system

Abstract

To provide a system that enables all children to receive an equal and personalized education regardless of their parents' educational level or financial resources, thereby reducing educational disparities. [Solution] A system including a means for inputting and transmitting user information, a means for receiving and storing transmitted user information in a database, a means for collecting user learning progress data and transmitting it to a server, a means for receiving and analyzing the learning progress data, a means for generating a learning plan based on the analysis results, a means for transmitting the generated learning plan to a user terminal, a means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to a server, a means for analyzing the feedback and fine-tuning the learning plan, a means for searching for and providing answers to questions from users, and a means for providing a community platform for sharing knowledge and experience among users.

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 the modern education system, it is extremely important to provide equal educational opportunities to all children, regardless of their parents' educational level or financial resources. However, the current education system has created large disparities based on family environment and financial resources, and is far from providing an equal education to all children. To solve this problem, a system is needed that can easily provide personalized education tailored to individual learning needs. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. Specifically, the system includes a means for inputting and transmitting user information, a means for receiving the transmitted user information and storing it in a database, a means for collecting user learning progress data and transmitting it to a server, a means for receiving and analyzing the learning progress data, a means for generating a learning plan based on the analysis results, a means for transmitting the generated learning plan to a user terminal, a means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to a server, a means for analyzing the feedback and fine-tuning the learning plan, a means for searching for and providing answers to questions from users, and a means for providing a community platform for users to share knowledge and experiences. This system enables all children to receive an equal, personalized education regardless of their parents' educational level or financial situation, thereby reducing educational disparities.

[0006] "User Information" refers to basic data such as an individual's name, age, grade, and areas of interest provided by a user when registering with the system.

[0007] A "terminal" is a device such as a computer or smartphone that allows a user to access the system and input information, perform learning activities, provide feedback, etc.

[0008] A "server" is a central computer system that receives, stores, and analyzes data sent by users, generates study plans and answers, and sends them to terminals.

[0009] "Database" refers to information storage within the system for organizing and storing submitted user information, learning progress data, and feedback.

[0010] "Study progress data" is data that records the progress status, such as test results, study time, and completed tasks, that occurs when a user carries out a learning activity.

[0011] "Analysis" is the process of using models to analyze collected data and identify users' strengths and weaknesses.

[0012] A "learning plan" is a plan that is generated based on the analysis results and includes specific learning content and tasks that the user should follow.

[0013] "Feedback" is information about the difficulty level of questions and the level of understanding that is input by the user after the user has completed the learning activity based on the learning plan.

[0014] "Answers to questions" are explanations and information that the server searches for and provides in response to questions that arise during the user's studies.

[0015] A "community platform" is an online exchange space where users can share their learning experiences and knowledge with each other. [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 system according to the present invention aims to reduce educational disparities within families and provide equal educational opportunities to all children through cooperation between users, terminals, and servers. Specific embodiments for carrying out the present invention are described below.

[0038] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. The user enters their personal information (name, age, grade, areas of interest) and sends it to the server via their device. The server receives the information and registers it in a database.

[0039] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time. The collected learning progress data is sent from the device to the server. The server receives this data and stores it in a database.

[0040] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server determines that the user is good at math but weak at history. Based on this analysis result, the server generates a study plan. The generated study plan is sent to the user via the device. For example, the server may suggest a plan that includes many applied math problems and a plan that includes basic history review problems.

[0041] The user uses the device to carry out learning activities according to the provided learning plan. If a question arises during the learning activity, the user uses the device to input the question. This question is sent from the device to the server. The server searches a database or external resource for an appropriate answer to the question, generates an answer, and sends it to the device. The device displays the generated answer to the user.

[0042] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[0043] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and to ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[0044] The above is a specific embodiment of the system according to the present invention. This system can provide equal educational opportunities to all children, regardless of their parents' educational level or financial resources, thereby reducing educational disparities.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The user installs and launches the app. The device displays a first-time launch screen and offers a sign-up button.

[0048] Step 2:

[0049] The user clicks the new registration button and enters personal information such as name, age, grade, area of ​​interest, etc. The terminal stores this information in an input form and provides a submit button.

[0050] Step 3:

[0051] The user clicks the send button. The device sends the registration information to the server.

[0052] Step 4:

[0053] The server analyzes the received user information and registers it in the database.

[0054] Step 5:

[0055] The user initiates a learning activity through the app. The device logs the start and end times of each learning activity (e.g., test, assignment, quiz, etc.) and progress data.

[0056] Step 6:

[0057] The terminal transmits learning progress data to the server at regular intervals.

[0058] Step 7:

[0059] The server stores the received learning progress data in a database and executes a learning performance analysis algorithm.

[0060] Step 8:

[0061] The server identifies the user's strengths and weaknesses based on the analysis results and generates a personalized learning plan.

[0062] Step 9:

[0063] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[0064] Step 10:

[0065] The user uses the terminal to carry out the learning activities according to the provided learning plan. If a question arises during the learning activities, the user inputs the question into the terminal.

[0066] Step 11:

[0067] The device sends a question to the server, which receives the question and searches for an appropriate answer from a database or external resource.

[0068] Step 12:

[0069] The server generates a response and sends it to the terminal, which displays it to the user.

[0070] Step 13:

[0071] The user completes the learning activity based on the learning plan and inputs feedback (such as difficulty of the questions and level of understanding) into the terminal.

[0072] Step 14:

[0073] The device sends feedback to the server, which receives the feedback and fine-tunes the learning plan.

[0074] Step 15:

[0075] The server resends the fine-tuned study plan to the device, and the user begins the next study cycle.

[0076] Step 16:

[0077] A user accesses a community forum to share learning experiences and knowledge with other users, and the terminal displays the forum interface.

[0078] Step 17:

[0079] The server monitors interactions on the community and filters inappropriate content.

[0080] Example 1

[0081] 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."

[0082] In today's educational environment, it is difficult to provide individualized learning plans that match each child's learning progress and level of understanding. Furthermore, educational disparities and economic situations within families can easily lead to unequal educational opportunities. There is also a lack of systems that can quickly and accurately respond to questions that arise during learning. Furthermore, there is also a lack of monitoring systems to ensure the safe sharing of knowledge and experience between users. To solve these issues, a personalized learning support system is needed.

[0083] 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.

[0084] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for generating answers to user questions using a generative AI model, and means for optimizing the learning plan based on the generated learning plan and user feedback. This makes it possible to provide individualized learning plans and resolve questions, thereby eliminating educational disparities and providing equal learning opportunities.

[0085] "User information" is information used to identify individual users and grasp their characteristics, such as the user's name, age, grade, and areas of interest.

[0086] "Study progress data" refers to data such as test results, study time, and completed assignments obtained when a user engages in study activities.

[0087] "Analysis" is the process of analyzing the collected data to identify the user's strengths and weaknesses and reflect them in future learning plans.

[0088] A "study plan" is a specific study content and schedule created based on the user's study progress data and analysis results to help the user study effectively.

[0089] "Feedback" refers to information such as an evaluation or impression provided by a user regarding the difficulty level or level of understanding of a learning plan after the user has completed the learning activity.

[0090] A "generative AI model" is a model that uses artificial intelligence to analyze data and automatically generate optimal learning plans and answers to questions for users.

[0091] A "community platform" is an online environment for users to share knowledge and experiences, and is a place where interactions take place in the form of forums or chats.

[0092] "Interaction" refers to the interaction and information exchange activities between users on the community platform.

[0093] "Filtering" is the process of monitoring interactions on a community platform and removing inappropriate content.

[0094] "Optimization" means adjusting the learning plan to be most effective for the user based on user feedback and collected data.

[0095] The system according to the present invention is linked by users, terminals, and a server, and aims to reduce educational disparities within families and provide equal educational opportunities to all children. This specification describes the specific configuration and operation of this system.

[0096] Hardware and Software Used

[0097] Hardware: User devices (smartphones and tablets), servers

[0098] Software: Learning apps, database management systems, data analysis engines, community platforms

[0099] Program processing overview

[0100] The system begins when the user downloads and installs the app on their device. After installation, the user launches the app and completes the registration process. The user enters their personal information (name, age, grade, and areas of interest) and sends it from their device to the server. The server stores the received information in a database.

[0101] As users engage in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time and sends it to the server. The server receives this data and stores it in a database. The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. A generative AI model is used for this analysis. Based on the analysis results, the server generates an individual learning plan and sends it to the device.

[0102] For example, if a user scores high on a math test but low on a history test, the server will determine that the user is strong in math but weak in history, and suggest a plan that includes many applied math questions and a plan that includes basic history questions.

[0103] The user proceeds with the learning activities according to the provided learning plan. If a question arises during the learning process, the user can input the question using the device. This question is sent from the device to the server, and the server searches for an appropriate answer to the question from a database or external resources and generates an answer using a generative AI model. The answer is sent to the device and displayed to the user.

[0104] After completing a study activity based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server analyzes this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[0105] Furthermore, the system provides a community platform for users to share their knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this way, users can maintain a safe and effective learning environment.

[0106] Examples and prompts

[0107] Example scenario:

[0108] While studying a math problem, a user types into the device, "There was something difficult to understand in history class. Could you briefly explain the economy of medieval Europe?"

[0109] The server receives this question, uses a generative AI model to generate an appropriate answer, and sends it to the user's device.

[0110] The user continues learning based on the answers displayed on the device.

[0111] The above is a specific embodiment of the system according to the present invention, which allows users to study efficiently, eliminate educational disparities, and enjoy equal learning opportunities.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1: Installing the app and initial setup

[0114] User:

[0115] Users download and install the learning app onto their smartphones or tablets.

[0116] Launch the app and complete the new registration process.

[0117] Users enter personal information such as name, age, grade, and areas of interest.

[0118] Input: User's personal information

[0119] Output: Initialized app

[0120] Device:

[0121] The personal information entered by the user is sent to the server.

[0122] Input: User's personal information

[0123] Output: User information sent to the server

[0124] server:

[0125] Receives the personal information you submit and stores it in a database.

[0126] Input: User information sent from the device

[0127] Output: User information stored in the database

[0128] Step 2: Collecting learning progress data

[0129] User:

[0130] Users use the learning app to carry out learning activities.

[0131] Input: Learning activities using learning apps

[0132] Output: None

[0133] Device:

[0134] Learning progress data (test results, study time, completed assignments) is collected in real time and sent to the server.

[0135] Input: Data based on learning activities

[0136] Output: Learning progress data sent to the server

[0137] server:

[0138] The transmitted learning progress data is received and stored in a database.

[0139] Input: Learning progress data sent from the device

[0140] Output: Learning progress data stored in a database

[0141] Step 3: Data analysis and learning plan generation

[0142] server:

[0143] Analyze learning progress data using generative AI models to identify user strengths and weaknesses.

[0144] For example, if a student scores high on a math test but low on history, they will be judged as strong in math but weak in history.

[0145] Input: Learning progress data stored in a database

[0146] Output: Analysis of user's strengths and weaknesses

[0147] server:

[0148] An individual learning plan is generated based on the analysis results and sent to the device.

[0149] For example, a plan containing many applied mathematics questions and a plan containing basic history questions are generated.

[0150] Input: Analysis of user strengths and weaknesses

[0151] Output: Generated lesson plan

[0152] Device:

[0153] Display the generated learning plan to the user.

[0154] Input: Learning plan from the server

[0155] Output: The lesson plan displayed to the user

[0156] Step 4: Supporting learning activities

[0157] User:

[0158] Follow the provided learning plan and proceed with the learning activities.

[0159] If you have any questions while studying, use the device to type in your question.

[0160] Input: Learning activities and questions based on the lesson plan

[0161] Output: The question sent to the server

[0162] Device:

[0163] Send the question to the server.

[0164] Input: User question

[0165] Output: The question sent to the server

[0166] server:

[0167] Search for answers to questions and generate appropriate answers using generative AI models.

[0168] The response is sent to the device.

[0169] Input: User question

[0170] Output: The generated answer

[0171] Device:

[0172] The submitted response is displayed to the user.

[0173] Input: Response from the server

[0174] Output: The answer displayed to the user

[0175] Step 5: Gather feedback and fine-tune your learning plan

[0176] User:

[0177] After the learning activity, feedback on the difficulty and level of understanding of the learning plan is sent to the server via the device.

[0178] Input: Feedback obtained from the learning activity

[0179] Output: Feedback sent to the server

[0180] Device:

[0181] Send feedback to the server.

[0182] Input: User feedback

[0183] Output: Feedback sent to the server

[0184] server:

[0185] Receive feedback and analyze it to fine-tune your learning plan.

[0186] For example, the system regenerates an optimal study plan by adjusting the difficulty of the questions.

[0187] Input: User feedback

[0188] Output: A fine-tuned learning plan

[0189] Step 6: Operating a community platform

[0190] User:

[0191] Visit the community forum to share your learning experiences, knowledge, and ask questions with other users.

[0192] Input: Forum interactions

[0193] Output: Shared knowledge and experience

[0194] server:

[0195] Monitor community interactions and filter inappropriate content.

[0196] Input: Forum interactions

[0197] Output: Filtered content

[0198] (Application example 1)

[0199] 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."

[0200] While conventional learning platforms offer various features to reduce learning gaps, they lack real-time learning support and rapid response to questions. Furthermore, it is difficult for users to receive real-time lectures from virtual teachers. The present invention aims to solve these problems and provide a system that supports learning more efficiently and effectively.

[0201] 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.

[0202] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for receiving lectures from a virtual teacher in real time using a smart device, and means for generating prompt sentences and obtaining appropriate answers to questions using a generative AI model, thereby enabling more efficient learning support and faster question answering.

[0203] "User information" refers to data such as the personal information, grade, and areas of interest of system users.

[0204] "Study progress data" refers to data such as test results, study time, and completed assignments obtained as the user progresses through their studies.

[0205] "Server" refers to a computer system that manages, analyzes, and processes user information and learning progress data.

[0206] "Database" means structured data storage for storing user information and learning progress data.

[0207] "Analysis" refers to the process of processing the collected learning progress data to evaluate and identify the user's learning status.

[0208] A "learning plan" is a plan for providing optimal learning content to individual users based on the analysis results.

[0209] "Feedback" refers to an evaluation of the user's impressions and level of understanding after carrying out learning activities based on the learning plan.

[0210] A "virtual teacher's lecture" is a lecture that a user receives in real time via a smart device.

[0211] A "smart device" is a digital device that can connect to the Internet and run applications, such as a smartphone, smart glasses, or a head-mounted display.

[0212] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers or content based on a given prompt.

[0213] A "prompt sentence" is text data containing questions or instructions that are input into a generative AI model.

[0214] A "community platform" is an online space where users can share knowledge and experiences and ask and answer questions.

[0215] "Search" is the process of retrieving relevant answers to a user's question from databases or external resources.

[0216] The system according to the present invention provides consistent support for everything from managing user information to creating learning plans and providing lectures by virtual teachers. This system is realized through cooperation between users, terminals, and a server.

[0217] First, the user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process. The user enters their personal information, such as name, age, grade, and areas of interest. The entered information is sent to the server via the device. The server receives this information and stores it in a database.

[0218] When a user is engaged in learning activities, the device collects learning progress data (e.g., test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to a server. The server receives this data and stores it in a database.

[0219] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server will determine that the user is good at math but weak at history. Based on the results of this analysis, the server generates a study plan. The generated study plan is sent to the user via the device. For example, a plan containing many applied math questions and a plan containing basic history review questions may be suggested.

[0220] Users can use their smart devices to receive real-time lectures from virtual teachers. These lectures are delivered through smart glasses or head-mounted displays. If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server uses a generative AI model to obtain an appropriate answer to the question. For example, if a user sends the prompt "Tell me about the French Revolution," the server will generate a specific answer such as "The French Revolution was a social revolution that took place in France from 1789 to 1799..."

[0221] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[0222] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access this community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[0223] This allows the system to support users in efficiently progressing with their studies and reduce learning gaps.

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] The user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process, entering their personal information (name, age, grade, areas of interest). The entered information is then sent by the device to the server. The server receives the sent information and stores it in a database. In this step, the user's personal information is the input, and the user information stored in the database is obtained as the output.

[0227] Step 2:

[0228] When a user starts learning activities, the device collects learning progress data (test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to the server. The server receives this data and stores it in a database. In this step, the user's learning progress data is input, and after going through the process of being sent and stored on the server, the learning progress data is accumulated in the database.

[0229] Step 3:

[0230] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, learning progress data is given as input, and the AI ​​analyzes it, and the output identifies the user's strong and weak subjects.

[0231] Step 4:

[0232] The server generates a study plan based on the analysis results, and the generated study plan is sent to the user via the terminal. For example, the user's analysis results are input, the server generates a study plan based on them, and the plan is sent to the user terminal as output.

[0233] Step 5:

[0234] Users use smart devices to receive real-time lectures from virtual teachers. The lectures are delivered through smart glasses or head-mounted displays. In this step, the input is the virtual lecture content, and the output is the lecture as visual information for the user.

[0235] Step 6:

[0236] If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server then inputs the prompt into the generative AI model to obtain an appropriate answer to the question. For example, if the prompt "Tell me about the French Revolution" is given as input, the generative AI model will output the answer "The French Revolution was a social revolution that took place in France between 1789 and 1799..." and send it to the user's device.

[0237] Step 7:

[0238] After completing a learning activity based on the learning plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the learning plan. For example, the server may take the user's feedback data as input, adjust the difficulty of the learning plan based on this, and output a new learning plan.

[0239] Step 8:

[0240] This system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this step, the input is data posted on the community forum, and the output is the server monitoring this and filtering inappropriate content.

[0241] 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.

[0242] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes the user's emotions and reflects them in the learning plans. Specific embodiments for implementing the present invention are described below.

