based on the analysis results, it generates feedback messages for the user, pointing out their weaknesses and areas for improvement. Based on the emotional data, it also provides messages of encouragement and relaxation.

[1439] Terminal: Displays feedback messages sent by the server to the user.

[1440] Providing individually optimized learning plans

[1441] 6. Generate personalized learning plans

[1442] Server: Based on the collected progress and emotion data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[1443] Device: Displays the generated learning plan to the user.

[1444] 7. Implementing your learning plan

[1445] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[1446] Example: When a junior high school student uses the system

[1447] If the user is a junior high school student, the following is a specific example.

[1448] 1. User Registration

[1449] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, they provide information such as age 13, local resident, and second-year junior high school student.

[1450] Server: Stores the received information in a database and sends a confirmation email.

[1451] User: Receives a confirmation email and clicks on the link to complete registration.

[1452] 2. Content Recommendation

[1453] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[1454] Devices: Show recommended content to middle school students.

[1455] User: Selects and watches a mathematics geometry lesson from the recommended content.

[1456] 3. Collecting and analyzing progress and emotion data

[1457] User: After solving a geometry exercise, the user enters the results and emotions (e.g., stress or accomplishment) into the device.

[1458] Server: Analyzes the input data and detects the level of understanding and emotional state of the specific problem.

[1459] Terminal: Feedback informs the user that additional geometry practice problems are needed, and also displays encouraging messages to reduce stress.

[1460] 4. Providing personalized learning plans

[1461] Server: An AI engine uses progress and emotional data to generate a study plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks to relax.

[1462] Device: Display the new learning plan to the user.

[1463] Users: Follow a plan and solve geometry exercises every day to improve their understanding and manage stress.

[1464] In this way, the present invention manages the user's emotional state in real time along with their learning progress, providing a personalized and optimized educational experience, thereby enabling more effective learning.

[1465] The processing flow will be explained below.

[1466] Step 1:

[1467] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​residence, email address, and password, and presses the submit button.

[1468] Step 2:

[1469] The terminal transmits the user's input information to the server.

[1470] Step 3:

[1471] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[1472] Step 4:

[1473] The user receives a confirmation email and clicks the link in the email to activate their account.

[1474] Step 5:

[1475] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[1476] Step 6:

[1477] The server generates a list of selected learning content and sends it to the terminal.

[1478] Step 7:

[1479] The terminal displays the received content list to the user, who can then click to select the content of interest.

[1480] Step 8:

[1481] The terminal provides the selected learning content in streaming or download format for the user to access.

[1482] Step 9:

[1483] After completing a study session, the user inputs their learning progress and emotions into the device. Emotional data is collected using a facial recognition camera and sensors.

[1484] Step 10:

[1485] The terminal transmits the input learning progress data and emotion data to the server.

[1486] Step 11:

[1487] The server analyzes the received learning progress data. For example, if the percentage of correct answers to a particular math problem is low, it can be determined that the user is struggling with that subject.

[1488] Step 12:

[1489] The server uses an emotion engine to analyze the user's emotion data, for example, to detect if the user is feeling confused or stressed.

[1490] Step 13:

[1491] The server generates a feedback message based on the learning progress data and emotion data, such as "It seems you found this part difficult. Let's start with an easier problem next time."

[1492] Step 14:

[1493] The terminal displays the feedback message sent from the server to the user.

[1494] Step 15:

[1495] Users periodically (e.g., on weekends) enter their study time and results into the device, and emotional data is also collected.

[1496] Step 16:

[1497] The terminal sends the input data to the server.

[1498] Step 17:

[1499] The server analyzes the accumulated data using an AI engine and generates an optimized study plan for each user, including daily study time, tasks to focus on, and study materials to use.

[1500] Step 18:

[1501] The server sends the generated learning plan to the device.

[1502] Step 19:

[1503] The device displays the new study plan to the user, who then follows the plan to study.

[1504] Step 20:

[1505] The server continuously collects and analyzes learning progress and emotional data, and adjusts feedback and learning plans accordingly to continually optimize the user's learning experience.

[1506] In this way, the system manages the user's learning progress and emotional state in real time, providing a personalized educational experience.

[1507] Example 2

[1508] 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."

[1509] In conventional online education systems, it was difficult to grasp individual users' learning progress and emotions in real time and provide optimal learning plans. As a result, it was not possible to provide an effective and continuous learning experience for users, which could lead to a decline in learning effectiveness. In addition, there was a lack of means to provide feedback based on learning progress and changes in emotions, making it difficult to maintain users' motivation to learn.

[1510] 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.

[1511] In this invention, the server includes means for the user to input necessary information and register, means for recommending appropriate study content based on the user's registration information, means for collecting the user's study progress data and emotion data, means for analyzing the collected data and generating an individually optimized study plan, means for providing the generated study plan to the user, and means for displaying the provided feedback message to the user, thereby making it possible to provide the user with an individually optimized study plan and feedback messages based on their emotions.

[1512] "User" refers to an individual learner who uses the online education system.

[1513] "Information" or "Required Information" refers to personal information such as name, age, grade, location, email address, and password that a User provides to register with the System.

[1514] "Registration" refers to the process by which a User enters required information into the System to create and activate an account.

[1515] "Server" refers to a computer system that stores and processes user information, progress data, learning content, etc.

[1516] "Learning Content" refers to educational resources such as teaching materials, video lessons, and textbooks provided on the System.

[1517] "Recommendation" refers to the process of selecting and presenting the most appropriate learning content based on the user's registration information and learning history.

[1518] "Study progress data" refers to records of what parts a user has studied, the content of their studies, test results, and so on.

[1519] "Emotional data" refers to data on a user's emotional state based on facial expressions and physical reactions collected using facial recognition cameras and sensors.

[1520] "Analysis" refers to the process of analyzing the collected learning progress data and emotional data to evaluate the user's learning status and emotional state.

[1521] "Individually optimized learning plan" refers to a plan that includes a learning schedule and assignments that are optimal for a specific user, generated based on the user's learning progress data and emotional data.

[1522] "Feedback message" refers to a message of evaluation or encouragement that is generated based on the analysis results and provided to the user.

[1523] "Providing" refers to the process by which the server presents the generated learning plan and feedback messages to the user.

[1524] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. This system has the following functions: user registration, learning content recommendation, collection and analysis of learning progress data and emotion data, generation and provision of personalized and optimized learning plans, and provision of feedback messages.

[1525] 1. User Registration

[1526] Device: The user uses a device (such as a PC or smartphone) to open the system's registration page in a web browser or application. The user enters the required information (name, age, grade, location, email address, password, etc.) and clicks the "Register" button.

[1527] Server: Receives user information sent from the device and stores it in a secure database. The stored data is protected by encryption technology. A registration confirmation email is then automatically generated and sent to the user's email address.

[1528] User: The user receives a confirmation email and clicks the link in the email to activate their account.

[1529] 2. Educational content recommendations

[1530] Server: Based on the information registered by the user (age, grade, interests, etc.), the algorithm runs and automatically selects the most suitable learning content. The selected content is customized according to the user's grade, region, and interests. An AI-based recommendation system is used.

[1531] Device: Receives the selected content recommendation list and displays it to the user, who can then select the content they want to study from the list.

[1532] 3. Content Access

[1533] User: The user clicks to select the content they want to watch (e.g., a math video lesson) from the recommendations list.

[1534] Device: Providing selected content for user access in streaming or download format.

[1535] 4. Collecting learning progress data

[1536] User: After studying, the user enters progress data (which part they studied, what they learned, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[1537] Terminal: Sends the input learning progress data and emotion data to the server.

