System

Abstract

A system is provided.SOLUTION: A system including a server means for hosting a generated AI model, a server means for providing e-learning content, a server means for authenticating a user, a server means for transmitting a prompt to the generated AI model and obtaining a response thereto, a server means for recording and analyzing an activity of the user as a log, a terminal means for providing an interface for accessing the e-learning content, a terminal means for accepting content reproduction and prompt input, and a terminal means for displaying a progress status of the user.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] When using generative AI, it is necessary for users to acquire the correct knowledge and methods to provide appropriate prompts and effectively utilize the results in educational settings. However, in current educational settings, it is not clear how to use generative AI or how to provide correct prompts, and effective education is not being carried out. For this reason, there is a need to provide educational content that utilizes generative AI and to build a system for learning how to use it correctly. [Means for solving the problem]

[0005] To address the above-mentioned challenges, we provide a system that includes a server for hosting a generative AI model, a server for providing e-learning content, a server for authenticating users, a server for sending prompts to the generative AI model and receiving responses, a server for recording and analyzing user activity as a log, a terminal for providing an interface for accessing e-learning content, a terminal for playing content and accepting prompt input, and a terminal for displaying the user's progress. This allows users to learn how to properly use the generative AI and how to provide prompts, enabling them to use the generative AI effectively and safely. Furthermore, the system includes a means for formatting and displaying prompt responses and a means for updating the user's learning progress based on the generative AI model's responses, thereby supporting the user's learning.

[0006] Okay, now let's create definitions for each of the important terms included in the claims.

[0007] "Generative AI model" refers to an artificial intelligence algorithm that generates responses to user-provided prompts.

[0008] "Server means" refers to a computer system that hosts the generative AI model, provides e-learning content, authenticates users, and records and analyzes data.

[0009] "Terminal means" refers to a device (e.g., PC, tablet, smartphone) that provides an interface for users to access e-learning content and input prompts.

[0010] "User" refers to a person (such as a student, teacher, or corporate training officer) who uses generative AI in an educational setting and engages in learning activities.

[0011] A "prompt" refers to a question or instruction that is input into a generative AI model.

[0012] "Response" refers to the answer or information generated by a generative AI model in response to a prompt.

[0013] "E-learning content" refers to digital educational materials (videos, texts, quizzes, etc.) provided for educational purposes.

[0014] "Authentication" refers to the process of using a user's identity to verify that the user has valid access rights.

[0015] "Log" refers to data that records the activities of users when using generated AI or systems.

[0016] "Interface" refers to the input and output means (screen, buttons, menus, etc.) required for a user to operate a system.

[0017] "Progress" refers to information that indicates the degree to which a user has completed a learning course or e-learning content. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention provides specific embodiments for implementing a system that utilizes a generative AI model to provide educational content and help users learn how to use AI correctly.

[0040] The system mainly consists of three entities: a server, a device, and a user. The server plays a central role in hosting the generative AI model and providing e-learning content. It also authenticates users, processes prompts, and records and analyzes their activities.

[0041] server

[0042] When the server receives a new user request, it first registers the user's authentication information in the database and automatically creates a new AI account. The created account is notified to the user along with the initial setup information.

[0043] The educational content includes videos, texts, quizzes, etc., and is hosted on a server. The content is streamed in response to user requests. As the user progresses through the content, progress information is recorded in real time on the server and analyzed as needed. When a prompt is entered, the server sends it to a generative AI model, obtains a response, and returns it to the user.

[0044] Terminal

[0045] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select learning content from the courses offered. The login information is sent to the server, and after successful authentication, a list of available courses is displayed on the terminal.

[0046] The user browses the content through the device and inputs the necessary prompts into the generation AI. The response received from the server is displayed on the device in a format that is easy for the user to understand. The device also displays the user's progress and manages their learning progress.

[0047] User

[0048] First, users log in to the system through their terminal. Then, they select an appropriate course from the available e-learning content. As they study the content, they input questions and instructions (prompts) to the generative AI and receive its responses. This allows them to learn how to use the generative AI and how to create appropriate prompts.

[0049] Learning progress is displayed in real time on the dashboard, allowing users to check their own progress as they study. Furthermore, based on the responses of the generating AI, users can deepen their own understanding and progress more efficiently.

[0050] Specific examples

[0051] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. As the user watches the video and answers the quiz, the results are sent to the server and recorded as progress information. Next, the user enters a prompt, "Teach me how to summarize a text," which is sent via the server to the generative AI. The generative AI's response is formatted by the server and returned to the device. The user can review the response and enter additional prompts to further their understanding.

[0052] As described above, this system effectively utilizes generative AI models in educational settings and provides a series of methods for users to correctly understand and use them.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The server receives a new user request: the user requests to create an account to access the system.

[0056] Step 2:

[0057] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[0058] Step 3:

[0059] A user logs into the system using a terminal, which sends the user's login information to the server.

[0060] Step 4:

[0061] The server authenticates the user, and if authentication is successful, sends a list of available e-learning content to the terminal.

[0062] Step 5:

[0063] The terminal displays the received course list to the user, who selects the desired course and clicks the start button.

[0064] Step 6:

[0065] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[0066] Step 7:

[0067] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[0068] Step 8:

[0069] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[0070] Step 9:

[0071] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[0072] Step 10:

[0073] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[0074] Step 11:

[0075] The generative AI model generates a response and returns it to the server, which formats it and converts it into a form that is easy for the user to understand.

[0076] Step 12:

[0077] The server sends the formatted response to the terminal, which displays the response to the user.

[0078] Step 13:

[0079] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[0080] Step 14:

[0081] The server logs all user activity and analyzes it as needed to understand the user's learning patterns and progress.

[0082] In this way, this system provides educational content using generative AI, realizing a series of processes that enable users to learn correct usage.

[0083] Example 1

[0084] 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."

[0085] Conventional e-learning systems have struggled to effectively utilize generative AI models to improve users' learning experiences. They lacked adequate support for users to understand how to use generative AI and apply its responses to their learning. Furthermore, real-time progress recording and analysis were insufficient, necessitating a systematic approach to maximize user learning outcomes.

[0086] 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.

[0087] In this invention, the server includes an information processing device means for hosting the generative AI model, an information processing device means for providing educational content, an information processing device means for authenticating the user, an information processing device means for sending prompts to the generative AI model and obtaining its response, an information processing device means for recording and analyzing user activity, a means for notifying the user of generated user account information, a means for recording the progress of the educational content in real time, and a means for formatting the generative AI's response in a format optimal for the user. This enables the user to receive educational content through the generative AI model, understand the progress in real time, and receive optimized learning support.

[0088] A "generative AI model" is a technical means of automatically generating responses based on user input.

[0089] An "information processing device" is a device for recording, analyzing, and communicating data.

[0090] "User authentication" is the process of verifying a user's identity and their access privileges.

[0091] A "prompt" is input such as a question or instruction that a user sends to a generative AI model.

[0092] An "input / output device" is an interface device that allows a user to input data into the system and display the results.

[0093] "Progress" indicates the user's level of achievement and history information as they progress through the learning content.

[0094] "Formatting" is the process of preparing data in a particular form.

[0095] "Educational content" refers to teaching materials and resources provided to aid users in their learning and understanding.

[0096] The present invention is an educational content provision system that includes a generative AI model, and aims to enable users to learn how to utilize generative AI. Specific embodiments for implementing this system are described below.

[0097] System Structure

[0098] server

[0099] The server is the core of this system and includes an information processing device with the following functions:

[0100] 1. Hosting a generative AI model: A generative AI model is a technology that receives prompts from users and generates responses accordingly. A server hosts this model and responds to user requests in real time.

[0101] 2. Providing educational content: The server hosts educational content such as videos, texts, and quizzes, and streams them to the device upon user request.

[0102] 3. User authentication: The server registers the new user's authentication information in the database and notifies the created user account information and initial setting information.

[0103] 4. Prompt processing and response: The server sends the prompts entered by the user to the generative AI model, receives the responses, formats them in a format that is optimal for the user, and returns them to the model.

[0104] 5. Activity recording and analysis: Recording and analyzing user progress and activity data in real time helps maximize learning outcomes.

[0105] Terminal

[0106] The terminal includes input / output devices that allow the user to interact with the system:

[0107] 1. Login and Course Selection: The user logs into the system using a terminal and selects the appropriate course from the list of available courses.

[0108] 2. Playback of educational content and input of prompts: The content of the course selected by the user is displayed on the device, and the user can watch videos, read text, and answer quizzes. The user can also input prompts to the generation AI through the device.

[0109] 3. Response display and progress management: The response from the AI ​​is displayed on the device, providing appropriate answers to the user's questions. In addition, the device displays the user's learning progress in real time and helps manage their learning plan.

[0110] User

[0111] Users are the users of the system and interact with it in the following ways:

[0112] 1. Login and course selection: The user logs into the system via a terminal and selects the course to study from the provided e-learning content.

[0113] 2. Content learning: The user studies the educational content displayed on the device, providing prompts to the AI ​​generator as needed, and receives responses that allow the user to deepen their understanding.

[0114] 3. Track your progress: Users can view their progress on the dashboard and adjust their learning methods as needed. For example, they can take a quiz after watching a video to gauge their understanding.

[0115] Specific examples

[0116] For example, if a user selects the "Basic Operations of Generative AI" course, the following specific examples might be possible:

[0117] 1. After logging in, the user selects the "Basic Operations of Generative AI" course.

[0118] 2. The server streams the relevant video content to the device.

[0119] 3. Users watch videos and take quizzes.

[0120] 4. The quiz results are sent to the server and recorded as progress information.

[0121] 5. Next, the user enters the prompt "Please tell me how to summarize a text."

[0122] 6. The server sends the prompt to the generative AI model and receives a response.

[0123] 7. The generated AI's response is formatted on the server and displayed on the device.

[0124] 8. The user uses the responses to deepen their understanding and continue learning.

[0125] As described above, this system effectively utilizes generative AI models and provides a series of methods for users to correctly understand and use them.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] A user logs in to the system. The user uses a terminal to enter login information and clicks the login button. The terminal sends this input information to the server. The server collates the user's authentication information stored in the database, and if authentication is successful, it generates a login success message and a list of available courses and returns them to the terminal. The user can check the list of courses on the terminal screen.

[0129] Step 2:

[0130] The user selects the course they wish to study. They choose the course they wish to study from the list of courses displayed on the device screen and click the select button. The device sends the selected course information to the server. The server prepares educational content (videos, text, quizzes, etc.) corresponding to the selected course and returns the URL of that content to the device. The device uses this URL to display the content to the user.

[0131] Step 3:

[0132] The user views the educational content. The user watches the educational content on the device screen and learns the necessary information. The server records the timestamps of the user's start and end times and saves them in a database as progress. In addition, the server records and analyzes the user's progress in real time as they progress through the content.

[0133] Step 4:

[0134] The user answers the quiz. The quiz is presented as part of the learning content, and the user enters their answers and clicks the submit button. The device sends the user's answers to the server. The server determines whether the quiz is correct or incorrect, returns the results to the user, and records the results as progress data. The user can view the quiz results and explanations.

[0135] Step 5:

[0136] The user enters a prompt into the generative AI. The user enters a question or instruction related to the learning content as a prompt into the device and clicks the send button. The device sends the prompt to the server. The server sends the prompt to the generative AI model and receives the generated response. The server formats this response in a format optimal for the user and returns it to the device. The user can review the response from the generative AI to deepen their understanding.

[0137] Step 6:

[0138] The user checks their learning progress. The user checks their learning progress on the dashboard screen of their device. The server updates the progress data in real time and displays it on the dashboard. The user can adjust their learning plan based on this information.

[0139] The above is the specific processing flow of the system program.

[0140] (Application example 1)

[0141] 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."

[0142] Conventional educational content systems have difficulty in providing individualized support for users as they learn, and lack real-time management of learning progress and interactive learning assistance using generative AI. Therefore, it was necessary to provide a system that would enable users to progress through their studies effectively and efficiently. Furthermore, support for users to create appropriate prompts through generative AI and receive responses to deepen their learning was limited, so it was necessary to solve these issues.

[0143] 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.

[0144] In this invention, the server includes a means for hosting the generative AI model, a means for providing educational content, a means for authenticating users, a means for sending prompts to the generative AI model and obtaining responses, and a means for recording and analyzing user activities, thereby enabling real-time management of educational content, interactive learning support by the generative AI, and effective updates on the user's learning progress.

[0145] The terminal also includes a means for providing an interface for accessing educational content, a means for playing content and accepting prompt input, a means for displaying the user's progress, a means for inputting prompts to the generating AI and displaying the responses, and a means for updating progress information in real time, thereby enabling the user to effectively use the generating AI and deepen their learning while checking their learning progress in real time.

[0146] A "generative AI model" is a type of artificial intelligence trained on large datasets to generate responses in natural language based on prompts from users.

[0147] "Server means" refers to a computer system that provides services to users via a network and has the function of processing and storing data.

[0148] "Educational content" refers to a set of information or materials provided for educational purposes, and may consist of videos, texts, quizzes, etc.

[0149] "User authentication" is the process of verifying a user's identity and granting appropriate privileges when accessing a system.

[0150] A "prompt" is text that is input as an instruction or question to a generative AI model, which causes the generative AI to return a corresponding response.

[0151] "User activity" refers to all operations and actions performed by a user while using the system, which are recorded as logs and analyzed.

[0152] "Terminal means" refers to a physical or virtual device through which a user interacts with the system, such as a personal computer, smartphone, or tablet.

[0153] "Content playback" is the process of presenting educational content visually or audibly to a user.

[0154] "Progress" is information that indicates how far the user has progressed in their studies, and the system visually displays this information.

[0155] "Updating progress information in real time" refers to the process of instantly updating the system with new information obtained as the user progresses with their studies, and always displaying the latest status.

[0156] This invention relates to an educational content distribution system that utilizes generative AI models, and the system consists of three main entities: a server, a terminal, and a user.

[0157] Server Features

[0158] The server has the following functions:

[0159] 1. Hosting generative AI models: Hosting large-scale language models to generate natural language responses based on user prompts, such as GPT-3®.

[0160] 2. Providing educational content: We manage and host a variety of educational content and provide it to users upon request, including video, text, and quiz-style learning materials.

[0161] 3. User Authentication: Authenticating new and existing users and setting up appropriate learning accounts.

[0162] 4. Sending and receiving prompts: Receive prompts from the user, send them to the generative AI model to get a response, format this response and send it to the device.

[0163] 5. User activity recording and progress management: Record users' learning activities and analyze the data to manage their progress.

[0164] Device Features

[0165] The terminal has the following features:

[0166] 1. Provide an interface: Provide an interface for accessing educational content, allowing users to enter login information and access available courses.

[0167] 2. Content playback and prompt input: Plays the provided educational content and provides an interface for users to input prompts to the generated AI. For example, users can input prompts such as, "Please tell me the basic operations of the generated AI."

[0168] 3. Progress display: Displays the user's learning progress in real time and supports learning progress management.

[0169] User Roles

[0170] The user uses the system as follows:

[0171] 1. Log in to the system: The user logs in to the system using a terminal and selects an appropriate course from the available educational content.

[0172] 2. Viewing content and answering quizzes: View the content of the selected course and answer the related quizzes. These activities are recorded on the server and managed as progress information.

[0173] 3. Prompt input to the generative AI: During the learning process, questions or instructions (prompts) can be input to the generative AI and a response can be received. For example, you can input a prompt such as, "Please tell me how to summarize a text."

[0174] 4. Track your progress and keep learning: See your progress in real time as you learn, and enter additional prompts to further your understanding.

[0175] Hardware and software used

[0176] This system uses the following hardware and software:

[0177] Server: A server for data processing and storage. Generative AI models and databases (e.g., MySQL (registered trademark), MongoDB) are hosted on the server.

[0178] Terminal: The device with which the user interacts. This can be a smartphone, tablet, or computer.

[0179] Software: Web frameworks such as Flask are used for server-side processing, and the user interface is built using HTML, CSS, JavaScript (registered trademark), etc.

[0180] The system constructed in this way enables users to effectively and efficiently study using generative AI. By asking questions to the generative AI and checking its responses, users can deepen their learning and further their understanding. Furthermore, by checking progress in real time, it becomes easier to manage the learning process.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] A user logs into the system using a terminal. After entering login information (user name and password), the terminal sends this to the server. The server refers to the user authentication database and verifies that the authentication information is correct. If authentication is successful, the server returns a message of successful authentication and a list of available courses to the user. The terminal receives this and displays it to the user.

[0184] Step 2:

[0185] The user selects the course they wish to study from a list of courses available on the device. The device sends a course selection request to the server. The server searches for the appropriate educational content and prepares to stream the desired course content to the device. The device then plays the received course content to the user.

[0186] Step 3:

[0187] Users progress through their studies by viewing educational videos and textbooks according to the course content. Every time an interaction occurs, such as playing a video, viewing textbooks, or answering a quiz, a detailed activity log is sent from the device to the server. The server records and analyzes these activity logs and updates the user's progress data.

[0188] Step 4:

[0189] During the learning process, the user inputs questions and instructions to the generative AI model. For example, they input a prompt such as, "Please tell me the basic operations of generative AI." The device sends this prompt to the server. The server passes the prompt to the generative AI model and receives its response. The server formats the response and returns it to the device. The device displays this response to the user.

[0190] Step 5:

[0191] If the user enters an additional question as a prompt, the process repeats: the server sends the prompt to the generative AI model again and receives a response, which the user responds to further deepen their learning.

[0192] Step 6:

[0193] The user's progress is constantly updated in real time. Every time the user answers a quiz or watches a new video, the device sends progress data to the server. The server analyzes this data and updates the user's learning dashboard. The device then displays the latest progress information to the user in real time.

[0194] Step 7:

[0195] When the user finishes learning, he logs out of the system. The device sends a logout request to the server, which then terminates the user's session. All learning activity logs and progress data are stored on the server and can be reused in the future.

[0196] By following the above steps, users can efficiently advance their learning by using an interactive educational content delivery system that utilizes generative AI models.

