System

Abstract

A system is provided.SOLUTION: A system comprising: means for dynamically generating individually optimized learning content using generative artificial intelligence; means for providing an adaptive learning path based on a user's skill level and learning style; means for storing and updating user progress information in a database; and means for providing learning content to a user's terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Today's job market is rapidly changing, making it difficult for traditional educational methods to provide up-to-date and relevant skills. This has created a need for quickly and effectively delivering individually optimized learning content. It is also important to provide adaptive learning paths tailored to each learner's skill level and learning style, but this is difficult to achieve with traditional systems. Furthermore, properly managing learner progress and adjusting content as needed is also a major challenge. [Means for solving the problem]

[0005] To address the above-mentioned challenges, the present invention provides a system that dynamically generates individually optimized learning content using generative artificial intelligence. The system includes a means for providing an adaptive learning path based on a user's skill level and learning style. The system also has a function for storing and updating user progress information in a database and providing learning content to the user's device. Furthermore, the system incorporates a function for adding visual aids and audio explanations according to the user's learning style. The system integrates the generated learning content into educational programs in collaboration with educational institutions, thereby enabling effective skill acquisition.

[0006] "Generative AI" is AI that has the ability to dynamically generate new information and content based on input data.

[0007] "Personally optimized learning content" refers to educational and training materials that are optimized for a user's skill level and learning style.

[0008] "Dynamic generation" refers to generating new data and content in real time based on the current situation and user requirements.

[0009] "Skill level" is an indicator that shows the level of skill or knowledge a user has in a particular field or topic.

[0010] "Learning style" refers to the type of method or technique in which a user learns most effectively, including visual, auditory, and tactile learning methods.

[0011] An "adaptive learning path" is a learning route or plan that dynamically adjusts based on a user's progress and performance.

[0012] "Progress Information" is data that records milestones and achievements achieved by a user during the course of their learning or training.

[0013] A "database" is a structured collection of data for efficiently storing, searching, and managing large amounts of data.

[0014] "Device" means a physical device (e.g., computer, smartphone, tablet) through which a User accesses the System through an Interface.

[0015] "Visual aids" refer to graphical elements or images that aid comprehension through the eyes.

[0016] "Auditory description" refers to announcements or narrations that convey information through sound.

[0017] An "educational institution" is an organization or facility that provides formal education, such as a school, university, or college.

[0018] An "educational program" is a set of educational activities or curriculum designed for a specific purpose. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] overview

[0041] This invention utilizes generative artificial intelligence to dynamically generate and optimize learning content to enhance a user's learning experience. The system provides an adaptive learning path that takes into account a user's skill level, learning style, and progress information. It also has the ability to add visual aids and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[0042] System configuration

[0043] server

[0044] The server performs the following main functions:

[0045] 1. Management of User Information

[0046] Store and update a database with user skill levels, learning styles, and progress information.

[0047] Returns data in response to requests to get and update user information.

[0048] 2. Generating learning content

[0049] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[0050] Add custom visual aids and audio descriptions.

[0051] 3. Providing adaptive learning paths

[0052] Dynamically adjusts learning paths based on user progress to provide optimal learning paths.

[0053] 4. Collaboration with educational institutions

[0054] Partner with educational institutions to integrate the generated learning content into their educational programs.

[0055] Terminal

[0056] The terminal performs the following main functions:

[0057] 1. Viewing learning content

[0058] Display individually optimized learning content provided by the server.

[0059] Provide users with visual aids and auditory instructions.

[0060] 2. Sending progress information

[0061] As the user progresses through their studies, they send progress information to the server.

[0062] User

[0063] The user performs the following main functions:

[0064] 1. Use of learning content

[0065] Study using learning content provided through the device.

[0066] Check your progress and adjust your learning path if necessary.

[0067] 2. Updating Information

[0068] Send information to the server to update your skill level and learning style.

[0069] Specific examples

[0070] Retrieving User Information

[0071] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[0072] json

[0073] {

[0074] "skill_level": 1,

[0075] "learning_style": "visual",

[0076] "progress": 0

[0077] }

[0078] Update user information

[0079] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, when user "user1" updates their progress, the following information is returned:

[0080] json

[0081] {

[0082] "skill_level": 1,

[0083] "learning_style": "visual",

[0084] "progress": 1

[0085] }

[0086] Get course information

[0087] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[0088] json

[0089] {

[0090] "course_name": "Basic Python",

[0091] "content": ["print('Hello World')", "variables", "loops"]

[0092] }

[0093] Customize your learning content

[0094] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level and learning style. For example, user "user1" might be served the following customized content:

[0095] json

[0096] {

[0097] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[0098] }

[0099] This allows users to use learning materials optimized for their own learning style and progress effectively with their studies.

[0100] The processing flow will be explained below.

[0101] Retrieving User Information

[0102] Step 1:

[0103] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[0104] Step 2:

[0105] The server receives the request and the Flask route function user(username) is called.

[0106] Step 3:

[0107] The server verifies that the request method is GET.

[0108] Step 4:

[0109] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0110] Step 5:

[0111] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[0112] Update user information

[0113] Step 1:

[0114] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[0115] Step 2:

[0116] The server receives the request and the Flask route function user(username) is called.

[0117] Step 3:

[0118] The server verifies that the request method is PUT.

[0119] Step 4:

[0120] The server retrieves the JSON data from the request body.

[0121] Step 5:

[0122] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0123] Step 6:

[0124] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[0125] Get course information

[0126] Step 1:

[0127] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[0128] Step 2:

[0129] The server receives the request and the Flask route function course(course_id) is called.

[0130] Step 3:

[0131] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[0132] Step 4:

[0133] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[0134] Customize your learning content

[0135] Step 1:

[0136] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[0137] Step 2:

[0138] The server receives the request and the Flask route function customized_content(username) is called.

[0139] Step 3:

[0140] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0141] Step 4:

[0142] If the server finds the user data, it retrieves the skill level and learning style.

[0143] Step 5:

[0144] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[0145] Step 6:

[0146] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[0147] Step 7:

[0148] The server returns customized learning content to the user in JSON format.

[0149] Example 1

[0150] 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."

[0151] Conventional learning systems have struggled to provide an optimized learning experience for each user, and have not adequately provided learning content tailored to individual learning styles and skill levels. Furthermore, few systems manage users' progress information in real time and dynamically provide optimal learning paths. This has led to problems such as reduced learning effectiveness and a loss of user motivation to learn. The purpose of this invention is to solve these problems and improve users' learning experience.

[0152] 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.

[0153] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for providing learning content to the user's device, means for integrating the generated learning content into an educational program in collaboration with an educational institution, means for acquiring user information and returning it in JSON format, means for updating the user information and updating the database with the new user information, and means for customizing and providing learning content based on the user's skill level and learning style. This makes it possible to provide learning content and a learning path optimized for each user, improving the individual learning experience.

[0154] "Generative AI" is an AI technology that learns from huge datasets and generates text in natural language like a human.

[0155] "Individually optimized learning content" means learning materials that are individually tailored to each user based on their skill level and learning style.

[0156] An "adaptive learning path" is a learning path that dynamically adjusts based on the user's progress.

[0157] "Progress information" is data that indicates how far the user has progressed in their studies.

[0158] A "database" is a digital system that organizes and stores data so that it can be searched and updated.

[0159] "Terminal" refers to a device (computer, smartphone, etc.) used by a user to use the service.

[0160] An "educational program" is a curriculum of courses or courses offered by an educational institution.

[0161] "User Information" is data including a user's personal identification information, skill level, learning style, progress, etc.

[0162] "JSON format" stands for JavaScript Object Notation and is a lightweight text-based format for exchanging data.

[0163] "Customized content" is learning material that is generated or tailored to a user's specific needs.

[0164] "Visual aids" are visual learning aids such as slides, charts, and videos.

[0165] "Auditory description" means an audio commentary or explanation.

[0166] MODE FOR CARRYING OUT THE INVENTION

[0167] This invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve users' learning experiences. This system consists of three main components: a server, a terminal, and a user.

[0168] Server Roles

[0169] The server performs the following main functions:

[0170] 1. Management of User Information

[0171] The server uses a database management system (DBMS) to store and update the user-provided information on skill level, learning style, and progress as needed. Specifically, a SQL database or a NoSQL database may be used.

[0172] 2. Generating learning content

[0173] The server uses a generative artificial intelligence (generative AI model) to generate personalized learning content based on the user's skill level and learning style. For example, it can use OpenAI's GPT-3 model. In this case, the server sends a prompt to the generative AI model as follows:

[0174] - "The user has a beginner skill level and prefers a visual learning style. Please generate an introductory Python course that is appropriate for this user."

[0175] 3. Providing adaptive learning paths

[0176] The server dynamically adjusts the learning path based on the user's progress, for example by implementing an algorithm that automatically recommends the next chapter once the user has completed a chapter.

[0177] 4. Collaboration with educational institutions

[0178] The server provides an API for integrating the generated learning content into the educational program of the educational institution. Specifically, the learning content can be incorporated into the educational program using a RESTful API.

[0179] Device Role

[0180] The terminal mainly performs the following functions:

[0181] 1. Viewing learning content

[0182] The device displays personalized learning content provided by the server to the user, using mobile and web applications and providing visual aids and audio explanations.

[0183] 2. Sending progress information

[0184] As the user progresses, the device sends real-time progress information to the server. For example, when the user completes a particular chapter, the device marks it as "completed."

[0185] User Roles

[0186] The user mainly performs the following functions:

[0187] 1. Use of learning content

[0188] Users can learn by accessing learning content provided through their devices, for example by watching videos and answering interactive quizzes.

[0189] 2. Updating Information

[0190] Users send information to the server to update their progress, skill level, learning style, etc. For example, if a user wants to try a new learning method, they send that information to the server and update their account.

[0191] As described above, the server, terminal, and user each play their respective roles and work together to provide the user with the optimal learning experience. To specifically realize this system, technologies such as generative artificial intelligence, database management systems, and APIs are combined.

[0192] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0193] Program processing flow

[0194] Server Processing

[0195] Step 1:

[0196] The server receives a registration or login request from a user. As input, it receives the user's identifying information (username, password, etc.). It queries the database to retrieve the user's information or registers a new user. As output, it returns user information including the user's skill level, learning style, and progress information.

[0197] Step 2:

[0198] When a user makes a learning request, the server sends a prompt to a generative AI model (e.g., GPT-3). As input, it receives the user's skill level and learning style and generates a prompt based on this. The generative AI model generates individually optimized learning content based on this prompt. As output, the generated learning content is returned.

[0199] Step 3:

[0200] The server adds visual aids and auditory explanations to the generated learning content. As input, it receives the text data returned by the generative AI model. It then adds visual aids (e.g., slides, diagrams) and auditory explanations (e.g., audio guides). As output, it obtains the complete learning content, including both visual and auditory elements.

[0201] Step 4:

[0202] The server stores the user's progress in a database and dynamically adjusts the learning path. As input, it receives the user's progress information in real time. Based on this, it updates the database and recommends the next learning content. As output, it obtains information that indicates the next learning step.

[0203] Step 5:

[0204] The server integrates the generated learning content into the educational program of the educational institution. It receives the generated learning content and the educational institution's request as input. It integrates the content into the educational institution's system using RESTful APIs. As output, it obtains the learning content integrated into the educational institution's program.

[0205] Terminal handling

[0206] Step 6:

[0207] The terminal receives the learning content provided by the server and displays it to the user. As input, it receives the learning content from the server. It plays back the multimedia content including the visual support information and the audio explanation. As output, it obtains the learning content displayed to the user.

[0208] Step 7:

[0209] As the user progresses with their studies, the device sends their progress information to the server in real time. As input, it receives the user's learning actions (e.g., chapter completion, question answering), converts them into data packets to send to the server, and as output, sends the user's progress information to the server.

[0210] User Action

[0211] Step 8:

[0212] Users learn using learning content provided through their devices. As input, they consume learning content displayed on their devices and perform learning activities (e.g., watching videos or taking interactive quizzes). As output, progress information is generated.

[0213] Step 9:

[0214] The user sends information to the server to update their skill level and learning style. As input, it receives the new skill level and learning style information. It converts this into a data packet to send to the server. As output, it sends the updated user information to the server.

[0215] (Application example 1)

[0216] 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."

[0217] Conventional learning systems have difficulty adapting to the different skill levels and learning styles of each user, and are unable to dynamically provide individually optimized learning content. This means that users' learning effectiveness is not maximized, and it is difficult to manage progress or provide customized learning paths. In particular, the fact that learning content is static and cannot be adapted in real time to reflect progress is a major problem.

[0218] 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.

[0219] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for displaying the learning content on the user's device, and means for providing learning content customized by generative artificial intelligence, thereby enabling the dynamic provision of individually optimized learning content.

[0220] "Generative AI" is an AI technology that dynamically generates optimized learning content based on a user's skill level and learning style.

[0221] "Individually optimized learning content" means learning materials that are customized to a user's specific skill level and learning style.

[0222] "Adaptive learning paths" are a part of the system that provides optimal learning paths based on a user's progress and learning style.

[0223] "Progress information" is data that indicates the progress and history of a user's use of learning content.

[0224] A "database" is a system for storing and managing data such as a user's skill level, learning style, and progress information.

[0225] "Device" refers to a device (e.g., smartphone, tablet PC, desktop PC, etc.) that a user uses to display and use learning content.

[0226] "Visual aids" are visual information such as illustrations and videos that are provided to make learning content easier to understand.

[0227] "Auditory explanation" refers to auditory information such as audio guides or commentary audio that is provided to complement learning content.

[0228] An "educational program" is a systematic learning curriculum offered by an educational institution.

[0229] The present invention is a system that utilizes generative artificial intelligence to individually optimize a user's learning experience. Specific embodiments of the system are described below.

[0230] Server Features

[0231] The server performs the following main functions:

[0232] 1. Management of User Information

[0233] The server stores and updates the user's skill level, learning style, and progress information in a database. When a user uses the system, this information is retrieved from the database and updated as necessary.

[0234] 2. Generating learning content

[0235] The server uses a generative artificial intelligence (hereinafter referred to as a generative AI model) to dynamically generate individually optimized learning content based on the user's skill level and learning style. This generative AI model automatically generates optimized learning materials based on past learning data and general educational content.

[0236] 3. Providing adaptive learning paths

[0237] Based on the user's progress, the server provides an adaptive learning path that is structured to best suit the user's current skill level and learning style, maximizing the user's learning efficiency.

[0238] 4. Collaboration with educational institutions

[0239] The server works with educational institutions to integrate the generated learning content into their existing educational programs, improving student learning by tailoring the content to fit the institution's curriculum.

[0240] Functions of the device used

[0241] Devices (e.g. smartphones, tablet PCs, desktop PCs) perform the following main functions:

[0242] 1. Viewing learning content

[0243] Individually optimized learning content provided by the server is displayed on the device, including visual aids and audio explanations.

[0244] 2. Sending progress information

[0245] As users progress through their studies, they send progress information to the server, which then tracks their progress in real time and adjusts their learning path as needed.

[0246] User Actions

[0247] Users interact with the system as follows:

[0248] 1. Use of learning content

[0249] Learn using learning content delivered through the device, and users can track their progress and adjust their learning path as needed.

[0250] 2. Updating Information

[0251] Users can update their skill level and learning style by sending information to the server, which optimizes the generated learning content in real time.

[0252] Hardware and software used

[0253] Server side: Python, Flask, SQLAlchemy

[0254] Client side: Python, Requests

[0255] Database: SQLite

[0256] Generative AI model: Custom module (e.g., generation_ai_model)

[0257] Specific examples

[0258] Get user information:

[0259] User "user1" accesses the system and retrieves the following information from the server:

[0260] json

[0261] {

[0262] "skill_level": 1,

[0263] "learning_style": "visual",

[0264] "progress": 0

[0265] }

[0266] Update your user information:

[0267] User "user1" updates his progress and sends the following information to the server:

[0268] json

[0269] {

[0270] "skill_level": 1,

[0271] "learning_style": "visual",

[0272] "progress": 1

[0273] }

[0274] Request customized learning content:

[0275] When user "user1" requests customized learning content, the server returns the following content:

[0276] Print Statement Basics

[0277] How to use variables

[0278] How to use loops

[0279] Visual aids for loops

[0280] In this way, users can learn effectively through dynamically generated optimized content.

[0281] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0282] Step 1:

[0283] A user logs in to the system from a terminal. The terminal prompts for a username and password on a login page, and once the user enters this information, it sends a login request to the server. The server checks the database to see if there is a user that matches the entered information. If there is a match, it generates an authentication token and returns it to the user.

[0284] Input: Username, Password

[0285] Output: Authentication token

[0286] Step 2:

[0287] After logging in, a user sends a request from their device to obtain their learning skill level, learning style, and progress information. The server retrieves the corresponding user information from the database and returns it to the device in JSON format. The user can then check their current status based on this information.

[0288] Input: Authentication Token

[0289] Output: User's skill level, learning style, and progress information

[0290] Step 3:

[0291] To advance their learning, the user requests customized learning content. The device sends the request along with an authentication token, and the server uses a generative AI model to dynamically generate learning content optimized for the user. The generated content is customized based on the user's skill level and learning style and sent to the device.

[0292] Inputs: Authentication token, user skill level, learning style

[0293] Output: Customized learning content

[0294] Step 4:

[0295] The terminal displays the learning content provided by the server, and the user begins learning. The content includes visual aids such as text, illustrations, and audio guides, as well as auditory explanations. The user uses the displayed content to progress through the learning process.

[0296] Input: Customized learning content

[0297] Output: Providing a learning experience (including visual aids and audio explanations)

[0298] Step 5:

[0299] As the user progresses through the learning process, the device sends real-time progress information to the server, including learning achievement, content completed, time, etc. The server stores this progress information in a database and dynamically adjusts the learning path as needed.

[0300] Input: Progress information

[0301] Output: Updated learning path

[0302] Step 6:

[0303] When the user finishes their study, the device sends a request to the server to end the study session. The server saves the final progress information in a database and ends the study session. This information is saved for use the next time the user starts studying, and is used when the user starts studying again.

[0304] Input: Request to end study session

[0305] Output: Final saved progress information

[0306] In this way, users can effectively progress through individually optimized learning content.

[0307] 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.