[1538] 5. Data analysis and feedback

[1539] Server: The AI ​​engine analyzes the collected data (learning progress data and emotional data) and evaluates the user's learning progress and emotional state. For example, if the user has a low success rate on a particular math problem and feels confused or stressed, the server will urge the user to pay special attention.

[1540] Server: Generates feedback messages based on the analysis results. The feedback messages include points out the user's weaknesses and areas for improvement, as well as messages of encouragement or relaxation based on emotional data.

[1541] Terminal: Receives feedback messages sent from the server and displays them to the user.

[1542] 6. Generate personalized learning plans

[1543] Server: The AI ​​engine generates an optimal study plan for each user based on the progress and emotion data collected. The plan includes daily study time, priority tasks, and study materials to use.

[1544] Device: Displays the generated learning plan to the user.

[1545] 7. Implementing your learning plan

[1546] User: The user follows the displayed study plan, for example, working on new suggested math exercises.

[1547] Specific examples

[1548] For example, if a user is a 13-year-old junior high school student, he or she accesses the system and registers by entering his or her name, age, grade, location, email address, password, etc. After receiving a confirmation email and completing registration, the server will recommend math and science learning content for eighth-grade students based on the user's information. The user can then select the video lesson they want to watch from the recommended list.

[1549] After learning, the user inputs their progress data and emotional state into the device. For example, they input the results of solving geometry exercises and the stress or sense of accomplishment they felt while solving the problems. The server analyzes this data and generates feedback to the user, such as the need for additional geometry exercises, or encouraging messages to reduce stress.

[1550] The generated feedback is displayed on the device, and the AI ​​engine generates a personalized learning plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks for relaxation. By following this plan, users can maximize their learning and reduce mental strain.

[1551] Prompt Sentence Examples

[1552] "Enter math progress data and emotional data to suggest new learning plans."

[1553] As described above, the present invention can achieve more effective learning by managing a user's learning progress and emotional state in real time and providing an individually optimized educational experience.

[1554] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1555] Step 1:

[1556] Fill in and submit the user registration form

[1557] Device: The user operates a device (PC or smartphone) to open a web browser or application, accesses the registration page, enters their name, age, grade, location, email address, and password in the form, and clicks the "Register" button.

[1558] Input: Personal information entered by the user (name, age, grade, location, email address, password)

[1559] Output: Registration request sent to the server

[1560] Specific operation: The user enters information into the form and presses the submit button. The device transfers the submitted data to the server.

[1561] Step 2:

[1562] Receiving and storing user information

[1563] Server: The server receives the user information sent from the device and stores it in a secure database using encryption technology.

[1564] Input: User information sent from the device

[1565] Output: User information stored in the database, preparation for sending a registration confirmation email

[1566] Specific operation: The server inserts and saves data into the database. After saving, a confirmation email is automatically generated.

[1567] Step 3:

[1568] Sending a confirmation email

[1569] Server: Automatically generates a registration confirmation email and sends it to the user's email address, which contains a link to activate the account.

[1570] Input: The user's email address stored in the database

[1571] Output: A confirmation email sent to the user's mailbox.

[1572] Specific operation: The server calls the email sending API and sends a confirmation email.

[1573] Step 4:

[1574] Activating your account

[1575] User: The user receives a confirmation email and activates their account by clicking the link in the email, which allows them to officially log in to the system.

[1576] Input: Activation link in confirmation email

[1577] Output: The user's account is enabled and they can log in.

[1578] Specific action: A user clicking on a link in an email.

[1579] Step 5:

[1580] Learning content recommendations

[1581] Server: Retrieves user registration information (age, grade, interests, etc.) from a database and uses an AI algorithm to select the most appropriate learning content.

[1582] Input: User registration information stored in the database

[1583] Output: A list of recommended learning content

[1584] How it works: The server executes a query to obtain user information and uses an AI model to generate recommended content.

[1585] Step 6:

[1586] Delivery and display of recommendation lists

[1587] Terminal: Receives the list of recommended learning content sent from the server and displays it to the user. The user can select the content they want to learn.

[1588] Input: Recommended learning content list sent from the server

[1589] Output: Content list displayed in the user interface

[1590] Specific operation: The device analyzes the data and displays it on the user interface.

[1591] Step 7:

[1592] Content Selection and Access

[1593] User: The user clicks to select the content they want to watch from the recommended list.

[1594] Device: Providing selected content for user access in streaming or download format.

[1595] Input: Information about content leaked or selected by the user

[1596] Output: Learning content delivered in streaming or download format

[1597] Specific operation: The user selects content, and the device communicates with the content server to retrieve and display the content in the appropriate format.

[1598] Step 8:

[1599] Collection of learning progress and emotion data

[1600] User: After studying, the user enters progress data (study content, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[1601] Terminal: Sends collected progress data and emotion data to the server.

[1602] Input: Progress data entered by the user, emotion data collected by the emotion engine

[1603] Output: Progress and emotion data sent to the server

[1604] Specific operation: The user inputs the learning results, cameras and sensors collect data, and the device sends the data to the server.

[1605] Step 9:

[1606] Data analysis

[1607] Server: Analyzes the collected data (learning progress data and emotional data) using an AI engine to evaluate the user's learning progress and emotional state.

[1608] Input: Learning progress data and emotion data stored on the server

[1609] Output: Analysis results and evaluation report

[1610] What it does: AI algorithms run, analyze the data, and generate reports.

[1611] Step 10:

[1612] Generating and providing feedback messages

[1613] Server: Based on the analysis results, it generates feedback messages that point out the user's weaknesses and areas for improvement, as well as encouragement and relaxation based on emotional data.

[1614] Input: Analysis results and evaluation report

[1615] Output: The generated feedback message

[1616] Terminal: Receives feedback messages sent from the server and displays them to the user.

[1617] Input: The generated feedback message

[1618] Output: Feedback message displayed in the user interface

[1619] Specific operation: The server generates and sends a message, and the terminal displays it.

[1620] Step 11:

[1621] Generate personalized learning plans

[1622] Server: The AI ​​engine uses progress and emotion data to generate a personalized learning plan for each user, including daily study time, focus points, and learning materials.

[1623] Input: Progress data and emotion data

[1624] Output: Generated personalized optimized learning plan

[1625] How it works: The AI ​​model analyzes the input data and generates a new learning plan.

[1626] Step 12:

[1627] Providing and implementing a learning plan

[1628] Terminal: Receives the generated learning plan and displays it on the user interface.

[1629] User: Follows the study plan, for example, works through new suggested math exercises.

[1630] Input: Generated lesson plan

[1631] Output: Learning plan displayed in the user interface, user's learning execution

[1632] Specific operation: The device displays the study plan and the user studies according to it.

[1633] (Application example 2)

[1634] 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."

[1635] Current online education systems are unable to fully address the individual needs of users, particularly in the real-time monitoring of learning progress and emotional state. They also struggle to provide effective learning plans for specific environments, often limiting learning effectiveness. Similar problems can occur in training factory workers, making it difficult for them to effectively acquire skills.

[1636] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to input and register necessary information, a means for recommending appropriate study content based on the user's registration information, a means for collecting and analyzing the user's study progress data and emotional data, a means for generating an individually optimized study plan based on the analyzed data, and a means for providing the generated study plan to the user. This makes it possible to grasp the user's study progress and emotional state in real time and provide an individually optimized study plan and feedback messages.

[1637] The "means for users to enter and register the necessary information" is an interface that allows users to enter necessary information such as name, age, job title, and work content, and register it in the system.

