system

Abstract

Provide a system. [Solution] means for receiving user registration information and performing registration; means for invoking a generative artificial intelligence to provide learning content based on requests from a user; A means to choose the language in which lectures are delivered; A means for accepting user test takers and answers and scoring them using generative artificial intelligence; A means for correcting reports submitted by users using generative artificial intelligence; A research support method using generative artificial intelligence to support information gathering based on research themes, and A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional online education systems have limitations on the quality of instructors and the number of supported languages, making it difficult to receive appropriate education in specific fields or languages. Additionally, grading tests and correcting reports takes time, resulting in a lack of real-time feedback. Furthermore, in research activities, a lack of support for efficient information gathering and analysis can slow down research progress. The present invention aims to provide an efficient, multifunctional online education system that utilizes generative artificial intelligence to solve these problems. [Means for solving the problem]

[0005] The present invention provides a system that includes the following means: means for receiving user registration information and performing registration; means for activating a generative artificial intelligence (AI) to provide learning content based on a user's request; means for selecting the language used in lectures; means for accepting user test responses and answers and grading them using a generative AI; means for correcting user-submitted reports using a generative AI; and means for supporting research using a generative AI to support information gathering based on a research topic. This system provides efficient, multilingual online education and real-time feedback. The addition of real-time question and answering, test evaluation, and specialized information search capabilities significantly improves the quality of the learning experience and research activities.

[0006] "User registration information" refers to information such as name, email address, password, and desired field of study that a user enters to use the system.

[0007] "Generative AI" is an AI technology for natural language generation and task-specific content generation.

[0008] "Learning content" refers to materials and information necessary for education, such as lectures, textbooks, videos, and test questions.

[0009] "Language selection" is the act of a user specifying the language to be used within the system.

[0010] "Testing" is the process in which a user answers test questions to gauge their understanding of the material.

[0011] "Scoring" is the act of evaluating test answers submitted by users and assigning them scores.

[0012] "Report submission" refers to the act of a user uploading a report given as an assignment to the system.

[0013] "Correction" is the act of reviewing a submitted report and pointing out errors and areas for improvement.

[0014] A "research theme" is a specific issue or topic that a user tackles during research activities.

[0015] "Generative AI that supports information gathering" is an AI technology that has the ability to search for literature and materials related to the user's research topic and provide the necessary information.

[0016] "Real-time feedback" refers to providing instant feedback and comments on users' tests and submissions.

[0017] "Question answering" is the process by which a generative artificial intelligence provides a response to a question posed to the system by a user.

[0018] "Specialized information search means" is a function that efficiently searches for detailed information on a specific field or topic and provides it to users. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

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

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[0041] User Registration and Login

[0042] When a user uses the system for the first time, they must first register. The user registers by entering their name, email address, password, and the field they wish to study. The server receives this information and stores it in a database. Once registration is complete, the user logs in using the email address and password they used when registering. When logging in, the server compares the information in the database with the information they entered and authenticates them. If authentication is successful, the user is redirected to the dashboard.

[0043] Lecture selection and attendance

[0044] After logging in, users can select the lectures they wish to take from the dashboard. Lectures are categorized by field and can be freely selected according to the user's interests and needs. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4 (registered trademark)) corresponding to that lecture. This allows the generative AI to provide the user with an interactive lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[0045] Language Selection

[0046] Before the lecture begins, the user can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to deliver lectures in multiple languages.

[0047] Test administration and scoring

[0048] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[0049] Report submission and correction

[0050] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[0051] Support for research activities

[0052] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[0053] Specific examples

[0054] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server has the generative AI prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[0055] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[0056] The processing flow will be explained below.

[0057] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[0058] User Registration and Login

[0059] Step 1:

[0060] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[0061] Step 2:

[0062] The server receives these inputs and stores them in a database.

[0063] Step 3:

[0064] The server displays a message to the user confirming registration.

[0065] Step 4:

[0066] The user enters their email address and password on the login page and clicks the login button.

[0067] Step 5:

[0068] The server compares the input information with a database and performs authentication.

[0069] Step 6:

[0070] If authentication is successful, the server displays the user's dashboard.

[0071] Lecture selection and attendance

[0072] Step 1:

[0073] The user selects the desired lecture from the list of lectures on the dashboard.

[0074] Step 2:

[0075] The server receives the selected lecture information and activates the generative artificial intelligence.

[0076] Step 3:

[0077] Generative artificial intelligence provides users with interactive lectures.

[0078] Step 4:

[0079] A chat box is provided for users to enter questions during the lecture.

[0080] Step 5:

[0081] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[0082] Step 6:

[0083] The server displays the generated answer to the user.

[0084] Language Selection

[0085] Step 1:

[0086] The user selects the language to use before the lecture begins.

[0087] Step 2:

[0088] The server transmits the selected language information to the generative artificial intelligence.

[0089] Step 3:

[0090] Configure the generative artificial intelligence to deliver lectures in the language of your choice.

[0091] Test administration and scoring

[0092] Step 1:

[0093] After the lecture, the user presses a button to start the section test.

[0094] Step 2:

[0095] The server requests the generative artificial intelligence to generate test questions.

[0096] Step 3:

[0097] Test questions generated by the generative artificial intelligence are displayed to the user via the server.

[0098] Step 4:

[0099] The user answers the test questions and presses the answer button.

[0100] Step 5:

[0101] The server receives the user's answer and sends it to the generative artificial intelligence.

[0102] Step 6:

[0103] Generative AI grades answers and generates results and feedback.

[0104] Step 7:

[0105] The server displays the score and feedback to the user.

[0106] Report submission and correction

[0107] Step 1:

[0108] A user creates a report in a specified format and accesses the upload page.

[0109] Step 2:

[0110] The user uploads the report to the server.

[0111] Step 3:

[0112] The server receives the submitted report and sends it to the generative artificial intelligence.

[0113] Step 4:

[0114] Generative AI corrects reports and generates feedback including errors and areas for improvement.