[0308] overview

[0309] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust learning content based on the user's emotions. The system provides an optimal adaptive learning path by taking into account the user's skill level, learning style, progress information, and emotional state. The system also has the ability to add visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[0310] System configuration

[0311] server

[0312] The server performs the following main functions:

[0313] 1. Management of User Information

[0314] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database.

[0315] Returns data in response to requests to get and update user information.

[0316] 2. Generating learning content

[0317] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[0318] Add custom visual aids and audio descriptions.

[0319] It uses an emotion engine to assess the user's emotional state and dynamically adjust learning.

[0320] 3. Providing adaptive learning paths

[0321] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[0322] 4. Collaboration with educational institutions

[0323] Partner with educational institutions to integrate the generated learning content into their educational programs.

[0324] Terminal

[0325] The terminal performs the following main functions:

[0326] 1. Viewing learning content

[0327] Display individually optimized learning content provided by the server.

[0328] Provide users with visual aids and auditory instructions.

[0329] 2. Sending progress and emotional information

[0330] As the user progresses through their learning, they send progress information and the output of the emotion engine to the server.

[0331] User

[0332] The user performs the following main functions:

[0333] 1. Use of learning content

[0334] Study using learning content provided through the device.

[0335] Check in on your progress and emotional state and adjust your learning path as needed.

[0336] 2. Updating Information

[0337] Send information to the server to update your skill level, learning style, and emotional state.

[0338] Specific examples

[0339] Retrieving User Information

[0340] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[0341] json

[0342] {

[0343] "skill_level": 1,

[0344] "learning_style": "visual",

[0345] "progress": 0,

[0346] "emotional_state": "neutral"

[0347] }

[0348] Update user information

[0349] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, if user "user1" updates his progress and emotional state, the following information will be returned:

[0350] json

[0351] {

[0352] "skill_level": 1,

[0353] "learning_style": "visual",

[0354] "progress": 1,

[0355] "emotional_state": "happy"

[0356] }

[0357] Get course information

[0358] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[0359] json

[0360] {

[0361] "course_name": "Basic Python",

[0362] "content": ["print('Hello World')", "variables", "loops"]

[0363] }

[0364] Customize your learning content

[0365] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level, learning style, and emotional state. For example, the following customized content is served to user "user1":

[0366] json

[0367] {

[0368] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[0369] }

[0370] Regulation of learning content by emotional state

[0371] As the user progresses with their learning, the emotion engine evaluates the user's emotional state in real time and sends the results to the server. For example, if the emotion engine detects frustration while the user is learning, the server will adjust the content by simplifying it or displaying encouraging messages.

[0372] json

[0373] {

[0374] "customized_content": ["Take a short break and return later.", "Remember to stay positive!"]

[0375] }

[0376] This allows users to have an optimal learning experience based on their emotional state.

[0377] The processing flow will be explained below.

[0378] Retrieving User Information

[0379] Step 1:

[0380] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[0381] Step 2:

[0382] The server receives the request and the Flask route function user(username) is called.

[0383] Step 3:

[0384] The server verifies that the request method is GET.

[0385] Step 4:

[0386] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0387] Step 5:

[0388] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[0389] Update user information

[0390] Step 1:

[0391] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[0392] Step 2:

[0393] The server receives the request and the Flask route function user(username) is called.

[0394] Step 3:

[0395] The server verifies that the request method is PUT.

[0396] Step 4:

[0397] The server retrieves the JSON data from the request body.

[0398] Step 5:

[0399] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0400] Step 6:

[0401] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[0402] Get course information

[0403] Step 1:

[0404] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[0405] Step 2:

[0406] The server receives the request and the Flask route function course(course_id) is called.

[0407] Step 3:

[0408] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[0409] Step 4:

[0410] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[0411] Customize your learning content

[0412] Step 1:

[0413] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[0414] Step 2:

[0415] The server receives the request and the Flask route function customized_content(username) is called.

[0416] Step 3:

[0417] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0418] Step 4:

[0419] If the server finds the user data, it retrieves the skill level and learning style.

[0420] Step 5:

[0421] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[0422] Step 6:

[0423] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[0424] Step 7:

[0425] The server returns customized learning content to the user in JSON format.

[0426] Regulation of learning content by emotional state

[0427] Step 1:

[0428] As users engage with learning content, the emotion engine assesses their emotional state in real time.

[0429] Step 2:

[0430] The terminal transmits the output results of the emotion engine to the server.

[0431] Step 3:

[0432] The server analyzes the received emotional state information and determines whether the learning content needs to be adjusted based on the user's emotional state.

[0433] Step 4:

[0434] If the server determines that the emotional state is negative, such as "frustration," it will adjust the learning content to be simpler or display encouraging messages.

[0435] Step 5:

[0436] The server transmits the tailored learning content or message to the terminal.

[0437] Step 6:

[0438] The terminal displays the tailored learning content or message to the user.

[0439] Specific examples

[0440] Regulation of learning content by emotional state

[0441] If it can be explained as a diagram:

[0442] Step 1:

[0443] The user is learning basic Python syntax and is tackling difficult topics.

[0444] Step 2:

[0445] Using the device's camera and sensors, the emotion engine analyzes the user's facial expressions and biometric information to detect when the user is feeling frustrated.

[0446] Step 3:

[0447] The terminal transmits this emotion information to the server.

[0448] Step 4:

[0449] The server analyzes the information it receives and determines that the user is frustrated and that the learning needs to be adjusted.

[0450] Step 5:

[0451] The server generates a message encouraging the user to take a short break to relax.

[0452] Step 6:

[0453] The server transmits the adjusted learning content and the recommendation message to the terminal.

[0454] Step 7:

[0455] The device will display a message to the user such as "Take a short break and refresh yourself."

[0456] In this way, the learning content and pace can be flexibly adjusted according to the user's emotional state, providing an optimal learning environment.

[0457] Example 2

[0458] 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."

[0459] Conventional learning systems struggled to provide learning content that adequately reflected a user's individual skill level and learning style, resulting in insufficient adjustments to improve learning efficiency. Furthermore, they were unable to provide adaptive learning paths based on the user's emotional state, making it difficult to maintain motivation. Furthermore, there was little collaboration with educational institutions, making it difficult to integrate individually optimized learning content into educational programs.

[0460] 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.

[0461] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for evaluating the user's emotional state in real time and dynamically adjusting the learning content, and means for providing the learning content to the user's device, thereby providing an individually optimized learning experience for each user, enabling efficient adjustment of the learning content and maintaining motivation.

[0462] "Generative AI" is an AI technology that dynamically generates content based on user information.

[0463] "Individually optimized learning content" refers to learning materials that are customized based on each user's skill level and learning style.

[0464] An "adaptive learning path" is a learning path that is individually optimized using the user's progress and emotional information.

[0465] "Progress information" is data about the user's learning progress and achievement level that is recorded as the user progresses with their learning.

[0466] "Emotion information" is data that indicates the user's emotional state and is obtained by the emotion engine.

[0467] An "emotion engine" is a system that assesses a user's emotional state and adjusts learning content based on that.

[0468] "Learning style" is information that indicates a user's preferences for the most effective ways and means of learning.

[0469] "Visual aids" are visual materials such as charts, graphs, and images that are provided to support learning.

[0470] "Auditory explanation" refers to the explanation of learning content using audio or narration.

[0471] An "educational program" is a set of courses or curriculum offered by an educational institution.

[0472] "Real-time" refers to a state in which processing is carried out immediately on the spot without delay.

[0473] A "database" is a system that efficiently stores, searches, and manages large amounts of information.

[0474] MODE FOR CARRYING OUT THE INVENTION

[0475] overview

[0476] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. The system takes into account a user's skill level, learning style, progress information, and emotional state to provide an optimal adaptive learning path. The system also has an emotion engine that recognizes the user's emotions and adjusts the learning content based on the user's emotions. Furthermore, the system has the ability to provide visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[0477] System configuration

[0478] The system consists of three elements: the server, the terminal, and the user.

[0479] server

[0480] The server performs the following main functions:

[0481] 1. Management of User Information

[0482] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database. The server receives requests sent by the user from a web browser or API client and accesses the database to retrieve or update information.

[0483] For example, if a user requests "GET / user / <username>When you send a request like "PUT / user / ", the server retrieves the user information from the database and returns it in JSON format. <username>” If a request is submitted, update the database with the new information.

[0484] 2. Generating learning content

[0485] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[0486] For example, if a user GET / customized-content / <username>When you send a request like ", the server sends a prompt to the generative AI model to generate the optimal learning content.

[0487] Example prompt: "User is skill level 1 and has a visual learning style. Generate some basic Python programming content."

[0488] 3. Addition of visual and auditory support information

[0489] Dynamically add visual supplementary information and audio explanations to the generated learning content.

[0490] For example, the generated program code is provided to the user with associated diagrams and audio explanations.

[0491] 4. Emotional Engine Adjustment

[0492] Evaluates the user's emotional state and dynamically adjusts learning.

[0493] For example, if the emotion engine detects "dissatisfaction" during learning, the server can simplify the content or display an encouraging message.

[0494] 5. Providing adaptive learning paths

[0495] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[0496] 6. Collaboration with educational institutions

[0497] Partner with educational institutions to integrate the generated learning content into their educational programs.

[0498] Terminal

[0499] The terminal performs the following main functions:

[0500] 1. Viewing learning content

[0501] Display individually optimized learning content provided by the server.

[0502] For example, a user may view learning content through a device, such as a web browser or mobile application.

[0503] 2. Providing supporting information

[0504] Providing users with visual aids or auditory instructions, for example, displaying or playing images or audio files related to the learning content.

[0505] 3. Sending progress and emotional information

[0506] As the user progresses through their learning, progress information and the output of the emotion engine are sent to the server in real time.

[0507] User

[0508] The user performs the following main functions:

[0509] 1. Use of learning content

[0510] Students advance their studies using learning content provided through their devices.

[0511] For example, a user solves a programming problem and checks the results.

[0512] 2. Check in on your progress and feelings

[0513] Check in on your progress and emotional state and adjust your learning path as needed.

[0514] 3. Updating Information

[0515] Send information to the server to update your skill level, learning style, and emotional state.

[0516] As a result, this system can provide an optimal learning experience tailored to the individual needs of each user, improving learning efficiency and motivation.

[0517] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0518] Step 1:

[0519] The server receives a request to obtain user information.

[0520] Input: A user enters GET / user / from a web browser or API client. <username>" request.

[0521] Specific operation: The server receives the request and accesses the database to retrieve the user's information.

[0522] Output: Returns the retrieved user information in JSON format.

[0523] Step 2:

[0524] The server receives a request to update user information.

[0525] Input: User enters PUT / user / <username>" request and provide the new user information in JSON format.

[0526] Specific operation: The server receives the request and updates the corresponding user's information in the database.

[0527] Output: Returns a response confirming the updated user information.

[0528] Step 3:

[0529] The server receives a learning content generation request.

[0530] Input: When the user types "GET / customized-content / <username>" request.

[0531] How it works: The server receives the request and sends a prompt to the generative AI model based on the user's skill level and learning style. An example of a prompt is: "User's skill level is 1, and their learning style is visual. Please generate some basic Python programming content."

[0532] Output: Get the generated learning content.

[0533] Step 4:

[0534] The server adds visual and audio aid information.

[0535] Input: Generated learning content.

[0536] Specific operation: The server adds relevant visual support information (e.g., graphs and charts) and auditory explanations (e.g., audio files and narration) to the learning content.

[0537] Output: Learning content with additional information added.

[0538] Step 5:

[0539] The server uses an emotion engine to adjust the learning content.

[0540] Input: The user's emotional state (e.g., if the emotion engine detects "unhappy").

[0541] Specific operation: The server receives the evaluation results of the emotion engine and makes adjustments such as simplifying the learning content or adding encouraging messages.

[0542] Output: Tailored learning content.

[0543] Step 6:

[0544] The server provides an adaptive learning path.

[0545] Input: User progress and emotion information.

[0546] Specific operation: The server dynamically adjusts the optimal learning path based on this information.

[0547] Output: The adjusted learning path.

[0548] Step 7:

[0549] The server integrates the content into the educational program.

[0550] Input: Generated learning content.

[0551] Specific operation: The server collaborates with educational institutions and integrates the generated learning content into educational programs.

[0552] Output: Content integrated into an educational program.

[0553] Step 8:

[0554] The device displays individually optimized learning content.

[0555] Input: Learning content provided by the server.

[0556] What it does: The device renders learning content in a web browser or application.

[0557] Output: The learning content displayed to the user.

[0558] Step 9:

[0559] The device provides visual aids and audio instructions.

[0560] Input: Learning content with additional supporting information.

[0561] Specific action: The device displays or plays relevant visual aids (e.g., images, videos) or auditory instructions (e.g., audio files, narration) to the user.

[0562] Output: The visual aids and / or auditory instructions provided to the user.

[0563] Step 10:

[0564] The terminal transmits the progress information and emotion information to the server.

[0565] Input: Progress information as the user progresses through the learning process and the output of the emotion engine.

[0566] Specific operation: The device sends this information to the server in real time.

[0567] Output: Progress and emotion information sent to the server.

[0568] (Application example 2)

[0569] 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."

[0570] Conventional learning systems have the problem of insufficient individual optimization based on the user's skill level and learning style, resulting in reduced learning effectiveness. Furthermore, they do not adjust the learning content to take the user's emotional state into consideration, which often leads to a loss of motivation to learn and stress. Furthermore, a lack of visual and auditory supplementary information also poses the issue of reduced learning efficiency.

[0571] The identification processing by the identification 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 dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for providing learning content to the user's device, and means for analyzing the user's emotions in real time and dynamically adjusting the learning content. This makes it possible to provide content optimized for the user's skill level and learning style, and to adjust the learning content according to the user's emotional state.

[0572] "Generative AI" refers to AI that dynamically generates content based on user data.

[0573] "Optimized learning content" refers to educational materials that are tailored to a user's skill level and learning style.

[0574] An "adaptive learning path" refers to a learning path that dynamically changes depending on the user's progress and emotional state.

[0575] "User Progress Information" means information that indicates how far a user has progressed in their learning process.

[0576] "Emotional information" refers to data that assesses a user's emotional state in real time.

[0577] A "database" refers to a system for systematically managing, storing, and searching various types of information.

[0578] "Device" refers to the device used by a User to receive the Learning Content.

[0579] "Means for analyzing emotions in real time" refers to technologies and functions for instantly analyzing a user's emotional state.

[0580] "Visual aids" refers to visual elements (diagrams, illustrations, graphs, etc.) that are provided to make learning content easier to understand.

[0581] "Auditory explanation" refers to information that explains the learning content to the user using audio.

[0582] "Collaboration with educational institutions" refers to activities to collaborate with schools and professional institutions to integrate learning content into educational programs.

[0583] System Overview

[0584] This invention is a system that uses generative artificial intelligence to dynamically generate and optimize learning content to improve the user's learning experience. The system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust the learning content based on the user's emotions. Furthermore, by adding visual supplementary information and auditory explanations, the system provides comprehensive learning support.

[0585] server

[0586] 1. Generative artificial intelligence:

[0587] Using this technology, the server dynamically generates personalized learning content based on the user's skill level and learning style, and the learning content is customized according to the user's current learning progress and emotional state.

[0588] 2. Adaptive Learning Path:

[0589] The server stores the user's skill level, learning style, progress information, and emotional state in a database and uses this information to provide an adaptive learning path that is dynamically adjusted in real time.

[0590] 3. Emotion recognition and regulation:

[0591] The server analyzes the user's emotions in real time and dynamically adjusts the learning content based on the detected emotions. For example, if the user is in a "frustration" emotional state, the content will be simplified or an encouraging message will be displayed.

[0592] 4. Visual and auditory support:

[0593] The server provides visual aids and audio explanations according to the user's learning style, thereby enhancing the user's understanding.

[0594] 5. Collaboration with educational institutions:

[0595] The server provides a means for collaborating with educational institutions to integrate the generated learning content into their educational programs.

[0596] Terminal

[0597] 1. View learning content:

[0598] The device displays individually optimized learning content delivered from the server, including visual aids and audio explanations.

[0599] 2. Sending progress and emotional information:

[0600] As the user progresses through the learning process, the device sends progress information and the output of the emotion engine to the server, which updates the user's learning path in real time.

[0601] User

[0602] 1. Access to learning content:

[0603] Users can use learning content provided through their devices to progress through their studies, check their own progress and emotional state, and receive appropriate feedback.

[0604] 2. Example prompt:

[0605] An example of a prompt for a generative AI model is below: "Generative artificial intelligence model for education, please generate appropriate content for learning the basics of Python. User's skill level is 2, learning style is visual, and emotional state is 'happy'."

[0606] This allows users to have a comprehensive and optimized learning experience.

[0607] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0608] Step 1:

[0609] Upon receiving a request from a user, the server retrieves the user's skill level, learning style, progress information, and emotional information from the database. This information becomes input data for the generative AI to generate learning content. The output is JSON-formatted data of the user information.

[0610] Step 2:

[0611] The server generates prompts for the generative AI model based on the user's skill level and learning style. For example, the prompts might be in the form of "The user's skill level is 2 and their learning style is visual. Please generate appropriate content for learning the basics of Python." The prompts serve as input data for the generative AI, and the output is learning content optimized for the user.

[0612] Step 3:

[0613] The server retrieves the generated learning content and sends it to an emotion engine for evaluating the user's emotional information. The emotion engine further adjusts the learning content according to the user's emotional state (e.g., happy, frustrated). The input is the learning content and the emotional state data, and the output is the adjusted learning content based on the emotional state.

[0614] Step 4:

[0615] The server sends the optimized and tailored learning content to the user's device. The device displays the received data, including visual aids and audio instructions. The input is the tailored learning content, and the output is the learning content displayed on the user's device.

[0616] Step 5:

[0617] As the user progresses through the learning process, the device sends progress and emotional information to the server in real time. This information is updated in the database on the server and used to generate the next learning content. The input is the user's progress and emotional information, and the output is the updated database information.

[0618] Step 6:

[0619] The user checks their own progress and emotional state and adjusts their learning path as needed. The input is feedback information on progress and emotional state provided by the server, and the output is the result of the user adjusting their learning path.

[0620] This allows users to have a real-time optimized learning experience.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] [Second embodiment]

[0625] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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."