[1638] "Means for recommending appropriate learning content based on the user's registered information" refers to an algorithm and system for automatically selecting optimal learning content based on the user's registered information and recommending it to the user.

[1639] "Means for collecting and analyzing user learning progress data and emotional data" refers to a system for centrally collecting and analyzing learning progress information entered by users and emotional data collected using cameras and sensors.

[1640] The "means for generating an individually optimized learning plan based on the analyzed data" refers to an AI engine and system for analyzing a user's learning progress data and emotional data and generating an optimized learning plan for the user based on the results.

[1641] The "means for providing the generated study plan to the user" refers to an interface and system for presenting the generated individually optimized study plan to the user and allowing the user to study efficiently in accordance with the study plan.

[1642] The present invention provides a system for improving a user's learning environment and providing an individually optimized educational experience. Specific embodiments of the present invention will be described below.

[1643] 1. User Registration

[1644] The server provides a means for users to enter the required information and register. This means can be an interface, such as an HTML form, where users enter and submit information such as their name, age, job title, and job description. The registration information is securely stored in the server's database. The user then receives a confirmation email with a link to activate their account.

[1645] 2. Learning content recommendations

[1646] The server provides a means to recommend appropriate learning content based on the user's registered information. Specifically, it selects appropriate training content based on the user's work duties, job title, and past learning data using an algorithm. The selected content is optimized for the growth of factory workers and promotes efficient skill acquisition.

[1647] 3.Collection and analysis of learning progress and emotion data

[1648] The device provides a means to collect learning progress data and emotional data entered by the user. Emotional data is collected using an emotion recognition library (e.g., EmotionRecognizer) and analyzes the user's facial expressions using a camera sensor. The collected data is sent to a server and analyzed by an AI engine. This analysis provides a detailed understanding of the user's progress and emotional state.

[1649] 4.Generating an individualized learning plan

[1650] The server analyzes the user's learning progress data and emotional data and provides a means to generate an individually optimized learning plan. Specifically, an AI engine (e.g., AIEngine) analyzes the user's weaknesses and strengths and creates optimal training content and feedback. The generated learning plan includes daily training content, recommended learning materials, break times, etc.

[1651] 5. Providing study plans

[1652] The device has a means for providing the generated learning plan to the user, allowing the user to proceed with training according to the displayed plan. Feedback messages are provided according to the user's progress and emotional state, and may include encouraging messages such as "Your progress so far is great! Keep up the great work!"

[1653] Adding specific examples

[1654] For example, when a new factory worker is undergoing training on a certain process, if the emotion recognition engine detects that the worker is feeling stressed, the AI ​​engine will suggest encouraging messages or additional training content for the worker.

[1655] Prompt Sentence Examples

[1656] Analyze the training progress and sentiment data of your next worker to generate feedback and next steps:

[1657] User ID: 123

[1658] Progress data: { "Completion": 70, "Test result": 80}

[1659] Emotion data: { "Stress": High, "Concentration": Low}"

[1660] In this way, the system is able to grasp the user's learning progress and emotional state in real time, enabling it to provide individually optimized learning plans and feedback messages.

[1661] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1662] Step 1:

[1663] The user registers by entering the required information such as name, age, job title, and work details. The entered information is sent to the server through the device interface. The server securely stores the received information in a database and sends a registration confirmation email to the user's email address. The user activates their account by clicking the link in the confirmation email.

[1664] Step 2:

[1665] The server recommends appropriate learning content based on the user's registration information. It selects appropriate training content and generates a recommendation list based on the user's registration information (name, age, job title, and work content). The terminal displays this list to the user, who can then select training content from the provided options.

[1666] Step 3:

[1667] You watch or perform the training content you select. The device makes the selected content available to you in streaming or download format, and collects progress data (e.g., completion rate, test results) that occurs during the viewing or performance.

[1668] Step 4:

[1669] The device collects learning progress data and emotional data entered by the user. Emotional data is collected using a camera sensor and an emotion recognition library (e.g., EmotionRecognizer). Specifically, the camera captures a picture of the user's face, and the emotion recognition algorithm detects emotions from their facial expressions. This data is then sent to the server.

[1670] Step 5:

[1671] The server analyzes the user's learning progress data and emotional data. An AI engine (e.g., AIEngine) analyzes the input progress and emotional data. The analysis results reveal the user's progress and emotional state, and generates appropriate feedback messages based on that information.

[1672] Step 6:

[1673] The server then generates a personalized learning plan based on the analysis results. Based on the progress and emotional data, the AI ​​engine generates a learning plan that includes daily training content, specific learning materials, and break times. This plan is then sent to the device and displayed to the user.

[1674] Step 7:

[1675] Users progress through their training according to the individually optimized learning plan displayed on their device. The device has an interface that supports page transitions and training progress, and displays guides and reminders to help users progress through their studies efficiently. Continuous feedback messages are also provided to maintain motivation to study.

[1676] These are the specific processing steps of the system that realizes this application example. This makes it possible to grasp the user's learning progress and emotional state in real time and provide individually optimized learning plans and feedback messages.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] [Fourth embodiment]

[1681] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1682] 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.

[1683] 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).

[1684] 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.

[1685] 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.

[1686] 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).

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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."

[1694] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, learning content recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[1695] User Registration

[1696] 1. User Registration

[1697] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters their name, age, grade, location, email address, password, etc.

[1698] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[1699] Users: Receive a confirmation email and click the link provided to activate their account.

[1700] Educational content distribution

[1701] 2. Content Recommendation

[1702] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[1703] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[1704] 3. Content Access

[1705] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[1706] Device: Providing selected content for users to access in streaming or download format.

[1707] Learning progress management

[1708] 4. Collecting progress data

[1709] User: After studying, enter study progress data (which parts have been studied, test results, etc.) into the terminal.

[1710] Terminal: Sends the entered data to the server.

[1711] 5. Data analysis and feedback

[1712] Server: Analyzes the collected data and tracks progress. For example, if the percentage of correct answers to a particular math problem is low, it provides feedback on that task.

[1713] Terminal: Displays feedback messages sent by the server to the user.

[1714] Providing individually optimized learning plans

[1715] 6. Generate personalized learning plans

[1716] Server: Based on the collected progress data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[1717] Device: Displays the generated learning plan to the user.

[1718] 7. Implementing your learning plan

[1719] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[1720] Example: When a junior high school student uses the system

[1721] If the user is a junior high school student, the following is a specific example.

[1722] 1. User Registration

[1723] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, the user provides information such as 13 years old, living in Tokyo, and in the second year of junior high school.

[1724] Server: Stores the received information in a database and sends a confirmation email.

[1725] User: Receives a confirmation email and clicks on the link to complete registration.

[1726] 2. Content Recommendation

[1727] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[1728] Devices: Show recommended content to middle school students.

[1729] User: Selects and watches a mathematics geometry lesson from the recommended content.

[1730] 3. Collecting and analyzing progress data

[1731] User: After solving a geometry exercise, enter the result into the terminal.

[1732] Server: Analyzes the input data and detects low levels of understanding of specific problems.

[1733] Terminal: Feedback tells the user that additional geometry practice problems are needed.

[1734] 4. Providing personalized learning plans

[1735] Server: The AI ​​engine uses the user's progress data to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch.

[1736] Device: Display the new learning plan to the user.

[1737] Users: Follow the plan and solve geometry exercises every day to deepen their understanding.

[1738] In this way, the present invention achieves equalization and quality improvement of education by strictly managing users' learning progress and providing individually optimized educational experiences.

[1739] The processing flow will be explained below.