[0115] Step 5:

[0116] The server displays the feedback to the user.

[0117] Support for research activities

[0118] Step 1:

[0119] The user inputs a research topic into the research support function.

[0120] Step 2:

[0121] The server sends the input theme to the generative artificial intelligence.

[0122] Step 3:

[0123] The generative artificial intelligence searches for relevant literature and materials and provides them to users via a server.

[0124] Step 4:

[0125] The research will proceed based on the materials provided by the user, and if additional information is needed, the generative AI will be queried again.

[0126] Step 5:

[0127] The user completes the final product of the research and submits it to the system.

[0128] Step 6:

[0129] The server sends the final product to the generative artificial intelligence for review and feedback.

[0130] Step 7:

[0131] The server displays the review results to the user.

[0132] Specific examples

[0133] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server instructs the generative AI to prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[0134] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[0135] Example 1

[0136] 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."

[0137] Conventional online education systems have the problem of requiring time and effort to provide individual support for selecting lectures, conducting tests, correcting papers, and supporting research activities, which reduces the quality of the user experience. Furthermore, real-time responses to questions and the provision of lectures in multiple languages ​​are often insufficient, resulting in reduced learning efficiency. This has made it difficult to improve user satisfaction and learning outcomes.

[0138] 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.

[0139] In this invention, the server includes means for receiving user registration information and executing registration, means for launching a generative model that provides learning content based on a user request, means for selecting the language used in the lecture, means for accepting user tests and answers and grading them using the generative model, means for correcting reports submitted by the user using the generative model, means for research support using the generative model to support information gathering based on a research topic, and means for sending questions entered by the user during a lecture to the generative model and generating and returning answers. This improves the efficiency of the entire online education system, dramatically improves the user's learning experience, and enables multifaceted learning support.

[0140] "User registration information" is data used to identify a user and allow access to the system, such as name, email address, password, and desired field of study.

[0141] A "generative model" is an AI technology that uses natural language processing technology to generate content in response to user requests, and an example is a natural language generation model.

[0142] "Learning Content" means any information content provided for educational purposes, such as lectures, tests, reports, and research support.

[0143] The "language used in the lecture" is the language in which the lecture will be given, and can be selected by the user to enable lectures to be provided in multiple languages.

[0144] "Testing" refers to a test conducted to check the user's level of understanding, and includes processes such as asking questions, accepting answers, and grading.

[0145] An "answer" is an answer submitted by a user to a test question.

[0146] A "report" is a document or literature that a user creates and submits based on a specified assignment.

[0147] A "research theme" is a subject or issue that a user sets when conducting research activities.

[0148] "Information gathering" refers to the activity of collecting literature and materials related to a research topic and providing them to users.

[0149] "Questions from users" are points of uncertainty or doubt that users input in text format during a lecture and send to the system.

[0150] "Generating answers in real time" refers to the process in which a generative model receives a question from a user and immediately generates an answer and responds.

[0151] The present invention provides an online education system that utilizes a generative artificial intelligence model. This system is designed to enable users to efficiently access learning content. Specific embodiments of the system are described in detail below.

[0152] System Configuration

[0153] This system includes a user's device, a server, and a generative artificial intelligence model (generative model). The user's device is a device such as a computer, tablet, or smartphone, and communicates with the server via an internet connection. The server is located in a cloud environment or data center and manages and operates the user data and the generative model.

[0154] User Registration and Login

[0155] User registration: When a user uses the system for the first time, they enter their name, email address, password, and the field they want to study. The terminal sends this data to the server, which receives the user information and stores it in a database.

[0156] Login: Registered users log in by entering their email address and password. The server authenticates them by checking the information in its database. If authentication is successful, the user is redirected to the dashboard.

[0157] Lecture selection and attendance

[0158] Lecture selection: The user selects the lecture they want to take from the dashboard. The device sends the selected lecture information to the server. The server receives the lecture information and launches a generative model (e.g., GPT-4).

[0159] Attendance: The generative model provides the user with an interactive lecture. When the user enters a question during the lecture, the device sends the question to the server. The server sends the question as a prompt to the generative model, which then returns the generated answer to the user.

[0160] Language Selection

[0161] The user selects the desired language before the lecture begins. The terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture to be delivered in the selected language. This makes it possible to deliver lectures in multiple languages.

[0162] Test administration and scoring

[0163] Start test: The user selects a test from the dashboard and clicks the "Start test" button. The device sends a test start request to the server. The server asks the generative model to generate test questions and provides the generated questions to the user.

[0164] Test submission and scoring: The user enters their test answers and clicks the "Submit" button. The device sends the answer data to the server. The server then sends the answer data to the generative model and requests scoring. The scoring results are then fed back to the user.

[0165] Report submission and correction

[0166] The user creates a report based on a specified assignment and uploads it to the system from their device. The server sends the report to the generative model for correction. The generative model corrects the report and generates feedback pointing out errors and areas for improvement. The server returns this feedback to the user.

[0167] Support for research activities

[0168] The user enters a research topic and sends it from their device to the server. The server sends the topic information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates search results and provides them to the user via the server. The user can then continue their research based on the provided materials and ask the generative model additional questions.

[0169] Specific examples

[0170] For example, if a user selects the "Introduction to Python Programming" lecture, the device sends this information to the server. The server then prepares the first lecture of "Introduction to Python Programming" for the generative model. If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative model, which then generates and returns a specific answer, such as "Functions in Python are defined using the def keyword."

[0171] In this way, the present invention provides users with a highly efficient and multifunctional online learning environment through a series of operations.

[0172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0173] Step 1: User Registration

[0174] Input: The user enters their name, email address, password, and the subject they want to learn about.

[0175] Specific operation: When the user clicks the "Register" button, the device sends this data to the server. The server receives the user data and saves it in the database. After saving is complete, the server returns a response to the user indicating successful registration.

[0176] Output: The user data is saved in the database and a registration success message is displayed to the user.

[0177] Step 2: Log in

[0178] Input: The user enters their email address and password.