[0197] 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.

[0198] The present invention provides educational content using a generative AI model, and further combines it with an emotion engine that recognizes the user's emotions to implement a system that improves the user's learning experience. Specific embodiments for implementing the system are described below.

[0199] The system mainly consists of three components: a server, a device, and a user. The server hosts the generative AI model and provides e-learning content. It also has an emotion engine that recognizes user emotions and has the ability to collect and analyze user emotion data.

[0200] server

[0201] The server creates a new account for the user to access the system, automatically generates a generated AI account, and notifies the user of the generated login information and initial setting information.

[0202] Educational content (videos, texts, quizzes, etc.) is hosted on a server and streamed upon user request. The server also records user progress and provides real-time analysis as needed.

[0203] Once a prompt is entered, the server sends it to the generative AI model to get a response, where an emotion engine is used to tailor the generative AI model's response based on the user's emotional state, which is then formatted and finally returned to the user.

[0204] Terminal

[0205] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select the desired course from a list. The terminal receives the content of the selected course from the server and displays it.

[0206] Furthermore, the device uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine, which analyzes this data and determines the user's emotional state.

[0207] The user enters a prompt into the generation AI, and the device sends the prompt to the server, which formats the generated response and returns it to the device, where it is displayed to the user.

[0208] User

[0209] Users log in to the system using a terminal and select the provided e-learning content. As they study the content, they input prompts to the generation AI and receive responses. The emotion engine then checks the user's emotional state and assists in providing the optimal response.

[0210] The server also recommends appropriate e-learning content based on the user's emotional state: for example, if the user is tired, it recommends light review content, and if the user is concentrating, it recommends new, more challenging content.

[0211] Specific examples

[0212] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after the video ends, the emotion engine captures the emotion data. When the user enters the prompt "Please give me a summary of the text," the device sends the prompt to the server.

[0213] The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response, which is formatted on the server and displayed to the user via their device. The user can review the response and enter additional prompts if they have further questions.

[0214] The server logs all user activity and emotional data, capturing learning patterns and progress in real time, which can then be used to optimize the user's learning experience.

[0215] In this way, the system combines a generative AI model and an emotion engine to improve users' learning efficiency and provide individually optimized educational content.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The server receives a new user request: the user requests to create an account to access the system.

[0219] Step 2:

[0220] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[0221] Step 3:

[0222] A user logs into the system using a terminal, which sends the user's login information to the server.

[0223] Step 4:

[0224] The server authenticates the user and sends a list of available e-learning content to the terminal.

[0225] Step 5:

[0226] The terminal displays the received course list to the user, who selects the desired course and clicks Start.

[0227] Step 6:

[0228] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[0229] Step 7:

[0230] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[0231] Step 8:

[0232] The device uses its built-in camera and microphone to collect the user's emotional data (facial expressions and voice) and transmits it to the server.

[0233] Step 9:

[0234] The server uses an emotion engine to analyze the received emotion data and determine the user's emotional state.

[0235] Step 10:

[0236] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[0237] Step 11:

[0238] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[0239] Step 12:

[0240] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[0241] Step 13:

[0242] The generative AI model generates a response and returns it to the server, which then adjusts the response of the generative AI model based on the emotion recognition results of the emotion engine.

[0243] Step 14:

[0244] The server formats a response and sends it to the terminal, which displays it to the user.

[0245] Step 15:

[0246] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[0247] Step 16:

[0248] The server logs all user activity and emotional data and analyzes it as needed to understand the user's learning patterns and progress.

[0249] Step 17:

[0250] The server recommends optimal e-learning content based on the user's emotional state: for example, it recommends light review content if the user is tired, or new, more challenging content if the user is concentrating.

[0251] In this way, the system not only provides educational content that utilizes generative AI and helps users learn how to use it correctly, but also uses emotional data to individually optimize the user's learning experience.

[0252] Example 2

[0253] 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."

[0254] Conventional e-learning systems provide uniform educational content without considering the user's emotional state, resulting in reduced learning efficiency. Even response systems using generative AI models have difficulty providing appropriate responses based on the user's real-time emotions. Therefore, there is a need for a system that can improve the user's learning experience and provide individually optimized educational content.

[0255] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for hosting the generative AI model, a means for providing e-learning content, a means for authenticating the user, a means for sending prompts to the generative AI model and obtaining its response, a means for recording and analyzing the user's activity as a log, a means for analyzing emotional data and having an emotion engine for determining the user's emotional state, and a means for adjusting the response of the generative AI model based on the user's emotional state. This makes it possible to provide an optimal response according to the user's emotional state and improve learning efficiency.

[0256] A "generative AI model" is an algorithm or system that automatically generates natural language responses based on user prompts.

[0257] "Server means" means a server that has the functionality to host the generative AI model, provide e-learning content, authenticate users, and send prompts to the generative AI model to obtain responses.

[0258] "E-learning content" refers to educational content in digital format, such as videos, texts, and quizzes, provided for learning purposes.

[0259] A "prompt" is a question or instruction that a user inputs to a generative AI model.

[0260] An "emotion engine" is a system or algorithm that analyzes a user's emotional data and determines the user's emotional state.

[0261] "Terminal means" refers to a terminal that provides an interface for users to interact with the system, has the functions of playing content, accepting prompt input, and collecting emotion data.

[0262] "User activity" refers to a series of operations or actions that a user performs on a system.

[0263] A "log" is data that records the history of a user's activities, emotional data, and so on.

[0264] The present invention relates to a system that provides educational content using a generative AI model and combines it with an emotion engine that recognizes the user's emotions to improve the user's learning experience. Specific embodiments of the system are described below.

[0265] System configuration

[0266] The system is mainly composed of three entities: a server, a terminal, and a user.

[0267] Server Features

[0268] The server hosts the generative AI model and provides e-learning content. It also authenticates users, sends prompts to the generative AI model, and receives responses. It also logs and analyzes user activity and uses an emotion engine to determine the user's emotional state.

[0269] Specifically, the server uses the following software and hardware:

[0270] Generative AI models (e.g., GPT-3)

[0271] Emotion engines (e.g., Affectiva)

[0272] Database management system (e.g. MySQL)

[0273] Web server (e.g., Apache)

[0274] Device Features

[0275] The terminal provides an interface for users to interact with the system. Users can log in using the terminal and select the course they want. The terminal receives the content of the selected course from the server and displays it. It also uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine.

[0276] Specific hardware and software include:

[0277] Camera (to capture the user's facial expressions)

[0278] A microphone (to record your vocal inflections)

[0279] User interface (e.g., web browser or dedicated application)

[0280] User operations

[0281] Users log in to the system using their devices and study the provided e-learning content. If they have any questions while studying, they can input prompt sentences and send them to the generative AI model. Furthermore, appropriate responses and content are provided depending on the user's emotional state.

[0282] Specific examples

[0283] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after finishing, the emotion engine captures emotion data. When the user inputs the prompt "Please summarize the text," the data is sent to the server. The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response. This response is formatted by the server and displayed to the user via the device. The user can review the response and input additional prompts if they have further questions.

[0284] Examples of prompts:

[0285] "Please tell me more about the educational system using generative AI models."

[0286] "Please briefly summarize what you learned today."

[0287] As a result, a system is realized that can provide optimal educational content that takes into account the user's emotional state and improve learning efficiency.

[0288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0289] Step 1: User Login

[0290] 1. The device displays the login screen.

[0291] 2. The user enters their username and password.

[0292] 3. The device sends the entered login information to the server.

[0293] 4. The server receives the login information and authenticates it by checking it against the database (input is the user's login information, output is the authentication result).

[0294] 5. The server returns the authentication result to the terminal.

[0295] 6. The terminal displays a successful authentication message to the user.

[0296] Specific behavior:

[0297] The terminal's user interface provides a text box and a submit button.

[0298] The server queries a database (e.g. MySQL) to verify the user information.

[0299] Step 2: Course Selection and Content Acquisition

[0300] 1. After logging in, the user views the list of courses offered.

[0301] 2. The terminal accepts the user's course selection.

[0302] 3. The terminal sends a request for the selected course to the server (input is the selected course ID, output is the course content).

[0303] 4. The server receives the course request, retrieves the corresponding content from the database, and prepares it for streaming (data processing: content extraction).

[0304] 5. The server streams the content to the device.

[0305] 6. The device receives the content and displays it to the user.

[0306] Specific behavior:

[0307] The device records the user's selection through clicks.

[0308] The server responds to the HTTP request with content (e.g., video URL, text).

[0309] Step 3: Complete and submit prompts during training

[0310] 1. The user has a question while studying and enters a prompt.

[0311] 2. The terminal accepts the input prompt.

[0312] 3. The device sends a prompt to the server (the input is the user's prompt, and the output is a request to the generative AI model).

[0313] Specific behavior:

[0314] The terminal's user interface provides a text area and a submit button.

[0315] The server receives the prompt sentence and adds it to a queue for post-processing.

[0316] Step 4: Generative AI model generates a response

[0317] 1. The server sends a prompt to the generative AI model (data calculation: response generation).

[0318] 2. The generative AI model parses the prompt and generates a response (the input is the prompt sentence, and the output is the generated response).

[0319] 3. The server receives the generated response.

[0320] Specific behavior:

[0321] The server sends an API request to the generative AI model (e.g., GPT-3).

[0322] A generative AI model uses natural language processing algorithms to generate responses.

[0323] Step 5: Collect and analyze sentiment data

[0324] 1. The device's camera and microphone collect the user's emotional data (input is the user's facial expression data and voice data).

[0325] 2. The device sends the collected emotion data to the server (the output is emotion data).

[0326] Specific behavior:

[0327] Sensors on the device capture the user's facial expressions, and a microphone records the intonation of the voice.

[0328] The server receives this data and sends it to the emotion engine.

[0329] Step 6: Response adjustment with the emotion engine

[0330] 1. The server receives emotion data.

[0331] 2. The server sends the emotion data to the emotion engine, which analyzes the user's emotional state (the input is emotion data, and the output is the emotion analysis result).

[0332] 3. The emotion engine determines the emotional state and adjusts the response of the generative AI model (data processing: response adjustment).

[0333] Specific behavior:

[0334] The server sends data to the emotion engine in the form of an API request.

[0335] The emotion engine analyzes the data and adjusts the tone and content of the response accordingly.

[0336] Step 7: Formatting and displaying the response

[0337] 1. The server receives the adjusted response and converts it into the appropriate format (data processing: format conversion).

[0338] 2. The server sends the formatted response to the terminal (output is the formatted response).

[0339] 3. The terminal receives the response and displays it to the user.

[0340] Specific behavior:

[0341] The server formats the response in HTML or JSON.

[0342] The device will display the response in a chat box or pop-up format.

[0343] Step 8: Progress Recording and Analysis

[0344] 1. The server records the user's activity and emotion data (input is activity data and emotion data, output is recorded data).

[0345] 2. The server analyzes the recorded data and generates learning patterns and progress reports (data calculation: progress analysis).

[0346] Specific behavior:

[0347] The server stores the activity and emotion data in a database.

[0348] Big data analytics tools (e.g., Hadoop) analyze the data and generate reports.

[0349] Step 9: Recommending the right content

[0350] 1. The server selects appropriate content based on the user's emotional state and progress (data calculation: content recommendation).

[0351] 2. The server sends the recommended content to the terminal (the output is the recommended content).

[0352] 3. The device displays the recommended content to the user.

[0353] Specific behavior:

[0354] The server selects content using machine learning algorithms.

[0355] Your device will display recommended content in a list or pop-up notification.

[0356] The above are the specific processing steps in the embodiment of the invention.

[0357] (Application example 2)

[0358] 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."

[0359] Conventional e-learning systems and virtual stores have difficulty providing services that take into account the user's emotional state, making it difficult to recommend content or products that best suit individual needs. As a result, users' learning efficiency and purchasing experience are not sufficiently improved. In addition, the lack of optimization of real-time responses based on the user's emotional state can sometimes result in a poor user experience.

[0360] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0361] In this invention, the server includes means for hosting the generative AI model, means for providing e-learning content, means for authenticating a user, means for sending prompts to the generative AI model and obtaining responses, means for recording and analyzing user activity as a log, terminal means for providing an interface for accessing the e-learning content, terminal means for playing content and accepting prompt inputs, terminal means for displaying the user's progress, emotion analysis means for recognizing the user's emotional state, response optimization means for adjusting the response of the generative AI model based on the emotion analysis results, and shopping assistant means for recommending products in a virtual store. This enables optimal learning content and product recommendations based on the user's emotional state, improving the user experience.

[0362] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate sophisticated responses to user input.

[0363] "Server Means" refers to a central processing unit that allows users to access the system and stores, processes, and distributes data.

[0364] "E-learning content" refers to digital content such as online learning materials, videos, and quizzes provided for educational purposes.

[0365] "User authentication" refers to the process of verifying the legitimacy of a user when they access a system.

[0366] A "prompt" refers to text or voice input that a user uses to enter a question or request to a generative AI model.

[0367] "Activity logging" refers to data used to record and analyze a user's system usage history.

[0368] "Interface" refers to the screen and operating means by which a user interacts with a system.

[0369] "Terminal means" refers to a device that allows a user to access and operate the system.

[0370] "Emotion analysis means" refers to technology that uses sensors such as cameras and microphones to recognize a user's emotional state from their facial expressions and voice.

[0371] "Response optimization means" refers to technology that adjusts the response of a generative AI model based on the results of emotion analysis.

[0372] "Virtual store" refers to a virtual shopping environment provided on the Internet.

[0373] "Shopping assistant means" refers to a function for supporting the user's purchasing experience in a virtual store.

[0374] The present invention provides a system that improves the user's learning experience by providing educational content using a generative AI model and combining it with emotion analysis means that recognize the user's emotions. A specific embodiment of this system is described below.

[0375] Overall system configuration

[0376] The system mainly consists of the following three entities:

[0377] server

[0378] Terminal

[0379] User

[0380] server

[0381] The server hosts the generative AI model and provides e-learning content to users, and also authenticates users and includes an emotion analysis means to recognize their emotional state.

[0382] For specific hardware, we use cloud computing environments such as Amazon Web Services (AWS) and Microsoft Azure, which enable training and hosting of generative AI models. For sentiment analysis, we use Microsoft Azure's Cognitive Services.

[0383] The server provides the following main functions:

[0384] 1. Hosting a generative AI model: The generative AI model provides intelligent responses to prompts sent by the user.

[0385] 2. Providing e-learning content: Hosting educational content such as videos, texts, and quizzes and streaming them upon user request.

[0386] 3. User authentication: Provides functionality for authenticating users accessing the system.

[0387] 4. Emotion analysis: Analyzes the user's camera and microphone data to determine the user's emotional state in real time.

[0388] 5. Response optimization: Adjust the response of the generative AI model based on the results of sentiment analysis.

[0389] 6. Logging and Analysis: Recording user activity and analyzing learning progress.

[0390] Terminal

[0391] The terminal provides an interface between the user and the system, allowing the user to access the system and use e-learning content.

[0392] The specific hardware of the device includes smartphones, tablets, and PCs. The application is developed using Flutter (registered trademark). The device provides the following functions:

[0393] 1. Login interface: Provides a screen for users to log in to the system.

[0394] 2. Content playback: Play and display the content of the course selected by the user.

[0395] 3. Prompt input acceptance: Provides a screen where users can input prompts to the generated AI model.

[0396] 4. Transmission of emotion data: The user's facial expressions and voice are transmitted to the emotion analysis means using a camera and microphone.

[0397] 5. Progress display: Show users' learning progress in real time.

[0398] User

[0399] Users access the system using a terminal and use the provided e-learning content.

[0400] 1. Logging in to the system: A user logs in to the system using a terminal.

[0401] 2. Course selection: The user selects the desired course and begins learning.

[0402] 3. Prompt Creation: The user types a question or request to the generative AI model.

[0403] Example prompt: "Recommend a new smartphone."

[0404] 4. Sending emotional data: Using the device's camera and microphone, emotional data obtained from the user's facial expressions and voice is sent to the server.

[0405] 5. Receive response: Receive the response generated by the generative AI model and confirm the learnings and purchase suggestions.

[0406] Through the above process, the system is able to provide optimal learning content and recommend products based on the user's emotional state, improving the user's learning and purchasing experiences.

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] The server hosts the generative AI model and creates a new account for the user to access the system. It receives the user's registration information as input and notifies the user of the generated login information and initial setting information as output. At this time, it authenticates the user and saves the account information in the database.

[0410] Step 2:

[0411] A user logs into the system using a terminal. The terminal displays a login interface and sends the entered user ID and password to the server. The server performs authentication and, if successful, displays the user's dashboard as output.

[0412] Step 3:

[0413] The user selects the desired course and begins learning. The terminal sends a request for the selected course to the server, and the server provides the corresponding e-learning content to the terminal by streaming. As an output, the content is displayed on the user's terminal.

[0414] Step 4:

[0415] To recognize the user's emotional state, the device's camera and microphone are activated. The user's facial expression and voice data are acquired as input and sent to the server's emotion analysis means. The server then performs emotion analysis and outputs the user's emotional state (e.g., joy, excitement, fatigue) in real time.

[0416] Step 5:

[0417] The user uses a device to input a prompt to the generative AI model. The input prompt is sent to the server. The server takes into account the results of sentiment analysis and passes the prompt to the generative AI model, which then generates a response. The generated response is returned to the server.

[0418] Step 6:

[0419] The server formats the generated response and sends it to the device. The formatted response is displayed as output on the user's device. For example, if the user types the prompt "Recommend a new smartphone," the response might read, "Currently popular products are the latest smartphone models with excellent cameras."

[0420] Step 7:

[0421] User activity is recorded as a log and analyzed by the server. The input is user behavioral data, which is reflected in learning progress and product recommendations as output. At this time, emotional data stored in the database is also utilized to provide individually optimized content based on learning and purchasing history.