[1740] Step 1:

[1741] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​affiliation, email address, and password, and presses the submit button.

[1742] Step 2:

[1743] The terminal transmits the user's input information to the server.

[1744] Step 3:

[1745] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[1746] Step 4:

[1747] The user receives a confirmation email and clicks the link in the email to activate their account.

[1748] Step 5:

[1749] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[1750] Step 6:

[1751] The server generates a list of selected learning content and sends it to the terminal.

[1752] Step 7:

[1753] The terminal displays the received content list to the user, who can then click to select the content of interest.

[1754] Step 8:

[1755] The terminal provides the selected learning content in streaming or download format for the user to access.

[1756] Step 9:

[1757] After the user finishes the study session, they input information such as what they studied and test results into the terminal.

[1758] Step 10:

[1759] The terminal transmits the input learning progress data to the server.

[1760] Step 11:

[1761] The server analyzes the received learning progress data, for example determining if a particular task has not been understood and therefore requires special attention from the user.

[1762] Step 12:

[1763] Based on the analysis results, the server generates a feedback message for the user, pointing out the user's weaknesses and areas for improvement.

[1764] Step 13:

[1765] The terminal displays the feedback message sent from the server to the user.

[1766] Step 14:

[1767] The user periodically (e.g., on weekends) enters the study time and results into the terminal.

[1768] Step 15:

[1769] The terminal sends the input data to the server.

[1770] Step 16:

[1771] The server analyzes the accumulated data using an AI engine and generates a study plan optimized for the user (daily study time, tasks to focus on, study materials to use, etc.).

[1772] Step 17:

[1773] The server sends the generated learning plan to the device.

[1774] Step 18:

[1775] The device displays the new study plan to the user, who then follows the plan to study.

[1776] Step 19:

[1777] The user repeatedly inputs the learning progress into the terminal.

[1778] Step 20:

[1779] The server collects and analyzes data, and then adjusts feedback and learning plans to continuously optimize the user's learning experience.

[1780] In this way, the system manages the user's learning progress in real time and provides a personalized educational experience.

[1781] Example 1

[1782] 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."

[1783] In conventional online education systems, users are often provided with uniform learning content, making it difficult to provide an educational experience optimized for individual needs and progress. Furthermore, there is a lack of feedback based on the user's learning progress or the generation of individually optimized learning plans, which makes it difficult to provide effective learning support.

[1784] 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.

[1785] In this invention, the server includes a means for a user to input and register necessary information, a means for recommending appropriate educational materials based on the user's registration information, a means for collecting and analyzing the user's learning progress data, a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, and a means for creating prompt sentences when generating a learning plan from the user's progress data using a generative AI model. This enables a customized educational experience for each user, not only improving learning efficiency but also making it possible to provide an optimal learning plan based on each user's individual progress.

[1786] "User" refers to an individual who uses the online education system to learn.

[1787] "Information input" refers to the act of a user registering personal information such as name, age, grade, location, email address, and password into the system.

[1788] "Educational Materials" refers to educational content, such as teaching materials and video lessons, designed to enhance a user's learning.

[1789] "Recommendation" refers to the act of providing appropriate educational materials based on the user's registration information.

[1790] "Study progress data" refers to information indicating the user's learning progress, such as their learning status and test results.

[1791] "Analysis" refers to the act of analyzing collected learning progress data using data analysis tools and AI models.

[1792] "Individually optimized learning plan" refers to a learning plan that includes an optimal learning schedule and learning materials, which is generated based on each user's individual progress.

[1793] "Generation" refers to the act of creating a new learning plan based on collected data and analysis results.

[1794] "Providing" refers to the act of notifying or presenting the generated study plan to the user.

[1795] A "generative AI model" refers to an artificial intelligence model that automatically generates learning plans and feedback based on user progress data.

[1796] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[1797] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has the functions of user registration, educational material recommendation, learning progress data collection and analysis, and creation and provision of personalized and optimized learning plans.

[1798] User Registration

[1799] Users access the system via a web browser or smartphone app and navigate to the registration page. They enter required information such as their name, age, grade, location, email address, and password. This data is sent to the server via their device and stored in a secure database. The server then sends a registration confirmation email to the user's email address, and the user activates their account by clicking the link in the email.

[1800] Recommend educational materials

[1801] The server retrieves the user's registration information from the database and uses a generative AI model to recommend appropriate educational materials. For example, it automatically selects geometry or chemistry video lessons based on the user's grade, location, and interests. The recommended educational materials are sent to the device, which displays the list to the user. The user can then select the content they want to study from the list.

[1802] Learning progress management

[1803] After using educational materials, users input their learning progress data (study content, test results, etc.) into their device. The device then sends this data to a server. The server collects the progress data and analyzes it using analytical tools and AI models. For example, if the accuracy rate for a particular math problem is low, a feedback message is generated based on the results.

[1804] The server sends the generated feedback message to the terminal, and the terminal displays the feedback to the user, so that the user can work on the next learning activity based on the feedback.

[1805] Providing individually optimized learning plans

[1806] The server uses a generative AI model to generate an individually optimized study plan based on the collected progress data. For example, it uses a prompt such as, "Based on the user's progress data, please generate a study plan for next week. Please include key topics, recommended learning materials, and study time." The generated study plan is stored in a database and sent to the user's device. The device displays the plan to the user, and the user proceeds with their studies according to the plan.

[1807] Specific examples

[1808] For example, when a second-year junior high school student uses the system, they follow the steps below. The user registers necessary information such as their name, age, grade, location, email address, and password. The server recommends educational materials for mathematics and science based on the registered information. From the recommended content, the user selects and watches a geometry video lesson.

[1809] After studying, the user enters progress data into the device and sends it to the server, which analyzes it and detects insufficient understanding of a particular problem, generating a feedback message to the user via the device indicating the need for additional geometry practice problems.

[1810] The server then uses an AI engine to generate a study plan for the next week, including detailed geometry practice times and a list of video lessons to watch, allowing users to take their next learning steps based on scientific evidence.

[1811] In this way, the system provides a customized educational experience for each user, improving learning efficiency.

[1812] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1813] Step 1: Go to the user registration page

[1814] The user opens a web browser or smartphone app and accesses the system's user registration page.

[1815] The terminal receives the user's request and displays a registration page.

[1816] Input: User access request

[1817] Output: Display of registration page

[1818] What happens: The device interprets the URL and displays the appropriate registration page.

[1819] Step 2: Enter your information

[1820] The user enters necessary information such as name, age, grade, location, email address, and password.

[1821] The terminal temporarily stores the entered information in real time and displays it on the form.

[1822] Input: Personal information entered by the user

[1823] Output: Personal information displayed in the input form

[1824] Specific operation: The device receives user input and displays it in real time.

[1825] Step 3: Send information

[1826] The user clicks the send button to submit the information they have entered.

[1827] The terminal transmits the user's input information to the server.

[1828] Input: User submission requests and input information

[1829] Output: User information sent to the server

[1830] Specific operation: Create an HTTP request and send it to the server along with the input information.

[1831] Step 4: Receiving and storing information

[1832] The server checks the received information and stores it in a secure database, either an SQL database or a NoSQL database.

[1833] Input: User information sent from the device

[1834] Output: User information stored in the database

[1835] Specific operation: The server verifies the received information and stores it in a database using an SQL query, etc.

[1836] Step 5: Send a confirmation email

[1837] The server will send a registration confirmation email to the user's email address, which will contain an account activation link.

[1838] The user will receive a confirmation email and will need to click on the link to activate their account.

[1839] Input: User's email address

[1840] Output: A confirmation email sent to the user.