[0637] overview

[0638] This invention utilizes generative artificial intelligence to dynamically generate and optimize learning content to enhance a user's learning experience. The system provides an adaptive learning path that takes into account a user's skill level, learning style, and progress information. It also has the ability to add visual aids and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[0639] System configuration

[0640] server

[0641] The server performs the following main functions:

[0642] 1. Management of User Information

[0643] Store and update a database with user skill levels, learning styles, and progress information.

[0644] Returns data in response to requests to get and update user information.

[0645] 2. Generating learning content

[0646] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[0647] Add custom visual aids and audio descriptions.

[0648] 3. Providing adaptive learning paths

[0649] Dynamically adjusts learning paths based on user progress to provide optimal learning paths.

[0650] 4. Collaboration with educational institutions

[0651] Partner with educational institutions to integrate the generated learning content into their educational programs.

[0652] Terminal

[0653] The terminal performs the following main functions:

[0654] 1. Viewing learning content

[0655] Display individually optimized learning content provided by the server.

[0656] Provide users with visual aids and auditory instructions.

[0657] 2. Sending progress information

[0658] As the user progresses through their studies, they send progress information to the server.

[0659] User

[0660] The user performs the following main functions:

[0661] 1. Use of learning content

[0662] Study using learning content provided through the device.

[0663] Check your progress and adjust your learning path if necessary.

[0664] 2. Updating Information

[0665] Send information to the server to update your skill level and learning style.

[0666] Specific examples

[0667] Retrieving User Information

[0668] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[0669] json

[0670] {

[0671] "skill_level": 1,

[0672] "learning_style": "visual",

[0673] "progress": 0

[0674] }

[0675] Update user information

[0676] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, when user "user1" updates their progress, the following information is returned:

[0677] json

[0678] {

[0679] "skill_level": 1,

[0680] "learning_style": "visual",

[0681] "progress": 1

[0682] }

[0683] Get course information

[0684] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[0685] json

[0686] {

[0687] "course_name": "Basic Python",

[0688] "content": ["print('Hello World')", "variables", "loops"]

[0689] }

[0690] Customize your learning content

[0691] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level and learning style. For example, user "user1" might be served the following customized content:

[0692] json

[0693] {

[0694] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[0695] }

[0696] This allows users to use learning materials optimized for their own learning style and progress effectively with their studies.

[0697] The processing flow will be explained below.

[0698] Retrieving User Information

[0699] Step 1:

[0700] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[0701] Step 2:

[0702] The server receives the request and the Flask route function user(username) is called.

[0703] Step 3:

[0704] The server verifies that the request method is GET.

[0705] Step 4:

[0706] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0707] Step 5:

[0708] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[0709] Update user information

[0710] Step 1:

[0711] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[0712] Step 2:

[0713] The server receives the request and the Flask route function user(username) is called.

[0714] Step 3:

[0715] The server verifies that the request method is PUT.

[0716] Step 4:

[0717] The server retrieves the JSON data from the request body.

[0718] Step 5:

[0719] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0720] Step 6:

[0721] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[0722] Get course information

[0723] Step 1:

[0724] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[0725] Step 2:

[0726] The server receives the request and the Flask route function course(course_id) is called.

[0727] Step 3:

[0728] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[0729] Step 4:

[0730] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[0731] Customize your learning content

[0732] Step 1:

[0733] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[0734] Step 2:

[0735] The server receives the request and the Flask route function customized_content(username) is called.

[0736] Step 3:

[0737] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0738] Step 4:

[0739] If the server finds the user data, it retrieves the skill level and learning style.

[0740] Step 5:

[0741] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[0742] Step 6:

[0743] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[0744] Step 7:

[0745] The server returns customized learning content to the user in JSON format.

[0746] Example 1

[0747] 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."

[0748] Conventional learning systems have struggled to provide an optimized learning experience for each user, and have not adequately provided learning content tailored to individual learning styles and skill levels. Furthermore, few systems manage users' progress information in real time and dynamically provide optimal learning paths. This has led to problems such as reduced learning effectiveness and a loss of user motivation to learn. The purpose of this invention is to solve these problems and improve users' learning experience.

[0749] 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.

[0750] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for providing learning content to the user's device, means for integrating the generated learning content into an educational program in collaboration with an educational institution, means for acquiring user information and returning it in JSON format, means for updating the user information and updating the database with the new user information, and means for customizing and providing learning content based on the user's skill level and learning style. This makes it possible to provide learning content and a learning path optimized for each user, improving the individual learning experience.

[0751] "Generative AI" is an AI technology that learns from huge datasets and generates text in natural language like a human.

[0752] "Individually optimized learning content" means learning materials that are individually tailored to each user based on their skill level and learning style.

[0753] An "adaptive learning path" is a learning path that dynamically adjusts based on the user's progress.

[0754] "Progress information" is data that indicates how far the user has progressed in their studies.

[0755] A "database" is a digital system that organizes and stores data so that it can be searched and updated.

[0756] "Terminal" refers to a device (computer, smartphone, etc.) used by a user to use the service.

[0757] An "educational program" is a curriculum of courses or courses offered by an educational institution.

[0758] "User Information" is data including a user's personal identification information, skill level, learning style, progress, etc.

[0759] "JSON format" stands for JavaScript Object Notation and is a lightweight text-based format for exchanging data.

[0760] "Customized content" is learning material that is generated or tailored to a user's specific needs.

[0761] "Visual aids" are visual learning aids such as slides, charts, and videos.

[0762] "Auditory description" means an audio commentary or explanation.

[0763] MODE FOR CARRYING OUT THE INVENTION

[0764] This invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve users' learning experiences. This system consists of three main components: a server, a terminal, and a user.

[0765] Server Roles

[0766] The server performs the following main functions:

[0767] 1. Management of User Information

[0768] The server uses a database management system (DBMS) to store and update the user-provided information on skill level, learning style, and progress as needed. Specifically, a SQL database or a NoSQL database may be used.

[0769] 2. Generating learning content

[0770] The server uses a generative artificial intelligence (generative AI model) to generate personalized learning content based on the user's skill level and learning style. For example, it can use OpenAI's GPT-3 model. In this case, the server sends a prompt to the generative AI model as follows:

[0771] - "The user has a beginner skill level and prefers a visual learning style. Please generate an introductory Python course that is appropriate for this user."

[0772] 3. Providing adaptive learning paths

[0773] The server dynamically adjusts the learning path based on the user's progress, for example by implementing an algorithm that automatically recommends the next chapter once the user has completed a chapter.

[0774] 4. Collaboration with educational institutions

[0775] The server provides an API for integrating the generated learning content into the educational program of the educational institution. Specifically, the learning content can be incorporated into the educational program using a RESTful API.

[0776] Device Role

[0777] The terminal mainly performs the following functions:

[0778] 1. Viewing learning content

[0779] The device displays personalized learning content provided by the server to the user, using mobile and web applications and providing visual aids and audio explanations.

[0780] 2. Sending progress information

[0781] As the user progresses, the device sends real-time progress information to the server. For example, when the user completes a particular chapter, the device marks it as "completed."

[0782] User Roles

[0783] The user mainly performs the following functions:

[0784] 1. Use of learning content

[0785] Users can learn by accessing learning content provided through their devices, for example by watching videos and answering interactive quizzes.

[0786] 2. Updating Information

[0787] Users send information to the server to update their progress, skill level, learning style, etc. For example, if a user wants to try a new learning method, they send that information to the server and update their account.

[0788] As described above, the server, terminal, and user each play their respective roles and work together to provide the user with the optimal learning experience. To specifically realize this system, technologies such as generative artificial intelligence, database management systems, and APIs are combined.

[0789] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0790] Program processing flow

[0791] Server Processing

[0792] Step 1:

[0793] The server receives a registration or login request from a user. As input, it receives the user's identifying information (username, password, etc.). It queries the database to retrieve the user's information or registers a new user. As output, it returns user information including the user's skill level, learning style, and progress information.

[0794] Step 2:

[0795] When a user makes a learning request, the server sends a prompt to a generative AI model (e.g., GPT-3). As input, it receives the user's skill level and learning style and generates a prompt based on this. The generative AI model generates individually optimized learning content based on this prompt. As output, the generated learning content is returned.

[0796] Step 3:

[0797] The server adds visual aids and auditory explanations to the generated learning content. As input, it receives the text data returned by the generative AI model. It then adds visual aids (e.g., slides, diagrams) and auditory explanations (e.g., audio guides). As output, it obtains the complete learning content, including both visual and auditory elements.

[0798] Step 4:

[0799] The server stores the user's progress in a database and dynamically adjusts the learning path. As input, it receives the user's progress information in real time. Based on this, it updates the database and recommends the next learning content. As output, it obtains information that indicates the next learning step.

[0800] Step 5:

[0801] The server integrates the generated learning content into the educational program of the educational institution. It receives the generated learning content and the educational institution's request as input. It integrates the content into the educational institution's system using RESTful APIs. As output, it obtains the learning content integrated into the educational institution's program.

[0802] Terminal handling

[0803] Step 6:

[0804] The terminal receives the learning content provided by the server and displays it to the user. As input, it receives the learning content from the server. It plays back the multimedia content including the visual support information and the audio explanation. As output, it obtains the learning content displayed to the user.

[0805] Step 7:

[0806] As the user progresses with their studies, the device sends their progress information to the server in real time. As input, it receives the user's learning actions (e.g., chapter completion, question answering), converts them into data packets to send to the server, and as output, sends the user's progress information to the server.

[0807] User Action

[0808] Step 8:

[0809] Users learn using learning content provided through their devices. As input, they consume learning content displayed on their devices and perform learning activities (e.g., watching videos or taking interactive quizzes). As output, progress information is generated.

[0810] Step 9:

[0811] The user sends information to the server to update their skill level and learning style. As input, it receives the new skill level and learning style information. It converts this into a data packet to send to the server. As output, it sends the updated user information to the server.

[0812] (Application example 1)

[0813] 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."

[0814] Conventional learning systems have difficulty adapting to the different skill levels and learning styles of each user, and are unable to dynamically provide individually optimized learning content. This means that users' learning effectiveness is not maximized, and it is difficult to manage progress or provide customized learning paths. In particular, the fact that learning content is static and cannot be adapted in real time to reflect progress is a major problem.

[0815] 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.

[0816] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for displaying the learning content on the user's device, and means for providing learning content customized by generative artificial intelligence, thereby enabling the dynamic provision of individually optimized learning content.

[0817] "Generative AI" is an AI technology that dynamically generates optimized learning content based on a user's skill level and learning style.

[0818] "Individually optimized learning content" means learning materials that are customized to a user's specific skill level and learning style.

[0819] "Adaptive learning paths" are a part of the system that provides optimal learning paths based on a user's progress and learning style.

[0820] "Progress information" is data that indicates the progress and history of a user's use of learning content.

[0821] A "database" is a system for storing and managing data such as a user's skill level, learning style, and progress information.

[0822] "Device" refers to a device (e.g., smartphone, tablet PC, desktop PC, etc.) that a user uses to display and use learning content.

[0823] "Visual aids" are visual information such as illustrations and videos that are provided to make learning content easier to understand.

[0824] "Auditory explanation" refers to auditory information such as audio guides or commentary audio that is provided to complement learning content.

[0825] An "educational program" is a systematic learning curriculum offered by an educational institution.

[0826] The present invention is a system that utilizes generative artificial intelligence to individually optimize a user's learning experience. Specific embodiments of the system are described below.

[0827] Server Features

[0828] The server performs the following main functions:

[0829] 1. Management of User Information

[0830] The server stores and updates the user's skill level, learning style, and progress information in a database. When a user uses the system, this information is retrieved from the database and updated as necessary.

[0831] 2. Generating learning content

[0832] The server uses a generative artificial intelligence (hereinafter referred to as a generative AI model) to dynamically generate individually optimized learning content based on the user's skill level and learning style. This generative AI model automatically generates optimized learning materials based on past learning data and general educational content.

[0833] 3. Providing adaptive learning paths

[0834] Based on the user's progress, the server provides an adaptive learning path that is structured to best suit the user's current skill level and learning style, maximizing the user's learning efficiency.

[0835] 4. Collaboration with educational institutions

[0836] The server works with educational institutions to integrate the generated learning content into their existing educational programs, improving student learning by tailoring the content to fit the institution's curriculum.

[0837] Functions of the device used

[0838] Devices (e.g. smartphones, tablet PCs, desktop PCs) perform the following main functions:

[0839] 1. Viewing learning content

[0840] Individually optimized learning content provided by the server is displayed on the device, including visual aids and audio explanations.

[0841] 2. Sending progress information

[0842] As users progress through their studies, they send progress information to the server, which then tracks their progress in real time and adjusts their learning path as needed.

[0843] User Actions

[0844] Users interact with the system as follows:

[0845] 1. Use of learning content

[0846] Learn using learning content delivered through the device, and users can track their progress and adjust their learning path as needed.

[0847] 2. Updating Information

[0848] Users can update their skill level and learning style by sending information to the server, which optimizes the generated learning content in real time.

[0849] Hardware and software used

[0850] Server side: Python, Flask, SQLAlchemy

[0851] Client side: Python, Requests

[0852] Database: SQLite

[0853] Generative AI model: Custom module (e.g., generation_ai_model)

[0854] Specific examples

[0855] Get user information:

[0856] User "user1" accesses the system and retrieves the following information from the server:

[0857] json

[0858] {

[0859] "skill_level": 1,

[0860] "learning_style": "visual",

[0861] "progress": 0

[0862] }

[0863] Update your user information:

[0864] User "user1" updates his progress and sends the following information to the server:

[0865] json

[0866] {

[0867] "skill_level": 1,

[0868] "learning_style": "visual",

[0869] "progress": 1

[0870] }

[0871] Request customized learning content:

[0872] When user "user1" requests customized learning content, the server returns the following content:

[0873] Print Statement Basics

[0874] How to use variables

[0875] How to use loops

[0876] Visual aids for loops

[0877] In this way, users can learn effectively through dynamically generated optimized content.

[0878] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0879] Step 1:

[0880] A user logs in to the system from a terminal. The terminal prompts for a username and password on a login page, and once the user enters this information, it sends a login request to the server. The server checks the database to see if there is a user that matches the entered information. If there is a match, it generates an authentication token and returns it to the user.

[0881] Input: Username, Password

[0882] Output: Authentication token

[0883] Step 2:

[0884] After logging in, a user sends a request from their device to obtain their learning skill level, learning style, and progress information. The server retrieves the corresponding user information from the database and returns it to the device in JSON format. The user can then check their current status based on this information.

[0885] Input: Authentication Token

[0886] Output: User's skill level, learning style, and progress information

[0887] Step 3:

[0888] To advance their learning, the user requests customized learning content. The device sends the request along with an authentication token, and the server uses a generative AI model to dynamically generate learning content optimized for the user. The generated content is customized based on the user's skill level and learning style and sent to the device.

[0889] Inputs: Authentication token, user skill level, learning style

[0890] Output: Customized learning content

[0891] Step 4:

[0892] The terminal displays the learning content provided by the server, and the user begins learning. The content includes visual aids such as text, illustrations, and audio guides, as well as auditory explanations. The user uses the displayed content to progress through the learning process.

[0893] Input: Customized learning content

[0894] Output: Providing a learning experience (including visual aids and audio explanations)

[0895] Step 5:

[0896] As the user progresses through the learning process, the device sends real-time progress information to the server, including learning achievement, content completed, time, etc. The server stores this progress information in a database and dynamically adjusts the learning path as needed.

[0897] Input: Progress information

[0898] Output: Updated learning path

[0899] Step 6:

[0900] When the user finishes their study, the device sends a request to the server to end the study session. The server saves the final progress information in a database and ends the study session. This information is saved for use the next time the user starts studying, and is used when the user starts studying again.

[0901] Input: Request to end study session

[0902] Output: Final saved progress information

[0903] In this way, users can effectively progress through individually optimized learning content.

[0904] 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.

[0905] overview

[0906] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust learning content based on the user's emotions. The system provides an optimal adaptive learning path by taking into account the user's skill level, learning style, progress information, and emotional state. The system also has the ability to add visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[0907] System configuration

[0908] server

[0909] The server performs the following main functions:

[0910] 1. Management of User Information

[0911] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database.

[0912] Returns data in response to requests to get and update user information.

[0913] 2. Generating learning content

[0914] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[0915] Add custom visual aids and audio descriptions.

[0916] It uses an emotion engine to assess the user's emotional state and dynamically adjust learning.

[0917] 3. Providing adaptive learning paths

[0918] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[0919] 4. Collaboration with educational institutions

[0920] Partner with educational institutions to integrate the generated learning content into their educational programs.

[0921] Terminal

[0922] The terminal performs the following main functions:

[0923] 1. Viewing learning content

[0924] Display individually optimized learning content provided by the server.

[0925] Provide users with visual aids and auditory instructions.

[0926] 2. Sending progress and emotional information

[0927] As the user progresses through their learning, they send progress information and the output of the emotion engine to the server.

[0928] User

[0929] The user performs the following main functions:

[0930] 1. Use of learning content

[0931] Study using learning content provided through the device.

[0932] Check in on your progress and emotional state and adjust your learning path as needed.

[0933] 2. Updating Information

[0934] Send information to the server to update your skill level, learning style, and emotional state.

[0935] Specific examples

[0936] Retrieving User Information

[0937] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[0938] json

[0939] {

[0940] "skill_level": 1,

[0941] "learning_style": "visual",

[0942] "progress": 0,

[0943] "emotional_state": "neutral"

[0944] }

[0945] Update user information

[0946] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, if user "user1" updates his progress and emotional state, the following information will be returned:

[0947] json

[0948] {

[0949] "skill_level": 1,

[0950] "learning_style": "visual",

[0951] "progress": 1,

[0952] "emotional_state": "happy"

[0953] }

[0954] Get course information

[0955] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[0956] json

[0957] {

[0958] "course_name": "Basic Python",

[0959] "content": ["print('Hello World')", "variables", "loops"]

[0960] }

[0961] Customize your learning content

[0962] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level, learning style, and emotional state. For example, the following customized content is served to user "user1":

[0963] json

[0964] {

[0965] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[0966] }

[0967] Regulation of learning content by emotional state

[0968] As the user progresses with their learning, the emotion engine evaluates the user's emotional state in real time and sends the results to the server. For example, if the emotion engine detects frustration while the user is learning, the server will adjust the content by simplifying it or displaying encouraging messages.