[0179] Specific operation: When the user clicks the "Login" button, the device sends the entered data to the server, which then collates it with the user information stored in the database and performs authentication.

[0180] Output: If authentication is successful, the server returns the dashboard information to the terminal and the user is redirected to the dashboard.

[0181] Step 3: Select a course

[0182] Input: The user selects the course they want to take from the dashboard.

[0183] Specific operation: When the user clicks the "Select" button, the device sends the selected lecture information to the server. The server receives the lecture information and launches the corresponding generative model (e.g., GPT-4). It then asks the AI ​​to prepare the lecture and sends a command to the user device to start the lecture.

[0184] Output: The lecture start screen will be displayed on the user's device, and the AI ​​will be ready to provide the lecture.

[0185] Step 4: Language Selection

[0186] Input: The user selects the desired language before the lecture begins.

[0187] Specific operation: When the user selects a language, the terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture provision in the selected language.

[0188] Output: The user is now set up to receive lectures in the language of their choice.

[0189] Step 5: Answer questions during the lecture

[0190] Input: If a question arises during the lecture, the user inputs the question.

[0191] Specific operation: When a user clicks the "Ask" button, the device sends the question to the server. The server receives the question and sends it as a prompt to the generative model. The generative model generates an answer to the question and returns it to the server. The server receives the answer and sends it to the device.

[0192] Output: The answer generated by the generative model is displayed on the user's device.

[0193] Step 6: Testing

[0194] Input: User selects and starts a test from the dashboard.

[0195] Specific operation: When the user clicks the "Start Test" button, the device sends a test start request to the server. The server then requests the generative model to generate test questions and sends the generated questions to the user's device.

[0196] Output: The generated test questions are displayed on the user's terminal.

[0197] Step 7: Test submission and grading

[0198] Input: The user enters the test answers.

[0199] Specific operation: When the user clicks the "Submit" button, the device sends the answer data to the server. The server sends the answer data to the generative model and requests it to be graded. The generative model grades the answer and returns the results to the server. The server then sends the graded results to the device.

[0200] Output: The scoring results are displayed on the user's terminal.

[0201] Step 8: Submit your report and receive corrections

[0202] Input: The user creates and uploads a report based on the given assignment.

[0203] Specific operation: When a user uploads a report, the device sends the report data to the server. The server sends the report to the generative model and requests corrections. The generative model generates feedback pointing out errors and areas for improvement and returns it to the server. The server then sends the feedback to the device.

[0204] Output: The feedback generated by the generative model is displayed on the user's device.

[0205] Step 9: Enter your research topic and provide materials

[0206] Input: The user inputs the research topic.

[0207] Specific operation: When a user submits a theme, the device sends the theme information to the server. The server then sends the theme information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates related materials and returns them to the server. The server then sends the search results to the device.

[0208] Output: Related literature and materials are displayed on the user's terminal.

[0209] Step 10: Research work submission and feedback

[0210] Input: Users create and upload research artifacts.

[0211] Specific operation: When a user uploads an artifact, the device sends the artifact data to the server. The server sends the artifact to the generative model and requests review and feedback. The generative model generates reviews and feedback and returns them to the server. The server sends the feedback to the device.

[0212] Output: The feedback generated by the generative model is displayed on the user's device.

[0213] (Application example 1)

[0214] 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."

[0215] Modern education requires responding to the diverse needs of learners and providing optimal education for each individual. Traditional educational systems face challenges, such as difficulty providing real-time feedback and multilingual support. Furthermore, they lack the ability to provide an interactive learning experience using smart devices. A system is needed to solve these problems and provide an efficient and effective learning environment.

[0216] 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.

[0217] In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide educational content based on a user request, means for selecting the language of the educational content, means for accepting user tests and answers and grading them using the AI, means for correcting user-submitted reports using the AI, research support means using the AI ​​to support information gathering based on a research topic, means installed in a smartphone, smart glasses, a head-mounted display, or a robot, and response means using the AI ​​to support user learning through interactive dialogue. This allows for the provision of highly efficient and multifunctional education to individual learners and enables interactive learning experiences using a variety of devices.

[0218] "User registration information" refers to information such as name, email address, password, and desired field of study that a user provides when they start using the system.

[0219] "Educational content" refers to the lectures and teaching materials provided for learners to learn from.

[0220] "Generative artificial intelligence" is an AI model that can generate appropriate output based on input data.

[0221] "Language of Use" refers to the language in which the educational content is delivered.

[0222] A "test" is a confirmation test that learners take during or after a lecture to check their understanding.

[0223] "Answer" is the answer given by the learner to the test.

[0224] "Scoring" refers to the evaluation and assignment of scores to test answers.

[0225] A "report" is an assignment that a learner creates and submits in a specified format.

[0226] "Correction" refers to pointing out errors and areas for improvement in submitted reports and providing feedback.

[0227] A "research theme" is a specific subject that a learner sets for their research activities.

[0228] "Information gathering" means collecting materials and literature related to the research topic.

[0229] A "smartphone" is a highly functional mobile phone that can run a variety of applications in addition to making calls.

[0230] "Smart glasses" are a type of wearable device equipped with information display and communication functions.

[0231] A "head-mounted display" is a device worn on the head that displays visual information.

[0232] A "robot" is a machine that has a certain degree of autonomy and operates under human instructions.

[0233] "Interactive dialogue" refers to real-time, two-way communication between the learner and the system.

[0234] A "response mechanism" is a mechanism for providing answers to user questions.

[0235] The present invention provides a highly efficient and multifunctional educational system that utilizes generative artificial intelligence. This system allows users to register online, access educational content, ask questions, take tests, and submit reports. It also provides support for research activities. An embodiment of the system is described in detail below.

[0236] User Registration and Login

[0237] The server receives the user's name, email address, password, and desired field of study information and registers it in a database. After registering, the user logs in to the system using their email address and password. When logging in, the server compares the entered information with the information in the database and performs authentication. If authentication is successful, the user is redirected to the dashboard.