[0243] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the registration process. The user enters information such as their name, age, grade, and areas of interest, and sends this information to the server via their device. The server receives the information and registers it in a database.

[0244] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed assignments) in real time. The collected data is periodically sent from the device to the server. The server receives this data and stores it in a database.

[0245] The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan and sends it to the device. The device then displays the generated learning plan to the user.

[0246] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from text input and touch operations. This emotional data is sent to the server in real time. For example, if the user is feeling stressed, the system will detect this and respond by providing relaxing content.

[0247] The server analyzes the received emotional data and evaluates the user's level of stress and satisfaction. Based on this evaluation, the learning plan is adjusted. For example, if the user is tired, the plan is fine-tuned by regenerating it to include more easy tasks.

[0248] When a user has a question, they input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition indicates that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[0249] After completing a learning activity, users can input feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[0250] Furthermore, the system provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learnings and questions with other users. The server monitors interactions on the community and filters inappropriate content and behavior.

[0251] The above is a specific embodiment of the system according to the present invention. This system enables personalized learning support that takes into account the user's emotions, and is expected to reduce educational disparities.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user installs and launches the app. The device displays the new registration screen.

[0255] Step 2:

[0256] The user enters basic information such as name, age, grade, interests, etc. The device stores the information in an input form and provides a submit button.

[0257] Step 3:

[0258] The user clicks the send button. The device sends the registration information to the server.

[0259] Step 4:

[0260] The server analyzes the received user information and stores it in a database. The server sends a success message to the terminal.

[0261] Step 5:

[0262] The user starts a learning activity, and the device records learning progress data in real time, including start time, end time, test results, and study time.

[0263] Step 6:

[0264] The terminal transmits learning progress data to the server at regular intervals.

[0265] Step 7:

[0266] The server stores the received learning progress data in a database and begins analyzing it.

[0267] Step 8:

[0268] The server analyzes the learning data and runs algorithms to identify the user's strengths and weaknesses. For example, the server may see that the user has high scores in math and low scores in English.

[0269] Step 9:

[0270] The server generates an individually optimized study plan. For example, the server generates a plan that includes "advanced math problems" and "basic English problems."

[0271] Step 10:

[0272] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[0273] Step 11:

[0274] The user follows the study plan and proceeds with the study. If a question arises during the study, the user can input the question into the terminal.

[0275] Step 12:

[0276] The device sends a question to the server, which receives the question and searches for the appropriate answer from a database or external resource.

[0277] Step 13:

[0278] The server generates an answer and sends it to the device, which displays the answer to the user and resolves the question.

[0279] Step 14:

[0280] The device uses emotion recognition sensors such as a camera and microphone to collect emotion data in real time, for example, by analyzing the user's facial expressions and tone of voice.

[0281] Step 15:

[0282] The device sends the collected emotional data to a server, which analyzes the emotional data and evaluates the user's emotional state (e.g., stress, concentration, fatigue).

[0283] Step 16:

[0284] The server dynamically adjusts the learning plan based on emotional data. For example, if the user is feeling stressed, the server will change the plan to include more easy tasks that will help them relax.

[0285] Step 17:

[0286] When the user finishes the learning activity, the device displays a feedback form, and the user enters feedback on the difficulty of the questions and their level of understanding.

[0287] Step 18:

[0288] The device sends feedback to the server, which receives it and uses it to fine-tune the learning plan.

[0289] Step 19:

[0290] The server generates and resubmits a fine-tuned learning plan, and the device displays the new plan to the user and begins the next learning cycle.

[0291] Step 20:

[0292] A user accesses a community forum. The device displays the forum interface, allowing the user to post and ask questions.

[0293] Step 21:

[0294] The server monitors interactions on the forum and filters inappropriate content, and the device prevents inappropriate content from being displayed to the user.

[0295] Example 2

[0296] 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."

[0297] Conventional learning support systems have the problem that it is difficult to provide personalized learning plans for individual users, making it impossible to maximize users' learning efficiency. They also lack a function to reflect the stress and satisfaction felt by users during learning in real time, which can lead to a decrease in motivation to learn. Furthermore, they lack an appropriate filtering function to promote communication between users, making it difficult to provide a safe learning environment.

[0298] 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.

[0299] In this invention, the server includes a means for inputting and transmitting user information, a means for receiving the transmitted user information and storing it in a database, and a means for collecting the user's learning progress data and transmitting it to the server. This makes it possible to provide an individually optimized learning plan. The server also includes a means for collecting and analyzing the user's emotional data to reflect it in the learning plan, and a means for evaluating the user's stress and satisfaction and adjusting the learning plan. This makes it possible to dynamically adjust the user's learning plan based on real-time emotional recognition data. The server also includes a means for providing a community platform for users to share knowledge and experiences, and a means for monitoring interactions on the community platform and filtering inappropriate content. This allows users to safely share information and advance their learning.

[0300] "User information" refers to personal data such as the user's name, age, grade, and areas of interest.

[0301] "Database" refers to a system for systematically storing and managing information.

[0302] "Study progress data" refers to data about a user's learning activities, including test results, study time, completed assignments, and the like.

[0303] "Analysis" refers to the process of analyzing collected data and extracting meaningful information.

[0304] "Study Plan" refers to a plan that provides a user with optimized learning content and schedule.

[0305] "Feedback" refers to opinions and evaluations that users enter regarding learning activities.

[0306] "Emotional data" refers to information about a user's emotional state (e.g., stress, joy, fatigue, etc.).

[0307] A "community platform" refers to an online environment where users can share their knowledge and experiences.

[0308] "Filtering" refers to the process of automatically identifying and removing inappropriate content.

[0309] MODE FOR CARRYING OUT THE INVENTION

[0310] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes user emotions and reflects them in the learning plan.

[0311] First, the user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. When registering, the user enters information such as their name, age, grade, and areas of interest. This information is sent via the device to the server, which receives it and stores it in a database.

[0312] As users engage in learning activities, their devices collect learning progress data (e.g., test results, study time, and completed assignments) in real time. This data is periodically sent from the devices to a server, which receives it and stores it in a database.

[0313] The server analyzes the accumulated learning progress data using a program written in, for example, Python or Java (registered trademark) to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan to strengthen specific areas and sends it to the device, which then displays it to the user.

[0314] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from information such as text input and touch operations. This emotional data is sent to a server in real time, where it is analyzed. For example, if the user is feeling stressed, the server will detect this and respond by providing the device with relaxing content.

[0315] Furthermore, if a user has a question, they can input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition reveals that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[0316] After completing a learning activity, users can enter feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[0317] The system also provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learning experiences and questions with other users. The server monitors interactions in the community and filters inappropriate content and behavior, providing a safe and meaningful learning environment.

[0318] Related to the process described above, here are some example prompts for optimizing a lesson plan using a generative AI model:

[0319] "I'm 16 years old, a second-year high school student, and I'm interested in math. I'm particularly bad at studying geometry. How can I study more efficiently?"

[0320] In this way, the present invention is different from conventional systems in that it reflects the user's emotions and feedback in real time and provides personalized learning support.

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Step 1:

[0323] The user downloads and installs the app on their device

[0324] Specific actions

[0325] Users download the educational support app from the app store and install it on their device. During this process, the app requests necessary access permissions and settings. Once the app is successfully installed, the initial setup screen is displayed the first time the user launches the app.

[0326] Step 2:

[0327] The user launches the app and completes the new registration process.

[0328] Input and Output

[0329] The user enters information such as name, age, grade, and areas of interest.

[0330] As an output, the input user information is saved in the terminal.

[0331] Specific actions

[0332] A registration screen will appear, and the user will enter their name, age, grade, and areas of interest. The entered information will be temporarily stored on the device. When the user presses the "Done" button, the information will be sent to the server.

[0333] Step 3:

[0334] The device sends user information to the server

[0335] Input and Output

[0336] Input is information entered by the user.

[0337] The output is the user information received by the server.

[0338] Specific actions

[0339] The terminal encrypts the entered user information and sends it securely to the server, which then travels over the network to the server.

[0340] Step 4:

[0341] The server registers the user information in the database

[0342] Input and Output

[0343] The input is user information sent from the terminal.

[0344] The output is a message confirming successful registration.

[0345] Specific actions

[0346] The server analyzes the received user information, stores it in a database, and then sends a confirmation message to the terminal confirming registration completion.

[0347] Step 5:

[0348] The user begins learning, and the device collects learning progress data.

[0349] Input and Output

[0350] The input is the user's learning activity.

[0351] The output is the collected progress data.

[0352] Specific actions

[0353] The user selects learning content and begins studying. The device collects real-time progress data, such as the user's study time, completed assignments, and test results.

[0354] Step 6:

[0355] The device sends learning progress data to the server

[0356] Input and Output

[0357] The input is collected learning progress data.

[0358] The output is the progress data received by the server.

[0359] Specific actions

[0360] The device will send the collected data to the server at regular intervals or when the user pauses or finishes learning. This data is encrypted and sent securely.

[0361] Step 7:

[0362] The server analyzes the learning progress data and generates an individual learning plan

[0363] Input and Output

[0364] The input is the received learning progress data.

[0365] The output is a generated lesson plan.

[0366] Specific actions

[0367] The server analyzes the received learning progress data using an AI model to identify the user's strengths and weaknesses, and then generates an individually optimized learning plan based on the analysis results.

[0368] Step 8:

[0369] The device displays the generated learning plan to the user

[0370] Input and Output

[0371] The input is the generated lesson plan.

[0372] The output is a lesson plan that is displayed to the user.

[0373] Specific actions

[0374] The device receives the learning plan sent from the server and displays it to the user. The display format can be a dashboard or a notification.

[0375] Step 9:

[0376] The device collects the user's emotional data and sends it to the server.

[0377] Input and Output

[0378] The input is the user's emotional data.

[0379] The output is the emotion data received by the server.

[0380] Specific actions

[0381] During training, the device's camera and microphone are used to collect emotional data, which is then inferred from text input and touch patterns and sent to the server.

[0382] Step 10:

[0383] The server analyzes the emotional data and adjusts the learning plan.

[0384] Input and Output

[0385] The input is the received emotion data.

[0386] The output is a tailored lesson plan.

[0387] Specific actions

[0388] The server analyzes the emotional data, evaluates the stress and satisfaction felt by the user, and adjusts the learning plan based on the analysis results.

[0389] Step 11:

[0390] The device sends the user's question to the server, which returns the answer

[0391] Input and Output

[0392] The input is the user's question.

[0393] The output is the answer returned by the server.

[0394] Specific actions

[0395] The user types a question and sends it to the terminal, which then sends it to the server, which searches for the appropriate answer and sends it to the terminal, which then displays the answer to the user.

[0396] Step 12:

[0397] The user enters feedback, which the device sends to the server.

[0398] Input and Output

[0399] The input is the user's feedback.

[0400] The output is the feedback received by the server.

[0401] Specific actions

[0402] After the user has completed the learning, they input feedback and send it from their device to the server.

[0403] Step 13:

[0404] The server analyzes the feedback and reflects it in the learning plan.

[0405] Input and Output

[0406] The input is the feedback received.

[0407] The output is a learning plan that reflects the feedback.

[0408] Specific actions

[0409] The server analyzes the feedback and incorporates it into the learning plan. Based on the analysis results, a learning plan is regenerated to strengthen understanding of specific areas.

[0410] Step 14:

[0411] Users access community forums and share information with other users

[0412] Specific actions

[0413] Users access a community forum to share their learnings and questions with other users, and the server monitors community interactions to filter inappropriate content and behavior.

[0414] (Application example 2)

[0415] 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."

[0416] While conventional learning support systems can generate personalized learning plans based on a user's learning progress and adjust them based on feedback, they lack learning support that takes into account the user's emotional state. Furthermore, they lack a mechanism for recognizing the stress and fatigue a user feels during learning in real time and adjusting the learning plan accordingly, which results in a problem of not maximizing learning effectiveness.

[0417] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting the user's learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for collecting the user's emotional data and transmitting it to the server, means for analyzing the user's emotional data and reflecting it in the learning plan, means for searching for and providing answers to user questions, and means for providing a community platform for users to share knowledge and experiences. This enables personalized learning support that takes the user's emotional state into consideration, maximizing learning effectiveness.

[0418] "User information" refers to data about individual users of the learning system, including basic information such as name, age, grade, and areas of interest.

[0419] A "database" is a storage system for systematically storing and managing user information, learning progress data, feedback data, and the like.

[0420] "Study progress data" is data that indicates the progress of a user's learning activities, and includes test results, study time, completed assignments, and the like.

[0421] "Analysis" is the process of evaluating information and extracting meaning from collected data.

[0422] A "learning plan" is a plan that shows individually optimized learning content and learning methods based on the analysis results.

[0423] "Emotional data" is data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, written input, etc. collected through a camera or microphone.

[0424] "Feedback" refers to users' opinions and impressions about learning activities and systems, and is information that is useful for improving the system and fine-tuning the learning plan.

[0425] A "server" is a central computer that serves as a data processing and storage resource on a network.

[0426] A "user terminal" is a device used by a user to access the learning system, such as a smartphone, tablet, or PC.

[0427] A "community platform" is an online space for users to share knowledge and experiences, and interactions take place in a forum format.

[0428] A specific system for implementing this invention has a series of functions including managing user information, tracking learning progress, providing personalized learning plans, collecting and analyzing feedback, recognizing user emotions and reflecting them in learning plans, providing answers to questions, and operating a community platform.

[0429] Hardware and Software Configuration

[0430] The system uses the following hardware and software:

[0431] Hardware: Smartphone, tablet, PC (must have camera and microphone)

[0432] Software: Django (server-side), SQLite (database), Python (programming language), machine learning libraries (sklearn, etc.)

[0433] System Operation Overview

[0434] User Registration

[0435] Users enter user information such as name, age, grade, and areas of interest through an application on their device (smartphone or tablet) and send it to the server, which receives this information and registers it in a database.

[0436] Tracking your learning progress

[0437] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and sends it to the server, which receives and analyzes this data to identify the user's strengths and weaknesses.

[0438] Providing a personalized learning plan

[0439] The server generates an individually optimized learning plan based on the analysis of the learning progress data and transmits it to the terminal, which then displays the received learning plan to the user.

[0440] Emotion recognition and reflection

[0441] The device's camera and microphone are used to collect the user's emotional data (such as stress and satisfaction). This emotional data is sent to the server in real time, where it is analyzed and the learning plan is adjusted according to the user's emotional state. For example, if the user is tired, the system will regenerate a plan that includes many easy tasks.

[0442] Feedback and questions

[0443] After completing a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. If the user has any questions, they can input them via their device, and the server will search for the appropriate answer and send it to the device.

[0444] Community Platform

[0445] It provides a community platform for users to share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content.

[0446] Examples of concrete examples and prompts

[0447] For example, if Student A at a cram school feels tired, the system will provide relaxing content along with a "recommended break." An example of a prompt sentence for the generative AI model for this scenario is as follows:

[0448] "Student A is feeling tired. Can you suggest ways to provide content that will help him relax?"

[0449] In this way, the user's emotional state can be taken into consideration, enabling more effective learning support.

[0450] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0451] Step 1:

[0452] The user enters user information such as name, age, grade, and areas of interest from the terminal through the application and sends it to the server. The server receives this information and registers it in a database. The input is user information, and the output is storing the user information in the database. This process registers basic information for each user.

[0453] Step 2:

[0454] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and periodically sends it to the server. The server receives this learning progress data and stores it in a database. The input is the learning progress data, and the output is storing the learning progress data in the database.

[0455] Step 3:

[0456] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. This analysis uses a machine learning algorithm (e.g., the sklearn library). The input is the learning progress data, and the output is the individually identified strengths and weaknesses. Based on the analysis results, the server generates an individually optimized learning plan.

[0457] Step 4:

[0458] The server sends the generated study plan to the user's terminal. The terminal displays the received study plan to the user. The input is the generated study plan, and the output is the display of the study plan on the user's terminal. This allows the user to check the study plan that is optimized for them.

[0459] Step 5:

[0460] The device's camera and microphone are used to collect user emotional data. The collected emotional data (stress and satisfaction) is sent to the server in real time. The input is the collected emotional data, and the output is the transmission of the emotional data to the server.

[0461] Step 6:

[0462] The server analyzes the received emotional data and adjusts the study plan according to the user's emotional state. For example, if it senses that the user is tired, it regenerates a study plan that includes many easy tasks. The input is emotional data, and the output is the adjusted study plan. This provides flexible study support tailored to the user.

[0463] Step 7:

[0464] After a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. The input is feedback data, and the output is a fine-tuned learning plan. The accuracy of the system improves based on the feedback.

[0465] Step 8:

[0466] When a user inputs a question, the device sends the question to the server. The server searches for an appropriate answer and sends it to the device. The input is the user's question, and the output is the answer to the user. The user can instantly resolve their question.

[0467] Step 9:

[0468] The server provides a community platform where users can share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content. The input is community posted data, and the output is a monitored, safe community environment. Users can exchange information with confidence.

[0469] Through the above processing steps, the user's learning experience is maximized and efficient learning support is realized.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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. 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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."

[0486] The system according to the present invention aims to reduce educational disparities within families and provide equal educational opportunities to all children through cooperation between users, terminals, and servers. Specific embodiments for carrying out the present invention are described below.

[0487] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. The user enters their personal information (name, age, grade, areas of interest) and sends it to the server via their device. The server receives the information and registers it in a database.

[0488] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time. The collected learning progress data is sent from the device to the server. The server receives this data and stores it in a database.

[0489] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server determines that the user is good at math but weak at history. Based on this analysis result, the server generates a study plan. The generated study plan is sent to the user via the device. For example, the server may suggest a plan that includes many applied math problems and a plan that includes basic history review problems.