[0969] json

[0970] {

[0971] "customized_content": ["Take a short break and return later.", "Remember to stay positive!"]

[0972] }

[0973] This allows users to have an optimal learning experience based on their emotional state.

[0974] The processing flow will be explained below.

[0975] Retrieving User Information

[0976] Step 1:

[0977] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[0978] Step 2:

[0979] The server receives the request and the Flask route function user(username) is called.

[0980] Step 3:

[0981] The server verifies that the request method is GET.

[0982] Step 4:

[0983] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0984] Step 5:

[0985] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[0986] Update user information

[0987] Step 1:

[0988] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[0989] Step 2:

[0990] The server receives the request and the Flask route function user(username) is called.

[0991] Step 3:

[0992] The server verifies that the request method is PUT.

[0993] Step 4:

[0994] The server retrieves the JSON data from the request body.

[0995] Step 5:

[0996] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[0997] Step 6:

[0998] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[0999] Get course information

[1000] Step 1:

[1001] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[1002] Step 2:

[1003] The server receives the request and the Flask route function course(course_id) is called.

[1004] Step 3:

[1005] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[1006] Step 4:

[1007] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[1008] Customize your learning content

[1009] Step 1:

[1010] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[1011] Step 2:

[1012] The server receives the request and the Flask route function customized_content(username) is called.

[1013] Step 3:

[1014] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1015] Step 4:

[1016] If the server finds the user data, it retrieves the skill level and learning style.

[1017] Step 5:

[1018] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[1019] Step 6:

[1020] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[1021] Step 7:

[1022] The server returns customized learning content to the user in JSON format.

[1023] Regulation of learning content by emotional state

[1024] Step 1:

[1025] As users engage with learning content, the emotion engine assesses their emotional state in real time.

[1026] Step 2:

[1027] The terminal transmits the output results of the emotion engine to the server.

[1028] Step 3:

[1029] The server analyzes the received emotional state information and determines whether the learning content needs to be adjusted based on the user's emotional state.

[1030] Step 4:

[1031] If the server determines that the emotional state is negative, such as "frustration," it will adjust the learning content to be simpler or display encouraging messages.

[1032] Step 5:

[1033] The server transmits the tailored learning content or message to the terminal.

[1034] Step 6:

[1035] The terminal displays the tailored learning content or message to the user.

[1036] Specific examples

[1037] Regulation of learning content by emotional state

[1038] If it can be explained as a diagram:

[1039] Step 1:

[1040] The user is learning basic Python syntax and is tackling difficult topics.

[1041] Step 2:

[1042] Using the device's camera and sensors, the emotion engine analyzes the user's facial expressions and biometric information to detect when the user is feeling frustrated.

[1043] Step 3:

[1044] The terminal transmits this emotion information to the server.

[1045] Step 4:

[1046] The server analyzes the information it receives and determines that the user is frustrated and that the learning needs to be adjusted.

[1047] Step 5:

[1048] The server generates a message encouraging the user to take a short break to relax.

[1049] Step 6:

[1050] The server transmits the adjusted learning content and the recommendation message to the terminal.

[1051] Step 7:

[1052] The device will display a message to the user such as "Take a short break and refresh yourself."

[1053] In this way, the learning content and pace can be flexibly adjusted according to the user's emotional state, providing an optimal learning environment.

[1054] Example 2

[1055] 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."

[1056] Conventional learning systems struggled to provide learning content that adequately reflected a user's individual skill level and learning style, resulting in insufficient adjustments to improve learning efficiency. Furthermore, they were unable to provide adaptive learning paths based on the user's emotional state, making it difficult to maintain motivation. Furthermore, there was little collaboration with educational institutions, making it difficult to integrate individually optimized learning content into educational programs.

[1057] 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.

[1058] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for evaluating the user's emotional state in real time and dynamically adjusting the learning content, and means for providing the learning content to the user's device, thereby providing an individually optimized learning experience for each user, enabling efficient adjustment of the learning content and maintaining motivation.

[1059] "Generative AI" is an AI technology that dynamically generates content based on user information.

[1060] "Individually optimized learning content" refers to learning materials that are customized based on each user's skill level and learning style.

[1061] An "adaptive learning path" is a learning path that is individually optimized using the user's progress and emotional information.

[1062] "Progress information" is data about the user's learning progress and achievement level that is recorded as the user progresses with their learning.

[1063] "Emotion information" is data that indicates the user's emotional state and is obtained by the emotion engine.

[1064] An "emotion engine" is a system that assesses a user's emotional state and adjusts learning content based on that.

[1065] "Learning style" is information that indicates a user's preferences for the most effective ways and means of learning.

[1066] "Visual aids" are visual materials such as charts, graphs, and images that are provided to support learning.

[1067] "Auditory explanation" refers to the explanation of learning content using audio or narration.

[1068] An "educational program" is a set of courses or curriculum offered by an educational institution.

[1069] "Real-time" refers to a state in which processing is carried out immediately on the spot without delay.

[1070] A "database" is a system that efficiently stores, searches, and manages large amounts of information.

[1071] MODE FOR CARRYING OUT THE INVENTION

[1072] overview

[1073] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. The system takes into account a user's skill level, learning style, progress information, and emotional state to provide an optimal adaptive learning path. The system also has an emotion engine that recognizes the user's emotions and adjusts the learning content based on the user's emotions. Furthermore, the system has the ability to provide visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[1074] System configuration

[1075] The system consists of three elements: the server, the terminal, and the user.

[1076] server

[1077] The server performs the following main functions:

[1078] 1. Management of User Information

[1079] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database. The server receives requests sent by the user from a web browser or API client and accesses the database to retrieve or update information.

[1080] For example, if a user requests "GET / user / <username>When you send a request like "PUT / user / ", the server retrieves the user information from the database and returns it in JSON format. <username>” If a request is submitted, update the database with the new information.

[1081] 2. Generating learning content

[1082] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[1083] For example, if a user GET / customized-content / <username>When you send a request like ", the server sends a prompt to the generative AI model to generate the optimal learning content.

[1084] Example prompt: "User is skill level 1 and has a visual learning style. Generate some basic Python programming content."

[1085] 3. Addition of visual and auditory support information

[1086] Dynamically add visual supplementary information and audio explanations to the generated learning content.

[1087] For example, the generated program code is provided to the user with associated diagrams and audio explanations.

[1088] 4. Emotional Engine Adjustment

[1089] Evaluates the user's emotional state and dynamically adjusts learning.

[1090] For example, if the emotion engine detects "dissatisfaction" during learning, the server can simplify the content or display an encouraging message.

[1091] 5. Providing adaptive learning paths

[1092] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[1093] 6. Collaboration with educational institutions

[1094] Partner with educational institutions to integrate the generated learning content into their educational programs.

[1095] Terminal

[1096] The terminal performs the following main functions:

[1097] 1. Viewing learning content

[1098] Display individually optimized learning content provided by the server.

[1099] For example, a user may view learning content through a device, such as a web browser or mobile application.

[1100] 2. Providing supporting information

[1101] Providing users with visual aids or auditory instructions, for example, displaying or playing images or audio files related to the learning content.

[1102] 3. Sending progress and emotional information

[1103] As the user progresses through their learning, progress information and the output of the emotion engine are sent to the server in real time.

[1104] User

[1105] The user performs the following main functions:

[1106] 1. Use of learning content

[1107] Students advance their studies using learning content provided through their devices.

[1108] For example, a user solves a programming problem and checks the results.

[1109] 2. Check in on your progress and feelings

[1110] Check in on your progress and emotional state and adjust your learning path as needed.

[1111] 3. Updating Information

[1112] Send information to the server to update your skill level, learning style, and emotional state.

[1113] As a result, this system can provide an optimal learning experience tailored to the individual needs of each user, improving learning efficiency and motivation.

[1114] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1115] Step 1:

[1116] The server receives a request to obtain user information.

[1117] Input: A user enters GET / user / from a web browser or API client. <username>" request.

[1118] Specific operation: The server receives the request and accesses the database to retrieve the user's information.

[1119] Output: Returns the retrieved user information in JSON format.

[1120] Step 2:

[1121] The server receives a request to update user information.

[1122] Input: User enters PUT / user / <username>" request and provide the new user information in JSON format.

[1123] Specific operation: The server receives the request and updates the corresponding user's information in the database.

[1124] Output: Returns a response confirming the updated user information.

[1125] Step 3:

[1126] The server receives a learning content generation request.

[1127] Input: When the user types "GET / customized-content / <username>" request.

[1128] How it works: The server receives the request and sends a prompt to the generative AI model based on the user's skill level and learning style. An example of a prompt is: "User's skill level is 1, and their learning style is visual. Please generate some basic Python programming content."

[1129] Output: Get the generated learning content.

[1130] Step 4:

[1131] The server adds visual and audio aid information.

[1132] Input: Generated learning content.

[1133] Specific operation: The server adds relevant visual support information (e.g., graphs and charts) and auditory explanations (e.g., audio files and narration) to the learning content.

[1134] Output: Learning content with additional information added.

[1135] Step 5:

[1136] The server uses an emotion engine to adjust the learning content.

[1137] Input: The user's emotional state (e.g., if the emotion engine detects "unhappy").

[1138] Specific operation: The server receives the evaluation results of the emotion engine and makes adjustments such as simplifying the learning content or adding encouraging messages.

[1139] Output: Tailored learning content.

[1140] Step 6:

[1141] The server provides an adaptive learning path.

[1142] Input: User progress and emotion information.

[1143] Specific operation: The server dynamically adjusts the optimal learning path based on this information.

[1144] Output: The adjusted learning path.

[1145] Step 7:

[1146] The server integrates the content into the educational program.

[1147] Input: Generated learning content.

[1148] Specific operation: The server collaborates with educational institutions and integrates the generated learning content into educational programs.

[1149] Output: Content integrated into an educational program.

[1150] Step 8:

[1151] The device displays individually optimized learning content.

[1152] Input: Learning content provided by the server.

[1153] What it does: The device renders learning content in a web browser or application.

[1154] Output: The learning content displayed to the user.

[1155] Step 9:

[1156] The device provides visual aids and audio instructions.

[1157] Input: Learning content with additional supporting information.

[1158] Specific action: The device displays or plays relevant visual aids (e.g., images, videos) or auditory instructions (e.g., audio files, narration) to the user.

[1159] Output: The visual aids and / or auditory instructions provided to the user.

[1160] Step 10:

[1161] The terminal transmits the progress information and emotion information to the server.

[1162] Input: Progress information as the user progresses through the learning process and the output of the emotion engine.

[1163] Specific operation: The device sends this information to the server in real time.

[1164] Output: Progress and emotion information sent to the server.

[1165] (Application example 2)

[1166] 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."

[1167] Conventional learning systems have the problem of insufficient individual optimization based on the user's skill level and learning style, resulting in reduced learning effectiveness. Furthermore, they do not adjust the learning content to take the user's emotional state into consideration, which often leads to a loss of motivation to learn and stress. Furthermore, a lack of visual and auditory supplementary information also poses the issue of reduced learning efficiency.

[1168] The identification processing by the identification 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 dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for providing learning content to the user's device, and means for analyzing the user's emotions in real time and dynamically adjusting the learning content. This makes it possible to provide content optimized for the user's skill level and learning style, and to adjust the learning content according to the user's emotional state.

[1169] "Generative AI" refers to AI that dynamically generates content based on user data.

[1170] "Optimized learning content" refers to educational materials that are tailored to a user's skill level and learning style.

[1171] An "adaptive learning path" refers to a learning path that dynamically changes depending on the user's progress and emotional state.

[1172] "User Progress Information" means information that indicates how far a user has progressed in their learning process.

[1173] "Emotional information" refers to data that assesses a user's emotional state in real time.

[1174] A "database" refers to a system for systematically managing, storing, and searching various types of information.

[1175] "Device" refers to the device used by a User to receive the Learning Content.

[1176] "Means for analyzing emotions in real time" refers to technologies and functions for instantly analyzing a user's emotional state.

[1177] "Visual aids" refers to visual elements (diagrams, illustrations, graphs, etc.) that are provided to make learning content easier to understand.

[1178] "Auditory explanation" refers to information that explains the learning content to the user using audio.

[1179] "Collaboration with educational institutions" refers to activities to collaborate with schools and professional institutions to integrate learning content into educational programs.

[1180] System Overview

[1181] This invention is a system that uses generative artificial intelligence to dynamically generate and optimize learning content to improve the user's learning experience. The system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust the learning content based on the user's emotions. Furthermore, by adding visual supplementary information and auditory explanations, the system provides comprehensive learning support.

[1182] server

[1183] 1. Generative artificial intelligence:

[1184] Using this technology, the server dynamically generates personalized learning content based on the user's skill level and learning style, and the learning content is customized according to the user's current learning progress and emotional state.

[1185] 2. Adaptive Learning Path:

[1186] The server stores the user's skill level, learning style, progress information, and emotional state in a database and uses this information to provide an adaptive learning path that is dynamically adjusted in real time.

[1187] 3. Emotion recognition and regulation:

[1188] The server analyzes the user's emotions in real time and dynamically adjusts the learning content based on the detected emotions. For example, if the user is in a "frustration" emotional state, the content will be simplified or an encouraging message will be displayed.

[1189] 4. Visual and auditory support:

[1190] The server provides visual aids and audio explanations according to the user's learning style, thereby enhancing the user's understanding.

[1191] 5. Collaboration with educational institutions:

[1192] The server provides a means for collaborating with educational institutions to integrate the generated learning content into their educational programs.

[1193] Terminal

[1194] 1. View learning content:

[1195] The device displays individually optimized learning content delivered from the server, including visual aids and audio explanations.

[1196] 2. Sending progress and emotional information:

[1197] As the user progresses through the learning process, the device sends progress information and the output of the emotion engine to the server, which updates the user's learning path in real time.

[1198] User

[1199] 1. Access to learning content:

[1200] Users can use learning content provided through their devices to progress through their studies, check their own progress and emotional state, and receive appropriate feedback.

[1201] 2. Example prompt:

[1202] An example of a prompt for a generative AI model is below: "Generative artificial intelligence model for education, please generate appropriate content for learning the basics of Python. User's skill level is 2, learning style is visual, and emotional state is 'happy'."

[1203] This allows users to have a comprehensive and optimized learning experience.

[1204] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1205] Step 1:

[1206] Upon receiving a request from a user, the server retrieves the user's skill level, learning style, progress information, and emotional information from the database. This information becomes input data for the generative AI to generate learning content. The output is JSON-formatted data of the user information.

[1207] Step 2:

[1208] The server generates prompts for the generative AI model based on the user's skill level and learning style. For example, the prompts might be in the form of "The user's skill level is 2 and their learning style is visual. Please generate appropriate content for learning the basics of Python." The prompts serve as input data for the generative AI, and the output is learning content optimized for the user.

[1209] Step 3:

[1210] The server retrieves the generated learning content and sends it to an emotion engine for evaluating the user's emotional information. The emotion engine further adjusts the learning content according to the user's emotional state (e.g., happy, frustrated). The input is the learning content and the emotional state data, and the output is the adjusted learning content based on the emotional state.

[1211] Step 4:

[1212] The server sends the optimized and tailored learning content to the user's device. The device displays the received data, including visual aids and audio instructions. The input is the tailored learning content, and the output is the learning content displayed on the user's device.

[1213] Step 5:

[1214] As the user progresses through the learning process, the device sends progress and emotional information to the server in real time. This information is updated in the database on the server and used to generate the next learning content. The input is the user's progress and emotional information, and the output is the updated database information.

[1215] Step 6:

[1216] The user checks their own progress and emotional state and adjusts their learning path as needed. The input is feedback information on progress and emotional state provided by the server, and the output is the result of the user adjusting their learning path.

[1217] This allows users to have a real-time optimized learning experience.

[1218] 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.

[1219] 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.

[1220] 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.

[1221] [Third embodiment]

[1222] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1223] 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.

[1224] 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).

[1225] 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.

[1226] 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.

[1227] 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).

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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.

[1232] 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.

[1233] 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."

[1234] overview

[1235] This invention utilizes generative artificial intelligence to dynamically generate and optimize learning content to enhance a user's learning experience. The system provides an adaptive learning path that takes into account a user's skill level, learning style, and progress information. It also has the ability to add visual aids and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[1236] System configuration

[1237] server

[1238] The server performs the following main functions:

[1239] 1. Management of User Information

[1240] Store and update a database with user skill levels, learning styles, and progress information.

[1241] Returns data in response to requests to get and update user information.

[1242] 2. Generating learning content

[1243] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[1244] Add custom visual aids and audio descriptions.

[1245] 3. Providing adaptive learning paths

[1246] Dynamically adjusts learning paths based on user progress to provide optimal learning paths.

[1247] 4. Collaboration with educational institutions

[1248] Partner with educational institutions to integrate the generated learning content into their educational programs.

[1249] Terminal

[1250] The terminal performs the following main functions:

[1251] 1. Viewing learning content

[1252] Display individually optimized learning content provided by the server.

[1253] Provide users with visual aids and auditory instructions.

[1254] 2. Sending progress information

[1255] As the user progresses through their studies, they send progress information to the server.

[1256] User

[1257] The user performs the following main functions:

[1258] 1. Use of learning content

[1259] Study using learning content provided through the device.

[1260] Check your progress and adjust your learning path if necessary.

[1261] 2. Updating Information

[1262] Send information to the server to update your skill level and learning style.

[1263] Specific examples

[1264] Retrieving User Information

[1265] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[1266] json

[1267] {

[1268] "skill_level": 1,

[1269] "learning_style": "visual",

[1270] "progress": 0

[1271] }

[1272] Update user information

[1273] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, when user "user1" updates their progress, the following information is returned:

[1274] json

[1275] {

[1276] "skill_level": 1,

[1277] "learning_style": "visual",

[1278] "progress": 1

[1279] }

[1280] Get course information

[1281] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[1282] json

[1283] {

[1284] "course_name": "Basic Python",

[1285] "content": ["print('Hello World')", "variables", "loops"]

[1286] }

[1287] Customize your learning content

[1288] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level and learning style. For example, user "user1" might be served the following customized content:

[1289] json

[1290] {

[1291] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[1292] }

[1293] This allows users to use learning materials optimized for their own learning style and progress effectively with their studies.

[1294] The processing flow will be explained below.