[1841] Specific operation: The server connects to the mail server and sends a confirmation email.

[1842] Step 6: Information Acquisition for Content Recommendation

[1843] The server retrieves the user's registration information from a database, often using an SQL query.

[1844] Input: User registration information

[1845] Output: Retrieved user information

[1846] What happens: The server executes an SQL query to get the required information from the database.

[1847] Step 7: Running the Content Recommendation Engine

[1848] Based on the registration information, the server runs a recommendation engine (e.g., a machine learning model) to select appropriate learning content, taking into account the user's grade, location, interests, etc.

[1849] Input: User registration information

[1850] Output: A list of recommended educational materials

[1851] What it does: It uses an algorithm to select the most relevant content for users.

[1852] Step 8: Submit your recommended content

[1853] The server transmits the recommended content list to the terminal.

[1854] Input: A list of recommended educational materials

[1855] Output: Content list sent to device

[1856] Specific operation: Create an HTTP response and send the content list to the terminal.

[1857] Step 9: Displaying Content

[1858] The device displays the received content list to the user, such as a list of "geometry video lessons" and "chemistry experiment videos."

[1859] Input: Content list sent from the server

[1860] Output: Content list displayed to the user

[1861] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[1862] Step 10: Input training data

[1863] After using the educational materials, the user inputs learning progress data (learning content, test results, etc.) into the terminal.

[1864] Input: User's learning progress data

[1865] Output: Progress data temporarily stored on the device

[1866] Specific behavior: The device receives user input and displays it in real time.

[1867] Step 11: Send data

[1868] The device sends the entered progress data to the server, often by POSTing the data using an API endpoint.

[1869] Input: User's learning progress data

[1870] Output: Progress data sent to the server

[1871] Specific behavior: Makes an API request and sends progress data to the server.

[1872] Step 12: Analyze progress data

[1873] The server analyzes the received data, such as test accuracy and study time, using analytical tools and AI models.

[1874] Input: Received learning progress data

[1875] Output: Analysis results

[1876] Specific actions: Analyze progress data using data analysis tools and AI models.

[1877] Step 13: Feedback Generation

[1878] The server generates feedback based on the analysis results. If the level of understanding of a particular problem is low, it generates a feedback message recommending additional study materials or practice problems.

[1879] Input: Analysis results

[1880] Output: Feedback message

[1881] Specific operation: Perform text processing to generate a feedback message based on the analysis results.

[1882] Step 14: Submit your feedback

[1883] The server sends a feedback message to the terminal.

[1884] Input: Feedback message

[1885] Output: Feedback message sent to the terminal

[1886] Specific operation: Creates an HTTP response and sends a feedback message to the terminal.

[1887] Step 15: Viewing feedback

[1888] The device displays the received feedback message to the user, including specific improvements and recommendations for further learning.

[1889] Input: Feedback message sent by the server

[1890] Output: Feedback that is displayed to the user

[1891] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[1892] Step 16: Run the AI ​​Engine

[1893] The server uses the collected progress data to run a generative AI model and create prompts to generate an individually optimized study plan. For example, the server uses the prompt, "Based on the user's progress data, please generate a study plan for next week. Please include key tasks, recommended learning materials, and study time."

[1894] Input: Progress data and prompt text

[1895] Output: Personalized learning plan

[1896] Specific operation: Call the AI ​​engine and obtain the generated learning plan.

[1897] Step 17: Save and submit your study plan

[1898] The server stores the generated study plan in a database and transmits it to the user's terminal.

[1899] Input: Generated lesson plan

[1900] Output: Study plan stored in the database and study plan sent to the device

[1901] Specific behavior: Saves a learning plan using an SQL query and creates and sends an HTTP response.

[1902] Step 18: View your learning plan

[1903] The device displays the generated study plan to the user, which includes a daily study schedule and the learning materials to be used.

[1904] Input: Study plan sent from the server

[1905] Output: The learning plan that is displayed to the user

[1906] Specific operation: The terminal analyzes the received data and displays it on the user interface.

[1907] Step 19: Implementing your learning plan

[1908] The user progresses through the displayed study plan, for example by working through newly recommended math exercises.

[1909] Input: Study Plan

[1910] Output: User's learning progress

[1911] Specific actions: The user actually performs the learning activities according to the learning plan.

[1912] summary

[1913] This system aims to provide an individually optimized educational experience through collaboration between users, devices, and servers. By utilizing generative AI models and prompts, it is possible to generate and provide efficient and effective learning plans.

[1914] (Application example 1)

[1915] 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."

[1916] Conventional online education systems struggle to provide an individually optimized learning experience, and are unable to provide content tailored to each user's unique learning needs. As a result, learning effectiveness declines and user satisfaction declines. Furthermore, the inability to track learning progress in real time and provide appropriate feedback can impair learning efficiency.

[1917] 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.

[1918] In this invention, the server includes a means for the user to input necessary information and register, a means for recommending appropriate study content based on the user's registered information, and a means for collecting and analyzing the user's study progress data, thereby providing the user with an individually optimized learning experience and improving the effectiveness of their learning.

[1919] The server further includes a means for generating an individually optimized learning plan based on the analyzed data, a means for providing the generated learning plan to the user, a means for having the user input information via an application installed on the smartphone terminal, and collecting and analyzing progress data, and a means for using the collected progress data to generate a feedback message using an AI model and notifying the user. This makes it possible to grasp learning progress in real time and provide effective feedback, thereby improving the user's learning efficiency.

[1920] "User" refers to an individual who uses the online education system.

[1921] "Required information" refers to specific data required for registration, such as name, age, grade, location, email address, and password.

[1922] "Means for Registration" refers to the process by which a user enters required information, saves that information in the system, and activates an account.

[1923] "Learning Content" means educational materials, such as instructional materials, video lessons, and exercises, that are provided to assist users in their learning.

[1924] "Recommendation means" refers to a method of selecting and presenting appropriate learning content based on the user's registration information.

[1925] "Study progress data" refers to data such as the progress, grades, and test results achieved by the user during their studies.

[1926] "Means for collecting and analyzing" refers to a method for collecting learning progress data from users and analyzing the data to evaluate the state of learning.

[1927] "Individually optimized learning plan" refers to a learning plan that is optimized based on each user's learning progress data.

[1928] "Means of generation" refers to the method of creating an individually optimized learning plan based on the collected data.

[1929] The "means for providing" refers to a means for displaying the generated learning plan to the user and encouraging them to carry it out.

[1930] A "smartphone terminal" refers to a mobile communication device that can connect to the Internet and on which applications can be installed and used.

[1931] "Application" refers to software that users install and use on their smartphone devices.

[1932] "Means for collecting and analyzing progress data" refers to a method in which a user inputs learning progress data through an application and the data is sent to a server for analysis.

[1933] An "AI model" refers to an artificial intelligence algorithm that performs inference and optimization based on collected data and generates feedback and learning plans.

[1934] "Feedback message" refers to advice and evaluation provided based on the user's learning progress data.

[1935] "Means for notifying" refers to a method for notifying the user of the generated feedback message.

[1936] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. The system is operated using an application installed on a user's smartphone terminal.

[1937] First, the user downloads and installs the online education application on their smartphone. The user enters the required information (e.g., name, age, grade, location, email address, password, etc.) to register. The server stores the user's registration information in a secure database and sends the user an email to confirm their registration. The user receives the confirmation email and clicks the link contained in it to activate their account.

[1938] After registration is complete, the server recommends appropriate learning content (such as teaching materials and video lessons) based on the user's registration information. This content is customized according to the user's grade level and interests. The recommended learning content is displayed to the user through an application on their smartphone, and the user can select from the content and begin watching.