[0490] The user uses the device to carry out learning activities according to the provided learning plan. If a question arises during the learning activity, the user uses the device to input the question. This question is sent from the device to the server. The server searches a database or external resource for an appropriate answer to the question, generates an answer, and sends it to the device. The device displays the generated answer to the user.

[0491] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[0492] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and to ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[0493] The above is a specific embodiment of the system according to the present invention. This system can provide equal educational opportunities to all children, regardless of their parents' educational level or financial resources, thereby reducing educational disparities.

[0494] The processing flow will be explained below.

[0495] Step 1:

[0496] The user installs and launches the app. The device displays a first-time launch screen and offers a sign-up button.

[0497] Step 2:

[0498] The user clicks the new registration button and enters personal information such as name, age, grade, area of ​​interest, etc. The terminal stores this information in an input form and provides a submit button.

[0499] Step 3:

[0500] The user clicks the send button. The device sends the registration information to the server.

[0501] Step 4:

[0502] The server analyzes the received user information and registers it in the database.

[0503] Step 5:

[0504] The user initiates a learning activity through the app. The device logs the start and end times of each learning activity (e.g., test, assignment, quiz, etc.) and progress data.

[0505] Step 6:

[0506] The terminal transmits learning progress data to the server at regular intervals.

[0507] Step 7:

[0508] The server stores the received learning progress data in a database and executes a learning performance analysis algorithm.

[0509] Step 8:

[0510] The server identifies the user's strengths and weaknesses based on the analysis results and generates a personalized learning plan.

[0511] Step 9:

[0512] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[0513] Step 10:

[0514] The user uses the terminal to carry out the learning activities according to the provided learning plan. If a question arises during the learning activities, the user inputs the question into the terminal.

[0515] Step 11:

[0516] The device sends a question to the server, which receives the question and searches for an appropriate answer from a database or external resource.

[0517] Step 12:

[0518] The server generates a response and sends it to the terminal, which displays it to the user.

[0519] Step 13:

[0520] The user completes the learning activity based on the learning plan and inputs feedback (such as difficulty of the questions and level of understanding) into the terminal.

[0521] Step 14:

[0522] The device sends feedback to the server, which receives the feedback and fine-tunes the learning plan.

[0523] Step 15:

[0524] The server resends the fine-tuned study plan to the device, and the user begins the next study cycle.

[0525] Step 16:

[0526] A user accesses a community forum to share learning experiences and knowledge with other users, and the terminal displays the forum interface.

[0527] Step 17:

[0528] The server monitors interactions on the community and filters inappropriate content.

[0529] Example 1

[0530] 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."

[0531] In today's educational environment, it is difficult to provide individualized learning plans that match each child's learning progress and level of understanding. Furthermore, educational disparities and economic situations within families can easily lead to unequal educational opportunities. There is also a lack of systems that can quickly and accurately respond to questions that arise during learning. Furthermore, there is also a lack of monitoring systems to ensure the safe sharing of knowledge and experience between users. To solve these issues, a personalized learning support system is needed.

[0532] 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.

[0533] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for generating answers to user questions using a generative AI model, and means for optimizing the learning plan based on the generated learning plan and user feedback. This makes it possible to provide individualized learning plans and resolve questions, thereby eliminating educational disparities and providing equal learning opportunities.

[0534] "User information" is information used to identify individual users and grasp their characteristics, such as the user's name, age, grade, and areas of interest.

[0535] "Study progress data" refers to data such as test results, study time, and completed assignments obtained when a user engages in study activities.

[0536] "Analysis" is the process of analyzing the collected data to identify the user's strengths and weaknesses and reflect them in future learning plans.

[0537] A "study plan" is a specific study content and schedule created based on the user's study progress data and analysis results to help the user study effectively.

[0538] "Feedback" refers to information such as an evaluation or impression provided by a user regarding the difficulty level or level of understanding of a learning plan after the user has completed the learning activity.

[0539] A "generative AI model" is a model that uses artificial intelligence to analyze data and automatically generate optimal learning plans and answers to questions for users.

[0540] A "community platform" is an online environment for users to share knowledge and experiences, and is a place where interactions take place in the form of forums or chats.

[0541] "Interaction" refers to the interaction and information exchange activities between users on the community platform.

[0542] "Filtering" is the process of monitoring interactions on a community platform and removing inappropriate content.

[0543] "Optimization" means adjusting the learning plan to be most effective for the user based on user feedback and collected data.

[0544] The system according to the present invention is linked by users, terminals, and a server, and aims to reduce educational disparities within families and provide equal educational opportunities to all children. This specification describes the specific configuration and operation of this system.

[0545] Hardware and Software Used

[0546] Hardware: User devices (smartphones and tablets), servers

[0547] Software: Learning apps, database management systems, data analysis engines, community platforms

[0548] Program processing overview

[0549] The system begins when the user downloads and installs the app on their device. After installation, the user launches the app and completes the registration process. The user enters their personal information (name, age, grade, and areas of interest) and sends it from their device to the server. The server stores the received information in a database.

[0550] As users engage in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time and sends it to the server. The server receives this data and stores it in a database. The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. A generative AI model is used for this analysis. Based on the analysis results, the server generates an individual learning plan and sends it to the device.

[0551] For example, if a user scores high on a math test but low on a history test, the server will determine that the user is strong in math but weak in history, and suggest a plan that includes many applied math questions and a plan that includes basic history questions.

[0552] The user proceeds with the learning activities according to the provided learning plan. If a question arises during the learning process, the user can input the question using the device. This question is sent from the device to the server, and the server searches for an appropriate answer to the question from a database or external resources and generates an answer using a generative AI model. The answer is sent to the device and displayed to the user.

[0553] After completing a study activity based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server analyzes this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[0554] Furthermore, the system provides a community platform for users to share their knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this way, users can maintain a safe and effective learning environment.

[0555] Examples and prompts

[0556] Example scenario:

[0557] While studying a math problem, a user types into the device, "There was something difficult to understand in history class. Could you briefly explain the economy of medieval Europe?"

[0558] The server receives this question, uses a generative AI model to generate an appropriate answer, and sends it to the user's device.

[0559] The user continues learning based on the answers displayed on the device.

[0560] The above is a specific embodiment of the system according to the present invention, which allows users to study efficiently, eliminate educational disparities, and enjoy equal learning opportunities.

[0561] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0562] Step 1: Installing the app and initial setup

[0563] User:

[0564] Users download and install the learning app onto their smartphones or tablets.

[0565] Launch the app and complete the new registration process.

[0566] Users enter personal information such as name, age, grade, and areas of interest.

[0567] Input: User's personal information

[0568] Output: Initialized app

[0569] Device:

[0570] The personal information entered by the user is sent to the server.

[0571] Input: User's personal information

[0572] Output: User information sent to the server

[0573] server:

[0574] Receives the personal information you submit and stores it in a database.

[0575] Input: User information sent from the device

[0576] Output: User information stored in the database

[0577] Step 2: Collecting learning progress data

[0578] User:

[0579] Users use the learning app to carry out learning activities.

[0580] Input: Learning activities using learning apps

[0581] Output: None

[0582] Device:

[0583] Learning progress data (test results, study time, completed assignments) is collected in real time and sent to the server.

[0584] Input: Data based on learning activities

[0585] Output: Learning progress data sent to the server

[0586] server:

[0587] The transmitted learning progress data is received and stored in a database.

[0588] Input: Learning progress data sent from the device

[0589] Output: Learning progress data stored in a database

[0590] Step 3: Data analysis and learning plan generation

[0591] server:

[0592] Analyze learning progress data using generative AI models to identify user strengths and weaknesses.

[0593] For example, if a student scores high on a math test but low on history, they will be judged as strong in math but weak in history.

[0594] Input: Learning progress data stored in a database

[0595] Output: Analysis of user's strengths and weaknesses

[0596] server:

[0597] An individual learning plan is generated based on the analysis results and sent to the device.

[0598] For example, a plan containing many applied mathematics questions and a plan containing basic history questions are generated.

[0599] Input: Analysis of user strengths and weaknesses

[0600] Output: Generated lesson plan

[0601] Device:

[0602] Display the generated learning plan to the user.

[0603] Input: Learning plan from the server

[0604] Output: The lesson plan displayed to the user

[0605] Step 4: Supporting learning activities

[0606] User:

[0607] Follow the provided learning plan and proceed with the learning activities.

[0608] If you have any questions while studying, use the device to type in your question.

[0609] Input: Learning activities and questions based on the lesson plan

[0610] Output: The question sent to the server

[0611] Device:

[0612] Send the question to the server.

[0613] Input: User question

[0614] Output: The question sent to the server

[0615] server:

[0616] Search for answers to questions and generate appropriate answers using generative AI models.

[0617] The response is sent to the device.

[0618] Input: User question

[0619] Output: The generated answer

[0620] Device:

[0621] The submitted response is displayed to the user.

[0622] Input: Response from the server

[0623] Output: The answer displayed to the user

[0624] Step 5: Gather feedback and fine-tune your learning plan

[0625] User:

[0626] After the learning activity, feedback on the difficulty and level of understanding of the learning plan is sent to the server via the device.

[0627] Input: Feedback obtained from the learning activity

[0628] Output: Feedback sent to the server

[0629] Device:

[0630] Send feedback to the server.

[0631] Input: User feedback

[0632] Output: Feedback sent to the server

[0633] server:

[0634] Receive feedback and analyze it to fine-tune your learning plan.

[0635] For example, the system regenerates an optimal study plan by adjusting the difficulty of the questions.

[0636] Input: User feedback

[0637] Output: A fine-tuned learning plan

[0638] Step 6: Operating a community platform

[0639] User:

[0640] Visit the community forum to share your learning experiences, knowledge, and ask questions with other users.

[0641] Input: Forum interactions

[0642] Output: Shared knowledge and experience

[0643] server:

[0644] Monitor community interactions and filter inappropriate content.

[0645] Input: Forum interactions

[0646] Output: Filtered content

[0647] (Application example 1)

[0648] 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."

[0649] While conventional learning platforms offer various features to reduce learning gaps, they lack real-time learning support and rapid response to questions. Furthermore, it is difficult for users to receive real-time lectures from virtual teachers. The present invention aims to solve these problems and provide a system that supports learning more efficiently and effectively.

[0650] 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.

[0651] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for receiving lectures from a virtual teacher in real time using a smart device, and means for generating prompt sentences and obtaining appropriate answers to questions using a generative AI model, thereby enabling more efficient learning support and faster question answering.

[0652] "User information" refers to data such as the personal information, grade, and areas of interest of system users.

[0653] "Study progress data" refers to data such as test results, study time, and completed assignments obtained as the user progresses through their studies.

[0654] "Server" refers to a computer system that manages, analyzes, and processes user information and learning progress data.

[0655] "Database" means structured data storage for storing user information and learning progress data.

[0656] "Analysis" refers to the process of processing the collected learning progress data to evaluate and identify the user's learning status.

[0657] A "learning plan" is a plan for providing optimal learning content to individual users based on the analysis results.

[0658] "Feedback" refers to an evaluation of the user's impressions and level of understanding after carrying out learning activities based on the learning plan.

[0659] A "virtual teacher's lecture" is a lecture that a user receives in real time via a smart device.

[0660] A "smart device" is a digital device that can connect to the Internet and run applications, such as a smartphone, smart glasses, or a head-mounted display.

[0661] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers or content based on a given prompt.

[0662] A "prompt sentence" is text data containing questions or instructions that are input into a generative AI model.

[0663] A "community platform" is an online space where users can share knowledge and experiences and ask and answer questions.

[0664] "Search" is the process of retrieving relevant answers to a user's question from databases or external resources.

[0665] The system according to the present invention provides consistent support for everything from managing user information to creating learning plans and providing lectures by virtual teachers. This system is realized through cooperation between users, terminals, and a server.

[0666] First, the user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process. The user enters their personal information, such as name, age, grade, and areas of interest. The entered information is sent to the server via the device. The server receives this information and stores it in a database.

[0667] When a user is engaged in learning activities, the device collects learning progress data (e.g., test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to a server. The server receives this data and stores it in a database.

[0668] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server will determine that the user is good at math but weak at history. Based on the results of this analysis, the server generates a study plan. The generated study plan is sent to the user via the device. For example, a plan containing many applied math questions and a plan containing basic history review questions may be suggested.

[0669] Users can use their smart devices to receive real-time lectures from virtual teachers. These lectures are delivered through smart glasses or head-mounted displays. If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server uses a generative AI model to obtain an appropriate answer to the question. For example, if a user sends the prompt "Tell me about the French Revolution," the server will generate a specific answer such as "The French Revolution was a social revolution that took place in France from 1789 to 1799..."

[0670] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[0671] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access this community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[0672] This allows the system to support users in efficiently progressing with their studies and reduce learning gaps.

[0673] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0674] Step 1:

[0675] The user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process, entering their personal information (name, age, grade, areas of interest). The entered information is then sent by the device to the server. The server receives the sent information and stores it in a database. In this step, the user's personal information is the input, and the user information stored in the database is obtained as the output.

[0676] Step 2:

[0677] When a user starts learning activities, the device collects learning progress data (test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to the server. The server receives this data and stores it in a database. In this step, the user's learning progress data is input, and after going through the process of being sent and stored on the server, the learning progress data is accumulated in the database.

[0678] Step 3:

[0679] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, learning progress data is given as input, and the AI ​​analyzes it, and the output identifies the user's strong and weak subjects.

[0680] Step 4:

[0681] The server generates a study plan based on the analysis results, and the generated study plan is sent to the user via the terminal. For example, the user's analysis results are input, the server generates a study plan based on them, and the plan is sent to the user terminal as output.

[0682] Step 5:

[0683] Users use smart devices to receive real-time lectures from virtual teachers. The lectures are delivered through smart glasses or head-mounted displays. In this step, the input is the virtual lecture content, and the output is the lecture as visual information for the user.

[0684] Step 6:

[0685] If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server then inputs the prompt into the generative AI model to obtain an appropriate answer to the question. For example, if the prompt "Tell me about the French Revolution" is given as input, the generative AI model will output the answer "The French Revolution was a social revolution that took place in France between 1789 and 1799..." and send it to the user's device.

[0686] Step 7:

[0687] After completing a learning activity based on the learning plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the learning plan. For example, the server may take the user's feedback data as input, adjust the difficulty of the learning plan based on this, and output a new learning plan.

[0688] Step 8:

[0689] This system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this step, the input is data posted on the community forum, and the output is the server monitoring this and filtering inappropriate content.

[0690] 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.

[0691] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes the user's emotions and reflects them in the learning plans. Specific embodiments for implementing the present invention are described below.

[0692] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the registration process. The user enters information such as their name, age, grade, and areas of interest, and sends this information to the server via their device. The server receives the information and registers it in a database.

[0693] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed assignments) in real time. The collected data is periodically sent from the device to the server. The server receives this data and stores it in a database.

[0694] The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan and sends it to the device. The device then displays the generated learning plan to the user.

[0695] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from text input and touch operations. This emotional data is sent to the server in real time. For example, if the user is feeling stressed, the system will detect this and respond by providing relaxing content.

[0696] The server analyzes the received emotional data and evaluates the user's level of stress and satisfaction. Based on this evaluation, the learning plan is adjusted. For example, if the user is tired, the plan is fine-tuned by regenerating it to include more easy tasks.

[0697] When a user has a question, they input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition indicates that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[0698] After completing a learning activity, users can input feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[0699] Furthermore, the system provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learnings and questions with other users. The server monitors interactions on the community and filters inappropriate content and behavior.

[0700] The above is a specific embodiment of the system according to the present invention. This system enables personalized learning support that takes into account the user's emotions, and is expected to reduce educational disparities.

[0701] The processing flow will be explained below.

[0702] Step 1:

[0703] The user installs and launches the app. The device displays the new registration screen.

[0704] Step 2:

[0705] The user enters basic information such as name, age, grade, interests, etc. The device stores the information in an input form and provides a submit button.

[0706] Step 3:

[0707] The user clicks the send button. The device sends the registration information to the server.

[0708] Step 4:

[0709] The server analyzes the received user information and stores it in a database. The server sends a success message to the terminal.

[0710] Step 5:

[0711] The user starts a learning activity, and the device records learning progress data in real time, including start time, end time, test results, and study time.

[0712] Step 6:

[0713] The terminal transmits learning progress data to the server at regular intervals.

[0714] Step 7:

[0715] The server stores the received learning progress data in a database and begins analyzing it.

[0716] Step 8:

[0717] The server analyzes the learning data and runs algorithms to identify the user's strengths and weaknesses. For example, the server may see that the user has high scores in math and low scores in English.

[0718] Step 9:

[0719] The server generates an individually optimized study plan. For example, the server generates a plan that includes "advanced math problems" and "basic English problems."

[0720] Step 10:

[0721] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[0722] Step 11:

[0723] The user follows the study plan and proceeds with the study. If a question arises during the study, the user can input the question into the terminal.

[0724] Step 12:

[0725] The device sends a question to the server, which receives the question and searches for the appropriate answer from a database or external resource.

[0726] Step 13:

[0727] The server generates an answer and sends it to the device, which displays the answer to the user and resolves the question.

[0728] Step 14:

[0729] The device uses emotion recognition sensors such as a camera and microphone to collect emotion data in real time, for example, by analyzing the user's facial expressions and tone of voice.

[0730] Step 15:

[0731] The device sends the collected emotional data to a server, which analyzes the emotional data and evaluates the user's emotional state (e.g., stress, concentration, fatigue).

[0732] Step 16:

[0733] The server dynamically adjusts the learning plan based on emotional data. For example, if the user is feeling stressed, the server will change the plan to include more easy tasks that will help them relax.

[0734] Step 17:

[0735] When the user finishes the learning activity, the device displays a feedback form, and the user enters feedback on the difficulty of the questions and their level of understanding.