[1295] Retrieving User Information

[1296] Step 1:

[1297] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[1298] Step 2:

[1299] The server receives the request and the Flask route function user(username) is called.

[1300] Step 3:

[1301] The server verifies that the request method is GET.

[1302] Step 4:

[1303] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1304] Step 5:

[1305] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[1306] Update user information

[1307] Step 1:

[1308] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[1309] Step 2:

[1310] The server receives the request and the Flask route function user(username) is called.

[1311] Step 3:

[1312] The server verifies that the request method is PUT.

[1313] Step 4:

[1314] The server retrieves the JSON data from the request body.

[1315] Step 5:

[1316] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1317] Step 6:

[1318] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[1319] Get course information

[1320] Step 1:

[1321] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[1322] Step 2:

[1323] The server receives the request and the Flask route function course(course_id) is called.

[1324] Step 3:

[1325] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[1326] Step 4:

[1327] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[1328] Customize your learning content

[1329] Step 1:

[1330] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[1331] Step 2:

[1332] The server receives the request and the Flask route function customized_content(username) is called.

[1333] Step 3:

[1334] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1335] Step 4:

[1336] If the server finds the user data, it retrieves the skill level and learning style.

[1337] Step 5:

[1338] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[1339] Step 6:

[1340] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[1341] Step 7:

[1342] The server returns customized learning content to the user in JSON format.

[1343] Example 1

[1344] 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."

[1345] Conventional learning systems have struggled to provide an optimized learning experience for each user, and have not adequately provided learning content tailored to individual learning styles and skill levels. Furthermore, few systems manage users' progress information in real time and dynamically provide optimal learning paths. This has led to problems such as reduced learning effectiveness and a loss of user motivation to learn. The purpose of this invention is to solve these problems and improve users' learning experience.

[1346] 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.

[1347] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for providing learning content to the user's device, means for integrating the generated learning content into an educational program in collaboration with an educational institution, means for acquiring user information and returning it in JSON format, means for updating the user information and updating the database with the new user information, and means for customizing and providing learning content based on the user's skill level and learning style. This makes it possible to provide learning content and a learning path optimized for each user, improving the individual learning experience.

[1348] "Generative AI" is an AI technology that learns from huge datasets and generates text in natural language like a human.

[1349] "Individually optimized learning content" means learning materials that are individually tailored to each user based on their skill level and learning style.

[1350] An "adaptive learning path" is a learning path that dynamically adjusts based on the user's progress.

[1351] "Progress information" is data that indicates how far the user has progressed in their studies.

[1352] A "database" is a digital system that organizes and stores data so that it can be searched and updated.

[1353] "Terminal" refers to a device (computer, smartphone, etc.) used by a user to use the service.

[1354] An "educational program" is a curriculum of courses or courses offered by an educational institution.

[1355] "User Information" is data including a user's personal identification information, skill level, learning style, progress, etc.

[1356] "JSON format" stands for JavaScript Object Notation and is a lightweight text-based format for exchanging data.

[1357] "Customized content" is learning material that is generated or tailored to a user's specific needs.

[1358] "Visual aids" are visual learning aids such as slides, charts, and videos.

[1359] "Auditory description" means an audio commentary or explanation.

[1360] MODE FOR CARRYING OUT THE INVENTION

[1361] This invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve users' learning experiences. This system consists of three main components: a server, a terminal, and a user.

[1362] Server Roles

[1363] The server performs the following main functions:

[1364] 1. Management of User Information

[1365] The server uses a database management system (DBMS) to store and update the user-provided information on skill level, learning style, and progress as needed. Specifically, a SQL database or a NoSQL database may be used.

[1366] 2. Generating learning content

[1367] The server uses a generative artificial intelligence (generative AI model) to generate personalized learning content based on the user's skill level and learning style. For example, it can use OpenAI's GPT-3 model. In this case, the server sends a prompt to the generative AI model as follows:

[1368] - "The user has a beginner skill level and prefers a visual learning style. Please generate an introductory Python course that is appropriate for this user."

[1369] 3. Providing adaptive learning paths

[1370] The server dynamically adjusts the learning path based on the user's progress, for example by implementing an algorithm that automatically recommends the next chapter once the user has completed a chapter.

[1371] 4. Collaboration with educational institutions

[1372] The server provides an API for integrating the generated learning content into the educational program of the educational institution. Specifically, the learning content can be incorporated into the educational program using a RESTful API.

[1373] Device Role

[1374] The terminal mainly performs the following functions:

[1375] 1. Viewing learning content

[1376] The device displays personalized learning content provided by the server to the user, using mobile and web applications and providing visual aids and audio explanations.

[1377] 2. Sending progress information

[1378] As the user progresses, the device sends real-time progress information to the server. For example, when the user completes a particular chapter, the device marks it as "completed."

[1379] User Roles

[1380] The user mainly performs the following functions:

[1381] 1. Use of learning content

[1382] Users can learn by accessing learning content provided through their devices, for example by watching videos and answering interactive quizzes.

[1383] 2. Updating Information

[1384] Users send information to the server to update their progress, skill level, learning style, etc. For example, if a user wants to try a new learning method, they send that information to the server and update their account.

[1385] As described above, the server, terminal, and user each play their respective roles and work together to provide the user with the optimal learning experience. To specifically realize this system, technologies such as generative artificial intelligence, database management systems, and APIs are combined.

[1386] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1387] Program processing flow

[1388] Server Processing

[1389] Step 1:

[1390] The server receives a registration or login request from a user. As input, it receives the user's identifying information (username, password, etc.). It queries the database to retrieve the user's information or registers a new user. As output, it returns user information including the user's skill level, learning style, and progress information.

[1391] Step 2:

[1392] When a user makes a learning request, the server sends a prompt to a generative AI model (e.g., GPT-3). As input, it receives the user's skill level and learning style and generates a prompt based on this. The generative AI model generates individually optimized learning content based on this prompt. As output, the generated learning content is returned.

[1393] Step 3:

[1394] The server adds visual aids and auditory explanations to the generated learning content. As input, it receives the text data returned by the generative AI model. It then adds visual aids (e.g., slides, diagrams) and auditory explanations (e.g., audio guides). As output, it obtains the complete learning content, including both visual and auditory elements.

[1395] Step 4:

[1396] The server stores the user's progress in a database and dynamically adjusts the learning path. As input, it receives the user's progress information in real time. Based on this, it updates the database and recommends the next learning content. As output, it obtains information that indicates the next learning step.

[1397] Step 5:

[1398] The server integrates the generated learning content into the educational program of the educational institution. It receives the generated learning content and the educational institution's request as input. It integrates the content into the educational institution's system using RESTful APIs. As output, it obtains the learning content integrated into the educational institution's program.

[1399] Terminal handling

[1400] Step 6:

[1401] The terminal receives the learning content provided by the server and displays it to the user. As input, it receives the learning content from the server. It plays back the multimedia content including the visual support information and the audio explanation. As output, it obtains the learning content displayed to the user.

[1402] Step 7:

[1403] As the user progresses with their studies, the device sends their progress information to the server in real time. As input, it receives the user's learning actions (e.g., chapter completion, question answering), converts them into data packets to send to the server, and as output, sends the user's progress information to the server.

[1404] User Action

[1405] Step 8:

[1406] Users learn using learning content provided through their devices. As input, they consume learning content displayed on their devices and perform learning activities (e.g., watching videos or taking interactive quizzes). As output, progress information is generated.

[1407] Step 9:

[1408] The user sends information to the server to update their skill level and learning style. As input, it receives the new skill level and learning style information. It converts this into a data packet to send to the server. As output, it sends the updated user information to the server.

[1409] (Application example 1)

[1410] 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."

[1411] Conventional learning systems have difficulty adapting to the different skill levels and learning styles of each user, and are unable to dynamically provide individually optimized learning content. This means that users' learning effectiveness is not maximized, and it is difficult to manage progress or provide customized learning paths. In particular, the fact that learning content is static and cannot be adapted in real time to reflect progress is a major problem.

[1412] 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.

[1413] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for displaying the learning content on the user's device, and means for providing learning content customized by generative artificial intelligence, thereby enabling the dynamic provision of individually optimized learning content.

[1414] "Generative AI" is an AI technology that dynamically generates optimized learning content based on a user's skill level and learning style.

[1415] "Individually optimized learning content" means learning materials that are customized to a user's specific skill level and learning style.

[1416] "Adaptive learning paths" are a part of the system that provides optimal learning paths based on a user's progress and learning style.

[1417] "Progress information" is data that indicates the progress and history of a user's use of learning content.

[1418] A "database" is a system for storing and managing data such as a user's skill level, learning style, and progress information.

[1419] "Device" refers to a device (e.g., smartphone, tablet PC, desktop PC, etc.) that a user uses to display and use learning content.

[1420] "Visual aids" are visual information such as illustrations and videos that are provided to make learning content easier to understand.

[1421] "Auditory explanation" refers to auditory information such as audio guides or commentary audio that is provided to complement learning content.

[1422] An "educational program" is a systematic learning curriculum offered by an educational institution.

[1423] The present invention is a system that utilizes generative artificial intelligence to individually optimize a user's learning experience. Specific embodiments of the system are described below.

[1424] Server Features

[1425] The server performs the following main functions:

[1426] 1. Management of User Information

[1427] The server stores and updates the user's skill level, learning style, and progress information in a database. When a user uses the system, this information is retrieved from the database and updated as necessary.

[1428] 2. Generating learning content

[1429] The server uses a generative artificial intelligence (hereinafter referred to as a generative AI model) to dynamically generate individually optimized learning content based on the user's skill level and learning style. This generative AI model automatically generates optimized learning materials based on past learning data and general educational content.

[1430] 3. Providing adaptive learning paths

[1431] Based on the user's progress, the server provides an adaptive learning path that is structured to best suit the user's current skill level and learning style, maximizing the user's learning efficiency.

[1432] 4. Collaboration with educational institutions

[1433] The server works with educational institutions to integrate the generated learning content into their existing educational programs, improving student learning by tailoring the content to fit the institution's curriculum.

[1434] Functions of the device used

[1435] Devices (e.g. smartphones, tablet PCs, desktop PCs) perform the following main functions:

[1436] 1. Viewing learning content

[1437] Individually optimized learning content provided by the server is displayed on the device, including visual aids and audio explanations.

[1438] 2. Sending progress information

[1439] As users progress through their studies, they send progress information to the server, which then tracks their progress in real time and adjusts their learning path as needed.

[1440] User Actions

[1441] Users interact with the system as follows:

[1442] 1. Use of learning content

[1443] Learn using learning content delivered through the device, and users can track their progress and adjust their learning path as needed.

[1444] 2. Updating Information

[1445] Users can update their skill level and learning style by sending information to the server, which optimizes the generated learning content in real time.

[1446] Hardware and software used

[1447] Server side: Python, Flask, SQLAlchemy

[1448] Client side: Python, Requests

[1449] Database: SQLite

[1450] Generative AI model: Custom module (e.g., generation_ai_model)

[1451] Specific examples

[1452] Get user information:

[1453] User "user1" accesses the system and retrieves the following information from the server:

[1454] json

[1455] {

[1456] "skill_level": 1,

[1457] "learning_style": "visual",

[1458] "progress": 0

[1459] }

[1460] Update your user information:

[1461] User "user1" updates his progress and sends the following information to the server:

[1462] json

[1463] {

[1464] "skill_level": 1,

[1465] "learning_style": "visual",

[1466] "progress": 1

[1467] }

[1468] Request customized learning content:

[1469] When user "user1" requests customized learning content, the server returns the following content:

[1470] Print Statement Basics

[1471] How to use variables

[1472] How to use loops

[1473] Visual aids for loops

[1474] In this way, users can learn effectively through dynamically generated optimized content.

[1475] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1476] Step 1:

[1477] A user logs in to the system from a terminal. The terminal prompts for a username and password on a login page, and once the user enters this information, it sends a login request to the server. The server checks the database to see if there is a user that matches the entered information. If there is a match, it generates an authentication token and returns it to the user.

[1478] Input: Username, Password

[1479] Output: Authentication token

[1480] Step 2:

[1481] After logging in, a user sends a request from their device to obtain their learning skill level, learning style, and progress information. The server retrieves the corresponding user information from the database and returns it to the device in JSON format. The user can then check their current status based on this information.

[1482] Input: Authentication Token

[1483] Output: User's skill level, learning style, and progress information

[1484] Step 3:

[1485] To advance their learning, the user requests customized learning content. The device sends the request along with an authentication token, and the server uses a generative AI model to dynamically generate learning content optimized for the user. The generated content is customized based on the user's skill level and learning style and sent to the device.

[1486] Inputs: Authentication token, user skill level, learning style

[1487] Output: Customized learning content

[1488] Step 4:

[1489] The terminal displays the learning content provided by the server, and the user begins learning. The content includes visual aids such as text, illustrations, and audio guides, as well as auditory explanations. The user uses the displayed content to progress through the learning process.

[1490] Input: Customized learning content

[1491] Output: Providing a learning experience (including visual aids and audio explanations)

[1492] Step 5:

[1493] As the user progresses through the learning process, the device sends real-time progress information to the server, including learning achievement, content completed, time, etc. The server stores this progress information in a database and dynamically adjusts the learning path as needed.

[1494] Input: Progress information

[1495] Output: Updated learning path

[1496] Step 6:

[1497] When the user finishes their study, the device sends a request to the server to end the study session. The server saves the final progress information in a database and ends the study session. This information is saved for use the next time the user starts studying, and is used when the user starts studying again.

[1498] Input: Request to end study session

[1499] Output: Final saved progress information

[1500] In this way, users can effectively progress through individually optimized learning content.

[1501] 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.

[1502] overview

[1503] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust learning content based on the user's emotions. The system provides an optimal adaptive learning path by taking into account the user's skill level, learning style, progress information, and emotional state. The system also has the ability to add visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[1504] System configuration

[1505] server

[1506] The server performs the following main functions:

[1507] 1. Management of User Information

[1508] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database.

[1509] Returns data in response to requests to get and update user information.

[1510] 2. Generating learning content

[1511] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[1512] Add custom visual aids and audio descriptions.

[1513] It uses an emotion engine to assess the user's emotional state and dynamically adjust learning.

[1514] 3. Providing adaptive learning paths

[1515] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[1516] 4. Collaboration with educational institutions

[1517] Partner with educational institutions to integrate the generated learning content into their educational programs.

[1518] Terminal

[1519] The terminal performs the following main functions:

[1520] 1. Viewing learning content

[1521] Display individually optimized learning content provided by the server.

[1522] Provide users with visual aids and auditory instructions.

[1523] 2. Sending progress and emotional information

[1524] As the user progresses through their learning, they send progress information and the output of the emotion engine to the server.

[1525] User

[1526] The user performs the following main functions:

[1527] 1. Use of learning content

[1528] Study using learning content provided through the device.

[1529] Check in on your progress and emotional state and adjust your learning path as needed.

[1530] 2. Updating Information

[1531] Send information to the server to update your skill level, learning style, and emotional state.

[1532] Specific examples

[1533] Retrieving User Information

[1534] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[1535] json

[1536] {

[1537] "skill_level": 1,

[1538] "learning_style": "visual",

[1539] "progress": 0,

[1540] "emotional_state": "neutral"

[1541] }

[1542] Update user information

[1543] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, if user "user1" updates his progress and emotional state, the following information will be returned:

[1544] json

[1545] {

[1546] "skill_level": 1,

[1547] "learning_style": "visual",

[1548] "progress": 1,

[1549] "emotional_state": "happy"

[1550] }

[1551] Get course information

[1552] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[1553] json

[1554] {

[1555] "course_name": "Basic Python",

[1556] "content": ["print('Hello World')", "variables", "loops"]

[1557] }

[1558] Customize your learning content

[1559] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level, learning style, and emotional state. For example, the following customized content is served to user "user1":

[1560] json

[1561] {

[1562] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[1563] }

[1564] Regulation of learning content by emotional state

[1565] As the user progresses with their learning, the emotion engine evaluates the user's emotional state in real time and sends the results to the server. For example, if the emotion engine detects frustration while the user is learning, the server will adjust the content by simplifying it or displaying encouraging messages.

[1566] json

[1567] {

[1568] "customized_content": ["Take a short break and return later.", "Remember to stay positive!"]

[1569] }

[1570] This allows users to have an optimal learning experience based on their emotional state.

[1571] The processing flow will be explained below.

[1572] Retrieving User Information

[1573] Step 1:

[1574] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[1575] Step 2:

[1576] The server receives the request and the Flask route function user(username) is called.

[1577] Step 3:

[1578] The server verifies that the request method is GET.

[1579] Step 4:

[1580] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1581] Step 5:

[1582] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[1583] Update user information

[1584] Step 1:

[1585] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[1586] Step 2:

[1587] The server receives the request and the Flask route function user(username) is called.

[1588] Step 3:

[1589] The server verifies that the request method is PUT.

[1590] Step 4:

[1591] The server retrieves the JSON data from the request body.

[1592] Step 5:

[1593] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1594] Step 6:

[1595] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[1596] Get course information

[1597] Step 1:

[1598] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[1599] Step 2:

[1600] The server receives the request and the Flask route function course(course_id) is called.

[1601] Step 3:

[1602] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[1603] Step 4:

[1604] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[1605] Customize your learning content

[1606] Step 1:

[1607] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[1608] Step 2:

[1609] The server receives the request and the Flask route function customized_content(username) is called.

[1610] Step 3:

[1611] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1612] Step 4:

[1613] If the server finds the user data, it retrieves the skill level and learning style.

[1614] Step 5:

[1615] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[1616] Step 6:

[1617] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[1618] Step 7:

[1619] The server returns customized learning content to the user in JSON format.

[1620] Regulation of learning content by emotional state

[1621] Step 1:

[1622] As users engage with learning content, the emotion engine assesses their emotional state in real time.

[1623] Step 2:

[1624] The terminal transmits the output results of the emotion engine to the server.

[1625] Step 3:

[1626] The server analyzes the received emotional state information and determines whether the learning content needs to be adjusted based on the user's emotional state.

[1627] Step 4:

[1628] If the server determines that the emotional state is negative, such as "frustration," it will adjust the learning content to be simpler or display encouraging messages.

[1629] Step 5:

[1630] The server transmits the tailored learning content or message to the terminal.

[1631] Step 6:

[1632] The terminal displays the tailored learning content or message to the user.