[1939] As the user progresses with their studies, they input their learning progress data (such as which learning materials they have viewed and test results) into the application. The device then sends the input progress data to the server. The server analyzes the collected progress data and generates an individually optimized learning plan based on the analysis results. For example, if the user's understanding of a particular subject is insufficient, the plan will include additional learning content related to that subject.

[1940] Furthermore, the system uses an AI model to generate feedback messages based on the collected progress data and notifies the user of the generated feedback. The feedback messages include suggestions for additional practice in areas where understanding is insufficient and instructions on what to do next. This allows users to understand their own learning status in real time and study effectively.

[1941] As a concrete example, consider a middle school student watching a geometry video lesson and working on the exercises. The user opens the application and enters their progress data. For example, if they score 55 points on a geometry exercise, they enter that score. The server analyzes this score and uses an AI model to generate a feedback message to the user saying, "You need more practice in geometry. Please complete additional exercises."

[1942] Below are some examples of prompts used in this system:

[1943] "You are in eighth grade math class. Please solve a geometry problem. Enter the score you received for the problem."

[1944] In this way, the system of the present invention can closely manage the user's learning progress and provide a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[1945] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1946] Step 1:

[1947] A user downloads and installs an online education application on their smartphone. They register an account by entering required information such as their name, age, grade, location, email address, and password. The server receives the information, stores it in a secure database, and sends a confirmation email to the user. The user receives the confirmation email and clicks on the link included to activate their account.

[1948] Input: Registration information such as name, age, grade, location, email address, and password

[1949] Output: Verification email sent and account activated

[1950] Step 2:

[1951] The server recommends learning content (such as teaching materials and video lessons) based on the user's registration information. The recommended content is customized according to the user's grade and interests. The device displays the recommended learning content to the user, and the user selects the content they want to view from the displayed list.

[1952] Input: Registration information, grade, interests

[1953] Output: Display of customized learning content list

[1954] Step 3:

[1955] The user watches the selected learning content and proceeds with their learning. After studying, the user enters their learning progress data (viewed content, test results, etc.) into the device. The device then sends the entered progress data to the server.

[1956] Input: Learning progress data (content viewed, test results)

[1957] Output: Sending progress data

[1958] Step 4:

[1959] The server analyzes the collected progress data. For example, if a user scores low in a particular subject or topic, the server can identify the cause. Based on the analysis, the server generates a personalized, optimized learning plan, which includes focused learning content and additional practice questions.

[1960] Input: Progress data (content viewed, test results)

[1961] Output: Personalized learning plan

[1962] Step 5:

[1963] The server runs the generative AI model based on the collected progress data and generates feedback messages for the user. This feedback includes specific advice, such as "You need more practice on geometry. Please solve additional problems." The generated feedback messages are sent to the user's device.

[1964] Input: Progress data, execution results of generative AI model

[1965] Output: Feedback message

[1966] Step 6:

[1967] Users follow the feedback messages displayed on their device to progress through their learning according to an individually optimized learning plan. By using the device's application, they can improve their learning effectiveness by working on newly recommended content and practice problems.

[1968] Input: Feedback message, personalized learning plan

[1969] Output: Improved user learning progress

[1970] In this way, the system of the present invention closely manages the user's learning progress and provides a personalized and optimized educational experience, thereby improving the efficiency and effectiveness of education.

[1971] 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.

[1972] The present invention provides an online education system that improves a user's learning environment and provides a personalized and optimized educational experience. The system has functions for user registration, recommending learning content, collecting and analyzing learning progress data, and generating and providing personalized and optimized learning plans, as well as an emotion engine that recognizes the user's emotions.

[1973] User Registration

[1974] 1. User Registration

[1975] Terminal: The user accesses the system via a web browser or application and goes to the registration page. The user enters the required information such as name, age, grade, location, email address, and password, and presses the submit button.

[1976] Server: Receives the information submitted by the user and stores it in a secure database. The server then sends a registration confirmation email to the user's email address.

[1977] Users: Receive a confirmation email and click the link provided to activate their account.

[1978] Educational content distribution

[1979] 2. Content Recommendation

[1980] Server: Automatically selects appropriate learning content (teaching materials, video lessons, etc.) based on the user's registration information. The selected content is customized according to the user's grade, region, and interests.

[1981] Terminal: The received content recommendation list is displayed to the user, who can then select the content they want to study from the list.

[1982] 3. Content Access

[1983] User: Clicks to select the content they want to watch. For example, they might choose a math video lesson.

[1984] Device: Providing selected content for user access in streaming or download format.

[1985] Learning progress management

[1986] 4. Collecting progress data

[1987] User: After learning, the user inputs learning progress data (which parts they have learned, test results, etc.) into the device. In addition, the emotion engine collects the user's emotion data from the facial recognition camera and sensors.

[1988] Terminal: Sends the input learning progress data and emotion data to the server.

[1989] 5. Data analysis and feedback

[1990] Server: Analyzes the collected data (learning progress data and emotion data) to understand the progress of the user. For example, if the correct answer rate for a particular math problem is low and the user is feeling confused or stressed, it determines that the user needs special attention.

[1991] Server: Based on the analysis results, it generates feedback messages for the user, pointing out their weaknesses and areas for improvement. Based on the emotional data, it also provides messages of encouragement and relaxation.

[1992] Terminal: Displays feedback messages sent by the server to the user.

[1993] Providing individually optimized learning plans

[1994] 6. Generate personalized learning plans

[1995] Server: Based on the collected progress and emotion data, the AI ​​engine generates an optimal study plan for the user, including daily study time, priority tasks, and study materials to use.

[1996] Device: Displays the generated learning plan to the user.

[1997] 7. Implementing your learning plan

[1998] User: Follows the displayed study plan, for example, working on new suggested math exercises.

[1999] Example: When a junior high school student uses the system

[2000] If the user is a junior high school student, the following is a specific example.

[2001] 1. User Registration

[2002] User: A junior high school student accesses the system through a terminal and registers by entering the necessary information. For example, they provide information such as age 13, local resident, and second-year junior high school student.

[2003] Server: Stores the received information in a database and sends a confirmation email.

[2004] User: Receives a confirmation email and clicks on the link to complete registration.

[2005] 2. Content Recommendation

[2006] Server: Selects educational content for math and science for eighth graders, for example, recommending video lessons on geometry and chemistry.

[2007] Devices: Show recommended content to middle school students.

[2008] User: Selects and watches a mathematics geometry lesson from the recommended content.

[2009] 3. Collecting and analyzing progress and emotion data

[2010] User: After solving a geometry exercise, the user enters the results and emotions (e.g., stress or accomplishment) into the device.

[2011] Server: Analyzes the input data and detects the level of understanding and emotional state of the specific problem.

[2012] Terminal: Feedback informs the user that additional geometry practice problems are needed, and also displays encouraging messages to reduce stress.

[2013] 4. Providing personalized learning plans

[2014] Server: An AI engine uses progress and emotional data to generate a study plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks to relax.

[2015] Device: Display the new learning plan to the user.

[2016] Users: Follow a plan and solve geometry exercises every day to improve their understanding and manage stress.

[2017] In this way, the present invention manages the user's emotional state in real time along with their learning progress, providing a personalized and optimized educational experience, thereby enabling more effective learning.

[2018] The processing flow will be explained below.

[2019] Step 1:

[2020] A user accesses the system through a web browser or application, goes to the user registration page, enters the required information such as name, age, grade, area of ​​residence, email address, and password, and presses the submit button.