[0736] Step 18:

[0737] The device sends feedback to the server, which receives it and uses it to fine-tune the learning plan.

[0738] Step 19:

[0739] The server generates and resubmits a fine-tuned learning plan, and the device displays the new plan to the user and begins the next learning cycle.

[0740] Step 20:

[0741] A user accesses a community forum. The device displays the forum interface, allowing the user to post and ask questions.

[0742] Step 21:

[0743] The server monitors interactions on the forum and filters inappropriate content, and the device prevents inappropriate content from being displayed to the user.

[0744] Example 2

[0745] 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."

[0746] Conventional learning support systems have the problem that it is difficult to provide personalized learning plans for individual users, making it impossible to maximize users' learning efficiency. They also lack a function to reflect the stress and satisfaction felt by users during learning in real time, which can lead to a decrease in motivation to learn. Furthermore, they lack an appropriate filtering function to promote communication between users, making it difficult to provide a safe learning environment.

[0747] 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.

[0748] In this invention, the server includes a means for inputting and transmitting user information, a means for receiving the transmitted user information and storing it in a database, and a means for collecting the user's learning progress data and transmitting it to the server. This makes it possible to provide an individually optimized learning plan. The server also includes a means for collecting and analyzing the user's emotional data to reflect it in the learning plan, and a means for evaluating the user's stress and satisfaction and adjusting the learning plan. This makes it possible to dynamically adjust the user's learning plan based on real-time emotional recognition data. The server also includes a means for providing a community platform for users to share knowledge and experiences, and a means for monitoring interactions on the community platform and filtering inappropriate content. This allows users to safely share information and advance their learning.

[0749] "User information" refers to personal data such as the user's name, age, grade, and areas of interest.

[0750] "Database" refers to a system for systematically storing and managing information.

[0751] "Study progress data" refers to data about a user's learning activities, including test results, study time, completed assignments, and the like.

[0752] "Analysis" refers to the process of analyzing collected data and extracting meaningful information.

[0753] "Study Plan" refers to a plan that provides a user with optimized learning content and schedule.

[0754] "Feedback" refers to opinions and evaluations that users enter regarding learning activities.

[0755] "Emotional data" refers to information about a user's emotional state (e.g., stress, joy, fatigue, etc.).

[0756] A "community platform" refers to an online environment where users can share their knowledge and experiences.

[0757] "Filtering" refers to the process of automatically identifying and removing inappropriate content.

[0758] MODE FOR CARRYING OUT THE INVENTION

[0759] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes user emotions and reflects them in the learning plan.

[0760] First, the user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. When registering, the user enters information such as their name, age, grade, and areas of interest. This information is sent via the device to the server, which receives it and stores it in a database.

[0761] As users engage in learning activities, their devices collect learning progress data (e.g., test results, study time, and completed assignments) in real time. This data is periodically sent from the devices to a server, which receives it and stores it in a database.

[0762] The server analyzes the accumulated learning progress data using programs such as Python or Java to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan to strengthen specific areas and sends it to the device, which then displays it to the user.

[0763] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from information such as text input and touch operations. This emotional data is sent to a server in real time, where it is analyzed. For example, if the user is feeling stressed, the server will detect this and respond by providing the device with relaxing content.

[0764] Furthermore, if a user has a question, they can input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition reveals that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[0765] After completing a learning activity, users can enter feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[0766] The system also provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learning experiences and questions with other users. The server monitors interactions in the community and filters inappropriate content and behavior, providing a safe and meaningful learning environment.

[0767] Related to the process described above, here are some example prompts for optimizing a lesson plan using a generative AI model:

[0768] "I'm 16 years old, a second-year high school student, and I'm interested in math. I'm particularly bad at studying geometry. How can I study more efficiently?"

[0769] In this way, the present invention is different from conventional systems in that it reflects the user's emotions and feedback in real time and provides personalized learning support.

[0770] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0771] Step 1:

[0772] The user downloads and installs the app on their device

[0773] Specific actions

[0774] Users download the educational support app from the app store and install it on their device. During this process, the app requests necessary access permissions and settings. Once the app is successfully installed, the initial setup screen is displayed the first time the user launches the app.

[0775] Step 2:

[0776] The user launches the app and completes the new registration process.

[0777] Input and Output

[0778] The user enters information such as name, age, grade, and areas of interest.

[0779] As an output, the input user information is saved in the terminal.

[0780] Specific actions

[0781] A registration screen will appear, and the user will enter their name, age, grade, and areas of interest. The entered information will be temporarily stored on the device. When the user presses the "Done" button, the information will be sent to the server.

[0782] Step 3:

[0783] The device sends user information to the server

[0784] Input and Output

[0785] Input is information entered by the user.

[0786] The output is the user information received by the server.

[0787] Specific actions

[0788] The terminal encrypts the entered user information and sends it securely to the server, which then travels over the network to the server.

[0789] Step 4:

[0790] The server registers the user information in the database

[0791] Input and Output

[0792] The input is user information sent from the terminal.

[0793] The output is a message confirming successful registration.

[0794] Specific actions

[0795] The server analyzes the received user information, stores it in a database, and then sends a confirmation message to the terminal confirming registration completion.

[0796] Step 5:

[0797] The user begins learning, and the device collects learning progress data.

[0798] Input and Output

[0799] The input is the user's learning activity.

[0800] The output is the collected progress data.

[0801] Specific actions

[0802] The user selects learning content and begins studying. The device collects real-time progress data, such as the user's study time, completed assignments, and test results.

[0803] Step 6:

[0804] The device sends learning progress data to the server

[0805] Input and Output

[0806] The input is collected learning progress data.

[0807] The output is the progress data received by the server.

[0808] Specific actions

[0809] The device will send the collected data to the server at regular intervals or when the user pauses or finishes learning. This data is encrypted and sent securely.

[0810] Step 7:

[0811] The server analyzes the learning progress data and generates an individual learning plan

[0812] Input and Output

[0813] The input is the received learning progress data.

[0814] The output is a generated lesson plan.

[0815] Specific actions

[0816] The server analyzes the received learning progress data using an AI model to identify the user's strengths and weaknesses, and then generates an individually optimized learning plan based on the analysis results.

[0817] Step 8:

[0818] The device displays the generated learning plan to the user

[0819] Input and Output

[0820] The input is the generated lesson plan.

[0821] The output is a lesson plan that is displayed to the user.

[0822] Specific actions

[0823] The device receives the learning plan sent from the server and displays it to the user. The display format can be a dashboard or a notification.

[0824] Step 9:

[0825] The device collects the user's emotional data and sends it to the server.

[0826] Input and Output

[0827] The input is the user's emotional data.

[0828] The output is the emotion data received by the server.

[0829] Specific actions

[0830] During training, the device's camera and microphone are used to collect emotional data, which is then inferred from text input and touch patterns and sent to the server.

[0831] Step 10:

[0832] The server analyzes the emotional data and adjusts the learning plan.

[0833] Input and Output

[0834] The input is the received emotion data.

[0835] The output is a tailored lesson plan.

[0836] Specific actions

[0837] The server analyzes the emotional data, evaluates the stress and satisfaction felt by the user, and adjusts the learning plan based on the analysis results.

[0838] Step 11:

[0839] The device sends the user's question to the server, which returns the answer

[0840] Input and Output

[0841] The input is the user's question.

[0842] The output is the answer returned by the server.

[0843] Specific actions

[0844] The user types a question and sends it to the terminal, which then sends it to the server, which searches for the appropriate answer and sends it to the terminal, which then displays the answer to the user.

[0845] Step 12:

[0846] The user enters feedback, which the device sends to the server.

[0847] Input and Output

[0848] The input is the user's feedback.

[0849] The output is the feedback received by the server.

[0850] Specific actions

[0851] After the user has completed the learning, they input feedback and send it from their device to the server.

[0852] Step 13:

[0853] The server analyzes the feedback and reflects it in the learning plan.

[0854] Input and Output

[0855] The input is the feedback received.

[0856] The output is a learning plan that reflects the feedback.

[0857] Specific actions

[0858] The server analyzes the feedback and incorporates it into the learning plan. Based on the analysis results, a learning plan is regenerated to strengthen understanding of specific areas.

[0859] Step 14:

[0860] Users access community forums and share information with other users

[0861] Specific actions

[0862] Users access a community forum to share their learnings and questions with other users, and the server monitors community interactions to filter inappropriate content and behavior.

[0863] (Application example 2)

[0864] 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."

[0865] While conventional learning support systems can generate personalized learning plans based on a user's learning progress and adjust them based on feedback, they lack learning support that takes into account the user's emotional state. Furthermore, they lack a mechanism for recognizing the stress and fatigue a user feels during learning in real time and adjusting the learning plan accordingly, which results in a problem of not maximizing learning effectiveness.

[0866] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting the user's learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for collecting the user's emotional data and transmitting it to the server, means for analyzing the user's emotional data and reflecting it in the learning plan, means for searching for and providing answers to user questions, and means for providing a community platform for users to share knowledge and experiences. This enables personalized learning support that takes the user's emotional state into consideration, maximizing learning effectiveness.

[0867] "User information" refers to data about individual users of the learning system, including basic information such as name, age, grade, and areas of interest.

[0868] A "database" is a storage system for systematically storing and managing user information, learning progress data, feedback data, and the like.

[0869] "Study progress data" is data that indicates the progress of a user's learning activities, and includes test results, study time, completed assignments, and the like.

[0870] "Analysis" is the process of evaluating information and extracting meaning from collected data.

[0871] A "learning plan" is a plan that shows individually optimized learning content and learning methods based on the analysis results.

[0872] "Emotional data" is data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, written input, etc. collected through a camera or microphone.

[0873] "Feedback" refers to users' opinions and impressions about learning activities and systems, and is information that is useful for improving the system and fine-tuning the learning plan.

[0874] A "server" is a central computer that serves as a data processing and storage resource on a network.

[0875] A "user terminal" is a device used by a user to access the learning system, such as a smartphone, tablet, or PC.

[0876] A "community platform" is an online space for users to share knowledge and experiences, and interactions take place in a forum format.

[0877] A specific system for implementing this invention has a series of functions including managing user information, tracking learning progress, providing personalized learning plans, collecting and analyzing feedback, recognizing user emotions and reflecting them in learning plans, providing answers to questions, and operating a community platform.

[0878] Hardware and Software Configuration

[0879] The system uses the following hardware and software:

[0880] Hardware: Smartphone, tablet, PC (must have camera and microphone)

[0881] Software: Django (server-side), SQLite (database), Python (programming language), machine learning libraries (sklearn, etc.)

[0882] System Operation Overview

[0883] User Registration

[0884] Users enter user information such as name, age, grade, and areas of interest through an application on their device (smartphone or tablet) and send it to the server, which receives this information and registers it in a database.

[0885] Tracking your learning progress

[0886] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and sends it to the server, which receives and analyzes this data to identify the user's strengths and weaknesses.

[0887] Providing a personalized learning plan

[0888] The server generates an individually optimized learning plan based on the analysis of the learning progress data and transmits it to the terminal, which then displays the received learning plan to the user.

[0889] Emotion recognition and reflection

[0890] The device's camera and microphone are used to collect the user's emotional data (such as stress and satisfaction). This emotional data is sent to the server in real time, where it is analyzed and the learning plan is adjusted according to the user's emotional state. For example, if the user is tired, the system will regenerate a plan that includes many easy tasks.

[0891] Feedback and questions

[0892] After completing a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. If the user has any questions, they can input them via their device, and the server will search for the appropriate answer and send it to the device.

[0893] Community Platform

[0894] It provides a community platform for users to share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content.

[0895] Examples of concrete examples and prompts

[0896] For example, if Student A at a cram school feels tired, the system will provide relaxing content along with a "recommended break." An example of a prompt sentence for the generative AI model for this scenario is as follows:

[0897] "Student A is feeling tired. Can you suggest ways to provide content that will help him relax?"

[0898] In this way, the user's emotional state can be taken into consideration, enabling more effective learning support.

[0899] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0900] Step 1:

[0901] The user enters user information such as name, age, grade, and areas of interest from the terminal through the application and sends it to the server. The server receives this information and registers it in a database. The input is user information, and the output is storing the user information in the database. This process registers basic information for each user.

[0902] Step 2:

[0903] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and periodically sends it to the server. The server receives this learning progress data and stores it in a database. The input is the learning progress data, and the output is storing the learning progress data in the database.

[0904] Step 3:

[0905] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. This analysis uses a machine learning algorithm (e.g., the sklearn library). The input is the learning progress data, and the output is the individually identified strengths and weaknesses. Based on the analysis results, the server generates an individually optimized learning plan.

[0906] Step 4:

[0907] The server sends the generated study plan to the user's terminal. The terminal displays the received study plan to the user. The input is the generated study plan, and the output is the display of the study plan on the user's terminal. This allows the user to check the study plan that is optimized for them.

[0908] Step 5:

[0909] The device's camera and microphone are used to collect user emotional data. The collected emotional data (stress and satisfaction) is sent to the server in real time. The input is the collected emotional data, and the output is the transmission of the emotional data to the server.

[0910] Step 6:

[0911] The server analyzes the received emotional data and adjusts the study plan according to the user's emotional state. For example, if it senses that the user is tired, it regenerates a study plan that includes many easy tasks. The input is emotional data, and the output is the adjusted study plan. This provides flexible study support tailored to the user.

[0912] Step 7:

[0913] After a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. The input is feedback data, and the output is a fine-tuned learning plan. The accuracy of the system improves based on the feedback.

[0914] Step 8:

[0915] When a user inputs a question, the device sends the question to the server. The server searches for an appropriate answer and sends it to the device. The input is the user's question, and the output is the answer to the user. The user can instantly resolve their question.

[0916] Step 9:

[0917] The server provides a community platform where users can share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content. The input is community posted data, and the output is a monitored, safe community environment. Users can exchange information with confidence.

[0918] Through the above processing steps, the user's learning experience is maximized and efficient learning support is realized.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] [Third embodiment]

[0923] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0924] 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.

[0925] 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).

[0926] 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.

[0927] 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.

[0928] 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).

[0929] 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. 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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."

[0935] The system according to the present invention aims to reduce educational disparities within families and provide equal educational opportunities to all children through cooperation between users, terminals, and servers. Specific embodiments for carrying out the present invention are described below.

[0936] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. The user enters their personal information (name, age, grade, areas of interest) and sends it to the server via their device. The server receives the information and registers it in a database.

[0937] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time. The collected learning progress data is sent from the device to the server. The server receives this data and stores it in a database.

[0938] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server determines that the user is good at math but weak at history. Based on this analysis result, the server generates a study plan. The generated study plan is sent to the user via the device. For example, the server may suggest a plan that includes many applied math problems and a plan that includes basic history review problems.

[0939] The user uses the device to carry out learning activities according to the provided learning plan. If a question arises during the learning activity, the user uses the device to input the question. This question is sent from the device to the server. The server searches a database or external resource for an appropriate answer to the question, generates an answer, and sends it to the device. The device displays the generated answer to the user.

[0940] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[0941] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and to ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[0942] The above is a specific embodiment of the system according to the present invention. This system can provide equal educational opportunities to all children, regardless of their parents' educational level or financial resources, thereby reducing educational disparities.

[0943] The processing flow will be explained below.

[0944] Step 1:

[0945] The user installs and launches the app. The device displays a first-time launch screen and offers a sign-up button.

[0946] Step 2:

[0947] The user clicks the new registration button and enters personal information such as name, age, grade, area of ​​interest, etc. The terminal stores this information in an input form and provides a submit button.

[0948] Step 3:

[0949] The user clicks the send button. The device sends the registration information to the server.

[0950] Step 4:

[0951] The server analyzes the received user information and registers it in the database.

[0952] Step 5:

[0953] The user initiates a learning activity through the app. The device logs the start and end times of each learning activity (e.g., test, assignment, quiz, etc.) and progress data.

[0954] Step 6:

[0955] The terminal transmits learning progress data to the server at regular intervals.

[0956] Step 7:

[0957] The server stores the received learning progress data in a database and executes a learning performance analysis algorithm.

[0958] Step 8:

[0959] The server identifies the user's strengths and weaknesses based on the analysis results and generates a personalized learning plan.

[0960] Step 9:

[0961] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[0962] Step 10:

[0963] The user uses the terminal to carry out the learning activities according to the provided learning plan. If a question arises during the learning activities, the user inputs the question into the terminal.

[0964] Step 11:

[0965] The device sends a question to the server, which receives the question and searches for an appropriate answer from a database or external resource.

[0966] Step 12:

[0967] The server generates a response and sends it to the terminal, which displays it to the user.

[0968] Step 13:

[0969] The user completes the learning activity based on the learning plan and inputs feedback (such as difficulty of the questions and level of understanding) into the terminal.

[0970] Step 14:

[0971] The device sends feedback to the server, which receives the feedback and fine-tunes the learning plan.

[0972] Step 15:

[0973] The server resends the fine-tuned study plan to the device, and the user begins the next study cycle.

[0974] Step 16:

[0975] A user accesses a community forum to share learning experiences and knowledge with other users, and the terminal displays the forum interface.

[0976] Step 17:

[0977] The server monitors interactions on the community and filters inappropriate content.

[0978] Example 1

[0979] 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."

[0980] In today's educational environment, it is difficult to provide individualized learning plans that match each child's learning progress and level of understanding. Furthermore, educational disparities and economic situations within families can easily lead to unequal educational opportunities. There is also a lack of systems that can quickly and accurately respond to questions that arise during learning. Furthermore, there is also a lack of monitoring systems to ensure the safe sharing of knowledge and experience between users. To solve these issues, a personalized learning support system is needed.

[0981] 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.