[0422] Step 8:

[0423] The system displays the user's progress on the device. It receives progress data sent from the server as input and displays it in a visually easy-to-understand format on a dashboard as output, allowing the user to check their own learning progress.

[0424] 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.

[0425] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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."

[0440] The present invention provides specific embodiments for implementing a system that utilizes a generative AI model to provide educational content and help users learn how to use AI correctly.

[0441] The system mainly consists of three entities: a server, a device, and a user. The server plays a central role in hosting the generative AI model and providing e-learning content. It also authenticates users, processes prompts, and records and analyzes their activities.

[0442] server

[0443] When the server receives a new user request, it first registers the user's authentication information in the database and automatically creates a new AI account. The created account is notified to the user along with the initial setup information.

[0444] The educational content includes videos, texts, quizzes, etc., and is hosted on a server. The content is streamed in response to user requests. As the user progresses through the content, progress information is recorded in real time on the server and analyzed as needed. When a prompt is entered, the server sends it to a generative AI model, obtains a response, and returns it to the user.

[0445] Terminal

[0446] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select learning content from the courses offered. The login information is sent to the server, and after successful authentication, a list of available courses is displayed on the terminal.

[0447] The user browses the content through the device and inputs the necessary prompts into the generation AI. The response received from the server is displayed on the device in a format that is easy for the user to understand. The device also displays the user's progress and manages their learning progress.

[0448] User

[0449] First, users log in to the system through their terminal. Then, they select an appropriate course from the available e-learning content. As they study the content, they input questions and instructions (prompts) to the generative AI and receive its responses. This allows them to learn how to use the generative AI and how to create appropriate prompts.

[0450] Learning progress is displayed in real time on the dashboard, allowing users to check their own progress as they study. Furthermore, based on the responses of the generating AI, users can deepen their own understanding and progress more efficiently.

[0451] Specific examples

[0452] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. As the user watches the video and answers the quiz, the results are sent to the server and recorded as progress information. Next, the user enters a prompt, "Teach me how to summarize a text," which is sent via the server to the generative AI. The generative AI's response is formatted by the server and returned to the device. The user can review the response and enter additional prompts to further their understanding.

[0453] As described above, this system effectively utilizes generative AI models in educational settings and provides a series of methods for users to correctly understand and use them.

[0454] The processing flow will be explained below.

[0455] Step 1:

[0456] The server receives a new user request: the user requests to create an account to access the system.

[0457] Step 2:

[0458] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[0459] Step 3:

[0460] A user logs into the system using a terminal, which sends the user's login information to the server.

[0461] Step 4:

[0462] The server authenticates the user, and if authentication is successful, sends a list of available e-learning content to the terminal.

[0463] Step 5:

[0464] The terminal displays the received course list to the user, who selects the desired course and clicks the start button.

[0465] Step 6:

[0466] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[0467] Step 7:

[0468] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[0469] Step 8:

[0470] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[0471] Step 9:

[0472] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[0473] Step 10:

[0474] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[0475] Step 11:

[0476] The generative AI model generates a response and returns it to the server, which formats it and converts it into a form that is easy for the user to understand.

[0477] Step 12:

[0478] The server sends the formatted response to the terminal, which displays the response to the user.

[0479] Step 13:

[0480] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[0481] Step 14:

[0482] The server logs all user activity and analyzes it as needed to understand the user's learning patterns and progress.

[0483] In this way, this system provides educational content using generative AI, realizing a series of processes that enable users to learn correct usage.

[0484] Example 1

[0485] 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."

[0486] Conventional e-learning systems have struggled to effectively utilize generative AI models to improve users' learning experiences. They lacked adequate support for users to understand how to use generative AI and apply its responses to their learning. Furthermore, real-time progress recording and analysis were insufficient, necessitating a systematic approach to maximize user learning outcomes.

[0487] 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.

[0488] In this invention, the server includes an information processing device means for hosting the generative AI model, an information processing device means for providing educational content, an information processing device means for authenticating the user, an information processing device means for sending prompts to the generative AI model and obtaining its response, an information processing device means for recording and analyzing user activity, a means for notifying the user of generated user account information, a means for recording the progress of the educational content in real time, and a means for formatting the generative AI's response in a format optimal for the user. This enables the user to receive educational content through the generative AI model, understand the progress in real time, and receive optimized learning support.

[0489] A "generative AI model" is a technical means of automatically generating responses based on user input.

[0490] An "information processing device" is a device for recording, analyzing, and communicating data.

[0491] "User authentication" is the process of verifying a user's identity and their access privileges.

[0492] A "prompt" is input such as a question or instruction that a user sends to a generative AI model.

[0493] An "input / output device" is an interface device that allows a user to input data into the system and display the results.

[0494] "Progress" indicates the user's level of achievement and history information as they progress through the learning content.

[0495] "Formatting" is the process of preparing data in a particular form.

[0496] "Educational content" refers to teaching materials and resources provided to aid users in their learning and understanding.

[0497] The present invention is an educational content provision system that includes a generative AI model, and aims to enable users to learn how to utilize generative AI. Specific embodiments for implementing this system are described below.

[0498] System Structure

[0499] server

[0500] The server is the core of this system and includes an information processing device with the following functions:

[0501] 1. Hosting a generative AI model: A generative AI model is a technology that receives prompts from users and generates responses accordingly. A server hosts this model and responds to user requests in real time.

[0502] 2. Providing educational content: The server hosts educational content such as videos, texts, and quizzes, and streams them to the device upon user request.

[0503] 3. User authentication: The server registers the new user's authentication information in the database and notifies the created user account information and initial setting information.

[0504] 4. Prompt processing and response: The server sends the prompts entered by the user to the generative AI model, receives the responses, formats them in a format that is optimal for the user, and returns them to the model.

[0505] 5. Activity recording and analysis: Recording and analyzing user progress and activity data in real time helps maximize learning outcomes.

[0506] Terminal

[0507] The terminal includes input / output devices that allow the user to interact with the system:

[0508] 1. Login and Course Selection: The user logs into the system using a terminal and selects the appropriate course from the list of available courses.

[0509] 2. Playback of educational content and input of prompts: The content of the course selected by the user is displayed on the device, and the user can watch videos, read text, and answer quizzes. The user can also input prompts to the generation AI through the device.

[0510] 3. Response display and progress management: The response from the AI ​​is displayed on the device, providing appropriate answers to the user's questions. In addition, the device displays the user's learning progress in real time and helps manage their learning plan.

[0511] User

[0512] Users are the users of the system and interact with it in the following ways:

[0513] 1. Login and course selection: The user logs into the system via a terminal and selects the course to study from the provided e-learning content.

[0514] 2. Content learning: The user studies the educational content displayed on the device, providing prompts to the AI ​​generator as needed, and receives responses that allow the user to deepen their understanding.

[0515] 3. Track your progress: Users can view their progress on the dashboard and adjust their learning methods as needed. For example, they can take a quiz after watching a video to gauge their understanding.

[0516] Specific examples

[0517] For example, if a user selects the "Basic Operations of Generative AI" course, the following specific examples might be possible:

[0518] 1. After logging in, the user selects the "Basic Operations of Generative AI" course.

[0519] 2. The server streams the relevant video content to the device.

[0520] 3. Users watch videos and take quizzes.

[0521] 4. The quiz results are sent to the server and recorded as progress information.

[0522] 5. Next, the user enters the prompt "Please tell me how to summarize a text."

[0523] 6. The server sends the prompt to the generative AI model and receives a response.

[0524] 7. The generated AI's response is formatted on the server and displayed on the device.

[0525] 8. The user uses the responses to deepen their understanding and continue learning.

[0526] As described above, this system effectively utilizes generative AI models and provides a series of methods for users to correctly understand and use them.

[0527] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0528] Step 1:

[0529] A user logs in to the system. The user uses a terminal to enter login information and clicks the login button. The terminal sends this input information to the server. The server collates the user's authentication information stored in the database, and if authentication is successful, it generates a login success message and a list of available courses and returns them to the terminal. The user can check the list of courses on the terminal screen.

[0530] Step 2:

[0531] The user selects the course they wish to study. They choose the course they wish to study from the list of courses displayed on the device screen and click the select button. The device sends the selected course information to the server. The server prepares educational content (videos, text, quizzes, etc.) corresponding to the selected course and returns the URL of that content to the device. The device uses this URL to display the content to the user.

[0532] Step 3:

[0533] The user views the educational content. The user watches the educational content on the device screen and learns the necessary information. The server records the timestamps of the user's start and end times and saves them in a database as progress. In addition, the server records and analyzes the user's progress in real time as they progress through the content.

[0534] Step 4:

[0535] The user answers the quiz. The quiz is presented as part of the learning content, and the user enters their answers and clicks the submit button. The device sends the user's answers to the server. The server determines whether the quiz is correct or incorrect, returns the results to the user, and records the results as progress data. The user can view the quiz results and explanations.

[0536] Step 5:

[0537] The user enters a prompt into the generative AI. The user enters a question or instruction related to the learning content as a prompt into the device and clicks the send button. The device sends the prompt to the server. The server sends the prompt to the generative AI model and receives the generated response. The server formats this response in a format optimal for the user and returns it to the device. The user can review the response from the generative AI to deepen their understanding.

[0538] Step 6:

[0539] The user checks their learning progress. The user checks their learning progress on the dashboard screen of their device. The server updates the progress data in real time and displays it on the dashboard. The user can adjust their learning plan based on this information.

[0540] The above is the specific processing flow of the system program.

[0541] (Application example 1)

[0542] 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."

[0543] Conventional educational content systems have difficulty in providing individualized support for users as they learn, and lack real-time management of learning progress and interactive learning assistance using generative AI. Therefore, it was necessary to provide a system that would enable users to progress through their studies effectively and efficiently. Furthermore, support for users to create appropriate prompts through generative AI and receive responses to deepen their learning was limited, so it was necessary to solve these issues.

[0544] 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.

[0545] In this invention, the server includes a means for hosting the generative AI model, a means for providing educational content, a means for authenticating users, a means for sending prompts to the generative AI model and obtaining responses, and a means for recording and analyzing user activities, thereby enabling real-time management of educational content, interactive learning support by the generative AI, and effective updates on the user's learning progress.

[0546] The terminal also includes a means for providing an interface for accessing educational content, a means for playing content and accepting prompt input, a means for displaying the user's progress, a means for inputting prompts to the generating AI and displaying the responses, and a means for updating progress information in real time, thereby enabling the user to effectively use the generating AI and deepen their learning while checking their learning progress in real time.

[0547] A "generative AI model" is a type of artificial intelligence trained on large datasets to generate responses in natural language based on prompts from users.

[0548] "Server means" refers to a computer system that provides services to users via a network and has the function of processing and storing data.

[0549] "Educational content" refers to a set of information or materials provided for educational purposes, and may consist of videos, texts, quizzes, etc.

[0550] "User authentication" is the process of verifying a user's identity and granting appropriate privileges when accessing a system.

[0551] A "prompt" is text that is input as an instruction or question to a generative AI model, which causes the generative AI to return a corresponding response.

[0552] "User activity" refers to all operations and actions performed by a user while using the system, which are recorded as logs and analyzed.

[0553] "Terminal means" refers to a physical or virtual device through which a user interacts with the system, such as a personal computer, smartphone, or tablet.

[0554] "Content playback" is the process of presenting educational content visually or audibly to a user.

[0555] "Progress" is information that indicates how far the user has progressed in their studies, and the system visually displays this information.

[0556] "Updating progress information in real time" refers to the process of instantly updating the system with new information obtained as the user progresses with their studies, and always displaying the latest status.

[0557] This invention relates to an educational content distribution system that utilizes generative AI models, and the system consists of three main entities: a server, a terminal, and a user.

[0558] Server Features

[0559] The server has the following functions:

[0560] 1. Hosting generative AI models: Hosting large language models to generate natural language responses based on user prompts, such as GPT-3.

[0561] 2. Providing educational content: We manage and host a variety of educational content and provide it to users upon request, including video, text, and quiz-style learning materials.

[0562] 3. User Authentication: Authenticating new and existing users and setting up appropriate learning accounts.

[0563] 4. Sending and receiving prompts: Receive prompts from the user, send them to the generative AI model to get a response, format this response and send it to the device.

[0564] 5. User activity recording and progress management: Record users' learning activities and analyze the data to manage their progress.

[0565] Device Features

[0566] The terminal has the following features:

[0567] 1. Provide an interface: Provide an interface for accessing educational content, allowing users to enter login information and access available courses.

[0568] 2. Content playback and prompt input: Plays the provided educational content and provides an interface for users to input prompts to the generated AI. For example, users can input prompts such as, "Please tell me the basic operations of the generated AI."

[0569] 3. Progress display: Displays the user's learning progress in real time and supports learning progress management.

[0570] User Roles

[0571] The user uses the system as follows:

[0572] 1. Log in to the system: The user logs in to the system using a terminal and selects an appropriate course from the available educational content.

[0573] 2. Viewing content and answering quizzes: View the content of the selected course and answer the related quizzes. These activities are recorded on the server and managed as progress information.

[0574] 3. Prompt input to the generative AI: During the learning process, questions or instructions (prompts) can be input to the generative AI and a response can be received. For example, you can input a prompt such as, "Please tell me how to summarize a text."

[0575] 4. Track your progress and keep learning: See your progress in real time as you learn, and enter additional prompts to further your understanding.

[0576] Hardware and software used

[0577] This system uses the following hardware and software:

[0578] Server: A server for data processing and storage, where generative AI models and databases (e.g., MySQL, MongoDB) are hosted.

[0579] Terminal: The device with which the user interacts. This can be a smartphone, tablet, or computer.

[0580] Software: Web frameworks such as Flask are used for server-side processing, and the user interface is built using HTML, CSS, JavaScript, etc.

[0581] The system constructed in this way enables users to effectively and efficiently study using generative AI. By asking questions to the generative AI and checking its responses, users can deepen their learning and further their understanding. Furthermore, by checking progress in real time, it becomes easier to manage the learning process.

[0582] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0583] Step 1:

[0584] A user logs into the system using a terminal. After entering login information (user name and password), the terminal sends this to the server. The server refers to the user authentication database and verifies that the authentication information is correct. If authentication is successful, the server returns a message of successful authentication and a list of available courses to the user. The terminal receives this and displays it to the user.

[0585] Step 2:

[0586] The user selects the course they wish to study from a list of courses available on the device. The device sends a course selection request to the server. The server searches for the appropriate educational content and prepares to stream the desired course content to the device. The device then plays the received course content to the user.

[0587] Step 3:

[0588] Users progress through their studies by viewing educational videos and textbooks according to the course content. Every time an interaction occurs, such as playing a video, viewing textbooks, or answering a quiz, a detailed activity log is sent from the device to the server. The server records and analyzes these activity logs and updates the user's progress data.

[0589] Step 4:

[0590] During the learning process, the user inputs questions and instructions to the generative AI model. For example, they input a prompt such as, "Please tell me the basic operations of generative AI." The device sends this prompt to the server. The server passes the prompt to the generative AI model and receives its response. The server formats the response and returns it to the device. The device displays this response to the user.

[0591] Step 5:

[0592] If the user enters an additional question as a prompt, the process repeats: the server sends the prompt to the generative AI model again and receives a response, which the user responds to further deepen their learning.

[0593] Step 6:

[0594] The user's progress is constantly updated in real time. Every time the user answers a quiz or watches a new video, the device sends progress data to the server. The server analyzes this data and updates the user's learning dashboard. The device then displays the latest progress information to the user in real time.

[0595] Step 7:

[0596] When the user finishes learning, he logs out of the system. The device sends a logout request to the server, which then terminates the user's session. All learning activity logs and progress data are stored on the server and can be reused in the future.

[0597] By following the above steps, users can efficiently advance their learning by using an interactive educational content delivery system that utilizes generative AI models.

[0598] 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.

[0599] The present invention provides educational content using a generative AI model, and further combines it with an emotion engine that recognizes the user's emotions to implement a system that improves the user's learning experience. Specific embodiments for implementing the system are described below.

[0600] The system mainly consists of three components: a server, a device, and a user. The server hosts the generative AI model and provides e-learning content. It also has an emotion engine that recognizes user emotions and has the ability to collect and analyze user emotion data.

[0601] server

[0602] The server creates a new account for the user to access the system, automatically generates a generated AI account, and notifies the user of the generated login information and initial setting information.

[0603] Educational content (videos, texts, quizzes, etc.) is hosted on a server and streamed upon user request. The server also records user progress and provides real-time analysis as needed.

[0604] Once a prompt is entered, the server sends it to the generative AI model to get a response, where an emotion engine is used to tailor the generative AI model's response based on the user's emotional state, which is then formatted and finally returned to the user.

[0605] Terminal

[0606] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select the desired course from a list. The terminal receives the content of the selected course from the server and displays it.

[0607] Furthermore, the device uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine, which analyzes this data and determines the user's emotional state.

[0608] The user enters a prompt into the generation AI, and the device sends the prompt to the server, which formats the generated response and returns it to the device, where it is displayed to the user.

[0609] User

[0610] Users log in to the system using a terminal and select the provided e-learning content. As they study the content, they input prompts to the generation AI and receive responses. The emotion engine then checks the user's emotional state and assists in providing the optimal response.

[0611] The server also recommends appropriate e-learning content based on the user's emotional state: for example, if the user is tired, it recommends light review content, and if the user is concentrating, it recommends new, more challenging content.

[0612] Specific examples

[0613] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after the video ends, the emotion engine captures the emotion data. When the user enters the prompt "Please give me a summary of the text," the device sends the prompt to the server.

[0614] The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response, which is formatted on the server and displayed to the user via their device. The user can review the response and enter additional prompts if they have further questions.

[0615] The server logs all user activity and emotional data, capturing learning patterns and progress in real time, which can then be used to optimize the user's learning experience.

[0616] In this way, the system combines a generative AI model and an emotion engine to improve users' learning efficiency and provide individually optimized educational content.

[0617] The processing flow will be explained below.

[0618] Step 1:

[0619] The server receives a new user request: the user requests to create an account to access the system.