[0238] Lecture selection and attendance

[0239] After logging in, a user can select the lecture they want to take from the dashboard. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4). This allows the generative AI to provide an interactive lecture. If a user has a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[0240] Language Selection

[0241] Before the lecture begins, users can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to provide learning content in multiple different languages.

[0242] Test administration and scoring

[0243] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[0244] Report submission and correction

[0245] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[0246] Support for research activities

[0247] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[0248] Interactive Dialogue

[0249] Generative AI supports learning through interactive dialogue with users. This dialogue can be conducted using a smartphone, smart glasses, a head-mounted display, or a robot. Specifically, when a user inputs a question while studying, the AI ​​provides an appropriate answer in real time. This allows users to immediately resolve their doubts and improves learning efficiency.

[0250] Specific examples

[0251] For example, if a user selects a lecture on "Introduction to Python Programming," and asks a question about "how to define a function" during the lecture, the AI ​​will immediately respond with a specific answer such as, "Functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. Furthermore, if the user submits a report assignment on "writing a Python script," the AI ​​will correct it and provide detailed feedback.

[0252] Prompt Sentence Examples

[0253] "What are some data preprocessing techniques for data science?"

[0254] In this way, the educational system based on the present invention provides an advanced educational experience for individual learners and provides an interactive learning experience utilizing a variety of devices.

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] The server receives the user registration information and stores it in a database. As input, it takes the user's name, email address, password, and desired learning area and stores that information in a database, providing the data to recognize and authenticate the user on future logins.

[0258] Step 2:

[0259] A user logs in with the registered email address and password. When the login request is sent to the server, the server checks the information in the database and authenticates the user. If the authentication is successful, the user is redirected to the dashboard. The input is the user's login information, and the output is the login success or failure status.

[0260] Step 3:

[0261] The user selects the lecture they wish to take from the dashboard. The selected lecture information is sent to the server, which then launches the corresponding generative AI. The input is the user's lecture selection information, and the output is the start of the lecture content.

[0262] Step 4:

[0263] The user selects the language to use before the lecture begins. The server sends the selected language information to the generative AI, which then provides the lecture in that language. The input is the user's language selection, and the output is the presentation of the lecture in the selected language.

[0264] Step 5:

[0265] During a lecture, a user enters a question and sends it to the server. The server then sends the question to a generative AI, which generates an answer in real time and returns it to the user. The input is the user's question, and the output is the answer provided by the generative AI.

[0266] Step 6:

[0267] During or after a lecture, the user starts a test. The server requests the generative AI to generate test questions and provides them to the user. When the user submits their answers, the server sends them to the AI, which then scores them. The input is the user's test answers, and the output is the scoring results and feedback.

[0268] Step 7:

[0269] Users create report assignments and upload them to the system. The server sends the submitted reports to a generative AI, which corrects them and generates feedback. The input is the user's report, and the output is the corrections and feedback.

[0270] Step 8:

[0271] Users input the topic of their research activities and send it to the server. The server then sends the topic information to a generative AI, which searches for related literature and materials and provides them to the user. Furthermore, when a research product is submitted, the server sends it to the AI ​​for review and feedback. The input is the user's research topic and product, and the output is related materials and feedback.

[0272] Step 9:

[0273] The user engages in interactive dialogue using a smartphone, smart glasses, a head-mounted display, or a robot. When the user inputs a question during learning, the generative AI responds in real time through the device. The input is the user's dialogue request, and the output is a real-time response from the generative AI.

[0274] Through the above steps, the educational system of the present invention provides users with an integrated educational experience and realizes efficient and interactive learning.

[0275] 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.

[0276] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[0277] User Registration and Login

[0278] User Registration

[0279] First, a user accesses the system and registers by entering their name, email address, password, and desired field of study. The server receives this information and stores it in a database. After registration is complete, the server displays a message to the user indicating successful registration.

[0280] Log in

[0281] The user enters their email address and password on the login page and presses the login button. The server verifies the entered information against the database, and if authentication is successful, displays the user's dashboard.

[0282] Lecture selection and attendance

[0283] Lecture selection

[0284] The user selects the lecture they wish to take from the dashboard. The server receives the selected lecture information and activates the corresponding generative artificial intelligence to prepare for providing the interactive lecture.

[0285] Lecture provision

[0286] The generative AI provides the user with a selected lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, which then generates an answer in real time and returns it to the user.

[0287] Language Selection

[0288] The user selects the language to use before the lecture begins. The server transmits the selected language information to the generative AI, and configures the generative AI to provide the lecture in that language.

[0289] Test administration and scoring

[0290] Users can take tests during or after a lecture. When a user starts a test, the server requests the generative AI to generate test questions and displays the generated questions to the user. When the user answers the questions and submits them, the server sends the answers to the generative AI, which then scores them and returns the results and feedback to the user.

[0291] Report submission and correction

[0292] When a user submits a report assignment, they create the report in the specified format and upload it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, generates feedback, and returns it to the user via the server.

[0293] Support for research activities

[0294] When a user sets a research topic and enters it into the server, the server sends the topic information to the generative AI. The generative AI searches for related literature and materials and provides that information to the user. The user can then conduct their research based on the provided materials, and if additional information is required, they can ask the generative AI again.

[0295] Finally, the user submits the research results to the system, and the server sends them to the generative artificial intelligence for review and feedback. The server then displays the review results to the user.

[0296] Introducing the Emotion Engine

[0297] To further personalize and enhance the user's learning experience, the present invention introduces an emotion engine.

[0298] emotion recognition

[0299] While the user is using the system, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone to recognize their emotions. The server receives this emotional information in real time.

[0300] Emotion-based lecture adjustment

[0301] The server then sends the recognized emotional information to the generative AI, which then dynamically adjusts the lecture delivery method and content to match the user's emotional state. For example, if the user is struggling to understand something, the generative AI can provide more detailed explanations or additional support.