[0982] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for generating answers to user questions using a generative AI model, and means for optimizing the learning plan based on the generated learning plan and user feedback. This makes it possible to provide individualized learning plans and resolve questions, thereby eliminating educational disparities and providing equal learning opportunities.

[0983] "User information" is information used to identify individual users and grasp their characteristics, such as the user's name, age, grade, and areas of interest.

[0984] "Study progress data" refers to data such as test results, study time, and completed assignments obtained when a user engages in study activities.

[0985] "Analysis" is the process of analyzing the collected data to identify the user's strengths and weaknesses and reflect them in future learning plans.

[0986] A "study plan" is a specific study content and schedule created based on the user's study progress data and analysis results to help the user study effectively.

[0987] "Feedback" refers to information such as an evaluation or impression provided by a user regarding the difficulty level or level of understanding of a learning plan after the user has completed the learning activity.

[0988] A "generative AI model" is a model that uses artificial intelligence to analyze data and automatically generate optimal learning plans and answers to questions for users.

[0989] A "community platform" is an online environment for users to share knowledge and experiences, and is a place where interactions take place in the form of forums or chats.

[0990] "Interaction" refers to the interaction and information exchange activities between users on the community platform.

[0991] "Filtering" is the process of monitoring interactions on a community platform and removing inappropriate content.

[0992] "Optimization" means adjusting the learning plan to be most effective for the user based on user feedback and collected data.

[0993] The system according to the present invention is linked by users, terminals, and a server, and aims to reduce educational disparities within families and provide equal educational opportunities to all children. This specification describes the specific configuration and operation of this system.

[0994] Hardware and Software Used

[0995] Hardware: User devices (smartphones and tablets), servers

[0996] Software: Learning apps, database management systems, data analysis engines, community platforms

[0997] Program processing overview

[0998] The system begins when the user downloads and installs the app on their device. After installation, the user launches the app and completes the registration process. The user enters their personal information (name, age, grade, and areas of interest) and sends it from their device to the server. The server stores the received information in a database.

[0999] As users engage in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time and sends it to the server. The server receives this data and stores it in a database. The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. A generative AI model is used for this analysis. Based on the analysis results, the server generates an individual learning plan and sends it to the device.

[1000] For example, if a user scores high on a math test but low on a history test, the server will determine that the user is strong in math but weak in history, and suggest a plan that includes many applied math questions and a plan that includes basic history questions.

[1001] The user proceeds with the learning activities according to the provided learning plan. If a question arises during the learning process, the user can input the question using the device. This question is sent from the device to the server, and the server searches for an appropriate answer to the question from a database or external resources and generates an answer using a generative AI model. The answer is sent to the device and displayed to the user.

[1002] After completing a study activity based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server analyzes this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[1003] Furthermore, the system provides a community platform for users to share their knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this way, users can maintain a safe and effective learning environment.

[1004] Examples and prompts

[1005] Example scenario:

[1006] While studying a math problem, a user types into the device, "There was something difficult to understand in history class. Could you briefly explain the economy of medieval Europe?"

[1007] The server receives this question, uses a generative AI model to generate an appropriate answer, and sends it to the user's device.

[1008] The user continues learning based on the answers displayed on the device.

[1009] The above is a specific embodiment of the system according to the present invention, which allows users to study efficiently, eliminate educational disparities, and enjoy equal learning opportunities.

[1010] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1011] Step 1: Installing the app and initial setup

[1012] User:

[1013] Users download and install the learning app onto their smartphones or tablets.

[1014] Launch the app and complete the new registration process.

[1015] Users enter personal information such as name, age, grade, and areas of interest.

[1016] Input: User's personal information

[1017] Output: Initialized app

[1018] Device:

[1019] The personal information entered by the user is sent to the server.

[1020] Input: User's personal information

[1021] Output: User information sent to the server

[1022] server:

[1023] Receives the personal information you submit and stores it in a database.

[1024] Input: User information sent from the device

[1025] Output: User information stored in the database

[1026] Step 2: Collecting learning progress data

[1027] User:

[1028] Users use the learning app to carry out learning activities.

[1029] Input: Learning activities using learning apps

[1030] Output: None

[1031] Device:

[1032] Learning progress data (test results, study time, completed assignments) is collected in real time and sent to the server.

[1033] Input: Data based on learning activities

[1034] Output: Learning progress data sent to the server

[1035] server:

[1036] The transmitted learning progress data is received and stored in a database.

[1037] Input: Learning progress data sent from the device

[1038] Output: Learning progress data stored in a database

[1039] Step 3: Data analysis and learning plan generation

[1040] server:

[1041] Analyze learning progress data using generative AI models to identify user strengths and weaknesses.

[1042] For example, if a student scores high on a math test but low on history, they will be judged as strong in math but weak in history.

[1043] Input: Learning progress data stored in a database

[1044] Output: Analysis of user's strengths and weaknesses

[1045] server:

[1046] An individual learning plan is generated based on the analysis results and sent to the device.

[1047] For example, a plan containing many applied mathematics questions and a plan containing basic history questions are generated.

[1048] Input: Analysis of user strengths and weaknesses

[1049] Output: Generated lesson plan

[1050] Device:

[1051] Display the generated learning plan to the user.

[1052] Input: Learning plan from the server

[1053] Output: The lesson plan displayed to the user

[1054] Step 4: Supporting learning activities

[1055] User:

[1056] Follow the provided learning plan and proceed with the learning activities.

[1057] If you have any questions while studying, use the device to type in your question.

[1058] Input: Learning activities and questions based on the lesson plan

[1059] Output: The question sent to the server

[1060] Device:

[1061] Send the question to the server.

[1062] Input: User question

[1063] Output: The question sent to the server

[1064] server:

[1065] Search for answers to questions and generate appropriate answers using generative AI models.

[1066] The response is sent to the device.

[1067] Input: User question

[1068] Output: The generated answer

[1069] Device:

[1070] The submitted response is displayed to the user.

[1071] Input: Response from the server

[1072] Output: The answer displayed to the user

[1073] Step 5: Gather feedback and fine-tune your learning plan

[1074] User:

[1075] After the learning activity, feedback on the difficulty and level of understanding of the learning plan is sent to the server via the device.

[1076] Input: Feedback obtained from the learning activity

[1077] Output: Feedback sent to the server

[1078] Device:

[1079] Send feedback to the server.

[1080] Input: User feedback

[1081] Output: Feedback sent to the server

[1082] server:

[1083] Receive feedback and analyze it to fine-tune your learning plan.

[1084] For example, the system regenerates an optimal study plan by adjusting the difficulty of the questions.

[1085] Input: User feedback

[1086] Output: A fine-tuned learning plan

[1087] Step 6: Operating a community platform

[1088] User:

[1089] Visit the community forum to share your learning experiences, knowledge, and ask questions with other users.

[1090] Input: Forum interactions

[1091] Output: Shared knowledge and experience

[1092] server:

[1093] Monitor community interactions and filter inappropriate content.

[1094] Input: Forum interactions

[1095] Output: Filtered content

[1096] (Application example 1)

[1097] 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."

[1098] While conventional learning platforms offer various features to reduce learning gaps, they lack real-time learning support and rapid response to questions. Furthermore, it is difficult for users to receive real-time lectures from virtual teachers. The present invention aims to solve these problems and provide a system that supports learning more efficiently and effectively.

[1099] 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.

[1100] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for receiving lectures from a virtual teacher in real time using a smart device, and means for generating prompt sentences and obtaining appropriate answers to questions using a generative AI model, thereby enabling more efficient learning support and faster question answering.

[1101] "User information" refers to data such as the personal information, grade, and areas of interest of system users.

[1102] "Study progress data" refers to data such as test results, study time, and completed assignments obtained as the user progresses through their studies.

[1103] "Server" refers to a computer system that manages, analyzes, and processes user information and learning progress data.

[1104] "Database" means structured data storage for storing user information and learning progress data.

[1105] "Analysis" refers to the process of processing the collected learning progress data to evaluate and identify the user's learning status.

[1106] A "learning plan" is a plan for providing optimal learning content to individual users based on the analysis results.

[1107] "Feedback" refers to an evaluation of the user's impressions and level of understanding after carrying out learning activities based on the learning plan.

[1108] A "virtual teacher's lecture" is a lecture that a user receives in real time via a smart device.

[1109] A "smart device" is a digital device that can connect to the Internet and run applications, such as a smartphone, smart glasses, or a head-mounted display.

[1110] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers or content based on a given prompt.

[1111] A "prompt sentence" is text data containing questions or instructions that are input into a generative AI model.

[1112] A "community platform" is an online space where users can share knowledge and experiences and ask and answer questions.

[1113] "Search" is the process of retrieving relevant answers to a user's question from databases or external resources.

[1114] The system according to the present invention provides consistent support for everything from managing user information to creating learning plans and providing lectures by virtual teachers. This system is realized through cooperation between users, terminals, and a server.

[1115] First, the user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process. The user enters their personal information, such as name, age, grade, and areas of interest. The entered information is sent to the server via the device. The server receives this information and stores it in a database.

[1116] When a user is engaged in learning activities, the device collects learning progress data (e.g., test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to a server. The server receives this data and stores it in a database.

[1117] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server will determine that the user is good at math but weak at history. Based on the results of this analysis, the server generates a study plan. The generated study plan is sent to the user via the device. For example, a plan containing many applied math questions and a plan containing basic history review questions may be suggested.

[1118] Users can use their smart devices to receive real-time lectures from virtual teachers. These lectures are delivered through smart glasses or head-mounted displays. If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server uses a generative AI model to obtain an appropriate answer to the question. For example, if a user sends the prompt "Tell me about the French Revolution," the server will generate a specific answer such as "The French Revolution was a social revolution that took place in France from 1789 to 1799..."

[1119] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[1120] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access this community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[1121] This allows the system to support users in efficiently progressing with their studies and reduce learning gaps.

[1122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1123] Step 1:

[1124] The user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process, entering their personal information (name, age, grade, areas of interest). The entered information is then sent by the device to the server. The server receives the sent information and stores it in a database. In this step, the user's personal information is the input, and the user information stored in the database is obtained as the output.

[1125] Step 2:

[1126] When a user starts learning activities, the device collects learning progress data (test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to the server. The server receives this data and stores it in a database. In this step, the user's learning progress data is input, and after going through the process of being sent and stored on the server, the learning progress data is accumulated in the database.

[1127] Step 3:

[1128] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, learning progress data is given as input, and the AI ​​analyzes it, and the output identifies the user's strong and weak subjects.

[1129] Step 4:

[1130] The server generates a study plan based on the analysis results, and the generated study plan is sent to the user via the terminal. For example, the user's analysis results are input, the server generates a study plan based on them, and the plan is sent to the user terminal as output.

[1131] Step 5:

[1132] Users use smart devices to receive real-time lectures from virtual teachers. The lectures are delivered through smart glasses or head-mounted displays. In this step, the input is the virtual lecture content, and the output is the lecture as visual information for the user.

[1133] Step 6:

[1134] If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server then inputs the prompt into the generative AI model to obtain an appropriate answer to the question. For example, if the prompt "Tell me about the French Revolution" is given as input, the generative AI model will output the answer "The French Revolution was a social revolution that took place in France between 1789 and 1799..." and send it to the user's device.

[1135] Step 7:

[1136] After completing a learning activity based on the learning plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the learning plan. For example, the server may take the user's feedback data as input, adjust the difficulty of the learning plan based on this, and output a new learning plan.

[1137] Step 8:

[1138] This system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this step, the input is data posted on the community forum, and the output is the server monitoring this and filtering inappropriate content.

[1139] 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.

[1140] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes the user's emotions and reflects them in the learning plans. Specific embodiments for implementing the present invention are described below.

[1141] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the registration process. The user enters information such as their name, age, grade, and areas of interest, and sends this information to the server via their device. The server receives the information and registers it in a database.

[1142] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed assignments) in real time. The collected data is periodically sent from the device to the server. The server receives this data and stores it in a database.

[1143] The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan and sends it to the device. The device then displays the generated learning plan to the user.

[1144] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from text input and touch operations. This emotional data is sent to the server in real time. For example, if the user is feeling stressed, the system will detect this and respond by providing relaxing content.

[1145] The server analyzes the received emotional data and evaluates the user's level of stress and satisfaction. Based on this evaluation, the learning plan is adjusted. For example, if the user is tired, the plan is fine-tuned by regenerating it to include more easy tasks.

[1146] When a user has a question, they input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition indicates that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[1147] After completing a learning activity, users can input feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[1148] Furthermore, the system provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learnings and questions with other users. The server monitors interactions on the community and filters inappropriate content and behavior.

[1149] The above is a specific embodiment of the system according to the present invention. This system enables personalized learning support that takes into account the user's emotions, and is expected to reduce educational disparities.

[1150] The processing flow will be explained below.

[1151] Step 1:

[1152] The user installs and launches the app. The device displays the new registration screen.

[1153] Step 2:

[1154] The user enters basic information such as name, age, grade, interests, etc. The device stores the information in an input form and provides a submit button.

[1155] Step 3:

[1156] The user clicks the send button. The device sends the registration information to the server.

[1157] Step 4:

[1158] The server analyzes the received user information and stores it in a database. The server sends a success message to the terminal.

[1159] Step 5:

[1160] The user starts a learning activity, and the device records learning progress data in real time, including start time, end time, test results, and study time.

[1161] Step 6:

[1162] The terminal transmits learning progress data to the server at regular intervals.

[1163] Step 7:

[1164] The server stores the received learning progress data in a database and begins analyzing it.

[1165] Step 8:

[1166] The server analyzes the learning data and runs algorithms to identify the user's strengths and weaknesses. For example, the server may see that the user has high scores in math and low scores in English.

[1167] Step 9:

[1168] The server generates an individually optimized study plan. For example, the server generates a plan that includes "advanced math problems" and "basic English problems."

[1169] Step 10:

[1170] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[1171] Step 11:

[1172] The user follows the study plan and proceeds with the study. If a question arises during the study, the user can input the question into the terminal.

[1173] Step 12:

[1174] The device sends a question to the server, which receives the question and searches for the appropriate answer from a database or external resource.

[1175] Step 13:

[1176] The server generates an answer and sends it to the device, which displays the answer to the user and resolves the question.

[1177] Step 14:

[1178] The device uses emotion recognition sensors such as a camera and microphone to collect emotion data in real time, for example, by analyzing the user's facial expressions and tone of voice.

[1179] Step 15:

[1180] The device sends the collected emotional data to a server, which analyzes the emotional data and evaluates the user's emotional state (e.g., stress, concentration, fatigue).

[1181] Step 16:

[1182] The server dynamically adjusts the learning plan based on emotional data. For example, if the user is feeling stressed, the server will change the plan to include more easy tasks that will help them relax.

[1183] Step 17:

[1184] When the user finishes the learning activity, the device displays a feedback form, and the user enters feedback on the difficulty of the questions and their level of understanding.

[1185] Step 18:

[1186] The device sends feedback to the server, which receives it and uses it to fine-tune the learning plan.

[1187] Step 19:

[1188] The server generates and resubmits a fine-tuned learning plan, and the device displays the new plan to the user and begins the next learning cycle.

[1189] Step 20:

[1190] A user accesses a community forum. The device displays the forum interface, allowing the user to post and ask questions.

[1191] Step 21:

[1192] The server monitors interactions on the forum and filters inappropriate content, and the device prevents inappropriate content from being displayed to the user.

[1193] Example 2

[1194] 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."

[1195] Conventional learning support systems have the problem that it is difficult to provide personalized learning plans for individual users, making it impossible to maximize users' learning efficiency. They also lack a function to reflect the stress and satisfaction felt by users during learning in real time, which can lead to a decrease in motivation to learn. Furthermore, they lack an appropriate filtering function to promote communication between users, making it difficult to provide a safe learning environment.

[1196] 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.

[1197] In this invention, the server includes a means for inputting and transmitting user information, a means for receiving the transmitted user information and storing it in a database, and a means for collecting the user's learning progress data and transmitting it to the server. This makes it possible to provide an individually optimized learning plan. The server also includes a means for collecting and analyzing the user's emotional data to reflect it in the learning plan, and a means for evaluating the user's stress and satisfaction and adjusting the learning plan. This makes it possible to dynamically adjust the user's learning plan based on real-time emotional recognition data. The server also includes a means for providing a community platform for users to share knowledge and experiences, and a means for monitoring interactions on the community platform and filtering inappropriate content. This allows users to safely share information and advance their learning.

[1198] "User information" refers to personal data such as the user's name, age, grade, and areas of interest.

[1199] "Database" refers to a system for systematically storing and managing information.

[1200] "Study progress data" refers to data about a user's learning activities, including test results, study time, completed assignments, and the like.

[1201] "Analysis" refers to the process of analyzing collected data and extracting meaningful information.

[1202] "Study Plan" refers to a plan that provides a user with optimized learning content and schedule.

[1203] "Feedback" refers to opinions and evaluations that users enter regarding learning activities.

[1204] "Emotional data" refers to information about a user's emotional state (e.g., stress, joy, fatigue, etc.).

[1205] A "community platform" refers to an online environment where users can share their knowledge and experiences.

[1206] "Filtering" refers to the process of automatically identifying and removing inappropriate content.

[1207] MODE FOR CARRYING OUT THE INVENTION

[1208] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes user emotions and reflects them in the learning plan.

[1209] First, the user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. When registering, the user enters information such as their name, age, grade, and areas of interest. This information is sent via the device to the server, which receives it and stores it in a database.

[1210] As users engage in learning activities, their devices collect learning progress data (e.g., test results, study time, and completed assignments) in real time. This data is periodically sent from the devices to a server, which receives it and stores it in a database.