[0620] Step 2:

[0621] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[0622] Step 3:

[0623] A user logs into the system using a terminal, which sends the user's login information to the server.

[0624] Step 4:

[0625] The server authenticates the user and sends a list of available e-learning content to the terminal.

[0626] Step 5:

[0627] The terminal displays the received course list to the user, who selects the desired course and clicks Start.

[0628] Step 6:

[0629] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[0630] Step 7:

[0631] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[0632] Step 8:

[0633] The device uses its built-in camera and microphone to collect the user's emotional data (facial expressions and voice) and transmits it to the server.

[0634] Step 9:

[0635] The server uses an emotion engine to analyze the received emotion data and determine the user's emotional state.

[0636] Step 10:

[0637] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[0638] Step 11:

[0639] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[0640] Step 12:

[0641] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[0642] Step 13:

[0643] The generative AI model generates a response and returns it to the server, which then adjusts the response of the generative AI model based on the emotion recognition results of the emotion engine.

[0644] Step 14:

[0645] The server formats a response and sends it to the terminal, which displays it to the user.

[0646] Step 15:

[0647] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[0648] Step 16:

[0649] The server logs all user activity and emotional data and analyzes it as needed to understand the user's learning patterns and progress.

[0650] Step 17:

[0651] The server recommends optimal e-learning content based on the user's emotional state: for example, it recommends light review content if the user is tired, or new, more challenging content if the user is concentrating.

[0652] In this way, the system not only provides educational content that utilizes generative AI and helps users learn how to use it correctly, but also uses emotional data to individually optimize the user's learning experience.

[0653] Example 2

[0654] 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."

[0655] Conventional e-learning systems provide uniform educational content without considering the user's emotional state, resulting in reduced learning efficiency. Even response systems using generative AI models have difficulty providing appropriate responses based on the user's real-time emotions. Therefore, there is a need for a system that can improve the user's learning experience and provide individually optimized educational content.

[0656] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for hosting the generative AI model, a means for providing e-learning content, a means for authenticating the user, a means for sending prompts to the generative AI model and obtaining its response, a means for recording and analyzing the user's activity as a log, a means for analyzing emotional data and having an emotion engine for determining the user's emotional state, and a means for adjusting the response of the generative AI model based on the user's emotional state. This makes it possible to provide an optimal response according to the user's emotional state and improve learning efficiency.

[0657] A "generative AI model" is an algorithm or system that automatically generates natural language responses based on user prompts.

[0658] "Server means" means a server that has the functionality to host the generative AI model, provide e-learning content, authenticate users, and send prompts to the generative AI model to obtain responses.

[0659] "E-learning content" refers to educational content in digital format, such as videos, texts, and quizzes, provided for learning purposes.

[0660] A "prompt" is a question or instruction that a user inputs to a generative AI model.

[0661] An "emotion engine" is a system or algorithm that analyzes a user's emotional data and determines the user's emotional state.

[0662] "Terminal means" refers to a terminal that provides an interface for users to interact with the system, has the functions of playing content, accepting prompt input, and collecting emotion data.

[0663] "User activity" refers to a series of operations or actions that a user performs on a system.

[0664] A "log" is data that records the history of a user's activities, emotional data, and so on.

[0665] The present invention relates to a system that provides educational content using a generative AI model and combines it with an emotion engine that recognizes the user's emotions to improve the user's learning experience. Specific embodiments of the system are described below.

[0666] System configuration

[0667] The system is mainly composed of three entities: a server, a terminal, and a user.

[0668] Server Features

[0669] The server hosts the generative AI model and provides e-learning content. It also authenticates users, sends prompts to the generative AI model, and receives responses. It also logs and analyzes user activity and uses an emotion engine to determine the user's emotional state.

[0670] Specifically, the server uses the following software and hardware:

[0671] Generative AI models (e.g., GPT-3)

[0672] Emotion engines (e.g., Affectiva)

[0673] Database management system (e.g. MySQL)

[0674] Web server (e.g. Apache)

[0675] Device Features

[0676] The terminal provides an interface for users to interact with the system. Users can log in using the terminal and select the course they want. The terminal receives the content of the selected course from the server and displays it. It also uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine.

[0677] Specific hardware and software include:

[0678] Camera (to capture the user's facial expressions)

[0679] A microphone (to record your vocal inflections)

[0680] User interface (e.g., web browser or dedicated application)

[0681] User operations

[0682] Users log in to the system using their devices and study the provided e-learning content. If they have any questions while studying, they can input prompt sentences and send them to the generative AI model. Furthermore, appropriate responses and content are provided depending on the user's emotional state.

[0683] Specific examples

[0684] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after finishing, the emotion engine captures emotion data. When the user inputs the prompt "Please summarize the text," the data is sent to the server. The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response. This response is formatted by the server and displayed to the user via the device. The user can review the response and input additional prompts if they have further questions.

[0685] Examples of prompts:

[0686] "Please tell me more about the educational system using generative AI models."

[0687] "Please briefly summarize what you learned today."

[0688] As a result, a system is realized that can provide optimal educational content that takes into account the user's emotional state and improve learning efficiency.

[0689] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0690] Step 1: User Login

[0691] 1. The device displays the login screen.

[0692] 2. The user enters their username and password.

[0693] 3. The device sends the entered login information to the server.

[0694] 4. The server receives the login information and authenticates it by checking it against the database (input is the user's login information, output is the authentication result).

[0695] 5. The server returns the authentication result to the terminal.

[0696] 6. The terminal displays a successful authentication message to the user.

[0697] Specific behavior:

[0698] The terminal's user interface provides a text box and a submit button.

[0699] The server queries a database (e.g. MySQL) to verify the user information.

[0700] Step 2: Course Selection and Content Acquisition

[0701] 1. After logging in, the user views the list of courses offered.

[0702] 2. The terminal accepts the user's course selection.

[0703] 3. The terminal sends a request for the selected course to the server (input is the selected course ID, output is the course content).

[0704] 4. The server receives the course request, retrieves the corresponding content from the database, and prepares it for streaming (data processing: content extraction).

[0705] 5. The server streams the content to the device.

[0706] 6. The device receives the content and displays it to the user.

[0707] Specific behavior:

[0708] The device records the user's selection through clicks.

[0709] The server responds to the HTTP request with content (e.g., video URL, text).

[0710] Step 3: Complete and submit prompts during training

[0711] 1. The user has a question while studying and enters a prompt.

[0712] 2. The terminal accepts the input prompt.

[0713] 3. The device sends a prompt to the server (the input is the user's prompt, and the output is a request to the generative AI model).

[0714] Specific behavior:

[0715] The terminal's user interface provides a text area and a submit button.

[0716] The server receives the prompt sentence and adds it to a queue for post-processing.

[0717] Step 4: Generative AI model generates a response

[0718] 1. The server sends a prompt to the generative AI model (data calculation: response generation).

[0719] 2. The generative AI model parses the prompt and generates a response (the input is the prompt sentence, and the output is the generated response).

[0720] 3. The server receives the generated response.

[0721] Specific behavior:

[0722] The server sends an API request to the generative AI model (e.g., GPT-3).

[0723] A generative AI model uses natural language processing algorithms to generate responses.

[0724] Step 5: Collect and analyze sentiment data

[0725] 1. The device's camera and microphone collect the user's emotional data (input is the user's facial expression data and voice data).

[0726] 2. The device sends the collected emotion data to the server (the output is emotion data).

[0727] Specific behavior:

[0728] Sensors on the device capture the user's facial expressions, and a microphone records the intonation of the voice.

[0729] The server receives this data and sends it to the emotion engine.

[0730] Step 6: Response adjustment with the emotion engine

[0731] 1. The server receives emotion data.

[0732] 2. The server sends the emotion data to the emotion engine, which analyzes the user's emotional state (the input is emotion data, and the output is the emotion analysis result).

[0733] 3. The emotion engine determines the emotional state and adjusts the response of the generative AI model (data processing: response adjustment).

[0734] Specific behavior:

[0735] The server sends data to the emotion engine in the form of an API request.

[0736] The emotion engine analyzes the data and adjusts the tone and content of the response accordingly.

[0737] Step 7: Formatting and displaying the response

[0738] 1. The server receives the adjusted response and converts it into the appropriate format (data processing: format conversion).

[0739] 2. The server sends the formatted response to the terminal (output is the formatted response).

[0740] 3. The terminal receives the response and displays it to the user.

[0741] Specific behavior:

[0742] The server formats the response in HTML or JSON.

[0743] The device will display the response in a chat box or pop-up format.

[0744] Step 8: Progress Recording and Analysis

[0745] 1. The server records the user's activity and emotion data (input is activity data and emotion data, output is recorded data).

[0746] 2. The server analyzes the recorded data and generates learning patterns and progress reports (data calculation: progress analysis).

[0747] Specific behavior:

[0748] The server stores the activity and emotion data in a database.

[0749] Big data analytics tools (e.g., Hadoop) analyze the data and generate reports.

[0750] Step 9: Recommending the right content

[0751] 1. The server selects appropriate content based on the user's emotional state and progress (data calculation: content recommendation).

[0752] 2. The server sends the recommended content to the terminal (the output is the recommended content).

[0753] 3. The device displays the recommended content to the user.

[0754] Specific behavior:

[0755] The server selects content using machine learning algorithms.

[0756] Your device will display recommended content in a list or pop-up notification.

[0757] The above are the specific processing steps in the embodiment of the invention.

[0758] (Application example 2)

[0759] 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."

[0760] Conventional e-learning systems and virtual stores have difficulty providing services that take into account the user's emotional state, making it difficult to recommend content or products that best suit individual needs. As a result, users' learning efficiency and purchasing experience are not sufficiently improved. In addition, the lack of optimization of real-time responses based on the user's emotional state can sometimes result in a poor user experience.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0762] In this invention, the server includes means for hosting the generative AI model, means for providing e-learning content, means for authenticating a user, means for sending prompts to the generative AI model and obtaining responses, means for recording and analyzing user activity as a log, terminal means for providing an interface for accessing the e-learning content, terminal means for playing content and accepting prompt inputs, terminal means for displaying the user's progress, emotion analysis means for recognizing the user's emotional state, response optimization means for adjusting the response of the generative AI model based on the emotion analysis results, and shopping assistant means for recommending products in a virtual store. This enables optimal learning content and product recommendations based on the user's emotional state, improving the user experience.

[0763] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate sophisticated responses to user input.

[0764] "Server Means" refers to a central processing unit that allows users to access the system and stores, processes, and distributes data.

[0765] "E-learning content" refers to digital content such as online learning materials, videos, and quizzes provided for educational purposes.

[0766] "User authentication" refers to the process of verifying the legitimacy of a user when they access a system.

[0767] A "prompt" refers to text or voice input that a user uses to enter a question or request to a generative AI model.

[0768] "Activity logging" refers to data used to record and analyze a user's system usage history.

[0769] "Interface" refers to the screen and operating means by which a user interacts with a system.

[0770] "Terminal means" refers to a device that allows a user to access and operate the system.

[0771] "Emotion analysis means" refers to technology that uses sensors such as cameras and microphones to recognize a user's emotional state from their facial expressions and voice.

[0772] "Response optimization means" refers to technology that adjusts the response of a generative AI model based on the results of emotion analysis.

[0773] "Virtual store" refers to a virtual shopping environment provided on the Internet.

[0774] "Shopping assistant means" refers to a function for supporting the user's purchasing experience in a virtual store.

[0775] The present invention provides a system that improves the user's learning experience by providing educational content using a generative AI model and combining it with emotion analysis means that recognize the user's emotions. A specific embodiment of this system is described below.

[0776] Overall system configuration

[0777] The system mainly consists of the following three entities:

[0778] server

[0779] Terminal

[0780] User

[0781] server

[0782] The server hosts the generative AI model and provides e-learning content to users, and also authenticates users and includes an emotion analysis means to recognize their emotional state.

[0783] For specific hardware, the company uses cloud computing environments such as Amazon Web Services (AWS) and Microsoft Azure, which enable training and hosting of generative AI models, and Microsoft Azure's Cognitive Services for sentiment analysis.

[0784] The server provides the following main functions:

[0785] 1. Hosting a generative AI model: The generative AI model provides intelligent responses to prompts sent by the user.

[0786] 2. Providing e-learning content: Hosting educational content such as videos, texts, and quizzes and streaming them upon user request.

[0787] 3. User authentication: Provides functionality for authenticating users accessing the system.

[0788] 4. Emotion analysis: Analyzes the user's camera and microphone data to determine the user's emotional state in real time.

[0789] 5. Response optimization: Adjust the response of the generative AI model based on the results of sentiment analysis.

[0790] 6. Logging and Analysis: Recording user activity and analyzing learning progress.

[0791] Terminal

[0792] The terminal provides an interface between the user and the system, allowing the user to access the system and use e-learning content.

[0793] The specific hardware of the device includes smartphones, tablets, and PCs. Applications are developed using Flutter. The device provides the following features:

[0794] 1. Login interface: Provides a screen for users to log in to the system.

[0795] 2. Content playback: Play and display the content of the course selected by the user.

[0796] 3. Prompt input acceptance: Provides a screen where users can input prompts to the generated AI model.

[0797] 4. Transmission of emotion data: The user's facial expressions and voice are transmitted to the emotion analysis means using a camera and microphone.

[0798] 5. Progress display: Show users' learning progress in real time.

[0799] User

[0800] Users access the system using a terminal and use the provided e-learning content.

[0801] 1. Logging in to the system: A user logs in to the system using a terminal.

[0802] 2. Course selection: The user selects the desired course and begins learning.

[0803] 3. Prompt Creation: The user types a question or request to the generative AI model.

[0804] Example prompt: "Recommend a new smartphone."

[0805] 4. Sending emotional data: Using the device's camera and microphone, emotional data obtained from the user's facial expressions and voice is sent to the server.

[0806] 5. Receive response: Receive the response generated by the generative AI model and confirm the learnings and purchase suggestions.

[0807] Through the above process, the system is able to provide optimal learning content and recommend products based on the user's emotional state, improving the user's learning and purchasing experiences.

[0808] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0809] Step 1:

[0810] The server hosts the generative AI model and creates a new account for the user to access the system. It receives the user's registration information as input and notifies the user of the generated login information and initial setting information as output. At this time, it authenticates the user and saves the account information in the database.

[0811] Step 2:

[0812] A user logs into the system using a terminal. The terminal displays a login interface and sends the entered user ID and password to the server. The server performs authentication and, if successful, displays the user's dashboard as output.

[0813] Step 3:

[0814] The user selects the desired course and begins learning. The terminal sends a request for the selected course to the server, and the server provides the corresponding e-learning content to the terminal by streaming. As an output, the content is displayed on the user's terminal.

[0815] Step 4:

[0816] To recognize the user's emotional state, the device's camera and microphone are activated. The user's facial expression and voice data are acquired as input and sent to the server's emotion analysis means. The server then performs emotion analysis and outputs the user's emotional state (e.g., joy, excitement, fatigue) in real time.

[0817] Step 5:

[0818] The user uses a device to input a prompt to the generative AI model. The input prompt is sent to the server. The server takes into account the results of sentiment analysis and passes the prompt to the generative AI model, which then generates a response. The generated response is returned to the server.

[0819] Step 6:

[0820] The server formats the generated response and sends it to the device. The formatted response is displayed as output on the user's device. For example, if the user types the prompt "Recommend a new smartphone," the response might read, "Currently popular products are the latest smartphone models with excellent cameras."

[0821] Step 7:

[0822] User activity is recorded as a log and analyzed by the server. The input is user behavioral data, which is reflected in learning progress and product recommendations as output. At this time, emotional data stored in the database is also utilized to provide individually optimized content based on learning and purchasing history.

[0823] Step 8:

[0824] The system displays the user's progress on the device. It receives progress data sent from the server as input and displays it in a visually easy-to-understand format on a dashboard as output, allowing the user to check their own learning progress.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] [Third embodiment]

[0829] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0830] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0831] 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).

[0832] 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.

[0833] 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.

[0834] 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).

[0835] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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."

[0841] The present invention provides specific embodiments for implementing a system that utilizes a generative AI model to provide educational content and help users learn how to use AI correctly.

[0842] The system mainly consists of three entities: a server, a device, and a user. The server plays a central role in hosting the generative AI model and providing e-learning content. It also authenticates users, processes prompts, and records and analyzes their activities.

[0843] server

[0844] When the server receives a new user request, it first registers the user's authentication information in the database and automatically creates a new AI account. The created account is notified to the user along with the initial setup information.

[0845] The educational content includes videos, texts, quizzes, etc., and is hosted on a server. The content is streamed in response to user requests. As the user progresses through the content, progress information is recorded in real time on the server and analyzed as needed. When a prompt is entered, the server sends it to a generative AI model, obtains a response, and returns it to the user.

[0846] Terminal

[0847] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select learning content from the courses offered. The login information is sent to the server, and after successful authentication, a list of available courses is displayed on the terminal.

[0848] The user browses the content through the device and inputs the necessary prompts into the generation AI. The response received from the server is displayed on the device in a format that is easy for the user to understand. The device also displays the user's progress and manages their learning progress.

[0849] User

[0850] First, users log in to the system through their terminal. Then, they select an appropriate course from the available e-learning content. As they study the content, they input questions and instructions (prompts) to the generative AI and receive its responses. This allows them to learn how to use the generative AI and how to create appropriate prompts.

[0851] Learning progress is displayed in real time on the dashboard, allowing users to check their own progress as they study. Furthermore, based on the responses of the generating AI, users can deepen their own understanding and progress more efficiently.

[0852] Specific examples

[0853] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. As the user watches the video and answers the quiz, the results are sent to the server and recorded as progress information. Next, the user enters a prompt, "Teach me how to summarize a text," which is sent via the server to the generative AI. The generative AI's response is formatted by the server and returned to the device. The user can review the response and enter additional prompts to further their understanding.

[0854] As described above, this system effectively utilizes generative AI models in educational settings and provides a series of methods for users to correctly understand and use them.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] The server receives a new user request: the user requests to create an account to access the system.