[0302] Emotion regulation in question-answering

[0303] When a user asks a question, the emotion engine understands the user's emotional state, and the generative AI generates a response in a tone that corresponds to that. For example, if the user is judged to be irritated, the generative AI will respond in a more polite and calm tone.

[0304] Specific examples

[0305] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question during the lecture about "how to define a function." In this case, the server sends the question to the generative AI, which immediately responds with a specific answer: "Functions in Python are defined using the def keyword." At the same time, if the emotion engine analyzes the user's facial expression and determines that the user is having difficulty understanding, the generative AI will provide more detailed sample code and practice problems. Similarly, if a user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which then corrects it and provides detailed feedback.

[0306] In this way, the present invention can provide a highly efficient, multi-functional, and interactive online teaching system combined with emotion recognition functions.

[0307] The processing flow will be explained below.

[0308] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[0309] User Registration and Login

[0310] Step 1:

[0311] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[0312] Step 2:

[0313] The server receives these inputs and stores them in a database.

[0314] Step 3:

[0315] The server displays a message to the user confirming registration.

[0316] Step 4:

[0317] The user enters their email address and password on the login page and clicks the login button.

[0318] Step 5:

[0319] The server compares the input information with a database and performs authentication.

[0320] Step 6:

[0321] If authentication is successful, the server displays the user's dashboard.

[0322] Lecture selection and attendance

[0323] Step 1:

[0324] The user selects the desired lecture from the list of lectures on the dashboard.

[0325] Step 2:

[0326] The server receives the selected lecture information and activates the generative artificial intelligence.

[0327] Step 3:

[0328] Generative AI prepares to provide interactive lectures to users.

[0329] Step 4:

[0330] A chat box is provided for users to enter questions during the lecture.

[0331] Step 5:

[0332] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[0333] Step 6:

[0334] The server displays the generated answer to the user.

[0335] Language Selection

[0336] Step 1:

[0337] The user selects the language to use before the lecture begins.

[0338] Step 2:

[0339] The server transmits the selected language information to the generative artificial intelligence.

[0340] Step 3:

[0341] Configure the generative artificial intelligence to deliver lectures in the language of your choice.

[0342] Test administration and scoring

[0343] Step 1:

[0344] After the lecture, the user presses a button to start the section test.

[0345] Step 2:

[0346] The server requests the generative artificial intelligence to generate test questions.

[0347] Step 3:

[0348] Test questions generated by the generative artificial intelligence are displayed to the user via the server.

[0349] Step 4:

[0350] The user answers the test questions and presses the answer button.

[0351] Step 5:

[0352] The server receives the user's answer and sends it to the generative artificial intelligence.

[0353] Step 6:

[0354] Generative AI grades answers and generates results and feedback.

[0355] Step 7:

[0356] The server displays the score and feedback to the user.

[0357] Report submission and correction

[0358] Step 1:

[0359] A user creates a report in a specified format and accesses the upload page.

[0360] Step 2:

[0361] The user uploads the report to the server.

[0362] Step 3:

[0363] The server receives the submitted report and sends it to the generative artificial intelligence.

[0364] Step 4:

[0365] Generative AI corrects reports and generates feedback including errors and areas for improvement.

[0366] Step 5:

[0367] The server displays the feedback to the user.

[0368] Support for research activities

[0369] Step 1:

[0370] The user inputs a research topic into the research support function.

[0371] Step 2:

[0372] The server sends the input theme to the generative artificial intelligence.

[0373] Step 3:

[0374] The generative artificial intelligence searches for relevant literature and materials and provides them to users via a server.

[0375] Step 4:

[0376] The research will proceed based on the materials provided by the user, and if additional information is needed, the generative AI will be queried again.

[0377] Step 5:

[0378] The user completes the final product of the research and submits it to the system.

[0379] Step 6:

[0380] The server sends the final product to the generative artificial intelligence for review and feedback.

[0381] Step 7:

[0382] The server displays the review results to the user.

[0383] Introducing the Emotion Engine

[0384] emotion recognition

[0385] Step 1:

[0386] When a user uses the system, the device's built-in camera and microphone capture the user's facial expressions and tone of voice.

[0387] Step 2:

[0388] The server sends these capture data to the emotion engine.

[0389] Step 3:

[0390] The emotion engine analyzes the user's emotions and sends the recognition results to the server.

[0391] Emotion-based lecture adjustment

[0392] Step 1:

[0393] The server sends the recognized emotion information to the generative artificial intelligence.

[0394] Step 2:

[0395] Generative AI dynamically adjusts lecture content and delivery methods based on the user's emotional state.

[0396] Step 3:

[0397] The server provides the adjusted lecture content to the user.

[0398] Emotion regulation in question-answering

[0399] Step 1:

[0400] When a user asks a question, the device captures the user's tone of voice and facial expressions.

[0401] Step 2:

[0402] The server sends the captured data to the emotion engine to recognize the emotion.

[0403] Step 3:

[0404] The server sends the recognized emotional information to the generative artificial intelligence, which then generates a response in a corresponding tone.

[0405] Step 4:

[0406] The server displays the generated answer to the user.

[0407] Specific examples

[0408] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question about "how to define a function" during the lecture. In this case, the server sends the question to the generative AI, which immediately responds with a specific answer: "Functions in Python are defined using the def keyword." At the same time, if the emotion engine analyzes the user's facial expression and determines that the user is having difficulty understanding, the generative AI will provide more detailed sample code and practice problems. Similarly, if a user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which then corrects it and provides detailed feedback.

[0409] In this way, the present invention can provide a highly efficient, multi-functional, and interactive online teaching system combined with emotion recognition functions.

[0410] Example 2

[0411] 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."

[0412] Conventional online education systems lack the ability to customize to meet individual user needs or provide real-time question-and-answer functions. Furthermore, they are unable to adjust to take into account the user's level of understanding or emotional state, making it difficult to provide an effective learning experience. Furthermore, automation of the grading and correction of tests and reports submitted by users has not been fully realized. Furthermore, when it comes to supporting research activities, there is a problem of reduced learning efficiency because the information users need is not collected or feedback is not provided in real time. It is necessary to solve these issues and provide a more effective and customized online education system.