[1633] Specific examples

[1634] Regulation of learning content by emotional state

[1635] If it can be explained as a diagram:

[1636] Step 1:

[1637] The user is learning basic Python syntax and is tackling difficult topics.

[1638] Step 2:

[1639] Using the device's camera and sensors, the emotion engine analyzes the user's facial expressions and biometric information to detect when the user is feeling frustrated.

[1640] Step 3:

[1641] The terminal transmits this emotion information to the server.

[1642] Step 4:

[1643] The server analyzes the information it receives and determines that the user is frustrated and that the learning needs to be adjusted.

[1644] Step 5:

[1645] The server generates a message encouraging the user to take a short break to relax.

[1646] Step 6:

[1647] The server transmits the adjusted learning content and the recommendation message to the terminal.

[1648] Step 7:

[1649] The device will display a message to the user such as "Take a short break and refresh yourself."

[1650] In this way, the learning content and pace can be flexibly adjusted according to the user's emotional state, providing an optimal learning environment.

[1651] Example 2

[1652] 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."

[1653] Conventional learning systems struggled to provide learning content that adequately reflected a user's individual skill level and learning style, resulting in insufficient adjustments to improve learning efficiency. Furthermore, they were unable to provide adaptive learning paths based on the user's emotional state, making it difficult to maintain motivation. Furthermore, there was little collaboration with educational institutions, making it difficult to integrate individually optimized learning content into educational programs.

[1654] 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.

[1655] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for evaluating the user's emotional state in real time and dynamically adjusting the learning content, and means for providing the learning content to the user's device, thereby providing an individually optimized learning experience for each user, enabling efficient adjustment of the learning content and maintaining motivation.

[1656] "Generative AI" is an AI technology that dynamically generates content based on user information.

[1657] "Individually optimized learning content" refers to learning materials that are customized based on each user's skill level and learning style.

[1658] An "adaptive learning path" is a learning path that is individually optimized using the user's progress and emotional information.

[1659] "Progress information" is data about the user's learning progress and achievement level that is recorded as the user progresses with their learning.

[1660] "Emotion information" is data that indicates the user's emotional state and is obtained by the emotion engine.

[1661] An "emotion engine" is a system that assesses a user's emotional state and adjusts learning content based on that.

[1662] "Learning style" is information that indicates a user's preferences for the most effective ways and means of learning.

[1663] "Visual aids" are visual materials such as charts, graphs, and images that are provided to support learning.

[1664] "Auditory explanation" refers to the explanation of learning content using audio or narration.

[1665] An "educational program" is a set of courses or curriculum offered by an educational institution.

[1666] "Real-time" refers to a state in which processing is carried out immediately on the spot without delay.

[1667] A "database" is a system that efficiently stores, searches, and manages large amounts of information.

[1668] MODE FOR CARRYING OUT THE INVENTION

[1669] overview

[1670] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. The system takes into account a user's skill level, learning style, progress information, and emotional state to provide an optimal adaptive learning path. The system also has an emotion engine that recognizes the user's emotions and adjusts the learning content based on the user's emotions. Furthermore, the system has the ability to provide visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[1671] System configuration

[1672] The system consists of three elements: the server, the terminal, and the user.

[1673] server

[1674] The server performs the following main functions:

[1675] 1. Management of User Information

[1676] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database. The server receives requests sent by the user from a web browser or API client and accesses the database to retrieve or update information.

[1677] For example, if a user requests "GET / user / <username>When you send a request like "PUT / user / ", the server retrieves the user information from the database and returns it in JSON format. <username>” If a request is submitted, update the database with the new information.

[1678] 2. Generating learning content

[1679] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[1680] For example, if a user GET / customized-content / <username>When you send a request like ", the server sends a prompt to the generative AI model to generate the optimal learning content.

[1681] Example prompt: "User is skill level 1 and has a visual learning style. Generate some basic Python programming content."

[1682] 3. Addition of visual and auditory support information

[1683] Dynamically add visual supplementary information and audio explanations to the generated learning content.

[1684] For example, the generated program code is provided to the user with associated diagrams and audio explanations.

[1685] 4. Emotional Engine Adjustment

[1686] Evaluates the user's emotional state and dynamically adjusts learning.

[1687] For example, if the emotion engine detects "dissatisfaction" during learning, the server can simplify the content or display an encouraging message.

[1688] 5. Providing adaptive learning paths

[1689] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[1690] 6. Collaboration with educational institutions

[1691] Partner with educational institutions to integrate the generated learning content into their educational programs.

[1692] Terminal

[1693] The terminal performs the following main functions:

[1694] 1. Viewing learning content

[1695] Display individually optimized learning content provided by the server.

[1696] For example, a user may view learning content through a device, such as a web browser or mobile application.

[1697] 2. Providing supporting information

[1698] Providing users with visual aids or auditory instructions, for example, displaying or playing images or audio files related to the learning content.

[1699] 3. Sending progress and emotional information

[1700] As the user progresses through their learning, progress information and the output of the emotion engine are sent to the server in real time.

[1701] User

[1702] The user performs the following main functions:

[1703] 1. Use of learning content

[1704] Students advance their studies using learning content provided through their devices.

[1705] For example, a user solves a programming problem and checks the results.

[1706] 2. Check in on your progress and feelings

[1707] Check in on your progress and emotional state and adjust your learning path as needed.

[1708] 3. Updating Information

[1709] Send information to the server to update your skill level, learning style, and emotional state.

[1710] As a result, this system can provide an optimal learning experience tailored to the individual needs of each user, improving learning efficiency and motivation.

[1711] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1712] Step 1:

[1713] The server receives a request to obtain user information.

[1714] Input: A user enters GET / user / from a web browser or API client. <username>" request.

[1715] Specific operation: The server receives the request and accesses the database to retrieve the user's information.

[1716] Output: Returns the retrieved user information in JSON format.

[1717] Step 2:

[1718] The server receives a request to update user information.

[1719] Input: User enters PUT / user / <username>" request and provide the new user information in JSON format.

[1720] Specific operation: The server receives the request and updates the corresponding user's information in the database.

[1721] Output: Returns a response confirming the updated user information.

[1722] Step 3:

[1723] The server receives a learning content generation request.

[1724] Input: When the user types "GET / customized-content / <username>" request.

[1725] How it works: The server receives the request and sends a prompt to the generative AI model based on the user's skill level and learning style. An example of a prompt is: "User's skill level is 1, and their learning style is visual. Please generate some basic Python programming content."

[1726] Output: Get the generated learning content.

[1727] Step 4:

[1728] The server adds visual and audio aid information.

[1729] Input: Generated learning content.

[1730] Specific operation: The server adds relevant visual support information (e.g., graphs and charts) and auditory explanations (e.g., audio files and narration) to the learning content.

[1731] Output: Learning content with additional information added.

[1732] Step 5:

[1733] The server uses an emotion engine to adjust the learning content.

[1734] Input: The user's emotional state (e.g., if the emotion engine detects "unhappy").

[1735] Specific operation: The server receives the evaluation results of the emotion engine and makes adjustments such as simplifying the learning content or adding encouraging messages.

[1736] Output: Tailored learning content.

[1737] Step 6:

[1738] The server provides an adaptive learning path.

[1739] Input: User progress and emotion information.

[1740] Specific operation: The server dynamically adjusts the optimal learning path based on this information.

[1741] Output: The adjusted learning path.

[1742] Step 7:

[1743] The server integrates the content into the educational program.

[1744] Input: Generated learning content.

[1745] Specific operation: The server collaborates with educational institutions and integrates the generated learning content into educational programs.

[1746] Output: Content integrated into an educational program.

[1747] Step 8:

[1748] The device displays individually optimized learning content.

[1749] Input: Learning content provided by the server.

[1750] What it does: The device renders learning content in a web browser or application.

[1751] Output: The learning content displayed to the user.

[1752] Step 9:

[1753] The device provides visual aids and audio instructions.

[1754] Input: Learning content with additional supporting information.

[1755] Specific action: The device displays or plays relevant visual aids (e.g., images, videos) or auditory instructions (e.g., audio files, narration) to the user.

[1756] Output: The visual aids and / or auditory instructions provided to the user.

[1757] Step 10:

[1758] The terminal transmits the progress information and emotion information to the server.

[1759] Input: Progress information as the user progresses through the learning process and the output of the emotion engine.

[1760] Specific operation: The device sends this information to the server in real time.

[1761] Output: Progress and emotion information sent to the server.

[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 headset type terminal 314 will be referred to as a "terminal."

[1764] Conventional learning systems have the problem of insufficient individual optimization based on the user's skill level and learning style, resulting in reduced learning effectiveness. Furthermore, they do not adjust the learning content to take the user's emotional state into consideration, which often leads to a loss of motivation to learn and stress. Furthermore, a lack of visual and auditory supplementary information also poses the issue of reduced learning efficiency.

[1765] The identification processing by the identification 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 dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for providing learning content to the user's device, and means for analyzing the user's emotions in real time and dynamically adjusting the learning content. This makes it possible to provide content optimized for the user's skill level and learning style, and to adjust the learning content according to the user's emotional state.

[1766] "Generative AI" refers to AI that dynamically generates content based on user data.

[1767] "Optimized learning content" refers to educational materials that are tailored to a user's skill level and learning style.

[1768] An "adaptive learning path" refers to a learning path that dynamically changes depending on the user's progress and emotional state.

[1769] "User Progress Information" means information that indicates how far a user has progressed in their learning process.

[1770] "Emotional information" refers to data that assesses a user's emotional state in real time.

[1771] A "database" refers to a system for systematically managing, storing, and searching various types of information.

[1772] "Device" refers to the device used by a User to receive the Learning Content.

[1773] "Means for analyzing emotions in real time" refers to technologies and functions for instantly analyzing a user's emotional state.

[1774] "Visual aids" refers to visual elements (diagrams, illustrations, graphs, etc.) that are provided to make learning content easier to understand.

[1775] "Auditory explanation" refers to information that explains the learning content to the user using audio.

[1776] "Collaboration with educational institutions" refers to activities to collaborate with schools and professional institutions to integrate learning content into educational programs.

[1777] System Overview

[1778] This invention is a system that uses generative artificial intelligence to dynamically generate and optimize learning content to improve the user's learning experience. The system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust the learning content based on the user's emotions. Furthermore, by adding visual supplementary information and auditory explanations, the system provides comprehensive learning support.

[1779] server

[1780] 1. Generative artificial intelligence:

[1781] Using this technology, the server dynamically generates personalized learning content based on the user's skill level and learning style, and the learning content is customized according to the user's current learning progress and emotional state.

[1782] 2. Adaptive Learning Path:

[1783] The server stores the user's skill level, learning style, progress information, and emotional state in a database and uses this information to provide an adaptive learning path that is dynamically adjusted in real time.

[1784] 3. Emotion recognition and regulation:

[1785] The server analyzes the user's emotions in real time and dynamically adjusts the learning content based on the detected emotions. For example, if the user is in a "frustration" emotional state, the content will be simplified or an encouraging message will be displayed.

[1786] 4. Visual and auditory support:

[1787] The server provides visual aids and audio explanations according to the user's learning style, thereby enhancing the user's understanding.

[1788] 5. Collaboration with educational institutions:

[1789] The server provides a means for collaborating with educational institutions to integrate the generated learning content into their educational programs.

[1790] Terminal

[1791] 1. View learning content:

[1792] The device displays individually optimized learning content delivered from the server, including visual aids and audio explanations.

[1793] 2. Sending progress and emotional information:

[1794] As the user progresses through the learning process, the device sends progress information and the output of the emotion engine to the server, which updates the user's learning path in real time.

[1795] User

[1796] 1. Access to learning content:

[1797] Users can use learning content provided through their devices to progress through their studies, check their own progress and emotional state, and receive appropriate feedback.

[1798] 2. Example prompt:

[1799] An example of a prompt for a generative AI model is below: "Generative artificial intelligence model for education, please generate appropriate content for learning the basics of Python. User's skill level is 2, learning style is visual, and emotional state is 'happy'."

[1800] This allows users to have a comprehensive and optimized learning experience.

[1801] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1802] Step 1:

[1803] Upon receiving a request from a user, the server retrieves the user's skill level, learning style, progress information, and emotional information from the database. This information becomes input data for the generative AI to generate learning content. The output is JSON-formatted data of the user information.

[1804] Step 2:

[1805] The server generates prompts for the generative AI model based on the user's skill level and learning style. For example, the prompts might be in the form of "The user's skill level is 2 and their learning style is visual. Please generate appropriate content for learning the basics of Python." The prompts serve as input data for the generative AI, and the output is learning content optimized for the user.

[1806] Step 3:

[1807] The server retrieves the generated learning content and sends it to an emotion engine for evaluating the user's emotional information. The emotion engine further adjusts the learning content according to the user's emotional state (e.g., happy, frustrated). The input is the learning content and the emotional state data, and the output is the adjusted learning content based on the emotional state.

[1808] Step 4:

[1809] The server sends the optimized and tailored learning content to the user's device. The device displays the received data, including visual aids and audio instructions. The input is the tailored learning content, and the output is the learning content displayed on the user's device.

[1810] Step 5:

[1811] As the user progresses through the learning process, the device sends progress and emotional information to the server in real time. This information is updated in the database on the server and used to generate the next learning content. The input is the user's progress and emotional information, and the output is the updated database information.

[1812] Step 6:

[1813] The user checks their own progress and emotional state and adjusts their learning path as needed. The input is feedback information on progress and emotional state provided by the server, and the output is the result of the user adjusting their learning path.

[1814] This allows users to have a real-time optimized learning experience.

[1815] 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.

[1816] 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.

[1817] 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.

[1818] [Fourth embodiment]

[1819] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1820] 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.

[1821] 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).

[1822] 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.

[1823] 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.

[1824] 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).

[1825] 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.

[1826] 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.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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."

[1832] overview

[1833] This invention utilizes generative artificial intelligence to dynamically generate and optimize learning content to enhance a user's learning experience. The system provides an adaptive learning path that takes into account a user's skill level, learning style, and progress information. It also has the ability to add visual aids and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[1834] System configuration

[1835] server

[1836] The server performs the following main functions:

[1837] 1. Management of User Information

[1838] Store and update a database with user skill levels, learning styles, and progress information.

[1839] Returns data in response to requests to get and update user information.

[1840] 2. Generating learning content

[1841] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[1842] Add custom visual aids and audio descriptions.

[1843] 3. Providing adaptive learning paths

[1844] Dynamically adjusts learning paths based on user progress to provide optimal learning paths.

[1845] 4. Collaboration with educational institutions

[1846] Partner with educational institutions to integrate the generated learning content into their educational programs.

[1847] Terminal

[1848] The terminal performs the following main functions:

[1849] 1. Viewing learning content

[1850] Display individually optimized learning content provided by the server.

[1851] Provide users with visual aids and auditory instructions.

[1852] 2. Sending progress information

[1853] As the user progresses through their studies, they send progress information to the server.

[1854] User

[1855] The user performs the following main functions:

[1856] 1. Use of learning content

[1857] Study using learning content provided through the device.

[1858] Check your progress and adjust your learning path if necessary.

[1859] 2. Updating Information

[1860] Send information to the server to update your skill level and learning style.

[1861] Specific examples

[1862] Retrieving User Information

[1863] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[1864] json

[1865] {

[1866] "skill_level": 1,

[1867] "learning_style": "visual",

[1868] "progress": 0

[1869] }

[1870] Update user information

[1871] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, when user "user1" updates their progress, the following information is returned:

[1872] json

[1873] {

[1874] "skill_level": 1,

[1875] "learning_style": "visual",

[1876] "progress": 1

[1877] }

[1878] Get course information

[1879] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[1880] json

[1881] {

[1882] "course_name": "Basic Python",

[1883] "content": ["print('Hello World')", "variables", "loops"]

[1884] }

[1885] Customize your learning content

[1886] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level and learning style. For example, user "user1" might be served the following customized content:

[1887] json

[1888] {

[1889] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[1890] }

[1891] This allows users to use learning materials optimized for their own learning style and progress effectively with their studies.

[1892] The processing flow will be explained below.

[1893] Retrieving User Information

[1894] Step 1:

[1895] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[1896] Step 2:

[1897] The server receives the request and the Flask route function user(username) is called.

[1898] Step 3:

[1899] The server verifies that the request method is GET.

[1900] Step 4:

[1901] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1902] Step 5:

[1903] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[1904] Update user information

[1905] Step 1:

[1906] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[1907] Step 2:

[1908] The server receives the request and the Flask route function user(username) is called.

[1909] Step 3:

[1910] The server verifies that the request method is PUT.

[1911] Step 4:

[1912] The server retrieves the JSON data from the request body.

[1913] Step 5:

[1914] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1915] Step 6:

[1916] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[1917] Get course information

[1918] Step 1:

[1919] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[1920] Step 2:

[1921] The server receives the request and the Flask route function course(course_id) is called.

[1922] Step 3:

[1923] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[1924] Step 4:

[1925] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[1926] Customize your learning content

[1927] Step 1:

[1928] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[1929] Step 2:

[1930] The server receives the request and the Flask route function customized_content(username) is called.

[1931] Step 3:

[1932] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[1933] Step 4:

[1934] If the server finds the user data, it retrieves the skill level and learning style.

[1935] Step 5:

[1936] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[1937] Step 6:

[1938] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[1939] Step 7:

[1940] The server returns customized learning content to the user in JSON format.

[1941] Example 1

[1942] 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."

[1943] Conventional learning systems have struggled to provide an optimized learning experience for each user, and have not adequately provided learning content tailored to individual learning styles and skill levels. Furthermore, few systems manage users' progress information in real time and dynamically provide optimal learning paths. This has led to problems such as reduced learning effectiveness and a loss of user motivation to learn. The purpose of this invention is to solve these problems and improve users' learning experience.

[1944] 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.

[1945] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for providing learning content to the user's device, means for integrating the generated learning content into an educational program in collaboration with an educational institution, means for acquiring user information and returning it in JSON format, means for updating the user information and updating the database with the new user information, and means for customizing and providing learning content based on the user's skill level and learning style. This makes it possible to provide learning content and a learning path optimized for each user, improving the individual learning experience.

[1946] "Generative AI" is an AI technology that learns from huge datasets and generates text in natural language like a human.

[1947] "Individually optimized learning content" means learning materials that are individually tailored to each user based on their skill level and learning style.