[0858] Step 2:

[0859] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[0860] Step 3:

[0861] A user logs into the system using a terminal, which sends the user's login information to the server.

[0862] Step 4:

[0863] The server authenticates the user, and if authentication is successful, sends a list of available e-learning content to the terminal.

[0864] Step 5:

[0865] The terminal displays the received course list to the user, who selects the desired course and clicks the start button.

[0866] Step 6:

[0867] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[0868] Step 7:

[0869] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[0870] Step 8:

[0871] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[0872] Step 9:

[0873] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[0874] Step 10:

[0875] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[0876] Step 11:

[0877] The generative AI model generates a response and returns it to the server, which formats it and converts it into a form that is easy for the user to understand.

[0878] Step 12:

[0879] The server sends the formatted response to the terminal, which displays the response to the user.

[0880] Step 13:

[0881] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[0882] Step 14:

[0883] The server logs all user activity and analyzes it as needed to understand the user's learning patterns and progress.

[0884] In this way, this system provides educational content using generative AI, realizing a series of processes that enable users to learn correct usage.

[0885] Example 1

[0886] 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."

[0887] Conventional e-learning systems have struggled to effectively utilize generative AI models to improve users' learning experiences. They lacked adequate support for users to understand how to use generative AI and apply its responses to their learning. Furthermore, real-time progress recording and analysis were insufficient, necessitating a systematic approach to maximize user learning outcomes.

[0888] 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.

[0889] In this invention, the server includes an information processing device means for hosting the generative AI model, an information processing device means for providing educational content, an information processing device means for authenticating the user, an information processing device means for sending prompts to the generative AI model and obtaining its response, an information processing device means for recording and analyzing user activity, a means for notifying the user of generated user account information, a means for recording the progress of the educational content in real time, and a means for formatting the generative AI's response in a format optimal for the user. This enables the user to receive educational content through the generative AI model, understand the progress in real time, and receive optimized learning support.

[0890] A "generative AI model" is a technical means of automatically generating responses based on user input.

[0891] An "information processing device" is a device for recording, analyzing, and communicating data.

[0892] "User authentication" is the process of verifying a user's identity and their access privileges.

[0893] A "prompt" is input such as a question or instruction that a user sends to a generative AI model.

[0894] An "input / output device" is an interface device that allows a user to input data into the system and display the results.

[0895] "Progress" indicates the user's level of achievement and history information as they progress through the learning content.

[0896] "Formatting" is the process of preparing data in a particular form.

[0897] "Educational content" refers to teaching materials and resources provided to aid users in their learning and understanding.

[0898] The present invention is an educational content provision system that includes a generative AI model, and aims to enable users to learn how to utilize generative AI. Specific embodiments for implementing this system are described below.

[0899] System Structure

[0900] server

[0901] The server is the core of this system and includes an information processing device with the following functions:

[0902] 1. Hosting a generative AI model: A generative AI model is a technology that receives prompts from users and generates responses accordingly. A server hosts this model and responds to user requests in real time.

[0903] 2. Providing educational content: The server hosts educational content such as videos, texts, and quizzes, and streams them to the device upon user request.

[0904] 3. User authentication: The server registers the new user's authentication information in the database and notifies the created user account information and initial setting information.

[0905] 4. Prompt processing and response: The server sends the prompts entered by the user to the generative AI model, receives the responses, formats them in a format that is optimal for the user, and returns them to the model.

[0906] 5. Activity recording and analysis: Recording and analyzing user progress and activity data in real time helps maximize learning outcomes.

[0907] Terminal

[0908] The terminal includes input / output devices that allow the user to interact with the system:

[0909] 1. Login and Course Selection: The user logs into the system using a terminal and selects the appropriate course from the list of available courses.

[0910] 2. Playback of educational content and input of prompts: The content of the course selected by the user is displayed on the device, and the user can watch videos, read text, and answer quizzes. The user can also input prompts to the generation AI through the device.

[0911] 3. Response display and progress management: The response from the AI ​​is displayed on the device, providing appropriate answers to the user's questions. In addition, the device displays the user's learning progress in real time and helps manage their learning plan.

[0912] User

[0913] Users are the users of the system and interact with it in the following ways:

[0914] 1. Login and course selection: The user logs into the system via a terminal and selects the course to study from the provided e-learning content.

[0915] 2. Content learning: The user studies the educational content displayed on the device, providing prompts to the AI ​​generator as needed, and receives responses that allow the user to deepen their understanding.

[0916] 3. Track your progress: Users can view their progress on the dashboard and adjust their learning methods as needed. For example, they can take a quiz after watching a video to gauge their understanding.

[0917] Specific examples

[0918] For example, if a user selects the "Basic Operations of Generative AI" course, the following specific examples might be possible:

[0919] 1. After logging in, the user selects the "Basic Operations of Generative AI" course.

[0920] 2. The server streams the relevant video content to the device.

[0921] 3. Users watch videos and take quizzes.

[0922] 4. The quiz results are sent to the server and recorded as progress information.

[0923] 5. Next, the user enters the prompt "Please tell me how to summarize a text."

[0924] 6. The server sends the prompt to the generative AI model and receives a response.

[0925] 7. The generated AI's response is formatted on the server and displayed on the device.

[0926] 8. The user uses the responses to deepen their understanding and continue learning.

[0927] As described above, this system effectively utilizes generative AI models and provides a series of methods for users to correctly understand and use them.

[0928] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0929] Step 1:

[0930] A user logs in to the system. The user uses a terminal to enter login information and clicks the login button. The terminal sends this input information to the server. The server collates the user's authentication information stored in the database, and if authentication is successful, it generates a login success message and a list of available courses and returns them to the terminal. The user can check the list of courses on the terminal screen.

[0931] Step 2:

[0932] The user selects the course they wish to study. They choose the course they wish to study from the list of courses displayed on the device screen and click the select button. The device sends the selected course information to the server. The server prepares educational content (videos, text, quizzes, etc.) corresponding to the selected course and returns the URL of that content to the device. The device uses this URL to display the content to the user.

[0933] Step 3:

[0934] The user views the educational content. The user watches the educational content on the device screen and learns the necessary information. The server records the timestamps of the user's start and end times and saves them in a database as progress. In addition, the server records and analyzes the user's progress in real time as they progress through the content.

[0935] Step 4:

[0936] The user answers the quiz. The quiz is presented as part of the learning content, and the user enters their answers and clicks the submit button. The device sends the user's answers to the server. The server determines whether the quiz is correct or incorrect, returns the results to the user, and records the results as progress data. The user can view the quiz results and explanations.

[0937] Step 5:

[0938] The user enters a prompt into the generative AI. The user enters a question or instruction related to the learning content as a prompt into the device and clicks the send button. The device sends the prompt to the server. The server sends the prompt to the generative AI model and receives the generated response. The server formats this response in a format optimal for the user and returns it to the device. The user can review the response from the generative AI to deepen their understanding.

[0939] Step 6:

[0940] The user checks their learning progress. The user checks their learning progress on the dashboard screen of their device. The server updates the progress data in real time and displays it on the dashboard. The user can adjust their learning plan based on this information.

[0941] The above is the specific processing flow of the system program.

[0942] (Application example 1)

[0943] 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."

[0944] Conventional educational content systems have difficulty in providing individualized support for users as they learn, and lack real-time management of learning progress and interactive learning assistance using generative AI. Therefore, it was necessary to provide a system that would enable users to progress through their studies effectively and efficiently. Furthermore, support for users to create appropriate prompts through generative AI and receive responses to deepen their learning was limited, so it was necessary to solve these issues.

[0945] 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.

[0946] In this invention, the server includes a means for hosting the generative AI model, a means for providing educational content, a means for authenticating users, a means for sending prompts to the generative AI model and obtaining responses, and a means for recording and analyzing user activities, thereby enabling real-time management of educational content, interactive learning support by the generative AI, and effective updates on the user's learning progress.

[0947] The terminal also includes a means for providing an interface for accessing educational content, a means for playing content and accepting prompt input, a means for displaying the user's progress, a means for inputting prompts to the generating AI and displaying the responses, and a means for updating progress information in real time, thereby enabling the user to effectively use the generating AI and deepen their learning while checking their learning progress in real time.

[0948] A "generative AI model" is a type of artificial intelligence trained on large datasets to generate responses in natural language based on prompts from users.

[0949] "Server means" refers to a computer system that provides services to users via a network and has the function of processing and storing data.

[0950] "Educational content" refers to a set of information or materials provided for educational purposes, and may consist of videos, texts, quizzes, etc.

[0951] "User authentication" is the process of verifying a user's identity and granting appropriate privileges when accessing a system.

[0952] A "prompt" is text that is input as an instruction or question to a generative AI model, which causes the generative AI to return a corresponding response.

[0953] "User activity" refers to all operations and actions performed by a user while using the system, which are recorded as logs and analyzed.

[0954] "Terminal means" refers to a physical or virtual device through which a user interacts with the system, such as a personal computer, smartphone, or tablet.

[0955] "Content playback" is the process of presenting educational content visually or audibly to a user.

[0956] "Progress" is information that indicates how far the user has progressed in their studies, and the system visually displays this information.

[0957] "Updating progress information in real time" refers to the process of instantly updating the system with new information obtained as the user progresses with their studies, and always displaying the latest status.

[0958] This invention relates to an educational content distribution system that utilizes generative AI models, and the system consists of three main entities: a server, a terminal, and a user.

[0959] Server Features

[0960] The server has the following functions:

[0961] 1. Hosting generative AI models: Hosting large language models to generate natural language responses based on user prompts, such as GPT-3.

[0962] 2. Providing educational content: We manage and host a variety of educational content and provide it to users upon request, including video, text, and quiz-style learning materials.

[0963] 3. User Authentication: Authenticating new and existing users and setting up appropriate learning accounts.

[0964] 4. Sending and receiving prompts: Receive prompts from the user, send them to the generative AI model to get a response, format this response and send it to the device.

[0965] 5. User activity recording and progress management: Record users' learning activities and analyze the data to manage their progress.

[0966] Device Features

[0967] The terminal has the following features:

[0968] 1. Provide an interface: Provide an interface for accessing educational content, allowing users to enter login information and access available courses.

[0969] 2. Content playback and prompt input: Plays the provided educational content and provides an interface for users to input prompts to the generated AI. For example, users can input prompts such as, "Please tell me the basic operations of the generated AI."

[0970] 3. Progress display: Displays the user's learning progress in real time and supports learning progress management.

[0971] User Roles

[0972] The user uses the system as follows:

[0973] 1. Log in to the system: The user logs in to the system using a terminal and selects an appropriate course from the available educational content.

[0974] 2. Viewing content and answering quizzes: View the content of the selected course and answer the related quizzes. These activities are recorded on the server and managed as progress information.

[0975] 3. Prompt input to the generative AI: During the learning process, questions or instructions (prompts) can be input to the generative AI and a response can be received. For example, you can input a prompt such as, "Please tell me how to summarize a text."

[0976] 4. Track your progress and keep learning: See your progress in real time as you learn, and enter additional prompts to further your understanding.

[0977] Hardware and software used

[0978] This system uses the following hardware and software:

[0979] Server: A server for data processing and storage, where generative AI models and databases (e.g., MySQL, MongoDB) are hosted.

[0980] Terminal: The device with which the user interacts. This can be a smartphone, tablet, or computer.

[0981] Software: Web frameworks such as Flask are used for server-side processing, and the user interface is built using HTML, CSS, JavaScript, etc.

[0982] The system constructed in this way enables users to effectively and efficiently study using generative AI. By asking questions to the generative AI and checking its responses, users can deepen their learning and further their understanding. Furthermore, by checking progress in real time, it becomes easier to manage the learning process.

[0983] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0984] Step 1:

[0985] A user logs into the system using a terminal. After entering login information (user name and password), the terminal sends this to the server. The server refers to the user authentication database and verifies that the authentication information is correct. If authentication is successful, the server returns a message of successful authentication and a list of available courses to the user. The terminal receives this and displays it to the user.

[0986] Step 2:

[0987] The user selects the course they wish to study from a list of courses available on the device. The device sends a course selection request to the server. The server searches for the appropriate educational content and prepares to stream the desired course content to the device. The device then plays the received course content to the user.

[0988] Step 3:

[0989] Users progress through their studies by viewing educational videos and textbooks according to the course content. Every time an interaction occurs, such as playing a video, viewing textbooks, or answering a quiz, a detailed activity log is sent from the device to the server. The server records and analyzes these activity logs and updates the user's progress data.

[0990] Step 4:

[0991] During the learning process, the user inputs questions and instructions to the generative AI model. For example, they input a prompt such as, "Please tell me the basic operations of generative AI." The device sends this prompt to the server. The server passes the prompt to the generative AI model and receives its response. The server formats the response and returns it to the device. The device displays this response to the user.

[0992] Step 5:

[0993] If the user enters an additional question as a prompt, the process repeats: the server sends the prompt to the generative AI model again and receives a response, which the user responds to further deepen their learning.

[0994] Step 6:

[0995] The user's progress is constantly updated in real time. Every time the user answers a quiz or watches a new video, the device sends progress data to the server. The server analyzes this data and updates the user's learning dashboard. The device then displays the latest progress information to the user in real time.

[0996] Step 7:

[0997] When the user finishes learning, he logs out of the system. The device sends a logout request to the server, which then terminates the user's session. All learning activity logs and progress data are stored on the server and can be reused in the future.

[0998] By following the above steps, users can efficiently advance their learning by using an interactive educational content delivery system that utilizes generative AI models.

[0999] 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.

[1000] The present invention provides educational content using a generative AI model, and further combines it with an emotion engine that recognizes the user's emotions to implement a system that improves the user's learning experience. Specific embodiments for implementing the system are described below.

[1001] The system mainly consists of three components: a server, a device, and a user. The server hosts the generative AI model and provides e-learning content. It also has an emotion engine that recognizes user emotions and has the ability to collect and analyze user emotion data.

[1002] server

[1003] The server creates a new account for the user to access the system, automatically generates a generated AI account, and notifies the user of the generated login information and initial setting information.

[1004] Educational content (videos, texts, quizzes, etc.) is hosted on a server and streamed upon user request. The server also records user progress and provides real-time analysis as needed.

[1005] Once a prompt is entered, the server sends it to the generative AI model to get a response, where an emotion engine is used to tailor the generative AI model's response based on the user's emotional state, which is then formatted and finally returned to the user.

[1006] Terminal

[1007] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select the desired course from a list. The terminal receives the content of the selected course from the server and displays it.

[1008] Furthermore, the device uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine, which analyzes this data and determines the user's emotional state.

[1009] The user enters a prompt into the generation AI, and the device sends the prompt to the server, which formats the generated response and returns it to the device, where it is displayed to the user.

[1010] User

[1011] Users log in to the system using a terminal and select the provided e-learning content. As they study the content, they input prompts to the generation AI and receive responses. The emotion engine then checks the user's emotional state and assists in providing the optimal response.

[1012] The server also recommends appropriate e-learning content based on the user's emotional state: for example, if the user is tired, it recommends light review content, and if the user is concentrating, it recommends new, more challenging content.

[1013] Specific examples

[1014] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after the video ends, the emotion engine captures the emotion data. When the user enters the prompt "Please give me a summary of the text," the device sends the prompt to the server.

[1015] The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response, which is formatted on the server and displayed to the user via their device. The user can review the response and enter additional prompts if they have further questions.

[1016] The server logs all user activity and emotional data, capturing learning patterns and progress in real time, which can then be used to optimize the user's learning experience.

[1017] In this way, the system combines a generative AI model and an emotion engine to improve users' learning efficiency and provide individually optimized educational content.

[1018] The processing flow will be explained below.

[1019] Step 1:

[1020] The server receives a new user request: the user requests to create an account to access the system.

[1021] Step 2:

[1022] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[1023] Step 3:

[1024] A user logs into the system using a terminal, which sends the user's login information to the server.

[1025] Step 4:

[1026] The server authenticates the user and sends a list of available e-learning content to the terminal.

[1027] Step 5:

[1028] The terminal displays the received course list to the user, who selects the desired course and clicks Start.

[1029] Step 6:

[1030] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[1031] Step 7:

[1032] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[1033] Step 8:

[1034] The device uses its built-in camera and microphone to collect the user's emotional data (facial expressions and voice) and transmits it to the server.

[1035] Step 9:

[1036] The server uses an emotion engine to analyze the received emotion data and determine the user's emotional state.

[1037] Step 10:

[1038] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[1039] Step 11:

[1040] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[1041] Step 12:

[1042] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[1043] Step 13:

[1044] The generative AI model generates a response and returns it to the server, which then adjusts the response of the generative AI model based on the emotion recognition results of the emotion engine.

[1045] Step 14:

[1046] The server formats a response and sends it to the terminal, which displays it to the user.

[1047] Step 15:

[1048] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[1049] Step 16:

[1050] The server logs all user activity and emotional data and analyzes it as needed to understand the user's learning patterns and progress.

[1051] Step 17:

[1052] The server recommends optimal e-learning content based on the user's emotional state: for example, it recommends light review content if the user is tired, or new, more challenging content if the user is concentrating.

[1053] In this way, the system not only provides educational content that utilizes generative AI and helps users learn how to use it correctly, but also uses emotional data to individually optimize the user's learning experience.

[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 headset type terminal 314 will be referred to as a "terminal."

[1056] Conventional e-learning systems provide uniform educational content without considering the user's emotional state, resulting in reduced learning efficiency. Even response systems using generative AI models have difficulty providing appropriate responses based on the user's real-time emotions. Therefore, there is a need for a system that can improve the user's learning experience and provide individually optimized educational content.

[1057] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for hosting the generative AI model, a means for providing e-learning content, a means for authenticating the user, a means for sending prompts to the generative AI model and obtaining its response, a means for recording and analyzing the user's activity as a log, a means for analyzing emotional data and having an emotion engine for determining the user's emotional state, and a means for adjusting the response of the generative AI model based on the user's emotional state. This makes it possible to provide an optimal response according to the user's emotional state and improve learning efficiency.