[2021] Step 2:

[2022] The terminal transmits the user's input information to the server.

[2023] Step 3:

[2024] The server stores the received information in a secure database and also sends a registration confirmation email to the user's email address.

[2025] Step 4:

[2026] The user receives a confirmation email and clicks the link in the email to activate their account.

[2027] Step 5:

[2028] The server selects appropriate learning content based on the user's registration information, for example, automatically recommending learning materials and video lessons based on the user's grade level and region.

[2029] Step 6:

[2030] The server generates a list of selected learning content and sends it to the terminal.

[2031] Step 7:

[2032] The terminal displays the received content list to the user, who can then click to select the content of interest.

[2033] Step 8:

[2034] The terminal provides the selected learning content in streaming or download format for the user to access.

[2035] Step 9:

[2036] After completing a study session, the user inputs their learning progress and emotions into the device. Emotional data is collected using a facial recognition camera and sensors.

[2037] Step 10:

[2038] The terminal transmits the input learning progress data and emotion data to the server.

[2039] Step 11:

[2040] The server analyzes the received learning progress data. For example, if the percentage of correct answers to a particular math problem is low, it can be determined that the user is struggling with that subject.

[2041] Step 12:

[2042] The server uses an emotion engine to analyze the user's emotion data, for example, to detect if the user is feeling confused or stressed.

[2043] Step 13:

[2044] The server generates a feedback message based on the learning progress data and emotion data, such as "It seems you found this part difficult. Let's start with an easier problem next time."

[2045] Step 14:

[2046] The terminal displays the feedback message sent from the server to the user.

[2047] Step 15:

[2048] Users periodically (e.g., on weekends) enter their study time and results into the device, and emotional data is also collected.

[2049] Step 16:

[2050] The terminal sends the input data to the server.

[2051] Step 17:

[2052] The server analyzes the accumulated data using an AI engine and generates an optimized study plan for each user, including daily study time, tasks to focus on, and study materials to use.

[2053] Step 18:

[2054] The server sends the generated learning plan to the device.

[2055] Step 19:

[2056] The device displays the new study plan to the user, who then follows the plan to study.

[2057] Step 20:

[2058] The server continuously collects and analyzes learning progress and emotional data, and adjusts feedback and learning plans accordingly to continually optimize the user's learning experience.

[2059] In this way, the system manages the user's learning progress and emotional state in real time, providing a personalized educational experience.

[2060] Example 2

[2061] 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."

[2062] In conventional online education systems, it was difficult to grasp individual users' learning progress and emotions in real time and provide optimal learning plans. As a result, it was not possible to provide an effective and continuous learning experience for users, which could lead to a decline in learning effectiveness. In addition, there was a lack of means to provide feedback based on learning progress and changes in emotions, making it difficult to maintain users' motivation to learn.

[2063] 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.

[2064] In this invention, the server includes means for the user to input necessary information and register, means for recommending appropriate study content based on the user's registration information, means for collecting the user's study progress data and emotion data, means for analyzing the collected data and generating an individually optimized study plan, means for providing the generated study plan to the user, and means for displaying the provided feedback message to the user, thereby making it possible to provide the user with an individually optimized study plan and feedback messages based on their emotions.

[2065] "User" refers to an individual learner who uses the online education system.

[2066] "Information" or "Required Information" refers to personal information such as name, age, grade, location, email address, and password that a User provides to register with the System.

[2067] "Registration" refers to the process by which a User enters required information into the System to create and activate an account.

[2068] "Server" refers to a computer system that stores and processes user information, progress data, learning content, etc.

[2069] "Learning Content" refers to educational resources such as teaching materials, video lessons, and textbooks provided on the System.

[2070] "Recommendation" refers to the process of selecting and presenting the most appropriate learning content based on the user's registration information and learning history.

[2071] "Study progress data" refers to records of what parts a user has studied, the content of their studies, test results, and so on.

[2072] "Emotional data" refers to data on a user's emotional state based on facial expressions and physical reactions collected using facial recognition cameras and sensors.

[2073] "Analysis" refers to the process of analyzing the collected learning progress data and emotional data to evaluate the user's learning status and emotional state.

[2074] "Individually optimized learning plan" refers to a plan that includes a learning schedule and assignments that are optimal for a specific user, generated based on the user's learning progress data and emotional data.

[2075] "Feedback message" refers to a message of evaluation or encouragement that is generated based on the analysis results and provided to the user.

[2076] "Providing" refers to the process by which the server presents the generated learning plan and feedback messages to the user.

[2077] The present invention provides an online education system that improves the user's learning environment and provides a personalized and optimized educational experience. This system has the following functions: user registration, learning content recommendation, collection and analysis of learning progress data and emotion data, generation and provision of personalized and optimized learning plans, and provision of feedback messages.

[2078] 1. User Registration

[2079] Device: The user uses a device (such as a PC or smartphone) to open the system's registration page in a web browser or application. The user enters the required information (name, age, grade, location, email address, password, etc.) and clicks the "Register" button.

[2080] Server: Receives user information sent from the device and stores it in a secure database. The stored data is protected by encryption technology. A registration confirmation email is then automatically generated and sent to the user's email address.

[2081] User: The user receives a confirmation email and clicks the link in the email to activate their account.

[2082] 2. Educational content recommendations

[2083] Server: Based on the information registered by the user (age, grade, interests, etc.), the algorithm runs and automatically selects the most suitable learning content. The selected content is customized according to the user's grade, region, and interests. An AI-based recommendation system is used.

[2084] Device: Receives the selected content recommendation list and displays it to the user, who can then select the content they want to study from the list.

[2085] 3. Content Access

[2086] User: The user clicks to select the content they want to watch (e.g., a math video lesson) from the recommendations list.

[2087] Device: Providing selected content for user access in streaming or download format.

[2088] 4. Collecting learning progress data

[2089] User: After studying, the user enters progress data (which part they studied, what they learned, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[2090] Terminal: Sends the input learning progress data and emotion data to the server.

[2091] 5. Data analysis and feedback

[2092] Server: The AI ​​engine analyzes the collected data (learning progress data and emotional data) and evaluates the user's learning progress and emotional state. For example, if the user has a low success rate on a particular math problem and feels confused or stressed, the server will urge the user to pay special attention.

[2093] Server: Generates feedback messages based on the analysis results. The feedback messages include points out the user's weaknesses and areas for improvement, as well as messages of encouragement or relaxation based on emotional data.

[2094] Terminal: Receives feedback messages sent from the server and displays them to the user.

[2095] 6. Generate personalized learning plans

[2096] Server: The AI ​​engine generates an optimal study plan for each user based on the progress and emotion data collected. The plan includes daily study time, priority tasks, and study materials to use.

[2097] Device: Displays the generated learning plan to the user.

[2098] 7. Implementing your learning plan

[2099] User: The user follows the displayed study plan, for example, working on new suggested math exercises.

[2100] Specific examples

[2101] For example, if a user is a 13-year-old junior high school student, he or she accesses the system and registers by entering his or her name, age, grade, location, email address, password, etc. After receiving a confirmation email and completing registration, the server will recommend math and science learning content for eighth-grade students based on the user's information. The user can then select the video lesson they want to watch from the recommended list.

[2102] After learning, the user inputs their progress data and emotional state into the device. For example, they input the results of solving geometry exercises and the stress or sense of accomplishment they felt while solving the problems. The server analyzes this data and generates feedback to the user, such as the need for additional geometry exercises, or encouraging messages to reduce stress.