[1211] The server analyzes the accumulated learning progress data using programs such as Python or Java to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan to strengthen specific areas and sends it to the device, which then displays it to the user.

[1212] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from information such as text input and touch operations. This emotional data is sent to a server in real time, where it is analyzed. For example, if the user is feeling stressed, the server will detect this and respond by providing the device with relaxing content.

[1213] Furthermore, if a user has a question, they can input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition reveals that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[1214] After completing a learning activity, users can enter feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[1215] The system also provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learning experiences and questions with other users. The server monitors interactions in the community and filters inappropriate content and behavior, providing a safe and meaningful learning environment.

[1216] Related to the process described above, here are some example prompts for optimizing a lesson plan using a generative AI model:

[1217] "I'm 16 years old, a second-year high school student, and I'm interested in math. I'm particularly bad at studying geometry. How can I study more efficiently?"

[1218] In this way, the present invention is different from conventional systems in that it reflects the user's emotions and feedback in real time and provides personalized learning support.

[1219] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1220] Step 1:

[1221] The user downloads and installs the app on their device

[1222] Specific actions

[1223] Users download the educational support app from the app store and install it on their device. During this process, the app requests necessary access permissions and settings. Once the app is successfully installed, the initial setup screen is displayed the first time the user launches the app.

[1224] Step 2:

[1225] The user launches the app and completes the new registration process.

[1226] Input and Output

[1227] The user enters information such as name, age, grade, and areas of interest.

[1228] As an output, the input user information is saved in the terminal.

[1229] Specific actions

[1230] A registration screen will appear, and the user will enter their name, age, grade, and areas of interest. The entered information will be temporarily stored on the device. When the user presses the "Done" button, the information will be sent to the server.

[1231] Step 3:

[1232] The device sends user information to the server

[1233] Input and Output

[1234] Input is information entered by the user.

[1235] The output is the user information received by the server.

[1236] Specific actions

[1237] The terminal encrypts the entered user information and sends it securely to the server, which then travels over the network to the server.

[1238] Step 4:

[1239] The server registers the user information in the database

[1240] Input and Output

[1241] The input is user information sent from the terminal.

[1242] The output is a message confirming successful registration.

[1243] Specific actions

[1244] The server analyzes the received user information, stores it in a database, and then sends a confirmation message to the terminal confirming registration completion.

[1245] Step 5:

[1246] The user begins learning, and the device collects learning progress data.

[1247] Input and Output

[1248] The input is the user's learning activity.

[1249] The output is the collected progress data.

[1250] Specific actions

[1251] The user selects learning content and begins studying. The device collects real-time progress data, such as the user's study time, completed assignments, and test results.

[1252] Step 6:

[1253] The device sends learning progress data to the server

[1254] Input and Output

[1255] The input is collected learning progress data.

[1256] The output is the progress data received by the server.

[1257] Specific actions

[1258] The device will send the collected data to the server at regular intervals or when the user pauses or finishes learning. This data is encrypted and sent securely.

[1259] Step 7:

[1260] The server analyzes the learning progress data and generates an individual learning plan

[1261] Input and Output

[1262] The input is the received learning progress data.

[1263] The output is a generated lesson plan.

[1264] Specific actions

[1265] The server analyzes the received learning progress data using an AI model to identify the user's strengths and weaknesses, and then generates an individually optimized learning plan based on the analysis results.

[1266] Step 8:

[1267] The device displays the generated learning plan to the user

[1268] Input and Output

[1269] The input is the generated lesson plan.

[1270] The output is a lesson plan that is displayed to the user.

[1271] Specific actions

[1272] The device receives the learning plan sent from the server and displays it to the user. The display format can be a dashboard or a notification.

[1273] Step 9:

[1274] The device collects the user's emotional data and sends it to the server.

[1275] Input and Output

[1276] The input is the user's emotional data.

[1277] The output is the emotion data received by the server.

[1278] Specific actions

[1279] During training, the device's camera and microphone are used to collect emotional data, which is then inferred from text input and touch patterns and sent to the server.

[1280] Step 10:

[1281] The server analyzes the emotional data and adjusts the learning plan.

[1282] Input and Output

[1283] The input is the received emotion data.

[1284] The output is a tailored lesson plan.

[1285] Specific actions

[1286] The server analyzes the emotional data, evaluates the stress and satisfaction felt by the user, and adjusts the learning plan based on the analysis results.

[1287] Step 11:

[1288] The device sends the user's question to the server, which returns the answer

[1289] Input and Output

[1290] The input is the user's question.

[1291] The output is the answer returned by the server.

[1292] Specific actions

[1293] The user types a question and sends it to the terminal, which then sends it to the server, which searches for the appropriate answer and sends it to the terminal, which then displays the answer to the user.

[1294] Step 12:

[1295] The user enters feedback, which the device sends to the server.

[1296] Input and Output

[1297] The input is the user's feedback.

[1298] The output is the feedback received by the server.

[1299] Specific actions

[1300] After the user has completed the learning, they input feedback and send it from their device to the server.

[1301] Step 13:

[1302] The server analyzes the feedback and reflects it in the learning plan.

[1303] Input and Output

[1304] The input is the feedback received.

[1305] The output is a learning plan that reflects the feedback.

[1306] Specific actions

[1307] The server analyzes the feedback and incorporates it into the learning plan. Based on the analysis results, a learning plan is regenerated to strengthen understanding of specific areas.

[1308] Step 14:

[1309] Users access community forums and share information with other users

[1310] Specific actions

[1311] Users access a community forum to share their learnings and questions with other users, and the server monitors community interactions to filter inappropriate content and behavior.

[1312] (Application example 2)

[1313] 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."

[1314] While conventional learning support systems can generate personalized learning plans based on a user's learning progress and adjust them based on feedback, they lack learning support that takes into account the user's emotional state. Furthermore, they lack a mechanism for recognizing the stress and fatigue a user feels during learning in real time and adjusting the learning plan accordingly, which results in a problem of not maximizing learning effectiveness.

[1315] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting the user's learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for collecting the user's emotional data and transmitting it to the server, means for analyzing the user's emotional data and reflecting it in the learning plan, means for searching for and providing answers to user questions, and means for providing a community platform for users to share knowledge and experiences. This enables personalized learning support that takes the user's emotional state into consideration, maximizing learning effectiveness.

[1316] "User information" refers to data about individual users of the learning system, including basic information such as name, age, grade, and areas of interest.

[1317] A "database" is a storage system for systematically storing and managing user information, learning progress data, feedback data, and the like.

[1318] "Study progress data" is data that indicates the progress of a user's learning activities, and includes test results, study time, completed assignments, and the like.

[1319] "Analysis" is the process of evaluating information and extracting meaning from collected data.

[1320] A "learning plan" is a plan that shows individually optimized learning content and learning methods based on the analysis results.

[1321] "Emotional data" is data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, written input, etc. collected through a camera or microphone.

[1322] "Feedback" refers to users' opinions and impressions about learning activities and systems, and is information that is useful for improving the system and fine-tuning the learning plan.

[1323] A "server" is a central computer that serves as a data processing and storage resource on a network.

[1324] A "user terminal" is a device used by a user to access the learning system, such as a smartphone, tablet, or PC.

[1325] A "community platform" is an online space for users to share knowledge and experiences, and interactions take place in a forum format.

[1326] A specific system for implementing this invention has a series of functions including managing user information, tracking learning progress, providing personalized learning plans, collecting and analyzing feedback, recognizing user emotions and reflecting them in learning plans, providing answers to questions, and operating a community platform.

[1327] Hardware and Software Configuration

[1328] The system uses the following hardware and software:

[1329] Hardware: Smartphone, tablet, PC (must have camera and microphone)

[1330] Software: Django (server-side), SQLite (database), Python (programming language), machine learning libraries (sklearn, etc.)

[1331] System Operation Overview

[1332] User Registration

[1333] Users enter user information such as name, age, grade, and areas of interest through an application on their device (smartphone or tablet) and send it to the server, which receives this information and registers it in a database.

[1334] Tracking your learning progress

[1335] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and sends it to the server, which receives and analyzes this data to identify the user's strengths and weaknesses.

[1336] Providing a personalized learning plan

[1337] The server generates an individually optimized learning plan based on the analysis of the learning progress data and transmits it to the terminal, which then displays the received learning plan to the user.

[1338] Emotion recognition and reflection

[1339] The device's camera and microphone are used to collect the user's emotional data (such as stress and satisfaction). This emotional data is sent to the server in real time, where it is analyzed and the learning plan is adjusted according to the user's emotional state. For example, if the user is tired, the system will regenerate a plan that includes many easy tasks.

[1340] Feedback and questions

[1341] After completing a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. If the user has any questions, they can input them via their device, and the server will search for the appropriate answer and send it to the device.

[1342] Community Platform

[1343] It provides a community platform for users to share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content.

[1344] Examples of concrete examples and prompts

[1345] For example, if Student A at a cram school feels tired, the system will provide relaxing content along with a "recommended break." An example of a prompt sentence for the generative AI model for this scenario is as follows:

[1346] "Student A is feeling tired. Can you suggest ways to provide content that will help him relax?"

[1347] In this way, the user's emotional state can be taken into consideration, enabling more effective learning support.

[1348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1349] Step 1:

[1350] The user enters user information such as name, age, grade, and areas of interest from the terminal through the application and sends it to the server. The server receives this information and registers it in a database. The input is user information, and the output is storing the user information in the database. This process registers basic information for each user.

[1351] Step 2:

[1352] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and periodically sends it to the server. The server receives this learning progress data and stores it in a database. The input is the learning progress data, and the output is storing the learning progress data in the database.

[1353] Step 3:

[1354] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. This analysis uses a machine learning algorithm (e.g., the sklearn library). The input is the learning progress data, and the output is the individually identified strengths and weaknesses. Based on the analysis results, the server generates an individually optimized learning plan.

[1355] Step 4:

[1356] The server sends the generated study plan to the user's terminal. The terminal displays the received study plan to the user. The input is the generated study plan, and the output is the display of the study plan on the user's terminal. This allows the user to check the study plan that is optimized for them.

[1357] Step 5:

[1358] The device's camera and microphone are used to collect user emotional data. The collected emotional data (stress and satisfaction) is sent to the server in real time. The input is the collected emotional data, and the output is the transmission of the emotional data to the server.

[1359] Step 6:

[1360] The server analyzes the received emotional data and adjusts the study plan according to the user's emotional state. For example, if it senses that the user is tired, it regenerates a study plan that includes many easy tasks. The input is emotional data, and the output is the adjusted study plan. This provides flexible study support tailored to the user.

[1361] Step 7:

[1362] After a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. The input is feedback data, and the output is a fine-tuned learning plan. The accuracy of the system improves based on the feedback.

[1363] Step 8:

[1364] When a user inputs a question, the device sends the question to the server. The server searches for an appropriate answer and sends it to the device. The input is the user's question, and the output is the answer to the user. The user can instantly resolve their question.

[1365] Step 9:

[1366] The server provides a community platform where users can share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content. The input is community posted data, and the output is a monitored, safe community environment. Users can exchange information with confidence.

[1367] Through the above processing steps, the user's learning experience is maximized and efficient learning support is realized.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] [Fourth embodiment]

[1372] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1373] 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.

[1374] 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).

[1375] 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.

[1376] 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.

[1377] 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).

[1378] 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. 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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."

[1385] The system according to the present invention aims to reduce educational disparities within families and provide equal educational opportunities to all children through cooperation between users, terminals, and servers. Specific embodiments for carrying out the present invention are described below.

[1386] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. The user enters their personal information (name, age, grade, areas of interest) and sends it to the server via their device. The server receives the information and registers it in a database.

[1387] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time. The collected learning progress data is sent from the device to the server. The server receives this data and stores it in a database.

[1388] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server determines that the user is good at math but weak at history. Based on this analysis result, the server generates a study plan. The generated study plan is sent to the user via the device. For example, the server may suggest a plan that includes many applied math problems and a plan that includes basic history review problems.

[1389] The user uses the device to carry out learning activities according to the provided learning plan. If a question arises during the learning activity, the user uses the device to input the question. This question is sent from the device to the server. The server searches a database or external resource for an appropriate answer to the question, generates an answer, and sends it to the device. The device displays the generated answer to the user.

[1390] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[1391] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and to ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[1392] The above is a specific embodiment of the system according to the present invention. This system can provide equal educational opportunities to all children, regardless of their parents' educational level or financial resources, thereby reducing educational disparities.

[1393] The processing flow will be explained below.

[1394] Step 1:

[1395] The user installs and launches the app. The device displays a first-time launch screen and offers a sign-up button.

[1396] Step 2:

[1397] The user clicks the new registration button and enters personal information such as name, age, grade, area of ​​interest, etc. The terminal stores this information in an input form and provides a submit button.

[1398] Step 3:

[1399] The user clicks the send button. The device sends the registration information to the server.

[1400] Step 4:

[1401] The server analyzes the received user information and registers it in the database.

[1402] Step 5:

[1403] The user initiates a learning activity through the app. The device logs the start and end times of each learning activity (e.g., test, assignment, quiz, etc.) and progress data.

[1404] Step 6:

[1405] The terminal transmits learning progress data to the server at regular intervals.

[1406] Step 7:

[1407] The server stores the received learning progress data in a database and executes a learning performance analysis algorithm.

[1408] Step 8:

[1409] The server identifies the user's strengths and weaknesses based on the analysis results and generates a personalized learning plan.

[1410] Step 9:

[1411] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[1412] Step 10:

[1413] The user uses the terminal to carry out the learning activities according to the provided learning plan. If a question arises during the learning activities, the user inputs the question into the terminal.

[1414] Step 11:

[1415] The device sends a question to the server, which receives the question and searches for an appropriate answer from a database or external resource.

[1416] Step 12:

[1417] The server generates a response and sends it to the terminal, which displays it to the user.

[1418] Step 13:

[1419] The user completes the learning activity based on the learning plan and inputs feedback (such as difficulty of the questions and level of understanding) into the terminal.

[1420] Step 14:

[1421] The device sends feedback to the server, which receives the feedback and fine-tunes the learning plan.

[1422] Step 15:

[1423] The server resends the fine-tuned study plan to the device, and the user begins the next study cycle.

[1424] Step 16:

[1425] A user accesses a community forum to share learning experiences and knowledge with other users, and the terminal displays the forum interface.

[1426] Step 17:

[1427] The server monitors interactions on the community and filters inappropriate content.

[1428] Example 1

[1429] 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."

[1430] In today's educational environment, it is difficult to provide individualized learning plans that match each child's learning progress and level of understanding. Furthermore, educational disparities and economic situations within families can easily lead to unequal educational opportunities. There is also a lack of systems that can quickly and accurately respond to questions that arise during learning. Furthermore, there is also a lack of monitoring systems to ensure the safe sharing of knowledge and experience between users. To solve these issues, a personalized learning support system is needed.

[1431] 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.

[1432] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for generating answers to user questions using a generative AI model, and means for optimizing the learning plan based on the generated learning plan and user feedback. This makes it possible to provide individualized learning plans and resolve questions, thereby eliminating educational disparities and providing equal learning opportunities.

[1433] "User information" is information used to identify individual users and grasp their characteristics, such as the user's name, age, grade, and areas of interest.

[1434] "Study progress data" refers to data such as test results, study time, and completed assignments obtained when a user engages in study activities.

[1435] "Analysis" is the process of analyzing the collected data to identify the user's strengths and weaknesses and reflect them in future learning plans.

[1436] A "study plan" is a specific study content and schedule created based on the user's study progress data and analysis results to help the user study effectively.

[1437] "Feedback" refers to information such as an evaluation or impression provided by a user regarding the difficulty level or level of understanding of a learning plan after the user has completed the learning activity.

[1438] A "generative AI model" is a model that uses artificial intelligence to analyze data and automatically generate optimal learning plans and answers to questions for users.

[1439] A "community platform" is an online environment for users to share knowledge and experiences, and is a place where interactions take place in the form of forums or chats.

[1440] "Interaction" refers to the interaction and information exchange activities between users on the community platform.

[1441] "Filtering" is the process of monitoring interactions on a community platform and removing inappropriate content.

[1442] "Optimization" means adjusting the learning plan to be most effective for the user based on user feedback and collected data.

[1443] The system according to the present invention is linked by users, terminals, and a server, and aims to reduce educational disparities within families and provide equal educational opportunities to all children. This specification describes the specific configuration and operation of this system.

[1444] Hardware and Software Used

[1445] Hardware: User devices (smartphones and tablets), servers

[1446] Software: Learning apps, database management systems, data analysis engines, community platforms

[1447] Program processing overview

[1448] The system begins when the user downloads and installs the app on their device. After installation, the user launches the app and completes the registration process. The user enters their personal information (name, age, grade, and areas of interest) and sends it from their device to the server. The server stores the received information in a database.

[1449] As users engage in learning activities, the device collects learning progress data (test results, study time, completed tasks) in real time and sends it to the server. The server receives this data and stores it in a database. The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. A generative AI model is used for this analysis. Based on the analysis results, the server generates an individual learning plan and sends it to the device.

[1450] For example, if a user scores high on a math test but low on a history test, the server will determine that the user is strong in math but weak in history, and suggest a plan that includes many applied math questions and a plan that includes basic history questions.

[1451] The user proceeds with the learning activities according to the provided learning plan. If a question arises during the learning process, the user can input the question using the device. This question is sent from the device to the server, and the server searches for an appropriate answer to the question from a database or external resources and generates an answer using a generative AI model. The answer is sent to the device and displayed to the user.

[1452] After completing a study activity based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server analyzes this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[1453] Furthermore, the system provides a community platform for users to share their knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this way, users can maintain a safe and effective learning environment.

[1454] Examples and prompts

[1455] Example scenario:

[1456] While studying a math problem, a user types into the device, "There was something difficult to understand in history class. Could you briefly explain the economy of medieval Europe?"