[1058] A "generative AI model" is an algorithm or system that automatically generates natural language responses based on user prompts.

[1059] "Server means" means a server that has the functionality to host the generative AI model, provide e-learning content, authenticate users, and send prompts to the generative AI model to obtain responses.

[1060] "E-learning content" refers to educational content in digital format, such as videos, texts, and quizzes, provided for learning purposes.

[1061] A "prompt" is a question or instruction that a user inputs to a generative AI model.

[1062] An "emotion engine" is a system or algorithm that analyzes a user's emotional data and determines the user's emotional state.

[1063] "Terminal means" refers to a terminal that provides an interface for users to interact with the system, has the functions of playing content, accepting prompt input, and collecting emotion data.

[1064] "User activity" refers to a series of operations or actions that a user performs on a system.

[1065] A "log" is data that records the history of a user's activities, emotional data, and so on.

[1066] The present invention relates to a system that provides educational content using a generative AI model and combines it with an emotion engine that recognizes the user's emotions to improve the user's learning experience. Specific embodiments of the system are described below.

[1067] System configuration

[1068] The system is mainly composed of three entities: a server, a terminal, and a user.

[1069] Server Features

[1070] The server hosts the generative AI model and provides e-learning content. It also authenticates users, sends prompts to the generative AI model, and receives responses. It also logs and analyzes user activity and uses an emotion engine to determine the user's emotional state.

[1071] Specifically, the server uses the following software and hardware:

[1072] Generative AI models (e.g., GPT-3)

[1073] Emotion engines (e.g., Affectiva)

[1074] Database management system (e.g. MySQL)

[1075] Web server (e.g. Apache)

[1076] Device Features

[1077] The terminal provides an interface for users to interact with the system. Users can log in using the terminal and select the course they want. The terminal receives the content of the selected course from the server and displays it. It also uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine.

[1078] Specific hardware and software include:

[1079] Camera (to capture the user's facial expressions)

[1080] A microphone (to record your vocal inflections)

[1081] User interface (e.g., web browser or dedicated application)

[1082] User operations

[1083] Users log in to the system using their devices and study the provided e-learning content. If they have any questions while studying, they can input prompt sentences and send them to the generative AI model. Furthermore, appropriate responses and content are provided depending on the user's emotional state.

[1084] Specific examples

[1085] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after finishing, the emotion engine captures emotion data. When the user inputs the prompt "Please summarize the text," the data is sent to the server. The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response. This response is formatted by the server and displayed to the user via the device. The user can review the response and input additional prompts if they have further questions.

[1086] Examples of prompts:

[1087] "Please tell me more about the educational system using generative AI models."

[1088] "Please briefly summarize what you learned today."

[1089] As a result, a system is realized that can provide optimal educational content that takes into account the user's emotional state and improve learning efficiency.

[1090] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1091] Step 1: User Login

[1092] 1. The device displays the login screen.

[1093] 2. The user enters their username and password.

[1094] 3. The device sends the entered login information to the server.

[1095] 4. The server receives the login information and authenticates it by checking it against the database (input is the user's login information, output is the authentication result).

[1096] 5. The server returns the authentication result to the terminal.

[1097] 6. The terminal displays a successful authentication message to the user.

[1098] Specific behavior:

[1099] The terminal's user interface provides a text box and a submit button.

[1100] The server queries a database (e.g. MySQL) to verify the user information.

[1101] Step 2: Course Selection and Content Acquisition

[1102] 1. After logging in, the user views the list of courses offered.

[1103] 2. The terminal accepts the user's course selection.

[1104] 3. The terminal sends a request for the selected course to the server (input is the selected course ID, output is the course content).

[1105] 4. The server receives the course request, retrieves the corresponding content from the database, and prepares it for streaming (data processing: content extraction).

[1106] 5. The server streams the content to the device.

[1107] 6. The device receives the content and displays it to the user.

[1108] Specific behavior:

[1109] The device records the user's selection through clicks.

[1110] The server responds to the HTTP request with content (e.g., video URL, text).

[1111] Step 3: Complete and submit prompts during training

[1112] 1. The user has a question while studying and enters a prompt.

[1113] 2. The terminal accepts the input prompt.

[1114] 3. The device sends a prompt to the server (the input is the user's prompt, and the output is a request to the generative AI model).

[1115] Specific behavior:

[1116] The terminal's user interface provides a text area and a submit button.

[1117] The server receives the prompt sentence and adds it to a queue for post-processing.

[1118] Step 4: Generative AI model generates a response

[1119] 1. The server sends a prompt to the generative AI model (data calculation: response generation).

[1120] 2. The generative AI model parses the prompt and generates a response (the input is the prompt sentence, and the output is the generated response).

[1121] 3. The server receives the generated response.

[1122] Specific behavior:

[1123] The server sends an API request to the generative AI model (e.g., GPT-3).

[1124] A generative AI model uses natural language processing algorithms to generate responses.

[1125] Step 5: Collect and analyze sentiment data

[1126] 1. The device's camera and microphone collect the user's emotional data (input is the user's facial expression data and voice data).

[1127] 2. The device sends the collected emotion data to the server (the output is emotion data).

[1128] Specific behavior:

[1129] Sensors on the device capture the user's facial expressions, and a microphone records the intonation of the voice.

[1130] The server receives this data and sends it to the emotion engine.

[1131] Step 6: Response adjustment with the emotion engine

[1132] 1. The server receives emotion data.

[1133] 2. The server sends the emotion data to the emotion engine, which analyzes the user's emotional state (the input is emotion data, and the output is the emotion analysis result).

[1134] 3. The emotion engine determines the emotional state and adjusts the response of the generative AI model (data processing: response adjustment).

[1135] Specific behavior:

[1136] The server sends data to the emotion engine in the form of an API request.

[1137] The emotion engine analyzes the data and adjusts the tone and content of the response accordingly.

[1138] Step 7: Formatting and displaying the response

[1139] 1. The server receives the adjusted response and converts it into the appropriate format (data processing: format conversion).

[1140] 2. The server sends the formatted response to the terminal (output is the formatted response).

[1141] 3. The terminal receives the response and displays it to the user.

[1142] Specific behavior:

[1143] The server formats the response in HTML or JSON.

[1144] The device will display the response in a chat box or pop-up format.

[1145] Step 8: Progress Recording and Analysis

[1146] 1. The server records the user's activity and emotion data (input is activity data and emotion data, output is recorded data).

[1147] 2. The server analyzes the recorded data and generates learning patterns and progress reports (data calculation: progress analysis).

[1148] Specific behavior:

[1149] The server stores the activity and emotion data in a database.

[1150] Big data analytics tools (e.g., Hadoop) analyze the data and generate reports.

[1151] Step 9: Recommending the right content

[1152] 1. The server selects appropriate content based on the user's emotional state and progress (data calculation: content recommendation).

[1153] 2. The server sends the recommended content to the terminal (the output is the recommended content).

[1154] 3. The device displays the recommended content to the user.

[1155] Specific behavior:

[1156] The server selects content using machine learning algorithms.

[1157] Your device will display recommended content in a list or pop-up notification.

[1158] The above are the specific processing steps in the embodiment of the invention.

[1159] (Application example 2)

[1160] 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."

[1161] Conventional e-learning systems and virtual stores have difficulty providing services that take into account the user's emotional state, making it difficult to recommend content or products that best suit individual needs. As a result, users' learning efficiency and purchasing experience are not sufficiently improved. In addition, the lack of optimization of real-time responses based on the user's emotional state can sometimes result in a poor user experience.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1163] In this invention, the server includes means for hosting the generative AI model, means for providing e-learning content, means for authenticating a user, means for sending prompts to the generative AI model and obtaining responses, means for recording and analyzing user activity as a log, terminal means for providing an interface for accessing the e-learning content, terminal means for playing content and accepting prompt inputs, terminal means for displaying the user's progress, emotion analysis means for recognizing the user's emotional state, response optimization means for adjusting the response of the generative AI model based on the emotion analysis results, and shopping assistant means for recommending products in a virtual store. This enables optimal learning content and product recommendations based on the user's emotional state, improving the user experience.

[1164] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate sophisticated responses to user input.

[1165] "Server Means" refers to a central processing unit that allows users to access the system and stores, processes, and distributes data.

[1166] "E-learning content" refers to digital content such as online learning materials, videos, and quizzes provided for educational purposes.

[1167] "User authentication" refers to the process of verifying the legitimacy of a user when they access a system.

[1168] A "prompt" refers to text or voice input that a user uses to enter a question or request to a generative AI model.

[1169] "Activity logging" refers to data used to record and analyze a user's system usage history.

[1170] "Interface" refers to the screen and operating means by which a user interacts with a system.

[1171] "Terminal means" refers to a device that allows a user to access and operate the system.

[1172] "Emotion analysis means" refers to technology that uses sensors such as cameras and microphones to recognize a user's emotional state from their facial expressions and voice.

[1173] "Response optimization means" refers to technology that adjusts the response of a generative AI model based on the results of emotion analysis.

[1174] "Virtual store" refers to a virtual shopping environment provided on the Internet.

[1175] "Shopping assistant means" refers to a function for supporting the user's purchasing experience in a virtual store.

[1176] The present invention provides a system that improves the user's learning experience by providing educational content using a generative AI model and combining it with emotion analysis means that recognize the user's emotions. A specific embodiment of this system is described below.

[1177] Overall system configuration

[1178] The system mainly consists of the following three entities:

[1179] server

[1180] Terminal

[1181] User

[1182] server

[1183] The server hosts the generative AI model and provides e-learning content to users, and also authenticates users and includes an emotion analysis means to recognize their emotional state.

[1184] For specific hardware, the company uses cloud computing environments such as Amazon Web Services (AWS) and Microsoft Azure, which enable training and hosting of generative AI models, and Microsoft Azure's Cognitive Services for sentiment analysis.

[1185] The server provides the following main functions:

[1186] 1. Hosting a generative AI model: The generative AI model provides intelligent responses to prompts sent by the user.

[1187] 2. Providing e-learning content: Hosting educational content such as videos, texts, and quizzes and streaming them upon user request.

[1188] 3. User authentication: Provides functionality for authenticating users accessing the system.

[1189] 4. Emotion analysis: Analyzes the user's camera and microphone data to determine the user's emotional state in real time.

[1190] 5. Response optimization: Adjust the response of the generative AI model based on the results of sentiment analysis.

[1191] 6. Logging and Analysis: Recording user activity and analyzing learning progress.

[1192] Terminal

[1193] The terminal provides an interface between the user and the system, allowing the user to access the system and use e-learning content.

[1194] The specific hardware of the device includes smartphones, tablets, and PCs. Applications are developed using Flutter. The device provides the following features:

[1195] 1. Login interface: Provides a screen for users to log in to the system.

[1196] 2. Content playback: Play and display the content of the course selected by the user.

[1197] 3. Prompt input acceptance: Provides a screen where users can input prompts to the generated AI model.

[1198] 4. Transmission of emotion data: The user's facial expressions and voice are transmitted to the emotion analysis means using a camera and microphone.

[1199] 5. Progress display: Show users' learning progress in real time.

[1200] User

[1201] Users access the system using a terminal and use the provided e-learning content.

[1202] 1. Logging in to the system: A user logs in to the system using a terminal.

[1203] 2. Course selection: The user selects the desired course and begins learning.

[1204] 3. Prompt Creation: The user types a question or request to the generative AI model.

[1205] Example prompt: "Recommend a new smartphone."

[1206] 4. Sending emotional data: Using the device's camera and microphone, emotional data obtained from the user's facial expressions and voice is sent to the server.

[1207] 5. Receive response: Receive the response generated by the generative AI model and confirm the learnings and purchase suggestions.

[1208] Through the above process, the system is able to provide optimal learning content and recommend products based on the user's emotional state, improving the user's learning and purchasing experiences.

[1209] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1210] Step 1:

[1211] The server hosts the generative AI model and creates a new account for the user to access the system. It receives the user's registration information as input and notifies the user of the generated login information and initial setting information as output. At this time, it authenticates the user and saves the account information in the database.

[1212] Step 2:

[1213] A user logs into the system using a terminal. The terminal displays a login interface and sends the entered user ID and password to the server. The server performs authentication and, if successful, displays the user's dashboard as output.

[1214] Step 3:

[1215] The user selects the desired course and begins learning. The terminal sends a request for the selected course to the server, and the server provides the corresponding e-learning content to the terminal by streaming. As an output, the content is displayed on the user's terminal.

[1216] Step 4:

[1217] To recognize the user's emotional state, the device's camera and microphone are activated. The user's facial expression and voice data are acquired as input and sent to the server's emotion analysis means. The server then performs emotion analysis and outputs the user's emotional state (e.g., joy, excitement, fatigue) in real time.

[1218] Step 5:

[1219] The user uses a device to input a prompt to the generative AI model. The input prompt is sent to the server. The server takes into account the results of sentiment analysis and passes the prompt to the generative AI model, which then generates a response. The generated response is returned to the server.

[1220] Step 6:

[1221] The server formats the generated response and sends it to the device. The formatted response is displayed as output on the user's device. For example, if the user types the prompt "Recommend a new smartphone," the response might read, "Currently popular products are the latest smartphone models with excellent cameras."

[1222] Step 7:

[1223] User activity is recorded as a log and analyzed by the server. The input is user behavioral data, which is reflected in learning progress and product recommendations as output. At this time, emotional data stored in the database is also utilized to provide individually optimized content based on learning and purchasing history.

[1224] Step 8:

[1225] The system displays the user's progress on the device. It receives progress data sent from the server as input and displays it in a visually easy-to-understand format on a dashboard as output, allowing the user to check their own learning progress.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] [Fourth embodiment]

[1230] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1231] 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.

[1232] 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).

[1233] 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.

[1234] 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.

[1235] 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).

[1236] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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.

[1242] 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."

[1243] The present invention provides specific embodiments for implementing a system that utilizes a generative AI model to provide educational content and help users learn how to use AI correctly.

[1244] The system mainly consists of three entities: a server, a device, and a user. The server plays a central role in hosting the generative AI model and providing e-learning content. It also authenticates users, processes prompts, and records and analyzes their activities.

[1245] server

[1246] When the server receives a new user request, it first registers the user's authentication information in the database and automatically creates a new AI account. The created account is notified to the user along with the initial setup information.

[1247] The educational content includes videos, texts, quizzes, etc., and is hosted on a server. The content is streamed in response to user requests. As the user progresses through the content, progress information is recorded in real time on the server and analyzed as needed. When a prompt is entered, the server sends it to a generative AI model, obtains a response, and returns it to the user.

[1248] Terminal

[1249] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select learning content from the courses offered. The login information is sent to the server, and after successful authentication, a list of available courses is displayed on the terminal.

[1250] The user browses the content through the device and inputs the necessary prompts into the generation AI. The response received from the server is displayed on the device in a format that is easy for the user to understand. The device also displays the user's progress and manages their learning progress.

[1251] User

[1252] First, users log in to the system through their terminal. Then, they select an appropriate course from the available e-learning content. As they study the content, they input questions and instructions (prompts) to the generative AI and receive its responses. This allows them to learn how to use the generative AI and how to create appropriate prompts.

[1253] Learning progress is displayed in real time on the dashboard, allowing users to check their own progress as they study. Furthermore, based on the responses of the generating AI, users can deepen their own understanding and progress more efficiently.

[1254] Specific examples

[1255] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. As the user watches the video and answers the quiz, the results are sent to the server and recorded as progress information. Next, the user enters a prompt, "Teach me how to summarize a text," which is sent via the server to the generative AI. The generative AI's response is formatted by the server and returned to the device. The user can review the response and enter additional prompts to further their understanding.

[1256] As described above, this system effectively utilizes generative AI models in educational settings and provides a series of methods for users to correctly understand and use them.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] The server receives a new user request: the user requests to create an account to access the system.

[1260] Step 2:

[1261] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[1262] Step 3:

[1263] A user logs into the system using a terminal, which sends the user's login information to the server.

[1264] Step 4:

[1265] The server authenticates the user, and if authentication is successful, sends a list of available e-learning content to the terminal.

[1266] Step 5:

[1267] The terminal displays the received course list to the user, who selects the desired course and clicks the start button.

[1268] Step 6:

[1269] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[1270] Step 7:

[1271] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[1272] Step 8:

[1273] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[1274] Step 9:

[1275] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[1276] Step 10:

[1277] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[1278] Step 11:

[1279] The generative AI model generates a response and returns it to the server, which formats it and converts it into a form that is easy for the user to understand.

[1280] Step 12:

[1281] The server sends the formatted response to the terminal, which displays the response to the user.

[1282] Step 13:

[1283] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[1284] Step 14:

[1285] The server logs all user activity and analyzes it as needed to understand the user's learning patterns and progress.

[1286] In this way, this system provides educational content using generative AI, realizing a series of processes that enable users to learn correct usage.

[1287] Example 1

[1288] 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."

[1289] Conventional e-learning systems have struggled to effectively utilize generative AI models to improve users' learning experiences. They lacked adequate support for users to understand how to use generative AI and apply its responses to their learning. Furthermore, real-time progress recording and analysis were insufficient, necessitating a systematic approach to maximize user learning outcomes.

[1290] 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.

[1291] In this invention, the server includes an information processing device means for hosting the generative AI model, an information processing device means for providing educational content, an information processing device means for authenticating the user, an information processing device means for sending prompts to the generative AI model and obtaining its response, an information processing device means for recording and analyzing user activity, a means for notifying the user of generated user account information, a means for recording the progress of the educational content in real time, and a means for formatting the generative AI's response in a format optimal for the user. This enables the user to receive educational content through the generative AI model, understand the progress in real time, and receive optimized learning support.

[1292] A "generative AI model" is a technical means of automatically generating responses based on user input.

[1293] An "information processing device" is a device for recording, analyzing, and communicating data.

[1294] "User authentication" is the process of verifying a user's identity and their access privileges.