[2103] The generated feedback is displayed on the device, and the AI ​​engine generates a personalized learning plan for the next week, including detailed geometry practice times, a list of video lessons to watch, and breaks for relaxation. By following this plan, users can maximize their learning and reduce mental strain.

[2104] Prompt Sentence Examples

[2105] "Enter math progress data and emotional data to suggest new learning plans."

[2106] As described above, the present invention can achieve more effective learning by managing a user's learning progress and emotional state in real time and providing an individually optimized educational experience.

[2107] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2108] Step 1:

[2109] Fill in and submit the user registration form

[2110] Device: The user operates a device (PC or smartphone) to open a web browser or application, accesses the registration page, enters their name, age, grade, location, email address, and password in the form, and clicks the "Register" button.

[2111] Input: Personal information entered by the user (name, age, grade, location, email address, password)

[2112] Output: Registration request sent to the server

[2113] Specific operation: The user enters information into the form and presses the submit button. The device transfers the submitted data to the server.

[2114] Step 2:

[2115] Receiving and storing user information

[2116] Server: The server receives the user information sent from the device and stores it in a secure database using encryption technology.

[2117] Input: User information sent from the device

[2118] Output: User information stored in the database, preparation for sending a registration confirmation email

[2119] Specific operation: The server inserts and saves data into the database. After saving, a confirmation email is automatically generated.

[2120] Step 3:

[2121] Sending a confirmation email

[2122] Server: Automatically generates a registration confirmation email and sends it to the user's email address, which contains a link to activate the account.

[2123] Input: The user's email address stored in the database

[2124] Output: A confirmation email sent to the user's mailbox.

[2125] Specific operation: The server calls the email sending API and sends a confirmation email.

[2126] Step 4:

[2127] Activating your account

[2128] User: The user receives a confirmation email and activates their account by clicking the link in the email, which allows them to officially log in to the system.

[2129] Input: Activation link in confirmation email

[2130] Output: The user's account is enabled and they can log in.

[2131] Specific action: A user clicking on a link in an email.

[2132] Step 5:

[2133] Learning content recommendations

[2134] Server: Retrieves user registration information (age, grade, interests, etc.) from a database and uses an AI algorithm to select the most appropriate learning content.

[2135] Input: User registration information stored in the database

[2136] Output: A list of recommended learning content

[2137] How it works: The server executes a query to obtain user information and uses an AI model to generate recommended content.

[2138] Step 6:

[2139] Delivery and display of recommendation lists

[2140] Terminal: Receives the list of recommended learning content sent from the server and displays it to the user. The user can select the content they want to learn.

[2141] Input: Recommended learning content list sent from the server

[2142] Output: Content list displayed in the user interface

[2143] Specific operation: The device analyzes the data and displays it on the user interface.

[2144] Step 7:

[2145] Content Selection and Access

[2146] User: The user clicks to select the content they want to watch from the recommended list.

[2147] Device: Providing selected content for user access in streaming or download format.

[2148] Input: Information about content leaked or selected by the user

[2149] Output: Learning content delivered in streaming or download format

[2150] Specific operation: The user selects content, and the device communicates with the content server to retrieve and display the content in the appropriate format.

[2151] Step 8:

[2152] Collection of learning progress and emotion data

[2153] User: After studying, the user enters progress data (study content, test results, etc.) into the device. The emotion engine also collects the user's emotional data through a facial recognition camera and sensor devices.

[2154] Terminal: Sends collected progress data and emotion data to the server.

[2155] Input: Progress data entered by the user, emotion data collected by the emotion engine

[2156] Output: Progress and emotion data sent to the server

[2157] Specific operation: The user inputs the learning results, cameras and sensors collect data, and the device sends the data to the server.

[2158] Step 9:

[2159] Data analysis

[2160] Server: Analyzes the collected data (learning progress data and emotional data) using an AI engine to evaluate the user's learning progress and emotional state.

[2161] Input: Learning progress data and emotion data stored on the server

[2162] Output: Analysis results and evaluation report

[2163] What it does: AI algorithms run, analyze the data, and generate reports.

[2164] Step 10:

[2165] Generating and providing feedback messages

[2166] Server: Based on the analysis results, it generates feedback messages that point out the user's weaknesses and areas for improvement, as well as encouragement and relaxation based on emotional data.

[2167] Input: Analysis results and evaluation report

[2168] Output: The generated feedback message

[2169] Terminal: Receives feedback messages sent from the server and displays them to the user.

[2170] Input: The generated feedback message

[2171] Output: Feedback message displayed in the user interface

[2172] Specific operation: The server generates and sends a message, and the terminal displays it.

[2173] Step 11:

[2174] Generate personalized learning plans

[2175] Server: The AI ​​engine uses progress and emotion data to generate a personalized learning plan for each user, including daily study time, focus points, and learning materials.

[2176] Input: Progress data and emotion data

[2177] Output: Generated personalized optimized learning plan

[2178] How it works: The AI ​​model analyzes the input data and generates a new learning plan.

[2179] Step 12:

[2180] Providing and implementing a learning plan

[2181] Terminal: Receives the generated learning plan and displays it on the user interface.

[2182] User: Follows the study plan, for example, works through new suggested math exercises.

[2183] Input: Generated lesson plan

[2184] Output: Learning plan displayed in the user interface, user's learning execution

[2185] Specific operation: The device displays the study plan and the user studies according to it.

[2186] (Application example 2)

[2187] 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."

[2188] Current online education systems are unable to fully address the individual needs of users, particularly in the real-time monitoring of learning progress and emotional state. They also struggle to provide effective learning plans for specific environments, often limiting learning effectiveness. Similar problems can occur in training factory workers, making it difficult for them to effectively acquire skills.

[2189] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for the user to input and register necessary information, a means for recommending appropriate study content based on the user's registration information, a means for collecting and analyzing the user's study progress data and emotional data, a means for generating an individually optimized study plan based on the analyzed data, and a means for providing the generated study plan to the user. This makes it possible to grasp the user's study progress and emotional state in real time and provide an individually optimized study plan and feedback messages.

[2190] The "means for users to enter and register the necessary information" is an interface that allows users to enter necessary information such as name, age, job title, and work content, and register it in the system.

[2191] "Means for recommending appropriate learning content based on the user's registered information" refers to an algorithm and system for automatically selecting optimal learning content based on the user's registered information and recommending it to the user.

[2192] "Means for collecting and analyzing user learning progress data and emotional data" refers to a system for centrally collecting and analyzing learning progress information entered by users and emotional data collected using cameras and sensors.

[2193] The "means for generating an individually optimized learning plan based on the analyzed data" refers to an AI engine and system for analyzing a user's learning progress data and emotional data and generating an optimized learning plan for the user based on the results.

[2194] The "means for providing the generated study plan to the user" refers to an interface and system for presenting the generated individually optimized study plan to the user and allowing the user to study efficiently in accordance with the study plan.

[2195] The present invention provides a system for improving a user's learning environment and providing an individually optimized educational experience. Specific embodiments of the present invention will be described below.

[2196] 1. User Registration...

Claims

1. A means for the user to enter necessary information and register; A means for recommending appropriate learning content based on the user's registration information; means for collecting and analyzing user learning progress data; means for generating an individualized optimized learning plan based on the analyzed data; means for providing the generated study plan to a user; A system including:

2. The system of claim 1 , further comprising means for a user to input learning progress.

3. The system of claim 1 , further comprising means for generating and providing feedback messages based on the user's learning progress.

4. The system of claim 1 , further comprising means for providing the learning content by streaming or download.

5. 10. The system of claim 1, further comprising means for securely storing and encrypting user information and learning data.

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