[0413] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0414] In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide learning content based on a user request, means for selecting the language used in lectures, means for accepting user test and answer submissions and grading them using the generative AI, means for correcting user-submitted reports using the generative AI, means for supporting research using the generative AI to support information gathering based on a research topic, means for recognizing user emotions and transmitting that information to the generative AI, and means for adjusting the tone of learning content and Q&A based on the recognized emotional information. This enables customization according to individual user needs, real-time Q&A, and the provision of an effective learning experience that takes into account the user's emotional state, automatic grading and correction of tests and reports, and support for research activities.

[0415] "User registration information" refers to information such as name, email address, password, and desired field of study that a user provides when registering with the system.

[0416] "Generative artificial intelligence" is an AI technology that generates answers and content in real time based on input text or questions.

[0417] "Language of lecture" refers to the language that the user can choose to use when taking a lecture, and the system will provide learning content in accordance with that language.

[0418] A "test" is an exam that a user takes to check what they have learned, and the answers are graded by generative artificial intelligence.

[0419] A "report" is a document that a user creates based on a specified assignment and submits to the system.

[0420] A "research theme" is a subject or issue that a user sets when conducting research activities.

[0421] "Emotion recognition" is a technology that uses the device's camera and microphone to analyze the user's facial expressions and tone of voice to determine their emotional state.

[0422] "Means of providing lectures" refers to the method by which generative artificial intelligence interactively provides lecture content to users.

[0423] "Question-answering tone" is a way for generative AI to adjust the tone and expression of its answers depending on the user's emotional state.

[0424] This invention is an online education system that combines generative artificial intelligence and an emotion engine to personalize the user's learning experience and provide a more effective educational experience. This system allows users to not only select learning content and attend lectures, but also answer questions in real time and adjust the content and tone of lectures according to their emotional state.

[0425] Hardware and software used

[0426] The system is implemented using the following hardware and software:

[0427] Server: Manages user information and communicates with generative AI. MySQL (registered trademark) is used as the database.

[0428] Terminal: A device with a camera and microphone that allows a user to use the system.

[0429] Generative AI: For example, OpenAI's (registered trademark) GPT-3 (registered trademark) is used as a generative AI model.

[0430] Emotion engine: To analyze the user's emotional state, for example, we use the Emotion API from Microsoft® Azure®.

[0431] Data processing and calculation

[0432] 1. User Registration and Login:

[0433] Users register by entering their name, email address, password, and the field they want to study from their device. The server receives this information and stores it in a MySQL database. After registration, users log in using their email address and password. The server verifies the information entered against the database and performs authentication.

[0434] 2. Course selection and attendance:

[0435] After logging in, the user selects the lecture they wish to take from the dashboard. The server receives the selected lecture information and activates the generative AI model. During the lecture, when the user enters a question, the server sends the question to the generative AI and obtains an answer.

[0436] 3. Language Selection:

[0437] The user selects the language to use before the lecture begins, and the server sends the selected language information to the generative AI model, which then configures the AI ​​to provide the lecture in that language.

[0438] 4. Test Administration and Scoring:

[0439] When a user starts a test, the server requests the generative AI model to generate test questions and displays them to the user. When the user submits their answers, the server sends them to the generative AI model and returns the scoring results to the user.

[0440] 5. Report submission and correction:

[0441] Users create reports and upload them to the system from their devices. The server receives the submitted reports and sends them to the generative AI model for correction. The generative AI model then corrects the reports and returns feedback to the user via the server.

[0442] 6. Support for research activities:

[0443] Users set a research topic and input it into the server. The server sends that information to the generative AI model, which then provides relevant literature and materials. Users can use these as a basis for their research.

[0444] 7. Emotion recognition and response:

[0445] The emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone, and sends the emotional information to a server, which then sends it to a generative AI model, which then dynamically adjusts the content and tone of the lecture based on the recognized emotions.

[0446] Specific examples

[0447] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question during the lecture about "how to define a function." The server sends the question to the generative AI model, which immediately returns a specific answer: "Functions in Python are defined using the def keyword." At the same time, the emotion engine analyzes the user's facial expression, and if it determines that the user is having difficulty understanding, the generative AI model provides more detailed sample code and practice problems.

[0448] Prompt Sentence Examples

[0449] An example of a prompt for a user question is:

[0450] A user asked how to define a function in Python programming. Please explain it clearly with concrete code examples.

[0451] As described above, by combining generative artificial intelligence and an emotion engine, the system of the present invention is able to provide an effective and customized learning experience by customizing lessons to meet the individual needs of users, enabling real-time Q&A, and tailoring lectures based on emotions.

[0452] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0453] Step 1: User Registration

[0454] Specific explanation

[0455] A user enters their name, email address, password, and desired field of study into the registration form on a web page and presses the submit button.

[0456] concrete action

[0457] The terminal transmits the input user information to the server.

[0458] input

[0459] Enter your name, email address, password, and the field you want to study.

[0460] Data Processing

[0461] The server receives this information and stores it in a MySQL database using SQL queries.

[0462] output

[0463] A successful registration message such as "Registration complete" will be displayed on the user's screen.

[0464] Step 2: User Login

[0465] Specific explanation

[0466] The user enters their email address and password in the login form and clicks the submit button.

[0467] concrete action

[0468] The terminal transmits the input information to the server.

[0469] input

[0470] Entered email address and password

[0471] Data Processing

[0472] The server compares the received information with the user information stored in the MySQL database and performs authentication.

[0473] output

[0474] If authentication is successful, the server redirects the user to their dashboard, which displays a list of courses available for study.

[0475] Step 3: Select a course

[0476] Specific explanation

[0477] The user clicks on the desired lecture from the lecture list on the dashboard.

[0478] concrete action

[0479] The terminal transmits the selected lecture information to the server.

[0480] input

[0481] Lecture information selected by the user

[0482] Data Processing

[0483] The server records the lecture information, calls the generative AI model via an API, and prepares to start the lecture content.