[1457] The server receives this question, uses a generative AI model to generate an appropriate answer, and sends it to the user's device.

[1458] The user continues learning based on the answers displayed on the device.

[1459] The above is a specific embodiment of the system according to the present invention, which allows users to study efficiently, eliminate educational disparities, and enjoy equal learning opportunities.

[1460] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1461] Step 1: Installing the app and initial setup

[1462] User:

[1463] Users download and install the learning app onto their smartphones or tablets.

[1464] Launch the app and complete the new registration process.

[1465] Users enter personal information such as name, age, grade, and areas of interest.

[1466] Input: User's personal information

[1467] Output: Initialized app

[1468] Device:

[1469] The personal information entered by the user is sent to the server.

[1470] Input: User's personal information

[1471] Output: User information sent to the server

[1472] server:

[1473] Receives the personal information you submit and stores it in a database.

[1474] Input: User information sent from the device

[1475] Output: User information stored in the database

[1476] Step 2: Collecting learning progress data

[1477] User:

[1478] Users use the learning app to carry out learning activities.

[1479] Input: Learning activities using learning apps

[1480] Output: None

[1481] Device:

[1482] Learning progress data (test results, study time, completed assignments) is collected in real time and sent to the server.

[1483] Input: Data based on learning activities

[1484] Output: Learning progress data sent to the server

[1485] server:

[1486] The transmitted learning progress data is received and stored in a database.

[1487] Input: Learning progress data sent from the device

[1488] Output: Learning progress data stored in a database

[1489] Step 3: Data analysis and learning plan generation

[1490] server:

[1491] Analyze learning progress data using generative AI models to identify user strengths and weaknesses.

[1492] For example, if a student scores high on a math test but low on history, they will be judged as strong in math but weak in history.

[1493] Input: Learning progress data stored in a database

[1494] Output: Analysis of user's strengths and weaknesses

[1495] server:

[1496] An individual learning plan is generated based on the analysis results and sent to the device.

[1497] For example, a plan containing many applied mathematics questions and a plan containing basic history questions are generated.

[1498] Input: Analysis of user strengths and weaknesses

[1499] Output: Generated lesson plan

[1500] Device:

[1501] Display the generated learning plan to the user.

[1502] Input: Learning plan from the server

[1503] Output: The lesson plan displayed to the user

[1504] Step 4: Supporting learning activities

[1505] User:

[1506] Follow the provided learning plan and proceed with the learning activities.

[1507] If you have any questions while studying, use the device to type in your question.

[1508] Input: Learning activities and questions based on the lesson plan

[1509] Output: The question sent to the server

[1510] Device:

[1511] Send the question to the server.

[1512] Input: User question

[1513] Output: The question sent to the server

[1514] server:

[1515] Search for answers to questions and generate appropriate answers using generative AI models.

[1516] The response is sent to the device.

[1517] Input: User question

[1518] Output: The generated answer

[1519] Device:

[1520] The submitted response is displayed to the user.

[1521] Input: Response from the server

[1522] Output: The answer displayed to the user

[1523] Step 5: Gather feedback and fine-tune your learning plan

[1524] User:

[1525] After the learning activity, feedback on the difficulty and level of understanding of the learning plan is sent to the server via the device.

[1526] Input: Feedback obtained from the learning activity

[1527] Output: Feedback sent to the server

[1528] Device:

[1529] Send feedback to the server.

[1530] Input: User feedback

[1531] Output: Feedback sent to the server

[1532] server:

[1533] Receive feedback and analyze it to fine-tune your learning plan.

[1534] For example, the system regenerates an optimal study plan by adjusting the difficulty of the questions.

[1535] Input: User feedback

[1536] Output: A fine-tuned learning plan

[1537] Step 6: Operating a community platform

[1538] User:

[1539] Visit the community forum to share your learning experiences, knowledge, and ask questions with other users.

[1540] Input: Forum interactions

[1541] Output: Shared knowledge and experience

[1542] server:

[1543] Monitor community interactions and filter inappropriate content.

[1544] Input: Forum interactions

[1545] Output: Filtered content

[1546] (Application example 1)

[1547] 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."

[1548] While conventional learning platforms offer various features to reduce learning gaps, they lack real-time learning support and rapid response to questions. Furthermore, it is difficult for users to receive real-time lectures from virtual teachers. The present invention aims to solve these problems and provide a system that supports learning more efficiently and effectively.

[1549] 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.

[1550] In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting user learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for searching for and providing answers to questions from users, means for providing a community platform for users to share knowledge and experience, means for receiving lectures from a virtual teacher in real time using a smart device, and means for generating prompt sentences and obtaining appropriate answers to questions using a generative AI model, thereby enabling more efficient learning support and faster question answering.

[1551] "User information" refers to data such as the personal information, grade, and areas of interest of system users.

[1552] "Study progress data" refers to data such as test results, study time, and completed assignments obtained as the user progresses through their studies.

[1553] "Server" refers to a computer system that manages, analyzes, and processes user information and learning progress data.

[1554] "Database" means structured data storage for storing user information and learning progress data.

[1555] "Analysis" refers to the process of processing the collected learning progress data to evaluate and identify the user's learning status.

[1556] A "learning plan" is a plan for providing optimal learning content to individual users based on the analysis results.

[1557] "Feedback" refers to an evaluation of the user's impressions and level of understanding after carrying out learning activities based on the learning plan.

[1558] A "virtual teacher's lecture" is a lecture that a user receives in real time via a smart device.

[1559] A "smart device" is a digital device that can connect to the Internet and run applications, such as a smartphone, smart glasses, or a head-mounted display.

[1560] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers or content based on a given prompt.

[1561] A "prompt sentence" is text data containing questions or instructions that are input into a generative AI model.

[1562] A "community platform" is an online space where users can share knowledge and experiences and ask and answer questions.

[1563] "Search" is the process of retrieving relevant answers to a user's question from databases or external resources.

[1564] The system according to the present invention provides consistent support for everything from managing user information to creating learning plans and providing lectures by virtual teachers. This system is realized through cooperation between users, terminals, and a server.

[1565] First, the user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process. The user enters their personal information, such as name, age, grade, and areas of interest. The entered information is sent to the server via the device. The server receives this information and stores it in a database.

[1566] When a user is engaged in learning activities, the device collects learning progress data (e.g., test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to a server. The server receives this data and stores it in a database.

[1567] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, if a user scores high on a math test but low on a history test, the server will determine that the user is good at math but weak at history. Based on the results of this analysis, the server generates a study plan. The generated study plan is sent to the user via the device. For example, a plan containing many applied math questions and a plan containing basic history review questions may be suggested.

[1568] Users can use their smart devices to receive real-time lectures from virtual teachers. These lectures are delivered through smart glasses or head-mounted displays. If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server uses a generative AI model to obtain an appropriate answer to the question. For example, if a user sends the prompt "Tell me about the French Revolution," the server will generate a specific answer such as "The French Revolution was a social revolution that took place in France from 1789 to 1799..."

[1569] After completing a study based on the study plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the study plan. For example, it adjusts the difficulty of the questions based on the feedback, thereby regenerating a study plan that is more optimal for the user.

[1570] Furthermore, this system provides a community platform for users to share knowledge and experiences. Users can access this community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content.

[1571] This allows the system to support users in efficiently progressing with their studies and reduce learning gaps.

[1572] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1573] Step 1:

[1574] The user downloads and installs a dedicated application onto their smart device. After installation, the user launches the app and completes the new registration process, entering their personal information (name, age, grade, areas of interest). The entered information is then sent by the device to the server. The server receives the sent information and stores it in a database. In this step, the user's personal information is the input, and the user information stored in the database is obtained as the output.

[1575] Step 2:

[1576] When a user starts learning activities, the device collects learning progress data (test results, study time, completed assignments, etc.) in real time. The collected data is sent from the device to the server. The server receives this data and stores it in a database. In this step, the user's learning progress data is input, and after going through the process of being sent and stored on the server, the learning progress data is accumulated in the database.

[1577] Step 3:

[1578] The server uses artificial intelligence (AI) to analyze the accumulated learning progress data. The analysis is performed to identify the user's strengths and weaknesses. For example, learning progress data is given as input, and the AI ​​analyzes it, and the output identifies the user's strong and weak subjects.

[1579] Step 4:

[1580] The server generates a study plan based on the analysis results, and the generated study plan is sent to the user via the terminal. For example, the user's analysis results are input, the server generates a study plan based on them, and the plan is sent to the user terminal as output.

[1581] Step 5:

[1582] Users use smart devices to receive real-time lectures from virtual teachers. The lectures are delivered through smart glasses or head-mounted displays. In this step, the input is the virtual lecture content, and the output is the lecture as visual information for the user.

[1583] Step 6:

[1584] If a user has a question during a learning activity, they can input it using their device. This question is sent to the server as a prompt. The server then inputs the prompt into the generative AI model to obtain an appropriate answer to the question. For example, if the prompt "Tell me about the French Revolution" is given as input, the generative AI model will output the answer "The French Revolution was a social revolution that took place in France between 1789 and 1799..." and send it to the user's device.

[1585] Step 7:

[1586] After completing a learning activity based on the learning plan, the user sends feedback about the difficulty and level of understanding to the server via their device. The server receives this feedback and fine-tunes the learning plan. For example, the server may take the user's feedback data as input, adjust the difficulty of the learning plan based on this, and output a new learning plan.

[1587] Step 8:

[1588] This system provides a community platform for users to share knowledge and experiences. Users can access the community forum to share their learning experiences and knowledge with other users, and ask and answer questions. The server monitors interactions on the community and filters inappropriate content. In this step, the input is data posted on the community forum, and the output is the server monitoring this and filtering inappropriate content.

[1589] 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.

[1590] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes the user's emotions and reflects them in the learning plans. Specific embodiments for implementing the present invention are described below.

[1591] The user downloads the app and installs it on their device. After installation, the user launches the app and completes the registration process. The user enters information such as their name, age, grade, and areas of interest, and sends this information to the server via their device. The server receives the information and registers it in a database.

[1592] When a user is engaged in learning activities, the device collects learning progress data (test results, study time, completed assignments) in real time. The collected data is periodically sent from the device to the server. The server receives this data and stores it in a database.

[1593] The server analyzes the accumulated learning progress data to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan and sends it to the device. The device then displays the generated learning plan to the user.

[1594] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from text input and touch operations. This emotional data is sent to the server in real time. For example, if the user is feeling stressed, the system will detect this and respond by providing relaxing content.

[1595] The server analyzes the received emotional data and evaluates the user's level of stress and satisfaction. Based on this evaluation, the learning plan is adjusted. For example, if the user is tired, the plan is fine-tuned by regenerating it to include more easy tasks.

[1596] When a user has a question, they input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition indicates that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[1597] After completing a learning activity, users can input feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[1598] Furthermore, the system provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learnings and questions with other users. The server monitors interactions on the community and filters inappropriate content and behavior.

[1599] The above is a specific embodiment of the system according to the present invention. This system enables personalized learning support that takes into account the user's emotions, and is expected to reduce educational disparities.

[1600] The processing flow will be explained below.

[1601] Step 1:

[1602] The user installs and launches the app. The device displays the new registration screen.

[1603] Step 2:

[1604] The user enters basic information such as name, age, grade, interests, etc. The device stores the information in an input form and provides a submit button.

[1605] Step 3:

[1606] The user clicks the send button. The device sends the registration information to the server.

[1607] Step 4:

[1608] The server analyzes the received user information and stores it in a database. The server sends a success message to the terminal.

[1609] Step 5:

[1610] The user starts a learning activity, and the device records learning progress data in real time, including start time, end time, test results, and study time.

[1611] Step 6:

[1612] The terminal transmits learning progress data to the server at regular intervals.

[1613] Step 7:

[1614] The server stores the received learning progress data in a database and begins analyzing it.

[1615] Step 8:

[1616] The server analyzes the learning data and runs algorithms to identify the user's strengths and weaknesses. For example, the server may see that the user has high scores in math and low scores in English.

[1617] Step 9:

[1618] The server generates an individually optimized study plan. For example, the server generates a plan that includes "advanced math problems" and "basic English problems."

[1619] Step 10:

[1620] The server sends the generated learning plan to the terminal, which displays the learning plan to the user through a user interface.

[1621] Step 11:

[1622] The user follows the study plan and proceeds with the study. If a question arises during the study, the user can input the question into the terminal.

[1623] Step 12:

[1624] The device sends a question to the server, which receives the question and searches for the appropriate answer from a database or external resource.

[1625] Step 13:

[1626] The server generates an answer and sends it to the device, which displays the answer to the user and resolves the question.

[1627] Step 14:

[1628] The device uses emotion recognition sensors such as a camera and microphone to collect emotion data in real time, for example, by analyzing the user's facial expressions and tone of voice.

[1629] Step 15:

[1630] The device sends the collected emotional data to a server, which analyzes the emotional data and evaluates the user's emotional state (e.g., stress, concentration, fatigue).

[1631] Step 16:

[1632] The server dynamically adjusts the learning plan based on emotional data. For example, if the user is feeling stressed, the server will change the plan to include more easy tasks that will help them relax.

[1633] Step 17:

[1634] When the user finishes the learning activity, the device displays a feedback form, and the user enters feedback on the difficulty of the questions and their level of understanding.

[1635] Step 18:

[1636] The device sends feedback to the server, which receives it and uses it to fine-tune the learning plan.

[1637] Step 19:

[1638] The server generates and resubmits a fine-tuned learning plan, and the device displays the new plan to the user and begins the next learning cycle.

[1639] Step 20:

[1640] A user accesses a community forum. The device displays the forum interface, allowing the user to post and ask questions.

[1641] Step 21:

[1642] The server monitors interactions on the forum and filters inappropriate content, and the device prevents inappropriate content from being displayed to the user.

[1643] Example 2

[1644] 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."

[1645] Conventional learning support systems have the problem that it is difficult to provide personalized learning plans for individual users, making it impossible to maximize users' learning efficiency. They also lack a function to reflect the stress and satisfaction felt by users during learning in real time, which can lead to a decrease in motivation to learn. Furthermore, they lack an appropriate filtering function to promote communication between users, making it difficult to provide a safe learning environment.

[1646] 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.

[1647] In this invention, the server includes a means for inputting and transmitting user information, a means for receiving the transmitted user information and storing it in a database, and a means for collecting the user's learning progress data and transmitting it to the server. This makes it possible to provide an individually optimized learning plan. The server also includes a means for collecting and analyzing the user's emotional data to reflect it in the learning plan, and a means for evaluating the user's stress and satisfaction and adjusting the learning plan. This makes it possible to dynamically adjust the user's learning plan based on real-time emotional recognition data. The server also includes a means for providing a community platform for users to share knowledge and experiences, and a means for monitoring interactions on the community platform and filtering inappropriate content. This allows users to safely share information and advance their learning.

[1648] "User information" refers to personal data such as the user's name, age, grade, and areas of interest.

[1649] "Database" refers to a system for systematically storing and managing information.

[1650] "Study progress data" refers to data about a user's learning activities, including test results, study time, completed assignments, and the like.

[1651] "Analysis" refers to the process of analyzing collected data and extracting meaningful information.

[1652] "Study Plan" refers to a plan that provides a user with optimized learning content and schedule.

[1653] "Feedback" refers to opinions and evaluations that users enter regarding learning activities.

[1654] "Emotional data" refers to information about a user's emotional state (e.g., stress, joy, fatigue, etc.).

[1655] A "community platform" refers to an online environment where users can share their knowledge and experiences.

[1656] "Filtering" refers to the process of automatically identifying and removing inappropriate content.

[1657] MODE FOR CARRYING OUT THE INVENTION

[1658] The system of the present invention manages user information, tracks learning progress, provides personalized learning plans, collects and incorporates feedback, provides real-time learning support, and also recognizes user emotions and reflects them in the learning plan.

[1659] First, the user downloads the app and installs it on their device. After installation, the user launches the app and completes the new registration process. When registering, the user enters information such as their name, age, grade, and areas of interest. This information is sent via the device to the server, which receives it and stores it in a database.

[1660] As users engage in learning activities, their devices collect learning progress data (e.g., test results, study time, and completed assignments) in real time. This data is periodically sent from the devices to a server, which receives it and stores it in a database.

[1661] The server analyzes the accumulated learning progress data using programs such as Python or Java to identify the user's strengths and weaknesses. Based on the analysis results, it generates an individually optimized learning plan to strengthen specific areas and sends it to the device, which then displays it to the user.

[1662] As the user progresses through the learning process, emotional data is collected through emotion recognition sensors such as the device's camera and microphone. Emotions are also inferred from information such as text input and touch operations. This emotional data is sent to a server in real time, where it is analyzed. For example, if the user is feeling stressed, the server will detect this and respond by providing the device with relaxing content.

[1663] Furthermore, if a user has a question, they can input it through their device. The question is sent to the server, which searches for an appropriate answer and sends it to the device. The device then displays the answer to the user, resolving their question. If emotion recognition reveals that the user is dissatisfied with the question, the server provides additional information to make the answer easier to understand.

[1664] After completing a learning activity, users can enter feedback and send it to the server via their device. The server analyzes this feedback and uses it to fine-tune the learning plan. For example, if the feedback indicates that a user's understanding of a particular area is low, the server can create a learning plan that focuses on that area.

[1665] The system also provides a community platform for users to share knowledge and experiences. Users can access the community forum and share their learning experiences and questions with other users. The server monitors interactions in the community and filters inappropriate content and behavior, providing a safe and meaningful learning environment.

[1666] Related to the process described above, here are some example prompts for optimizing a lesson plan using a generative AI model:

[1667] "I'm 16 years old, a second-year high school student, and I'm interested in math. I'm particularly bad at studying geometry. How can I study more efficiently?"