[1948] An "adaptive learning path" is a learning path that dynamically adjusts based on the user's progress.

[1949] "Progress information" is data that indicates how far the user has progressed in their studies.

[1950] A "database" is a digital system that organizes and stores data so that it can be searched and updated.

[1951] "Terminal" refers to a device (computer, smartphone, etc.) used by a user to use the service.

[1952] An "educational program" is a curriculum of courses or courses offered by an educational institution.

[1953] "User Information" is data including a user's personal identification information, skill level, learning style, progress, etc.

[1954] "JSON format" stands for JavaScript Object Notation and is a lightweight text-based format for exchanging data.

[1955] "Customized content" is learning material that is generated or tailored to a user's specific needs.

[1956] "Visual aids" are visual learning aids such as slides, charts, and videos.

[1957] "Auditory description" means an audio commentary or explanation.

[1958] MODE FOR CARRYING OUT THE INVENTION

[1959] This invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve users' learning experiences. This system consists of three main components: a server, a terminal, and a user.

[1960] Server Roles

[1961] The server performs the following main functions:

[1962] 1. Management of User Information

[1963] The server uses a database management system (DBMS) to store and update the user-provided information on skill level, learning style, and progress as needed. Specifically, a SQL database or a NoSQL database may be used.

[1964] 2. Generating learning content

[1965] The server uses a generative artificial intelligence (generative AI model) to generate personalized learning content based on the user's skill level and learning style. For example, it can use OpenAI's GPT-3 model. In this case, the server sends a prompt to the generative AI model as follows:

[1966] - "The user has a beginner skill level and prefers a visual learning style. Please generate an introductory Python course that is appropriate for this user."

[1967] 3. Providing adaptive learning paths

[1968] The server dynamically adjusts the learning path based on the user's progress, for example by implementing an algorithm that automatically recommends the next chapter once the user has completed a chapter.

[1969] 4. Collaboration with educational institutions

[1970] The server provides an API for integrating the generated learning content into the educational program of the educational institution. Specifically, the learning content can be incorporated into the educational program using a RESTful API.

[1971] Device Role

[1972] The terminal mainly performs the following functions:

[1973] 1. Viewing learning content

[1974] The device displays personalized learning content provided by the server to the user, using mobile and web applications and providing visual aids and audio explanations.

[1975] 2. Sending progress information

[1976] As the user progresses, the device sends real-time progress information to the server. For example, when the user completes a particular chapter, the device marks it as "completed."

[1977] User Roles

[1978] The user mainly performs the following functions:

[1979] 1. Use of learning content

[1980] Users can learn by accessing learning content provided through their devices, for example by watching videos and answering interactive quizzes.

[1981] 2. Updating Information

[1982] Users send information to the server to update their progress, skill level, learning style, etc. For example, if a user wants to try a new learning method, they send that information to the server and update their account.

[1983] As described above, the server, terminal, and user each play their respective roles and work together to provide the user with the optimal learning experience. To specifically realize this system, technologies such as generative artificial intelligence, database management systems, and APIs are combined.

[1984] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1985] Program processing flow

[1986] Server Processing

[1987] Step 1:

[1988] The server receives a registration or login request from a user. As input, it receives the user's identifying information (username, password, etc.). It queries the database to retrieve the user's information or registers a new user. As output, it returns user information including the user's skill level, learning style, and progress information.

[1989] Step 2:

[1990] When a user makes a learning request, the server sends a prompt to a generative AI model (e.g., GPT-3). As input, it receives the user's skill level and learning style and generates a prompt based on this. The generative AI model generates individually optimized learning content based on this prompt. As output, the generated learning content is returned.

[1991] Step 3:

[1992] The server adds visual aids and auditory explanations to the generated learning content. As input, it receives the text data returned by the generative AI model. It then adds visual aids (e.g., slides, diagrams) and auditory explanations (e.g., audio guides). As output, it obtains the complete learning content, including both visual and auditory elements.

[1993] Step 4:

[1994] The server stores the user's progress in a database and dynamically adjusts the learning path. As input, it receives the user's progress information in real time. Based on this, it updates the database and recommends the next learning content. As output, it obtains information that indicates the next learning step.

[1995] Step 5:

[1996] The server integrates the generated learning content into the educational program of the educational institution. It receives the generated learning content and the educational institution's request as input. It integrates the content into the educational institution's system using RESTful APIs. As output, it obtains the learning content integrated into the educational institution's program.

[1997] Terminal handling

[1998] Step 6:

[1999] The terminal receives the learning content provided by the server and displays it to the user. As input, it receives the learning content from the server. It plays back the multimedia content including the visual support information and the audio explanation. As output, it obtains the learning content displayed to the user.

[2000] Step 7:

[2001] As the user progresses with their studies, the device sends their progress information to the server in real time. As input, it receives the user's learning actions (e.g., chapter completion, question answering), converts them into data packets to send to the server, and as output, sends the user's progress information to the server.

[2002] User Action

[2003] Step 8:

[2004] Users learn using learning content provided through their devices. As input, they consume learning content displayed on their devices and perform learning activities (e.g., watching videos or taking interactive quizzes). As output, progress information is generated.

[2005] Step 9:

[2006] The user sends information to the server to update their skill level and learning style. As input, it receives the new skill level and learning style information. It converts this into a data packet to send to the server. As output, it sends the updated user information to the server.

[2007] (Application example 1)

[2008] 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."

[2009] Conventional learning systems have difficulty adapting to the different skill levels and learning styles of each user, and are unable to dynamically provide individually optimized learning content. This means that users' learning effectiveness is not maximized, and it is difficult to manage progress or provide customized learning paths. In particular, the fact that learning content is static and cannot be adapted in real time to reflect progress is a major problem.

[2010] 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.

[2011] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating user progress information in a database, means for displaying the learning content on the user's device, and means for providing learning content customized by generative artificial intelligence, thereby enabling the dynamic provision of individually optimized learning content.

[2012] "Generative AI" is an AI technology that dynamically generates optimized learning content based on a user's skill level and learning style.

[2013] "Individually optimized learning content" means learning materials that are customized to a user's specific skill level and learning style.

[2014] "Adaptive learning paths" are a part of the system that provides optimal learning paths based on a user's progress and learning style.

[2015] "Progress information" is data that indicates the progress and history of a user's use of learning content.

[2016] A "database" is a system for storing and managing data such as a user's skill level, learning style, and progress information.

[2017] "Device" refers to a device (e.g., smartphone, tablet PC, desktop PC, etc.) that a user uses to display and use learning content.

[2018] "Visual aids" are visual information such as illustrations and videos that are provided to make learning content easier to understand.

[2019] "Auditory explanation" refers to auditory information such as audio guides or commentary audio that is provided to complement learning content.

[2020] An "educational program" is a systematic learning curriculum offered by an educational institution.

[2021] The present invention is a system that utilizes generative artificial intelligence to individually optimize a user's learning experience. Specific embodiments of the system are described below.

[2022] Server Features

[2023] The server performs the following main functions:

[2024] 1. Management of User Information

[2025] The server stores and updates the user's skill level, learning style, and progress information in a database. When a user uses the system, this information is retrieved from the database and updated as necessary.

[2026] 2. Generating learning content

[2027] The server uses a generative artificial intelligence (hereinafter referred to as a generative AI model) to dynamically generate individually optimized learning content based on the user's skill level and learning style. This generative AI model automatically generates optimized learning materials based on past learning data and general educational content.

[2028] 3. Providing adaptive learning paths

[2029] Based on the user's progress, the server provides an adaptive learning path that is structured to best suit the user's current skill level and learning style, maximizing the user's learning efficiency.

[2030] 4. Collaboration with educational institutions

[2031] The server works with educational institutions to integrate the generated learning content into their existing educational programs, improving student learning by tailoring the content to fit the institution's curriculum.

[2032] Functions of the device used

[2033] Devices (e.g. smartphones, tablet PCs, desktop PCs) perform the following main functions:

[2034] 1. Viewing learning content

[2035] Individually optimized learning content provided by the server is displayed on the device, including visual aids and audio explanations.

[2036] 2. Sending progress information

[2037] As users progress through their studies, they send progress information to the server, which then tracks their progress in real time and adjusts their learning path as needed.

[2038] User Actions

[2039] Users interact with the system as follows:

[2040] 1. Use of learning content

[2041] Learn using learning content delivered through the device, and users can track their progress and adjust their learning path as needed.

[2042] 2. Updating Information

[2043] Users can update their skill level and learning style by sending information to the server, which optimizes the generated learning content in real time.

[2044] Hardware and software used

[2045] Server side: Python, Flask, SQLAlchemy

[2046] Client side: Python, Requests

[2047] Database: SQLite

[2048] Generative AI model: Custom module (e.g., generation_ai_model)

[2049] Specific examples

[2050] Get user information:

[2051] User "user1" accesses the system and retrieves the following information from the server:

[2052] json

[2053] {

[2054] "skill_level": 1,

[2055] "learning_style": "visual",

[2056] "progress": 0

[2057] }

[2058] Update your user information:

[2059] User "user1" updates his progress and sends the following information to the server:

[2060] json

[2061] {

[2062] "skill_level": 1,

[2063] "learning_style": "visual",

[2064] "progress": 1

[2065] }

[2066] Request customized learning content:

[2067] When user "user1" requests customized learning content, the server returns the following content:

[2068] Print Statement Basics

[2069] How to use variables

[2070] How to use loops

[2071] Visual aids for loops

[2072] In this way, users can learn effectively through dynamically generated optimized content.

[2073] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2074] Step 1:

[2075] A user logs in to the system from a terminal. The terminal prompts for a username and password on a login page, and once the user enters this information, it sends a login request to the server. The server checks the database to see if there is a user that matches the entered information. If there is a match, it generates an authentication token and returns it to the user.

[2076] Input: Username, Password

[2077] Output: Authentication token

[2078] Step 2:

[2079] After logging in, a user sends a request from their device to obtain their learning skill level, learning style, and progress information. The server retrieves the corresponding user information from the database and returns it to the device in JSON format. The user can then check their current status based on this information.

[2080] Input: Authentication Token

[2081] Output: User's skill level, learning style, and progress information

[2082] Step 3:

[2083] To advance their learning, the user requests customized learning content. The device sends the request along with an authentication token, and the server uses a generative AI model to dynamically generate learning content optimized for the user. The generated content is customized based on the user's skill level and learning style and sent to the device.

[2084] Inputs: Authentication token, user skill level, learning style

[2085] Output: Customized learning content

[2086] Step 4:

[2087] The terminal displays the learning content provided by the server, and the user begins learning. The content includes visual aids such as text, illustrations, and audio guides, as well as auditory explanations. The user uses the displayed content to progress through the learning process.

[2088] Input: Customized learning content

[2089] Output: Providing a learning experience (including visual aids and audio explanations)

[2090] Step 5:

[2091] As the user progresses through the learning process, the device sends real-time progress information to the server, including learning achievement, content completed, time, etc. The server stores this progress information in a database and dynamically adjusts the learning path as needed.

[2092] Input: Progress information

[2093] Output: Updated learning path

[2094] Step 6:

[2095] When the user finishes their study, the device sends a request to the server to end the study session. The server saves the final progress information in a database and ends the study session. This information is saved for use the next time the user starts studying, and is used when the user starts studying again.

[2096] Input: Request to end study session

[2097] Output: Final saved progress information

[2098] In this way, users can effectively progress through individually optimized learning content.

[2099] 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.

[2100] overview

[2101] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust learning content based on the user's emotions. The system provides an optimal adaptive learning path by taking into account the user's skill level, learning style, progress information, and emotional state. The system also has the ability to add visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[2102] System configuration

[2103] server

[2104] The server performs the following main functions:

[2105] 1. Management of User Information

[2106] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database.

[2107] Returns data in response to requests to get and update user information.

[2108] 2. Generating learning content

[2109] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[2110] Add custom visual aids and audio descriptions.

[2111] It uses an emotion engine to assess the user's emotional state and dynamically adjust learning.

[2112] 3. Providing adaptive learning paths

[2113] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[2114] 4. Collaboration with educational institutions

[2115] Partner with educational institutions to integrate the generated learning content into their educational programs.

[2116] Terminal

[2117] The terminal performs the following main functions:

[2118] 1. Viewing learning content

[2119] Display individually optimized learning content provided by the server.

[2120] Provide users with visual aids and auditory instructions.

[2121] 2. Sending progress and emotional information

[2122] As the user progresses through their learning, they send progress information and the output of the emotion engine to the server.

[2123] User

[2124] The user performs the following main functions:

[2125] 1. Use of learning content

[2126] Study using learning content provided through the device.

[2127] Check in on your progress and emotional state and adjust your learning path as needed.

[2128] 2. Updating Information

[2129] Send information to the server to update your skill level, learning style, and emotional state.

[2130] Specific examples

[2131] Retrieving User Information

[2132] When a user issues a GET / user / <username>When you send a request to ", the server retrieves the user's information from the database and returns it in JSON format. For example, you can retrieve the following information for user "user1":

[2133] json

[2134] {

[2135] "skill_level": 1,

[2136] "learning_style": "visual",

[2137] "progress": 0,

[2138] "emotional_state": "neutral"

[2139] }

[2140] Update user information

[2141] A user issues a PUT command to / user / from a web browser or API client. <username>" and provide new user information in JSON format, the server will update the user's information. For example, if user "user1" updates his progress and emotional state, the following information will be returned:

[2142] json

[2143] {

[2144] "skill_level": 1,

[2145] "learning_style": "visual",

[2146] "progress": 1,

[2147] "emotional_state": "happy"

[2148] }

[2149] Get course information

[2150] When a user accesses the web browser or API client via GET / course / <int:course_id> When you send a request to ", the server retrieves the corresponding course information and returns it in JSON format. For example, the course information for course ID "1" is as follows:

[2151] json

[2152] {

[2153] "course_name": "Basic Python",

[2154] "content": ["print('Hello World')", "variables", "loops"]

[2155] }

[2156] Customize your learning content

[2157] When a user sends a GET / customized-content / <username>When a user sends a request to "user1," the server customizes and serves content based on the user's skill level, learning style, and emotional state. For example, the following customized content is served to user "user1":

[2158] json

[2159] {

[2160] "customized_content": ["print('Hello World')", "variables", "loops", "Visual aid for loops"]

[2161] }

[2162] Regulation of learning content by emotional state

[2163] As the user progresses with their learning, the emotion engine evaluates the user's emotional state in real time and sends the results to the server. For example, if the emotion engine detects frustration while the user is learning, the server will adjust the content by simplifying it or displaying encouraging messages.

[2164] json

[2165] {

[2166] "customized_content": ["Take a short break and return later.", "Remember to stay positive!"]

[2167] }

[2168] This allows users to have an optimal learning experience based on their emotional state.

[2169] The processing flow will be explained below.

[2170] Retrieving User Information

[2171] Step 1:

[2172] The user uses a web browser or API client to access the GET / user / <username>Send a request to.

[2173] Step 2:

[2174] The server receives the request and the Flask route function user(username) is called.

[2175] Step 3:

[2176] The server verifies that the request method is GET.

[2177] Step 4:

[2178] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[2179] Step 5:

[2180] If the server finds the data, it returns the user information in JSON format, otherwise it returns a "User not found" message and a 404 status code.

[2181] Update user information

[2182] Step 1:

[2183] The user uses a web browser or API client to PUT / user / <username>Send a request to and provide the new user information in JSON format.

[2184] Step 2:

[2185] The server receives the request and the Flask route function user(username) is called.

[2186] Step 3:

[2187] The server verifies that the request method is PUT.

[2188] Step 4:

[2189] The server retrieves the JSON data from the request body.

[2190] Step 5:

[2191] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[2192] Step 6:

[2193] If the server finds the data, it updates the user information with the new data and returns the updated user information in JSON format. If the data is not found, it returns a "User not found" message and a 404 status code.

[2194] Get course information

[2195] Step 1:

[2196] The user uses a web browser or API client to access the GET / course / <int:course_id> Send a request to.

[2197] Step 2:

[2198] The server receives the request and the Flask route function course(course_id) is called.

[2199] Step 3:

[2200] The server uses the course ID to search for the corresponding course information from the course database (mock database).

[2201] Step 4:

[2202] If the server finds the data, it returns the course information in JSON format. If the data is not found, it returns a "Course not found" message and a 404 status code.

[2203] Customize your learning content

[2204] Step 1:

[2205] The user uses a web browser or API client to access the page by calling GET / customized-content / <username>Send a request to.

[2206] Step 2:

[2207] The server receives the request and the Flask route function customized_content(username) is called.

[2208] Step 3:

[2209] The server uses the username specified in the argument to search for the corresponding data from the user database (mock database).

[2210] Step 4:

[2211] If the server finds the user data, it retrieves the skill level and learning style.

[2212] Step 5:

[2213] The server retrieves the content of the relevant course from the course database based on the user's skill level.

[2214] Step 6:

[2215] The server applies customization logic based on the learning style, for example, if the learning style is "visual", add visual aids to the course content, if the learning style is "auditory", add auditory explanations to the course content.

[2216] Step 7:

[2217] The server returns customized learning content to the user in JSON format.

[2218] Regulation of learning content by emotional state

[2219] Step 1:

[2220] As users engage with learning content, the emotion engine assesses their emotional state in real time.

[2221] Step 2:

[2222] The terminal transmits the output results of the emotion engine to the server.

[2223] Step 3:

[2224] The server analyzes the received emotional state information and determines whether the learning content needs to be adjusted based on the user's emotional state.

[2225] Step 4:

[2226] If the server determines that the emotional state is negative, such as "frustration," it will adjust the learning content to be simpler or display encouraging messages.

[2227] Step 5:

[2228] The server transmits the tailored learning content or message to the terminal.

[2229] Step 6:

[2230] The terminal displays the tailored learning content or message to the user.

[2231] Specific examples

[2232] Regulation of learning content by emotional state

[2233] If it can be explained as a diagram:

[2234] Step 1:

[2235] The user is learning basic Python syntax and is tackling difficult topics.

[2236] Step 2:

[2237] Using the device's camera and sensors, the emotion engine analyzes the user's facial expressions and biometric information to detect when the user is feeling frustrated.

[2238] Step 3:

[2239] The terminal transmits this emotion information to the server.

[2240] Step 4:

[2241] The server analyzes the information it receives and determines that the user is frustrated and that the learning needs to be adjusted.