[0484] output

[0485] The interactive lecture start screen is displayed to the user.

[0486] Step 4: Deliver the lecture

[0487] Specific explanation

[0488] A generative artificial intelligence provides selected lectures to users.

[0489] concrete action

[0490] The server displays lecture materials and content (text, images, code examples, etc.) generated by generative AI on the user's screen. The user enters a question in the question form and presses the submit button.

[0491] input

[0492] User Questions

[0493] Data Processing

[0494] The server sends the question to the generative AI as an API request, and the generative AI generates an answer in real time.

[0495] output

[0496] The server displays the generated answer on the user's screen.

[0497] Step 5: Language Selection

[0498] Specific explanation

[0499] The user selects the language to use before the lecture begins.

[0500] concrete action

[0501] The terminal transmits the selected language information to the server.

[0502] input

[0503] Selected Language

[0504] Data Processing

[0505] The server sends the language information to the generative AI as an API request, and configures the AI ​​to provide lectures in that language.

[0506] output

[0507] Lectures will be delivered in the language of your choice.

[0508] Step 6: Testing

[0509] Specific explanation

[0510] When a user starts a test, the server asks the generative AI model to generate test questions.

[0511] concrete action

[0512] The device presses the test start button.

[0513] input

[0514] Request to start testing

[0515] Data Processing

[0516] The server sends test questions to the generative AI model as an API request, and the generated test questions are displayed to the user. When the user enters and submits the answers, the server sends them to the generative AI model for grading, and the AI ​​grades them.

[0517] output

[0518] Scoring and feedback are displayed to the user.

[0519] Step 7: Submit your report

[0520] Specific explanation

[0521] The user creates a report and uploads it to the system from the terminal.

[0522] concrete action

[0523] The terminal sends the report file to the server via the upload form.

[0524] input

[0525] Uploaded report file

[0526] Data Processing

[0527] The server receives the report and sends it to the generative AI model as an API request to correct it. The generative AI model corrects the report and generates feedback.

[0528] output

[0529] Corrections and feedback are displayed to the user.

[0530] Step 8: Setting a research topic and gathering information

[0531] Specific explanation

[0532] The user sets a research topic and enters it into the server.

[0533] concrete action

[0534] The terminal transmits the entered research topic information to the server.

[0535] input

[0536] Research Theme

[0537] Data Processing

[0538] The server sends this information to the generative AI model, which then searches the web for relevant literature and materials and provides them.The same process is followed if the user requests additional information by entering a question again.

[0539] output

[0540] Related materials and bibliographic information is displayed to the user.

[0541] Step 9: Emotion Recognition

[0542] Specific explanation

[0543] Using the device's camera and microphone, the emotion engine analyzes the user's facial expressions and tone of voice.

[0544] concrete action

[0545] The facial expression and voice data collected by the device is sent to the server.

[0546] input

[0547] User facial and voice data

[0548] Data Processing

[0549] The server receives this data, analyzes the emotional state using an emotion engine (e.g., Emotion API), and sends the results to a generative AI model.

[0550] output

[0551] The results of the emotional state analysis are stored on a server and used to adjust the content and tone of the lecture.

[0552] Step 10: Emotionally Based Lecture Adjustments

[0553] Specific explanation

[0554] Based on the recognized emotional information, generative artificial intelligence dynamically adjusts the content and tone of the lecture.

[0555] concrete action

[0556] The server sends the emotional information to the generative artificial intelligence.

[0557] input

[0558] Emotional information from the emotion engine

[0559] Data Processing

[0560] The generative AI model uses that information to customize the content and tone of the lecture, offering more detailed explanations and additional support if the user is struggling to understand.

[0561] output

[0562] The adjusted content and tone of the lecture are displayed to the user.

[0563] Step 11: Emotional regulation of question responses

[0564] Specific explanation

[0565] When a user asks a question, the emotion engine grasps the user's emotional state, and the generative AI generates a response in a tone that corresponds to that.

[0566] concrete action

[0567] The terminal transmits the user's emotional state along with the question to the server.

[0568] input

[0569] User questions and sentiment information

[0570] Data Processing

[0571] The server sends this information to a generative artificial intelligence, which then generates responses in an emotional tone.

[0572] output

[0573] The generated answer is displayed to the user.

[0574] (Application example 2)

[0575] 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."

[0576] Conventional online education systems and shopping assistant systems provide uniform information and responses without considering the user's emotional state, making it difficult to provide a personalized experience. This makes it difficult to respond flexibly to the user's interests and level of understanding, resulting in a decline in the effectiveness of learning and the quality of the shopping experience.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide learning content based on a user request, means for selecting the language used in the lecture, means for accepting user test and answer submissions and grading them using the generative AI, means for correcting user-submitted reports using the generative AI, means for supporting research using the generative AI to support information gathering based on a research topic, means for recognizing the user's emotional state in real time using an emotion engine, means for dynamically adjusting the method for providing learning content based on the user's emotional information, and means for providing answers to user questions in a tone that reflects the user's emotional information. This allows for a personalized experience tailored to the user's emotional state, improving the quality of learning outcomes and shopping experiences.

[0578] "User registration information" is basic information such as the user's name, email address, password, and areas of interest.

[0579] "Generative AI" is an AI system that generates and provides learning content and real-time answers based on user requests.

[0580] The "language used in the lecture" refers to the language used when the user attends the lecture.

[0581] "Test administration and answers" refers to an evaluation test that the user takes to check what they have learned, and the user's answers to that test.

[0582] "Report correction" is the process in which a generative AI reviews a report submitted by a user and provides corrections and feedback.

[0583] A "research theme" refers to the theme or issue that the user is researching.

[0584] The "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state in real time.

[0585] "Affective tailoring" is the process of dynamically adjusting the tone of learning content and responses based on the perceived emotional state of the user.

[0586] "Means for providing answers in real time" refers to means for generating and providing answers immediately when a user asks a question.