[1295] A "prompt" is input such as a question or instruction that a user sends to a generative AI model.

[1296] An "input / output device" is an interface device that allows a user to input data into the system and display the results.

[1297] "Progress" indicates the user's level of achievement and history information as they progress through the learning content.

[1298] "Formatting" is the process of preparing data in a particular form.

[1299] "Educational content" refers to teaching materials and resources provided to aid users in their learning and understanding.

[1300] The present invention is an educational content provision system that includes a generative AI model, and aims to enable users to learn how to utilize generative AI. Specific embodiments for implementing this system are described below.

[1301] System Structure

[1302] server

[1303] The server is the core of this system and includes an information processing device with the following functions:

[1304] 1. Hosting a generative AI model: A generative AI model is a technology that receives prompts from users and generates responses accordingly. A server hosts this model and responds to user requests in real time.

[1305] 2. Providing educational content: The server hosts educational content such as videos, texts, and quizzes, and streams them to the device upon user request.

[1306] 3. User authentication: The server registers the new user's authentication information in the database and notifies the created user account information and initial setting information.

[1307] 4. Prompt processing and response: The server sends the prompts entered by the user to the generative AI model, receives the responses, formats them in a format that is optimal for the user, and returns them to the model.

[1308] 5. Activity recording and analysis: Recording and analyzing user progress and activity data in real time helps maximize learning outcomes.

[1309] Terminal

[1310] The terminal includes input / output devices that allow the user to interact with the system:

[1311] 1. Login and Course Selection: The user logs into the system using a terminal and selects the appropriate course from the list of available courses.

[1312] 2. Playback of educational content and input of prompts: The content of the course selected by the user is displayed on the device, and the user can watch videos, read text, and answer quizzes. The user can also input prompts to the generation AI through the device.

[1313] 3. Response display and progress management: The response from the AI ​​is displayed on the device, providing appropriate answers to the user's questions. In addition, the device displays the user's learning progress in real time and helps manage their learning plan.

[1314] User

[1315] Users are the users of the system and interact with it in the following ways:

[1316] 1. Login and course selection: The user logs into the system via a terminal and selects the course to study from the provided e-learning content.

[1317] 2. Content learning: The user studies the educational content displayed on the device, providing prompts to the AI ​​generator as needed, and receives responses that allow the user to deepen their understanding.

[1318] 3. Track your progress: Users can view their progress on the dashboard and adjust their learning methods as needed. For example, they can take a quiz after watching a video to gauge their understanding.

[1319] Specific examples

[1320] For example, if a user selects the "Basic Operations of Generative AI" course, the following specific examples might be possible:

[1321] 1. After logging in, the user selects the "Basic Operations of Generative AI" course.

[1322] 2. The server streams the relevant video content to the device.

[1323] 3. Users watch videos and take quizzes.

[1324] 4. The quiz results are sent to the server and recorded as progress information.

[1325] 5. Next, the user enters the prompt "Please tell me how to summarize a text."

[1326] 6. The server sends the prompt to the generative AI model and receives a response.

[1327] 7. The generated AI's response is formatted on the server and displayed on the device.

[1328] 8. The user uses the responses to deepen their understanding and continue learning.

[1329] As described above, this system effectively utilizes generative AI models and provides a series of methods for users to correctly understand and use them.

[1330] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1331] Step 1:

[1332] A user logs in to the system. The user uses a terminal to enter login information and clicks the login button. The terminal sends this input information to the server. The server collates the user's authentication information stored in the database, and if authentication is successful, it generates a login success message and a list of available courses and returns them to the terminal. The user can check the list of courses on the terminal screen.

[1333] Step 2:

[1334] The user selects the course they wish to study. They choose the course they wish to study from the list of courses displayed on the device screen and click the select button. The device sends the selected course information to the server. The server prepares educational content (videos, text, quizzes, etc.) corresponding to the selected course and returns the URL of that content to the device. The device uses this URL to display the content to the user.

[1335] Step 3:

[1336] The user views the educational content. The user watches the educational content on the device screen and learns the necessary information. The server records the timestamps of the user's start and end times and saves them in a database as progress. In addition, the server records and analyzes the user's progress in real time as they progress through the content.

[1337] Step 4:

[1338] The user answers the quiz. The quiz is presented as part of the learning content, and the user enters their answers and clicks the submit button. The device sends the user's answers to the server. The server determines whether the quiz is correct or incorrect, returns the results to the user, and records the results as progress data. The user can view the quiz results and explanations.

[1339] Step 5:

[1340] The user enters a prompt into the generative AI. The user enters a question or instruction related to the learning content as a prompt into the device and clicks the send button. The device sends the prompt to the server. The server sends the prompt to the generative AI model and receives the generated response. The server formats this response in a format optimal for the user and returns it to the device. The user can review the response from the generative AI to deepen their understanding.

[1341] Step 6:

[1342] The user checks their learning progress. The user checks their learning progress on the dashboard screen of their device. The server updates the progress data in real time and displays it on the dashboard. The user can adjust their learning plan based on this information.

[1343] The above is the specific processing flow of the system program.

[1344] (Application example 1)

[1345] 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."

[1346] Conventional educational content systems have difficulty in providing individualized support for users as they learn, and lack real-time management of learning progress and interactive learning assistance using generative AI. Therefore, it was necessary to provide a system that would enable users to progress through their studies effectively and efficiently. Furthermore, support for users to create appropriate prompts through generative AI and receive responses to deepen their learning was limited, so it was necessary to solve these issues.

[1347] 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.

[1348] In this invention, the server includes a means for hosting the generative AI model, a means for providing educational content, a means for authenticating users, a means for sending prompts to the generative AI model and obtaining responses, and a means for recording and analyzing user activities, thereby enabling real-time management of educational content, interactive learning support by the generative AI, and effective updates on the user's learning progress.

[1349] The terminal also includes a means for providing an interface for accessing educational content, a means for playing content and accepting prompt input, a means for displaying the user's progress, a means for inputting prompts to the generating AI and displaying the responses, and a means for updating progress information in real time, thereby enabling the user to effectively use the generating AI and deepen their learning while checking their learning progress in real time.

[1350] A "generative AI model" is a type of artificial intelligence trained on large datasets to generate responses in natural language based on prompts from users.

[1351] "Server means" refers to a computer system that provides services to users via a network and has the function of processing and storing data.

[1352] "Educational content" refers to a set of information or materials provided for educational purposes, and may consist of videos, texts, quizzes, etc.

[1353] "User authentication" is the process of verifying a user's identity and granting appropriate privileges when accessing a system.

[1354] A "prompt" is text that is input as an instruction or question to a generative AI model, which causes the generative AI to return a corresponding response.

[1355] "User activity" refers to all operations and actions performed by a user while using the system, which are recorded as logs and analyzed.

[1356] "Terminal means" refers to a physical or virtual device through which a user interacts with the system, such as a personal computer, smartphone, or tablet.

[1357] "Content playback" is the process of presenting educational content visually or audibly to a user.

[1358] "Progress" is information that indicates how far the user has progressed in their studies, and the system visually displays this information.

[1359] "Updating progress information in real time" refers to the process of instantly updating the system with new information obtained as the user progresses with their studies, and always displaying the latest status.

[1360] This invention relates to an educational content distribution system that utilizes generative AI models, and the system consists of three main entities: a server, a terminal, and a user.

[1361] Server Features

[1362] The server has the following functions:

[1363] 1. Hosting generative AI models: Hosting large language models to generate natural language responses based on user prompts, such as GPT-3.

[1364] 2. Providing educational content: We manage and host a variety of educational content and provide it to users upon request, including video, text, and quiz-style learning materials.

[1365] 3. User Authentication: Authenticating new and existing users and setting up appropriate learning accounts.

[1366] 4. Sending and receiving prompts: Receive prompts from the user, send them to the generative AI model to get a response, format this response and send it to the device.

[1367] 5. User activity recording and progress management: Record users' learning activities and analyze the data to manage their progress.

[1368] Device Features

[1369] The terminal has the following features:

[1370] 1. Provide an interface: Provide an interface for accessing educational content, allowing users to enter login information and access available courses.

[1371] 2. Content playback and prompt input: Plays the provided educational content and provides an interface for users to input prompts to the generated AI. For example, users can input prompts such as, "Please tell me the basic operations of the generated AI."

[1372] 3. Progress display: Displays the user's learning progress in real time and supports learning progress management.

[1373] User Roles

[1374] The user uses the system as follows:

[1375] 1. Log in to the system: The user logs in to the system using a terminal and selects an appropriate course from the available educational content.

[1376] 2. Viewing content and answering quizzes: View the content of the selected course and answer the related quizzes. These activities are recorded on the server and managed as progress information.

[1377] 3. Prompt input to the generative AI: During the learning process, questions or instructions (prompts) can be input to the generative AI and a response can be received. For example, you can input a prompt such as, "Please tell me how to summarize a text."

[1378] 4. Track your progress and keep learning: See your progress in real time as you learn, and enter additional prompts to further your understanding.

[1379] Hardware and software used

[1380] This system uses the following hardware and software:

[1381] Server: A server for data processing and storage, where generative AI models and databases (e.g., MySQL, MongoDB) are hosted.

[1382] Terminal: The device with which the user interacts. This can be a smartphone, tablet, or computer.

[1383] Software: Web frameworks such as Flask are used for server-side processing, and the user interface is built using HTML, CSS, JavaScript, etc.

[1384] The system constructed in this way enables users to effectively and efficiently study using generative AI. By asking questions to the generative AI and checking its responses, users can deepen their learning and further their understanding. Furthermore, by checking progress in real time, it becomes easier to manage the learning process.

[1385] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1386] Step 1:

[1387] A user logs into the system using a terminal. After entering login information (user name and password), the terminal sends this to the server. The server refers to the user authentication database and verifies that the authentication information is correct. If authentication is successful, the server returns a message of successful authentication and a list of available courses to the user. The terminal receives this and displays it to the user.

[1388] Step 2:

[1389] The user selects the course they wish to study from a list of courses available on the device. The device sends a course selection request to the server. The server searches for the appropriate educational content and prepares to stream the desired course content to the device. The device then plays the received course content to the user.

[1390] Step 3:

[1391] Users progress through their studies by viewing educational videos and textbooks according to the course content. Every time an interaction occurs, such as playing a video, viewing textbooks, or answering a quiz, a detailed activity log is sent from the device to the server. The server records and analyzes these activity logs and updates the user's progress data.

[1392] Step 4:

[1393] During the learning process, the user inputs questions and instructions to the generative AI model. For example, they input a prompt such as, "Please tell me the basic operations of generative AI." The device sends this prompt to the server. The server passes the prompt to the generative AI model and receives its response. The server formats the response and returns it to the device. The device displays this response to the user.

[1394] Step 5:

[1395] If the user enters an additional question as a prompt, the process repeats: the server sends the prompt to the generative AI model again and receives a response, which the user responds to further deepen their learning.

[1396] Step 6:

[1397] The user's progress is constantly updated in real time. Every time the user answers a quiz or watches a new video, the device sends progress data to the server. The server analyzes this data and updates the user's learning dashboard. The device then displays the latest progress information to the user in real time.

[1398] Step 7:

[1399] When the user finishes learning, he logs out of the system. The device sends a logout request to the server, which then terminates the user's session. All learning activity logs and progress data are stored on the server and can be reused in the future.

[1400] By following the above steps, users can efficiently advance their learning by using an interactive educational content delivery system that utilizes generative AI models.

[1401] 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.

[1402] The present invention provides educational content using a generative AI model, and further combines it with an emotion engine that recognizes the user's emotions to implement a system that improves the user's learning experience. Specific embodiments for implementing the system are described below.

[1403] The system mainly consists of three components: a server, a device, and a user. The server hosts the generative AI model and provides e-learning content. It also has an emotion engine that recognizes user emotions and has the ability to collect and analyze user emotion data.

[1404] server

[1405] The server creates a new account for the user to access the system, automatically generates a generated AI account, and notifies the user of the generated login information and initial setting information.

[1406] Educational content (videos, texts, quizzes, etc.) is hosted on a server and streamed upon user request. The server also records user progress and provides real-time analysis as needed.

[1407] Once a prompt is entered, the server sends it to the generative AI model to get a response, where an emotion engine is used to tailor the generative AI model's response based on the user's emotional state, which is then formatted and finally returned to the user.

[1408] Terminal

[1409] The terminal provides an interface for users to interact with the system. Users log in using the terminal and select the desired course from a list. The terminal receives the content of the selected course from the server and displays it.

[1410] Furthermore, the device uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine, which analyzes this data and determines the user's emotional state.

[1411] The user enters a prompt into the generation AI, and the device sends the prompt to the server, which formats the generated response and returns it to the device, where it is displayed to the user.

[1412] User

[1413] Users log in to the system using a terminal and select the provided e-learning content. As they study the content, they input prompts to the generation AI and receive responses. The emotion engine then checks the user's emotional state and assists in providing the optimal response.

[1414] The server also recommends appropriate e-learning content based on the user's emotional state: for example, if the user is tired, it recommends light review content, and if the user is concentrating, it recommends new, more challenging content.

[1415] Specific examples

[1416] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after the video ends, the emotion engine captures the emotion data. When the user enters the prompt "Please give me a summary of the text," the device sends the prompt to the server.

[1417] The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response, which is formatted on the server and displayed to the user via their device. The user can review the response and enter additional prompts if they have further questions.

[1418] The server logs all user activity and emotional data, capturing learning patterns and progress in real time, which can then be used to optimize the user's learning experience.

[1419] In this way, the system combines a generative AI model and an emotion engine to improve users' learning efficiency and provide individually optimized educational content.

[1420] The processing flow will be explained below.

[1421] Step 1:

[1422] The server receives a new user request: the user requests to create an account to access the system.

[1423] Step 2:

[1424] The server registers the user information in the database, automatically creates a generated AI account, and notifies the user of the initial settings along with the generated login information.

[1425] Step 3:

[1426] A user logs into the system using a terminal, which sends the user's login information to the server.

[1427] Step 4:

[1428] The server authenticates the user and sends a list of available e-learning content to the terminal.

[1429] Step 5:

[1430] The terminal displays the received course list to the user, who selects the desired course and clicks Start.

[1431] Step 6:

[1432] The server retrieves the initial content (video, text, etc.) for the selected course and streams that content to the terminal.

[1433] Step 7:

[1434] The terminal displays the received content to the user, who then watches the content and answers quizzes as needed.

[1435] Step 8:

[1436] The device uses its built-in camera and microphone to collect the user's emotional data (facial expressions and voice) and transmits it to the server.

[1437] Step 9:

[1438] The server uses an emotion engine to analyze the received emotion data and determine the user's emotional state.

[1439] Step 10:

[1440] After watching the content, the user inputs a prompt to the AI ​​generating the content. For example, they can input a prompt such as, "Please give me a summary of the text."

[1441] Step 11:

[1442] The terminal sends the prompt entered by the user to the server, and requests the server to process the prompt.

[1443] Step 12:

[1444] The server sends the received prompt to the generative AI model, which generates a response to the prompt.

[1445] Step 13:

[1446] The generative AI model generates a response and returns it to the server, which then adjusts the response of the generative AI model based on the emotion recognition results of the emotion engine.

[1447] Step 14:

[1448] The server formats a response and sends it to the terminal, which displays it to the user.

[1449] Step 15:

[1450] The user reviews their response and enters additional prompts or proceeds to the next learning step, with progress updated in real time on the device.

[1451] Step 16:

[1452] The server logs all user activity and emotional data and analyzes it as needed to understand the user's learning patterns and progress.

[1453] Step 17:

[1454] The server recommends optimal e-learning content based on the user's emotional state: for example, it recommends light review content if the user is tired, or new, more challenging content if the user is concentrating.

[1455] In this way, the system not only provides educational content that utilizes generative AI and helps users learn how to use it correctly, but also uses emotional data to individually optimize the user's learning experience.

[1456] Example 2

[1457] 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."

[1458] Conventional e-learning systems provide uniform educational content without considering the user's emotional state, resulting in reduced learning efficiency. Even response systems using generative AI models have difficulty providing appropriate responses based on the user's real-time emotions. Therefore, there is a need for a system that can improve the user's learning experience and provide individually optimized educational content.

[1459] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for hosting the generative AI model, a means for providing e-learning content, a means for authenticating the user, a means for sending prompts to the generative AI model and obtaining its response, a means for recording and analyzing the user's activity as a log, a means for analyzing emotional data and having an emotion engine for determining the user's emotional state, and a means for adjusting the response of the generative AI model based on the user's emotional state. This makes it possible to provide an optimal response according to the user's emotional state and improve learning efficiency.

[1460] A "generative AI model" is an algorithm or system that automatically generates natural language responses based on user prompts.

[1461] "Server means" means a server that has the functionality to host the generative AI model, provide e-learning content, authenticate users, and send prompts to the generative AI model to obtain responses.

[1462] "E-learning content" refers to educational content in digital format, such as videos, texts, and quizzes, provided for learning purposes.

[1463] A "prompt" is a question or instruction that a user inputs to a generative AI model.

[1464] An "emotion engine" is a system or algorithm that analyzes a user's emotional data and determines the user's emotional state.

[1465] "Terminal means" refers to a terminal that provides an interface for users to interact with the system, has the functions of playing content, accepting prompt input, and collecting emotion data.

[1466] "User activity" refers to a series of operations or actions that a user performs on a system.

[1467] A "log" is data that records the history of a user's activities, emotional data, and so on.

[1468] The present invention relates to a system that provides educational content using a generative AI model and combines it with an emotion engine that recognizes the user's emotions to improve the user's learning experience. Specific embodiments of the system are described below.

[1469] System configuration

[1470] The system is mainly composed of three entities: a server, a terminal, and a user.

[1471] Server Features

[1472] The server hosts the generative AI model and provides e-learning content. It also authenticates users, sends prompts to the generative AI model, and receives responses. It also logs and analyzes user activity and uses an emotion engine to determine the user's emotional state.