[1668] In this way, the present invention is different from conventional systems in that it reflects the user's emotions and feedback in real time and provides personalized learning support.

[1669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1670] Step 1:

[1671] The user downloads and installs the app on their device

[1672] Specific actions

[1673] Users download the educational support app from the app store and install it on their device. During this process, the app requests necessary access permissions and settings. Once the app is successfully installed, the initial setup screen is displayed the first time the user launches the app.

[1674] Step 2:

[1675] The user launches the app and completes the new registration process.

[1676] Input and Output

[1677] The user enters information such as name, age, grade, and areas of interest.

[1678] As an output, the input user information is saved in the terminal.

[1679] Specific actions

[1680] A registration screen will appear, and the user will enter their name, age, grade, and areas of interest. The entered information will be temporarily stored on the device. When the user presses the "Done" button, the information will be sent to the server.

[1681] Step 3:

[1682] The device sends user information to the server

[1683] Input and Output

[1684] Input is information entered by the user.

[1685] The output is the user information received by the server.

[1686] Specific actions

[1687] The terminal encrypts the entered user information and sends it securely to the server, which then travels over the network to the server.

[1688] Step 4:

[1689] The server registers the user information in the database

[1690] Input and Output

[1691] The input is user information sent from the terminal.

[1692] The output is a message confirming successful registration.

[1693] Specific actions

[1694] The server analyzes the received user information, stores it in a database, and then sends a confirmation message to the terminal confirming registration completion.

[1695] Step 5:

[1696] The user begins learning, and the device collects learning progress data.

[1697] Input and Output

[1698] The input is the user's learning activity.

[1699] The output is the collected progress data.

[1700] Specific actions

[1701] The user selects learning content and begins studying. The device collects real-time progress data, such as the user's study time, completed assignments, and test results.

[1702] Step 6:

[1703] The device sends learning progress data to the server

[1704] Input and Output

[1705] The input is collected learning progress data.

[1706] The output is the progress data received by the server.

[1707] Specific actions

[1708] The device will send the collected data to the server at regular intervals or when the user pauses or finishes learning. This data is encrypted and sent securely.

[1709] Step 7:

[1710] The server analyzes the learning progress data and generates an individual learning plan

[1711] Input and Output

[1712] The input is the received learning progress data.

[1713] The output is a generated lesson plan.

[1714] Specific actions

[1715] The server analyzes the received learning progress data using an AI model to identify the user's strengths and weaknesses, and then generates an individually optimized learning plan based on the analysis results.

[1716] Step 8:

[1717] The device displays the generated learning plan to the user

[1718] Input and Output

[1719] The input is the generated lesson plan.

[1720] The output is a lesson plan that is displayed to the user.

[1721] Specific actions

[1722] The device receives the learning plan sent from the server and displays it to the user. The display format can be a dashboard or a notification.

[1723] Step 9:

[1724] The device collects the user's emotional data and sends it to the server.

[1725] Input and Output

[1726] The input is the user's emotional data.

[1727] The output is the emotion data received by the server.

[1728] Specific actions

[1729] During training, the device's camera and microphone are used to collect emotional data, which is then inferred from text input and touch patterns and sent to the server.

[1730] Step 10:

[1731] The server analyzes the emotional data and adjusts the learning plan.

[1732] Input and Output

[1733] The input is the received emotion data.

[1734] The output is a tailored lesson plan.

[1735] Specific actions

[1736] The server analyzes the emotional data, evaluates the stress and satisfaction felt by the user, and adjusts the learning plan based on the analysis results.

[1737] Step 11:

[1738] The device sends the user's question to the server, which returns the answer

[1739] Input and Output

[1740] The input is the user's question.

[1741] The output is the answer returned by the server.

[1742] Specific actions

[1743] The user types a question and sends it to the terminal, which then sends it to the server, which searches for the appropriate answer and sends it to the terminal, which then displays the answer to the user.

[1744] Step 12:

[1745] The user enters feedback, which the device sends to the server.

[1746] Input and Output

[1747] The input is the user's feedback.

[1748] The output is the feedback received by the server.

[1749] Specific actions

[1750] After the user has completed the learning, they input feedback and send it from their device to the server.

[1751] Step 13:

[1752] The server analyzes the feedback and reflects it in the learning plan.

[1753] Input and Output

[1754] The input is the feedback received.

[1755] The output is a learning plan that reflects the feedback.

[1756] Specific actions

[1757] The server analyzes the feedback and incorporates it into the learning plan. Based on the analysis results, a learning plan is regenerated to strengthen understanding of specific areas.

[1758] Step 14:

[1759] Users access community forums and share information with other users

[1760] Specific actions

[1761] Users access a community forum to share their learnings and questions with other users, and the server monitors community interactions to filter inappropriate content and behavior.

[1762] (Application example 2)

[1763] 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."

[1764] While conventional learning support systems can generate personalized learning plans based on a user's learning progress and adjust them based on feedback, they lack learning support that takes into account the user's emotional state. Furthermore, they lack a mechanism for recognizing the stress and fatigue a user feels during learning in real time and adjusting the learning plan accordingly, which results in a problem of not maximizing learning effectiveness.

[1765] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and transmitting user information, means for receiving the transmitted user information and storing it in a database, means for collecting the user's learning progress data and transmitting it to the server, means for receiving and analyzing the learning progress data, means for generating a learning plan based on the analysis results, means for transmitting the generated learning plan to the user terminal, means for collecting feedback obtained from learning activities based on the learning plan and transmitting it to the server, means for analyzing the feedback and fine-tuning the learning plan, means for collecting the user's emotional data and transmitting it to the server, means for analyzing the user's emotional data and reflecting it in the learning plan, means for searching for and providing answers to user questions, and means for providing a community platform for users to share knowledge and experiences. This enables personalized learning support that takes the user's emotional state into consideration, maximizing learning effectiveness.

[1766] "User information" refers to data about individual users of the learning system, including basic information such as name, age, grade, and areas of interest.

[1767] A "database" is a storage system for systematically storing and managing user information, learning progress data, feedback data, and the like.

[1768] "Study progress data" is data that indicates the progress of a user's learning activities, and includes test results, study time, completed assignments, and the like.

[1769] "Analysis" is the process of evaluating information and extracting meaning from collected data.

[1770] A "learning plan" is a plan that shows individually optimized learning content and learning methods based on the analysis results.

[1771] "Emotional data" is data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, written input, etc. collected through a camera or microphone.

[1772] "Feedback" refers to users' opinions and impressions about learning activities and systems, and is information that is useful for improving the system and fine-tuning the learning plan.

[1773] A "server" is a central computer that serves as a data processing and storage resource on a network.

[1774] A "user terminal" is a device used by a user to access the learning system, such as a smartphone, tablet, or PC.

[1775] A "community platform" is an online space for users to share knowledge and experiences, and interactions take place in a forum format.

[1776] A specific system for implementing this invention has a series of functions including managing user information, tracking learning progress, providing personalized learning plans, collecting and analyzing feedback, recognizing user emotions and reflecting them in learning plans, providing answers to questions, and operating a community platform.

[1777] Hardware and Software Configuration

[1778] The system uses the following hardware and software:

[1779] Hardware: Smartphone, tablet, PC (must have camera and microphone)

[1780] Software: Django (server-side), SQLite (database), Python (programming language), machine learning libraries (sklearn, etc.)

[1781] System Operation Overview

[1782] User Registration

[1783] Users enter user information such as name, age, grade, and areas of interest through an application on their device (smartphone or tablet) and send it to the server, which receives this information and registers it in a database.

[1784] Tracking your learning progress

[1785] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and sends it to the server, which receives and analyzes this data to identify the user's strengths and weaknesses.

[1786] Providing a personalized learning plan

[1787] The server generates an individually optimized learning plan based on the analysis of the learning progress data and transmits it to the terminal, which then displays the received learning plan to the user.

[1788] Emotion recognition and reflection

[1789] The device's camera and microphone are used to collect the user's emotional data (such as stress and satisfaction). This emotional data is sent to the server in real time, where it is analyzed and the learning plan is adjusted according to the user's emotional state. For example, if the user is tired, the system will regenerate a plan that includes many easy tasks.

[1790] Feedback and questions

[1791] After completing a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. If the user has any questions, they can input them via their device, and the server will search for the appropriate answer and send it to the device.

[1792] Community Platform

[1793] It provides a community platform for users to share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content.

[1794] Examples of concrete examples and prompts

[1795] For example, if Student A at a cram school feels tired, the system will provide relaxing content along with a "recommended break." An example of a prompt sentence for the generative AI model for this scenario is as follows:

[1796] "Student A is feeling tired. Can you suggest ways to provide content that will help him relax?"

[1797] In this way, the user's emotional state can be taken into consideration, enabling more effective learning support.

[1798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1799] Step 1:

[1800] The user enters user information such as name, age, grade, and areas of interest from the terminal through the application and sends it to the server. The server receives this information and registers it in a database. The input is user information, and the output is storing the user information in the database. This process registers basic information for each user.

[1801] Step 2:

[1802] The device collects the user's learning progress data (test results, study time, completed assignments, etc.) in real time and periodically sends it to the server. The server receives this learning progress data and stores it in a database. The input is the learning progress data, and the output is storing the learning progress data in the database.

[1803] Step 3:

[1804] The server analyzes the accumulated learning progress data and identifies the user's strengths and weaknesses. This analysis uses a machine learning algorithm (e.g., the sklearn library). The input is the learning progress data, and the output is the individually identified strengths and weaknesses. Based on the analysis results, the server generates an individually optimized learning plan.

[1805] Step 4:

[1806] The server sends the generated study plan to the user's terminal. The terminal displays the received study plan to the user. The input is the generated study plan, and the output is the display of the study plan on the user's terminal. This allows the user to check the study plan that is optimized for them.

[1807] Step 5:

[1808] The device's camera and microphone are used to collect user emotional data. The collected emotional data (stress and satisfaction) is sent to the server in real time. The input is the collected emotional data, and the output is the transmission of the emotional data to the server.

[1809] Step 6:

[1810] The server analyzes the received emotional data and adjusts the study plan according to the user's emotional state. For example, if it senses that the user is tired, it regenerates a study plan that includes many easy tasks. The input is emotional data, and the output is the adjusted study plan. This provides flexible study support tailored to the user.

[1811] Step 7:

[1812] After a learning activity, the user inputs feedback and sends it to the server via their device. The server uses this feedback to fine-tune the learning plan. The input is feedback data, and the output is a fine-tuned learning plan. The accuracy of the system improves based on the feedback.

[1813] Step 8:

[1814] When a user inputs a question, the device sends the question to the server. The server searches for an appropriate answer and sends it to the device. The input is the user's question, and the output is the answer to the user. The user can instantly resolve their question.

[1815] Step 9:

[1816] The server provides a community platform where users can share knowledge and experiences. The server monitors interactions on the platform and filters inappropriate content. The input is community posted data, and the output is a monitored, safe community environment. Users can exchange information with confidence.

[1817] Through the above processing steps, the user's learning experience is maximized and efficient learning support is realized.

[1818] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1819] 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.

[1820] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1821] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1822] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1823] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1824] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1825] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1826] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1827] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1828] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1829] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1830] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1831] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1832] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1833] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1834] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1835] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1836] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1837] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1838] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1839] The following is further disclosed regarding the above embodiment.

[1840] (Claim 1)

[1841] A means for inputting and transmitting user information;

[1842] means for receiving and storing the transmitted user information in a database;

[1843] means for collecting and transmitting user learning progress data to a server;

[1844] means for receiving and analyzing learning progress data;

[1845] A means for generating a learning plan based on the analysis results;

[1846] means for transmitting the generated learning plan to a user terminal;

[1847] a means for collecting feedback obtained from learning activities based on the learning plan and transmitting the feedback to a server;

[1848] A way to analyze feedback and fine-tune your learning plan,

[1849] a means for searching for and providing answers to user questions;

[1850] A means of providing a community platform for users to share knowledge and experiences

[1851] A system including:

[1852] (Claim 2)

[1853] 10. The system of claim 1, further comprising means for using artificial intelligence to analyze the learning progress data to identify the user's strengths and weaknesses.

[1854] (Claim 3)

[1855] 10. The system of claim 1, further comprising means for monitoring interactions on the community platform and filtering inappropriate content.

[1856] "Example 1"

[1857] (Claim 1)

[1858] A means for inputting and transmitting user information;

[1859] means for receiving and storing the transmitted user information in a database;

[1860] means for collecting and transmitting user learning progress data to a server;

[1861] means for receiving and analyzing learning progress data;

[1862] A means for generating a learning plan based on the analysis results;

[1863] means for transmitting the generated learning plan to a user terminal;

[1864] a means for collecting feedback obtained from learning activities based on the learning plan and transmitting the feedback to a server;

[1865] A way to analyze feedback and fine-tune your learning plan,

[1866] a means for searching for and providing answers to user questions;

[1867] A means for providing a community platform for users to share knowledge and experiences;

[1868] A means for generating answers to user questions using a generative AI model;

[1869] a means for optimizing the learning plan based on the generated learning plan and user feedback;

[1870] A system including:

[1871] (Claim 2)

[1872] 10. The system of claim 1, further comprising means for using artificial intelligence to analyze the learning progress data to identify the user's strengths and weaknesses.

[1873] (Claim 3)

[1874] 10. The system of claim 1, further comprising means for monitoring interactions on the community platform and filtering inappropriate content.

[1875] "Application Example 1"

[1876] (Claim 1)

[1877] A means for inputting and transmitting user information;

[1878] means for receiving and storing the transmitted user information in a database;

[1879] means for collecting and transmitting user learning progress data to a server;

[1880] means for receiving and analyzing learning progress data;

[1881] A means for generating a learning plan based on the analysis results;

[1882] means for transmitting the generated learning plan to a user terminal;

[1883] a means for collecting feedback obtained from learning activities based on the learning plan and transmitting the feedback to a server;

[1884] A way to analyze feedback and fine-tune your learning plan,

[1885] a means for searching for and providing answers to user questions;

[1886] A means for providing a community platform for users to share knowledge and experiences;

[1887] A means to receive lectures from virtual teachers in real time using smart devices;

[1888] A means for generating prompt sentences and using a generative AI model to obtain appropriate answers to questions;

[1889] A system including:

[1890] (Claim 2)

[1891] 10. The system of claim 1, further comprising means for using artificial intelligence to analyze the learning progress data to identify the user's strengths and weaknesses.

[1892] (Claim 3)

[1893] 10. The system of claim 1, further comprising means for monitoring interactions on the community platform and filtering inappropriate content.

[1894] "Example 2: Combining Emotion Engines"

[1895] (Claim 1)

[1896] A means for inputting and transmitting user information;

[1897] means for receiving and storing the transmitted user information in a database;

[1898] means for collecting and transmitting user learning progress data to a server;

[1899] means for receiving and analyzing learning progress data;

[1900] A means for generating a learning plan based on the analysis results;

[1901] means for transmitting the generated learning plan to a user terminal;

[1902] a means for collecting feedback obtained from learning activities based on the learning plan and transmitting the feedback to a server;

[1903] A way to analyze feedback and fine-tune your learning plan,

[1904] a means for searching for and providing answers to user questions;

[1905] A means for providing a community platform for users to share knowledge and experiences;

[1906] A means of collecting and analyzing user emotional data and reflecting it in learning plans;

[1907] A means to assess the stress and satisfaction felt by users and adjust their learning plans

[1908] A system including:

[1909] (Claim 2)

[1910] 10. The system of claim 1, further comprising means for using artificial intelligence to analyze the learning progress data to identify the user's strengths and weaknesses.

[1911] (Claim 3)

[1912] 10. The system of claim 1, further comprising means for monitoring interactions on the community platform and filtering inappropriate content.

[1913] "Application example 2 when combining emotion engines"

[1914] (Claim 1)

[1915] A means for inputting and transmitting user information;

[1916] means for receiving and storing the transmitted user information in a database;

[1917] means for collecting and transmitting user learning progress data to a server;

[1918] means for receiving and analyzing learning progress data;

[1919] A means for generating a learning plan based on the analysis results;

[1920] means for transmitting the generated learning plan to a user terminal;

[1921] a means for collecting feedback obtained from learning activities based on the learning plan and transmitting the feedback to a server;

[1922] A way to analyze feedback and fine-tune your learning plan,

[1923] means for collecting user emotion data and transmitting it to a server;

[1924] A means of analyzing user emotional data and reflecting it in learning plans;

[1925] a means for searching for and providing answers to user questions;

[1926] A means of providing a community platform for users to share knowledge and experiences

[1927] A system including:

[1928] (Claim 2)

[1929] 10. The system of claim 1, further comprising means for using artificial intelligence to analyze the learning progress data and emotion data to identify the user's strengths and weaknesses.

[1930] (Claim 3)

[1931] 10. The system of claim 1, further comprising means for monitoring interactions on the community platform and filtering inappropriate content. [Explanation of symbols]

[1932] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting and transmitting user information; means for receiving and storing the transmitted user information in a database; means for collecting and transmitting user learning progress data to a server; means for receiving and analyzing learning progress data; A means for generating a learning plan based on the analysis results; means for transmitting the generated learning plan to a user terminal; a means for collecting feedback obtained from learning activities based on the learning plan and transmitting the feedback to a server; A way to analyze feedback and fine-tune your learning plan, a means for searching for and providing answers to user questions; A means of providing a community platform for users to share knowledge and experiences A system including:

2. 10. The system of claim 1, further comprising means for using artificial intelligence to analyze the learning progress data to identify the user's strengths and weaknesses.

3. The system of claim 1 , further comprising means for monitoring interactions on the community platform and filtering inappropriate content.

Images