[2242] Step 5:

[2243] The server generates a message encouraging the user to take a short break to relax.

[2244] Step 6:

[2245] The server transmits the adjusted learning content and the recommendation message to the terminal.

[2246] Step 7:

[2247] The device will display a message to the user such as "Take a short break and refresh yourself."

[2248] In this way, the learning content and pace can be flexibly adjusted according to the user's emotional state, providing an optimal learning environment.

[2249] Example 2

[2250] 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."

[2251] Conventional learning systems struggled to provide learning content that adequately reflected a user's individual skill level and learning style, resulting in insufficient adjustments to improve learning efficiency. Furthermore, they were unable to provide adaptive learning paths based on the user's emotional state, making it difficult to maintain motivation. Furthermore, there was little collaboration with educational institutions, making it difficult to integrate individually optimized learning content into educational programs.

[2252] 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.

[2253] In this invention, the server includes means for dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for evaluating the user's emotional state in real time and dynamically adjusting the learning content, and means for providing the learning content to the user's device, thereby providing an individually optimized learning experience for each user, enabling efficient adjustment of the learning content and maintaining motivation.

[2254] "Generative AI" is an AI technology that dynamically generates content based on user information.

[2255] "Individually optimized learning content" refers to learning materials that are customized based on each user's skill level and learning style.

[2256] An "adaptive learning path" is a learning path that is individually optimized using the user's progress and emotional information.

[2257] "Progress information" is data about the user's learning progress and achievement level that is recorded as the user progresses with their learning.

[2258] "Emotion information" is data that indicates the user's emotional state and is obtained by the emotion engine.

[2259] An "emotion engine" is a system that assesses a user's emotional state and adjusts learning content based on that.

[2260] "Learning style" is information that indicates a user's preferences for the most effective ways and means of learning.

[2261] "Visual aids" are visual materials such as charts, graphs, and images that are provided to support learning.

[2262] "Auditory explanation" refers to the explanation of learning content using audio or narration.

[2263] An "educational program" is a set of courses or curriculum offered by an educational institution.

[2264] "Real-time" refers to a state in which processing is carried out immediately on the spot without delay.

[2265] A "database" is a system that efficiently stores, searches, and manages large amounts of information.

[2266] MODE FOR CARRYING OUT THE INVENTION

[2267] overview

[2268] The present invention is a system that utilizes generative artificial intelligence to dynamically generate and optimize learning content to improve a user's learning experience. The system takes into account a user's skill level, learning style, progress information, and emotional state to provide an optimal adaptive learning path. The system also has an emotion engine that recognizes the user's emotions and adjusts the learning content based on the user's emotions. Furthermore, the system has the ability to provide visual supplementary information and audio explanations, and integrates the generated learning content into educational programs through collaboration with educational institutions.

[2269] System configuration

[2270] The system consists of three elements: the server, the terminal, and the user.

[2271] server

[2272] The server performs the following main functions:

[2273] 1. Management of User Information

[2274] The user's skill level, learning style, progress information, and emotional information are stored and updated in a database. The server receives requests sent by the user from a web browser or API client and accesses the database to retrieve or update information.

[2275] For example, if a user requests "GET / user / <username>When you send a request like "PUT / user / ", the server retrieves the user information from the database and returns it in JSON format. <username>” If a request is submitted, update the database with the new information.

[2276] 2. Generating learning content

[2277] Using generative artificial intelligence, it dynamically generates personalized learning content based on the user's skill level and learning style.

[2278] For example, if a user GET / customized-content / <username>When you send a request like ", the server sends a prompt to the generative AI model to generate the optimal learning content.

[2279] Example prompt: "User is skill level 1 and has a visual learning style. Generate some basic Python programming content."

[2280] 3. Addition of visual and auditory support information

[2281] Dynamically add visual supplementary information and audio explanations to the generated learning content.

[2282] For example, the generated program code is provided to the user with associated diagrams and audio explanations.

[2283] 4. Emotional Engine Adjustment

[2284] Evaluates the user's emotional state and dynamically adjusts learning.

[2285] For example, if the emotion engine detects "dissatisfaction" during learning, the server can simplify the content or display an encouraging message.

[2286] 5. Providing adaptive learning paths

[2287] The learning path is dynamically adjusted based on the user's progress and emotional information, providing the optimal learning path.

[2288] 6. Collaboration with educational institutions

[2289] Partner with educational institutions to integrate the generated learning content into their educational programs.

[2290] Terminal

[2291] The terminal performs the following main functions:

[2292] 1. Viewing learning content

[2293] Display individually optimized learning content provided by the server.

[2294] For example, a user may view learning content through a device, such as a web browser or mobile application.

[2295] 2. Providing supporting information

[2296] Providing users with visual aids or auditory instructions, for example, displaying or playing images or audio files related to the learning content.

[2297] 3. Sending progress and emotional information

[2298] As the user progresses through their learning, progress information and the output of the emotion engine are sent to the server in real time.

[2299] User

[2300] The user performs the following main functions:

[2301] 1. Use of learning content

[2302] Students advance their studies using learning content provided through their devices.

[2303] For example, a user solves a programming problem and checks the results.

[2304] 2. Check in on your progress and feelings

[2305] Check in on your progress and emotional state and adjust your learning path as needed.

[2306] 3. Updating Information

[2307] Send information to the server to update your skill level, learning style, and emotional state.

[2308] As a result, this system can provide an optimal learning experience tailored to the individual needs of each user, improving learning efficiency and motivation.

[2309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2310] Step 1:

[2311] The server receives a request to obtain user information.

[2312] Input: A user enters GET / user / from a web browser or API client. <username>" request.

[2313] Specific operation: The server receives the request and accesses the database to retrieve the user's information.

[2314] Output: Returns the retrieved user information in JSON format.

[2315] Step 2:

[2316] The server receives a request to update user information.

[2317] Input: User enters PUT / user / <username>" request and provide the new user information in JSON format.

[2318] Specific operation: The server receives the request and updates the corresponding user's information in the database.

[2319] Output: Returns a response confirming the updated user information.

[2320] Step 3:

[2321] The server receives a learning content generation request.

[2322] Input: When the user types "GET / customized-content / <username>" request.

[2323] How it works: The server receives the request and sends a prompt to the generative AI model based on the user's skill level and learning style. An example of a prompt is: "User's skill level is 1, and their learning style is visual. Please generate some basic Python programming content."

[2324] Output: Get the generated learning content.

[2325] Step 4:

[2326] The server adds visual and audio aid information.

[2327] Input: Generated learning content.

[2328] Specific operation: The server adds relevant visual support information (e.g., graphs and charts) and auditory explanations (e.g., audio files and narration) to the learning content.

[2329] Output: Learning content with additional information added.

[2330] Step 5:

[2331] The server uses an emotion engine to adjust the learning content.

[2332] Input: The user's emotional state (e.g., if the emotion engine detects "unhappy").

[2333] Specific operation: The server receives the evaluation results of the emotion engine and makes adjustments such as simplifying the learning content or adding encouraging messages.

[2334] Output: Tailored learning content.

[2335] Step 6:

[2336] The server provides an adaptive learning path.

[2337] Input: User progress and emotion information.

[2338] Specific operation: The server dynamically adjusts the optimal learning path based on this information.

[2339] Output: The adjusted learning path.

[2340] Step 7:

[2341] The server integrates the content into the educational program.

[2342] Input: Generated learning content.

[2343] Specific operation: The server collaborates with educational institutions and integrates the generated learning content into educational programs.

[2344] Output: Content integrated into an educational program.

[2345] Step 8:

[2346] The device displays individually optimized learning content.

[2347] Input: Learning content provided by the server.

[2348] What it does: The device renders learning content in a web browser or application.

[2349] Output: The learning content displayed to the user.

[2350] Step 9:

[2351] The device provides visual aids and audio instructions.

[2352] Input: Learning content with additional supporting information.

[2353] Specific action: The device displays or plays relevant visual aids (e.g., images, videos) or auditory instructions (e.g., audio files, narration) to the user.

[2354] Output: The visual aids and / or auditory instructions provided to the user.

[2355] Step 10:

[2356] The terminal transmits the progress information and emotion information to the server.

[2357] Input: Progress information as the user progresses through the learning process and the output of the emotion engine.

[2358] Specific operation: The device sends this information to the server in real time.

[2359] Output: Progress and emotion information sent to the server.

[2360] (Application example 2)

[2361] 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."

[2362] Conventional learning systems have the problem of insufficient individual optimization based on the user's skill level and learning style, resulting in reduced learning effectiveness. Furthermore, they do not adjust the learning content to take the user's emotional state into consideration, which often leads to a loss of motivation to learn and stress. Furthermore, a lack of visual and auditory supplementary information also poses the issue of reduced learning efficiency.

[2363] The identification processing by the identification 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 dynamically generating individually optimized learning content using generative artificial intelligence, means for providing an adaptive learning path based on the user's skill level and learning style, means for storing and updating the user's progress information and emotional information in a database, means for providing learning content to the user's device, and means for analyzing the user's emotions in real time and dynamically adjusting the learning content. This makes it possible to provide content optimized for the user's skill level and learning style, and to adjust the learning content according to the user's emotional state.

[2364] "Generative AI" refers to AI that dynamically generates content based on user data.

[2365] "Optimized learning content" refers to educational materials that are tailored to a user's skill level and learning style.

[2366] An "adaptive learning path" refers to a learning path that dynamically changes depending on the user's progress and emotional state.

[2367] "User Progress Information" means information that indicates how far a user has progressed in their learning process.

[2368] "Emotional information" refers to data that assesses a user's emotional state in real time.

[2369] A "database" refers to a system for systematically managing, storing, and searching various types of information.

[2370] "Device" refers to the device used by a User to receive the Learning Content.

[2371] "Means for analyzing emotions in real time" refers to technologies and functions for instantly analyzing a user's emotional state.

[2372] "Visual aids" refers to visual elements (diagrams, illustrations, graphs, etc.) that are provided to make learning content easier to understand.

[2373] "Auditory explanation" refers to information that explains the learning content to the user using audio.

[2374] "Collaboration with educational institutions" refers to activities to collaborate with schools and professional institutions to integrate learning content into educational programs.

[2375] System Overview

[2376] This invention is a system that uses generative artificial intelligence to dynamically generate and optimize learning content to improve the user's learning experience. The system incorporates an emotion engine that recognizes the user's emotions and has the ability to adjust the learning content based on the user's emotions. Furthermore, by adding visual supplementary information and auditory explanations, the system provides comprehensive learning support.

[2377] server

[2378] 1. Generative artificial intelligence:

[2379] Using this technology, the server dynamically generates personalized learning content based on the user's skill level and learning style, and the learning content is customized according to the user's current learning progress and emotional state.

[2380] 2. Adaptive Learning Path:

[2381] The server stores the user's skill level, learning style, progress information, and emotional state in a database and uses this information to provide an adaptive learning path that is dynamically adjusted in real time.

[2382] 3. Emotion recognition and regulation:

[2383] The server analyzes the user's emotions in real time and dynamically adjusts the learning content based on the detected emotions. For example, if the user is in a "frustration" emotional state, the content will be simplified or an encouraging message will be displayed.

[2384] 4. Visual and auditory support:

[2385] The server provides visual aids and audio explanations according to the user's learning style, thereby enhancing the user's understanding.

[2386] 5. Collaboration with educational institutions:

[2387] The server provides a means for collaborating with educational institutions to integrate the generated learning content into their educational programs.

[2388] Terminal

[2389] 1. View learning content:

[2390] The device displays individually optimized learning content delivered from the server, including visual aids and audio explanations.

[2391] 2. Sending progress and emotional information:

[2392] As the user progresses through the learning process, the device sends progress information and the output of the emotion engine to the server, which updates the user's learning path in real time.

[2393] User

[2394] 1. Access to learning content:

[2395] Users can use learning content provided through their devices to progress through their studies, check their own progress and emotional state, and receive appropriate feedback.

[2396] 2. Example prompt:

[2397] An example of a prompt for a generative AI model is below: "Generative artificial intelligence model for education, please generate appropriate content for learning the basics of Python. User's skill level is 2, learning style is visual, and emotional state is 'happy'."

[2398] This allows users to have a comprehensive and optimized learning experience.

[2399] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2400] Step 1:

[2401] Upon receiving a request from a user, the server retrieves the user's skill level, learning style, progress information, and emotional information from the database. This information becomes input data for the generative AI to generate learning content. The output is JSON-formatted data of the user information.

[2402] Step 2:

[2403] The server generates prompts for the generative AI model based on the user's skill level and learning style. For example, the prompts might be in the form of "The user's skill level is 2 and their learning style is visual. Please generate appropriate content for learning the basics of Python." The prompts serve as input data for the generative AI, and the output is learning content optimized for the user.

[2404] Step 3:

[2405] The server retrieves the generated learning content and sends it to an emotion engine for evaluating the user's emotional information. The emotion engine further adjusts the learning content according to the user's emotional state (e.g., happy, frustrated). The input is the learning content and the emotional state data, and the output is the adjusted learning content based on the emotional state.

[2406] Step 4:

[2407] The server sends the optimized and tailored learning content to the user's device. The device displays the received data, including visual aids and audio instructions. The input is the tailored learning content, and the output is the learning content displayed on the user's device.

[2408] Step 5:

[2409] As the user progresses through the learning process, the device sends progress and emotional information to the server in real time. This information is updated in the database on the server and used to generate the next learning content. The input is the user's progress and emotional information, and the output is the updated database information.

[2410] Step 6:

[2411] The user checks their own progress and emotional state and adjusts their learning path as needed. The input is feedback information on progress and emotional state provided by the server, and the output is the result of the user adjusting their learning path.

[2412] This allows users to have a real-time optimized learning experience.

[2413] 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.

[2414] 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.

[2415] 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 robot 414.

[2416] 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.

[2417] 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.

[2418] 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.

[2419] 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).

[2420] 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, motorcycles, and other devices, 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.

[2421] 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."

[2422] 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.

[2423] 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).

[2424] 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.

[2425] 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.

[2426] 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.

[2427] 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.

[2428] 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.

[2429] 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.

[2430] 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.

[2431] 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.

[2432] 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.

[2433] 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.

[2434] The following is further disclosed regarding the above embodiment.

[2435] (Claim 1)

[2436] A means for dynamically generating individually optimized learning content using generative artificial intelligence;

[2437] A means to provide an adaptive learning path based on the user's skill level and learning style;

[2438] a means for storing and updating user progress information in a database;

[2439] A means for providing learning content to a user's device;

[2440] A system including:

[2441] (Claim 2)

[2442] 10. The system of claim 1, further comprising means for adding visual aids and / or audio explanations according to a user's learning style.

[2443] (Claim 3)

[2444] 10. The system of claim 1, further comprising means for working with an educational institution to integrate the generated learning content into an educational program.

[2445] "Example 1"

[2446] (Claim 1)

[2447] A means for dynamically generating individually optimized learning content using generative artificial intelligence;

[2448] A means to provide an adaptive learning path based on the user's skill level and learning style;

[2449] a means for storing and updating user progress information in a database;

[2450] A means for providing learning content to a user's device;

[2451] a means of working with educational institutions to integrate the generated learning content into their educational programs;

[2452] A means to retrieve user information and return it in JSON format,

[2453] A means of updating user information and updating the database with new user information;

[2454] A means to customize and deliver learning content based on a user's skill level and learning style; and

[2455] A system including:

[2456] (Claim 2)

[2457] 10. The system of claim 1, further comprising means for adding visual aids and / or audio explanations according to a user's learning style.

[2458] (Claim 3)

[2459] 10. The system of claim 1, further comprising means for a user to review their progress and adjust their learning path.

[2460] "Application Example 1"

[2461] (Claim 1)

[2462] A means for dynamically generating individually optimized learning content using generative artificial intelligence;

[2463] A means to provide an adaptive learning path based on the user's skill level and learning style;

[2464] a means for storing and updating user progress information in a database;

[2465] A means for displaying the learning content on the user's device;

[2466] A means to provide customized learning content using generative artificial intelligence;

[2467] A system including:

[2468] (Claim 2)

[2469] 10. The system of claim 1, further comprising means for adding visual aids and / or audio explanations according to a user's learning style.

[2470] (Claim 3)

[2471] 10. The system of claim 1, further comprising means for working with an educational institution to integrate the dynamically generated learning content into an educational program.

[2472] "Example 2: Combining Emotion Engines"

[2473] (Claim 1)

[2474] A means for dynamically generating individually optimized learning content using generative artificial intelligence;

[2475] A means to provide an adaptive learning path based on the user's skill level and learning style;

[2476] means for storing and updating user progress and emotion information in a database;

[2477] A means of assessing the user's emotional state in real time and dynamically adjusting learning; and

[2478] A means for providing learning content to a user's device;

[2479] A system including:

[2480] (Claim 2)

[2481] 10. The system of claim 1, further comprising means for adding visual aids and / or audio explanations according to a user's learning style.

[2482] (Claim 3)

[2483] 10. The system of claim 1, further comprising means for working with an educational institution to integrate the generated learning content into an educational program.

[2484] "Application example 2 when combining emotion engines"

[2485] (Claim 1)

[2486] A means for dynamically generating individually optimized learning content using generative artificial intelligence;

[2487] A means to provide an adaptive learning path based on the user's skill level and learning style;

[2488] means for storing and updating user progress and emotion information in a database;

[2489] A means for providing learning content to a user's device;

[2490] A means to analyze user sentiment in real time and dynamically adjust learning content;

[2491] A system including:

[2492] (Claim 2)

[2493] 10. The system of claim 1, further comprising means for adding visual aids and / or audio explanations according to a user's learning style.

[2494] (Claim 3)

[2495] 10. The system of claim 1, further comprising means for working with an educational institution to integrate the generated learning content into an educational program. [Explanation of symbols]

[2496] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / url:> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / url:> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / url:> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username> < / username>

Claims

1. A means for dynamically generating individually optimized learning content using generative artificial intelligence; A means to provide an adaptive learning path based on the user's skill level and learning style; a means for storing and updating user progress information in a database; A means for providing learning content to a user's device; A system including:

2. 10. The system of claim 1, further comprising means for adding visual aids and audio explanations according to the user's learning style.

3. The system of claim 1 , further comprising means for integrating the generated learning content into an educational program in collaboration with an educational institution.

Images