[1473] Specifically, the server uses the following software and hardware:

[1474] Generative AI models (e.g., GPT-3)

[1475] Emotion engines (e.g., Affectiva)

[1476] Database management system (e.g. MySQL)

[1477] Web server (e.g. Apache)

[1478] Device Features

[1479] The terminal provides an interface for users to interact with the system. Users can log in using the terminal and select the course they want. The terminal receives the content of the selected course from the server and displays it. It also uses sensors such as a camera and microphone to transmit the user's facial expressions and voice to the emotion engine.

[1480] Specific hardware and software include:

[1481] Camera (to capture the user's facial expressions)

[1482] A microphone (to record your vocal inflections)

[1483] User interface (e.g., web browser or dedicated application)

[1484] User operations

[1485] Users log in to the system using their devices and study the provided e-learning content. If they have any questions while studying, they can input prompt sentences and send them to the generative AI model. Furthermore, appropriate responses and content are provided depending on the user's emotional state.

[1486] Specific examples

[1487] For example, if a user selects the "Basic Operations of Generative AI" course, the server streams the relevant video content to the device. The user watches the video, and after finishing, the emotion engine captures emotion data. When the user inputs the prompt "Please summarize the text," the data is sent to the server. The emotion engine analyzes the user's emotional state and, if it determines that the user is tired, the generative AI model generates a concise, short response. This response is formatted by the server and displayed to the user via the device. The user can review the response and input additional prompts if they have further questions.

[1488] Examples of prompts:

[1489] "Please tell me more about the educational system using generative AI models."

[1490] "Please briefly summarize what you learned today."

[1491] As a result, a system is realized that can provide optimal educational content that takes into account the user's emotional state and improve learning efficiency.

[1492] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1493] Step 1: User Login

[1494] 1. The device displays the login screen.

[1495] 2. The user enters their username and password.

[1496] 3. The device sends the entered login information to the server.

[1497] 4. The server receives the login information and authenticates it by checking it against the database (input is the user's login information, output is the authentication result).

[1498] 5. The server returns the authentication result to the terminal.

[1499] 6. The terminal displays a successful authentication message to the user.

[1500] Specific behavior:

[1501] The terminal's user interface provides a text box and a submit button.

[1502] The server queries a database (e.g. MySQL) to verify the user information.

[1503] Step 2: Course Selection and Content Acquisition

[1504] 1. After logging in, the user views the list of courses offered.

[1505] 2. The terminal accepts the user's course selection.

[1506] 3. The terminal sends a request for the selected course to the server (input is the selected course ID, output is the course content).

[1507] 4. The server receives the course request, retrieves the corresponding content from the database, and prepares it for streaming (data processing: content extraction).

[1508] 5. The server streams the content to the device.

[1509] 6. The device receives the content and displays it to the user.

[1510] Specific behavior:

[1511] The device records the user's selection through clicks.

[1512] The server responds to the HTTP request with content (e.g., video URL, text).

[1513] Step 3: Complete and submit prompts during training

[1514] 1. The user has a question while studying and enters a prompt.

[1515] 2. The terminal accepts the input prompt.

[1516] 3. The device sends a prompt to the server (the input is the user's prompt, and the output is a request to the generative AI model).

[1517] Specific behavior:

[1518] The terminal's user interface provides a text area and a submit button.

[1519] The server receives the prompt sentence and adds it to a queue for post-processing.

[1520] Step 4: Generative AI model generates a response

[1521] 1. The server sends a prompt to the generative AI model (data calculation: response generation).

[1522] 2. The generative AI model parses the prompt and generates a response (the input is the prompt sentence, and the output is the generated response).

[1523] 3. The server receives the generated response.

[1524] Specific behavior:

[1525] The server sends an API request to the generative AI model (e.g., GPT-3).

[1526] A generative AI model uses natural language processing algorithms to generate responses.

[1527] Step 5: Collect and analyze sentiment data

[1528] 1. The device's camera and microphone collect the user's emotional data (input is the user's facial expression data and voice data).

[1529] 2. The device sends the collected emotion data to the server (the output is emotion data).

[1530] Specific behavior:

[1531] Sensors on the device capture the user's facial expressions, and a microphone records the intonation of the voice.

[1532] The server receives this data and sends it to the emotion engine.

[1533] Step 6: Response adjustment with the emotion engine

[1534] 1. The server receives emotion data.

[1535] 2. The server sends the emotion data to the emotion engine, which analyzes the user's emotional state (the input is emotion data, and the output is the emotion analysis result).

[1536] 3. The emotion engine determines the emotional state and adjusts the response of the generative AI model (data processing: response adjustment).

[1537] Specific behavior:

[1538] The server sends data to the emotion engine in the form of an API request.

[1539] The emotion engine analyzes the data and adjusts the tone and content of the response accordingly.

[1540] Step 7: Formatting and displaying the response

[1541] 1. The server receives the adjusted response and converts it into the appropriate format (data processing: format conversion).

[1542] 2. The server sends the formatted response to the terminal (output is the formatted response).

[1543] 3. The terminal receives the response and displays it to the user.

[1544] Specific behavior:

[1545] The server formats the response in HTML or JSON.

[1546] The device will display the response in a chat box or pop-up format.

[1547] Step 8: Progress Recording and Analysis

[1548] 1. The server records the user's activity and emotion data (input is activity data and emotion data, output is recorded data).

[1549] 2. The server analyzes the recorded data and generates learning patterns and progress reports (data calculation: progress analysis).

[1550] Specific behavior:

[1551] The server stores the activity and emotion data in a database.

[1552] Big data analytics tools (e.g., Hadoop) analyze the data and generate reports.

[1553] Step 9: Recommending the right content

[1554] 1. The server selects appropriate content based on the user's emotional state and progress (data calculation: content recommendation).

[1555] 2. The server sends the recommended content to the terminal (the output is the recommended content).

[1556] 3. The device displays the recommended content to the user.

[1557] Specific behavior:

[1558] The server selects content using machine learning algorithms.

[1559] Your device will display recommended content in a list or pop-up notification.

[1560] The above are the specific processing steps in the embodiment of the invention.

[1561] (Application example 2)

[1562] 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."

[1563] Conventional e-learning systems and virtual stores have difficulty providing services that take into account the user's emotional state, making it difficult to recommend content or products that best suit individual needs. As a result, users' learning efficiency and purchasing experience are not sufficiently improved. In addition, the lack of optimization of real-time responses based on the user's emotional state can sometimes result in a poor user experience.

[1564] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1565] In this invention, the server includes means for hosting the generative AI model, means for providing e-learning content, means for authenticating a user, means for sending prompts to the generative AI model and obtaining responses, means for recording and analyzing user activity as a log, terminal means for providing an interface for accessing the e-learning content, terminal means for playing content and accepting prompt inputs, terminal means for displaying the user's progress, emotion analysis means for recognizing the user's emotional state, response optimization means for adjusting the response of the generative AI model based on the emotion analysis results, and shopping assistant means for recommending products in a virtual store. This enables optimal learning content and product recommendations based on the user's emotional state, improving the user experience.

[1566] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate sophisticated responses to user input.

[1567] "Server Means" refers to a central processing unit that allows users to access the system and stores, processes, and distributes data.

[1568] "E-learning content" refers to digital content such as online learning materials, videos, and quizzes provided for educational purposes.

[1569] "User authentication" refers to the process of verifying the legitimacy of a user when they access a system.

[1570] A "prompt" refers to text or voice input that a user uses to enter a question or request to a generative AI model.

[1571] "Activity logging" refers to data used to record and analyze a user's system usage history.

[1572] "Interface" refers to the screen and operating means by which a user interacts with a system.

[1573] "Terminal means" refers to a device that allows a user to access and operate the system.

[1574] "Emotion analysis means" refers to technology that uses sensors such as cameras and microphones to recognize a user's emotional state from their facial expressions and voice.

[1575] "Response optimization means" refers to technology that adjusts the response of a generative AI model based on the results of emotion analysis.

[1576] "Virtual store" refers to a virtual shopping environment provided on the Internet.

[1577] "Shopping assistant means" refers to a function for supporting the user's purchasing experience in a virtual store.

[1578] The present invention provides a system that improves the user's learning experience by providing educational content using a generative AI model and combining it with emotion analysis means that recognize the user's emotions. A specific embodiment of this system is described below.

[1579] Overall system configuration

[1580] The system mainly consists of the following three entities:

[1581] server

[1582] Terminal

[1583] User

[1584] server

[1585] The server hosts the generative AI model and provides e-learning content to users, and also authenticates users and includes an emotion analysis means to recognize their emotional state.

[1586] For specific hardware, the company uses cloud computing environments such as Amazon Web Services (AWS) and Microsoft Azure, which enable training and hosting of generative AI models, and Microsoft Azure's Cognitive Services for sentiment analysis.

[1587] The server provides the following main functions:

[1588] 1. Hosting a generative AI model: The generative AI model provides intelligent responses to prompts sent by the user.

[1589] 2. Providing e-learning content: Hosting educational content such as videos, texts, and quizzes and streaming them upon user request.

[1590] 3. User authentication: Provides functionality for authenticating users accessing the system.

[1591] 4. Emotion analysis: Analyzes the user's camera and microphone data to determine the user's emotional state in real time.

[1592] 5. Response optimization: Adjust the response of the generative AI model based on the results of sentiment analysis.

[1593] 6. Logging and Analysis: Recording user activity and analyzing learning progress.

[1594] Terminal

[1595] The terminal provides an interface between the user and the system, allowing the user to access the system and use e-learning content.

[1596] The specific hardware of the device includes smartphones, tablets, and PCs. Applications are developed using Flutter. The device provides the following features:

[1597] 1. Login interface: Provides a screen for users to log in to the system.

[1598] 2. Content playback: Play and display the content of the course selected by the user.

[1599] 3. Prompt input acceptance: Provides a screen where users can input prompts to the generated AI model.

[1600] 4. Transmission of emotion data: The user's facial expressions and voice are transmitted to the emotion analysis means using a camera and microphone.

[1601] 5. Progress display: Show users' learning progress in real time.

[1602] User

[1603] Users access the system using a terminal and use the provided e-learning content.

[1604] 1. Logging in to the system: A user logs in to the system using a terminal.

[1605] 2. Course selection: The user selects the desired course and begins learning.

[1606] 3. Prompt Creation: The user types a question or request to the generative AI model.

[1607] Example prompt: "Recommend a new smartphone."

[1608] 4. Sending emotional data: Using the device's camera and microphone, emotional data obtained from the user's facial expressions and voice is sent to the server.

[1609] 5. Receive response: Receive the response generated by the generative AI model and confirm the learnings and purchase suggestions.

[1610] Through the above process, the system is able to provide optimal learning content and recommend products based on the user's emotional state, improving the user's learning and purchasing experiences.

[1611] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1612] Step 1:

[1613] The server hosts the generative AI model and creates a new account for the user to access the system. It receives the user's registration information as input and notifies the user of the generated login information and initial setting information as output. At this time, it authenticates the user and saves the account information in the database.

[1614] Step 2:

[1615] A user logs into the system using a terminal. The terminal displays a login interface and sends the entered user ID and password to the server. The server performs authentication and, if successful, displays the user's dashboard as output.

[1616] Step 3:

[1617] The user selects the desired course and begins learning. The terminal sends a request for the selected course to the server, and the server provides the corresponding e-learning content to the terminal by streaming. As an output, the content is displayed on the user's terminal.

[1618] Step 4:

[1619] To recognize the user's emotional state, the device's camera and microphone are activated. The user's facial expression and voice data are acquired as input and sent to the server's emotion analysis means. The server then performs emotion analysis and outputs the user's emotional state (e.g., joy, excitement, fatigue) in real time.

[1620] Step 5:

[1621] The user uses a device to input a prompt to the generative AI model. The input prompt is sent to the server. The server takes into account the results of sentiment analysis and passes the prompt to the generative AI model, which then generates a response. The generated response is returned to the server.

[1622] Step 6:

[1623] The server formats the generated response and sends it to the device. The formatted response is displayed as output on the user's device. For example, if the user types the prompt "Recommend a new smartphone," the response might read, "Currently popular products are the latest smartphone models with excellent cameras."

[1624] Step 7:

[1625] User activity is recorded as a log and analyzed by the server. The input is user behavioral data, which is reflected in learning progress and product recommendations as output. At this time, emotional data stored in the database is also utilized to provide individually optimized content based on learning and purchasing history.

[1626] Step 8:

[1627] The system displays the user's progress on the device. It receives progress data sent from the server as input and displays it in a visually easy-to-understand format on a dashboard as output, allowing the user to check their own learning progress.

[1628] 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.

[1629] 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.

[1630] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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).

[1635] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1636] 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."

[1637] 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.

[1638] 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).

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] The following is further disclosed regarding the above embodiment.

[1650] (Claim 1)

[1651] a server means for hosting the generative AI model;

[1652] a server means for providing e-learning content;

[1653] a server means for authenticating a user;

[1654] server means for sending prompts to the generative AI model and obtaining responses therefrom;

[1655] a server means for recording and analyzing user activity as a log;

[1656] a terminal means for providing an interface for accessing e-learning content;

[1657] a terminal means for playing content and accepting prompt input;

[1658] a terminal means for displaying the user's progress;

[1659] A system including:

[1660] (Claim 2)

[1661] 10. The system of claim 1, further comprising means for formatting and displaying prompt responses.

[1662] (Claim 3)

[1663] 10. The system of claim 1, further comprising means for updating a user's learning progress based on responses of the generative AI model.

[1664] The above is the draft of the patent claims regarding the characteristic parts of the invention.

[1665] "Example 1"

[1666] (Claim 1)

[1667] an information processing device means for hosting a generative AI model;

[1668] an information processing device for providing educational content;

[1669] an information processing device for authenticating a user;

[1670] an information processing device for sending prompts to the generative AI model and obtaining responses therefrom;

[1671] an information processing device for recording and analyzing user activity;

[1672] input / output device means for providing an interface for accessing educational content;

[1673] an input / output device means for playing content and accepting prompt input;

[1674] an input / output device means for displaying the user's progress;

[1675] a means for notifying the generated user account information;

[1676] A means of recording the progress of educational content in real time;

[1677] A means for formatting the generative AI's responses in a format that is optimal for the user; and

[1678] A system including means for receiving and displaying prompt processing results.

[1679] (Claim 2)

[1680] 10. The system of claim 1, further comprising means for notifying the created user account information and the initial setting information.

[1681] (Claim 3)

[1682] 10. The system of claim 1, further comprising means for analyzing the recorded user progress in real time.

[1683] "Application Example 1"

[1684] (Claim 1)

[1685] a server means for hosting the generative AI model;

[1686] a server means for providing educational content;

[1687] a server means for authenticating a user;

[1688] server means for sending prompts to the generative AI model and obtaining responses therefrom;

[1689] a server means for recording and analyzing user activity;

[1690] terminal means for providing an interface for accessing educational content;

[1691] a terminal means for playing content and accepting prompt input;

[1692] a terminal means for displaying the user's progress;

[1693] a terminal means for inputting a prompt sentence to the generation AI and displaying the response;

[1694] a terminal means for updating progress information in real time;

[1695] A system including:

[1696] (Claim 2)

[1697] 10. The system of claim 1, further comprising means for formatting and displaying prompt responses.

[1698] (Claim 3)

[1699] 10. The system of claim 1, further comprising means for updating a user's learning progress based on responses of the generative AI model.

[1700] "Example 2: Combining Emotion Engines"

[1701] (Claim 1)

[1702] a server means for hosting the generative AI model;

[1703] a server means for providing e-learning content;

[1704] a server means for authenticating a user;

[1705] server means for sending prompts to the generative AI model and obtaining responses therefrom;

[1706] a server means for recording and analyzing user activity as a log;

[1707] a server means having an emotion engine for analyzing emotion data and determining an emotional state of a user;

[1708] a server means for adjusting the response of the generative AI model based on the emotional state of the user;

[1709] a terminal means including a camera and a microphone for collecting emotion data and transmitting the data to a server;

[1710] a terminal means for providing an interface for accessing e-learning content;

[1711] a terminal means for playing content and accepting prompt input;

[1712] A terminal means for displaying a response of the generative AI model;

[1713] a terminal means for displaying the user's progress;

[1714] A system including:

[1715] (Claim 2)

[1716] 10. The system of claim 1, further comprising means for formatting and displaying prompt responses.

[1717] (Claim 3)

[1718] 10. The system of claim 1, further comprising means for updating a user's learning progress based on responses of the generative AI model.

[1719] "Application example 2 when combining emotion engines"

[1720] (Claim 1)

[1721] a server means for hosting the generative AI model;

[1722] a server means for providing e-learning content;

[1723] a server means for authenticating a user;

[1724] server means for sending prompts to the generative AI model and obtaining responses therefrom;

[1725] a server means for recording and analyzing user activity as a log;

[1726] a terminal means for providing an interface for accessing e-learning content;

[1727] a terminal means for playing content and accepting prompt input;

[1728] a terminal means for displaying the user's progress;

[1729] emotion analysis means for recognizing the emotional state of a user;

[1730] a response optimization means for adjusting the response of the generative AI model based on the emotion analysis results;

[1731] a shopping assistant means for recommending products in a virtual store;

[1732] A system including:

[1733] (Claim 2)

[1734] 10. The system of claim 1, further comprising means for formatting and displaying prompt responses.

[1735] (Claim 3)

[1736] 10. The system of claim 1, further comprising means for updating a user's learning progress based on responses of the generative AI model. [Explanation of symbols]

[1737] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a server means for hosting the generative AI model; a server means for providing e-learning content; a server means for authenticating a user; server means for sending prompts to the generative AI model and obtaining responses therefrom; a server means for recording and analyzing user activity as a log; a terminal means for providing an interface for accessing e-learning content; a terminal means for playing content and accepting prompt input; a terminal means for displaying the user's progress; A system including:

2. 2. The system of claim 1, further comprising means for formatting and displaying responses to prompts.

3. The system of claim 1 , further comprising means for updating the user's learning progress based on responses of the generative AI model.

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