[0587] This invention is an online education system that combines generative artificial intelligence and an emotion engine to provide a personalized learning experience based on the user's emotional state. The same technology can also be applied to smart shopping assistant systems in physical stores. The system for implementing this invention consists of hardware such as a smartphone, smart glasses, and a head-mounted display, as well as the following software modules:

[0588] System Program

[0589] The system first receives user registration information and stores it in a database on the backend, including the user's name, email address, password, interests, etc. Then, when the user logs in, a customized dashboard based on their interests is displayed.

[0590] Providing learning content

[0591] When a user selects a learning content, the generative AI is activated to generate interactive lectures and explanations for that specific content. The user can pre-select the corresponding language, and the AI ​​will provide the content based on the selected language.

[0592] Emotion Recognition and Dynamic Regulation

[0593] The system uses the cameras and microphones of smart glasses or head-mounted displays to recognize the user's emotional state in real time. Examples of technologies used include image processing libraries such as OpenCV and the emotion_recognition library. This analyzes the user's facial expressions and tone of voice, and transmits the user's emotional information to the server in real time. The server then sends this information to a generative artificial intelligence (AI) system, which dynamically adjusts the content delivery method based on the user's emotional state.

[0594] Real-time answers

[0595] When a user asks a question, the generative AI generates a real-time answer to the question, taking into account the emotional information from the emotion engine. If the user is frustrated, the AI ​​will use a polite tone, and if the user is interested, it will provide a detailed explanation.

[0596] Specific examples

[0597] For example, consider a scenario where a user is wearing smart glasses and walking through a store. When the user shows interest in a TV, the camera captures the user's facial expression, and the emotion engine recognizes it as "interested." As a result, the generative AI provides a detailed description, such as "This TV has 4K resolution and is equipped with the latest video technology." In this scenario, an example of a prompt sentence to be input to the generative AI model is as follows:

[0598] "Please enter a prompt to generate detailed descriptions of products that the user is interested in. User sentiment is interesting."

[0599] This allows users to receive appropriate information according to their emotional state, improving their purchasing and learning experiences. Dynamic adjustments based on emotional information maximize user satisfaction and effectiveness.

[0600] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0601] Step 1:

[0602] A user accesses the system using a terminal. As input, the user enters user information such as name, email address, password, and areas of interest. The server receives this information and stores it in a database. As output, it displays a message that the user registration was successful.

[0603] Step 2:

[0604] The user enters their email address and password on the login page and presses the login button. The server verifies the information entered against the database and performs authentication. If authentication is successful, the user's dashboard is displayed. The output is the dashboard page indicating successful authentication.

[0605] Step 3:

[0606] A user selects learning content from a dashboard. As input, a request for the learning content is sent to the server. The server receives the information of the selected learning content and launches the generative AI. As output, the AI ​​is ready to provide an interactive lecture.

[0607] Step 4:

[0608] The user selects the language to use before the lecture begins. The selected language information is sent to the server as input. The server then sends this information to the generative artificial intelligence, which configures the lecture to be provided in the selected language. The configured language information is displayed as output.

[0609] Step 5:

[0610] When a user has a question during a lecture, they input it using a terminal. The question is sent as input to the server. The server then sends it to a generative artificial intelligence, which generates an answer in real time. The generated answer is then displayed to the user as output.

[0611] Step 6:

[0612] During or after a lecture, the user takes a test. As input, a request to start the test is sent to the server. The server asks the generative AI to generate test questions and displays the generated questions to the user. When the user answers the questions and submits them, the answer data is sent to the server, and the AI ​​scores them. As output, the test results and feedback are displayed to the user.

[0613] Step 7:

[0614] When a user submits a report assignment, they create the report in the specified format and upload it to the system. The report upload data is sent to the server as input. The server then sends the submitted report to a generative AI, which then corrects the report. The correction results and feedback are displayed to the user as output.

[0615] Step 8:

[0616] The user sets a research topic and inputs it into the server. Information about the research topic is sent to the server as input. The server sends this information to a generative artificial intelligence, which searches for related literature and materials. As output, related information is provided to the user. The user then conducts their research based on the provided materials.

[0617] Step 9:

[0618] The system uses the device's built-in camera and microphone to recognize the user's emotional state in real time. As input, camera footage and audio data are sent to the server. The server processes this data using an emotion engine to recognize the user's emotional information. As output, the recognized emotional information is sent to the generative artificial intelligence.

[0619] Step 10:

[0620] The system dynamically adjusts the delivery method of learning content and the tone of answers based on the user's emotional information. As input, the emotional information and the user's request are sent to the server. The server then communicates this information to the generative AI, which then adjusts the delivery method. As output, the adjusted content and answers are displayed to the user.

[0621] Example prompt sentence:

[0622] "Please enter a prompt to generate detailed descriptions of products that the user is interested in. User sentiment is interesting."

[0623] The above are the specific processing steps for carrying out the invention, which allows for flexible responses to the user's emotional state, providing a personalized learning or shopping experience.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] [Second embodiment]

[0628] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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."

[0640] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[0641] User Registration and Login

[0642] When a user uses the system for the first time, they must first register. The user registers by entering their name, email address, password, and the field they wish to study. The server receives this information and stores it in a database. Once registration is complete, the user logs in using the email address and password they used when registering. When logging in, the server compares the information in the database with the information they entered and authenticates them. If authentication is successful, the user is redirected to the dashboard.

[0643] Lecture selection and attendance

[0644] After logging in, users can select the lectures they wish to take from the dashboard. Lectures are categorized by field and can be freely selected according to the user's interests and needs. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4) corresponding to that lecture. This allows the generative AI to provide the user with an interactive lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[0645] Language Selection

[0646] Before the lecture begins, the user can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to deliver lectures in multiple languages.

[0647] Test administration and scoring

[0648] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[0649] Report submission and correction

[0650] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[0651] Support for research activities

[0652] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[0653] Specific examples

[0654] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server has the generative AI prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[0655] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[0656] The processing flow will be explained below.

[0657] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[0658] User Registration and Login

[0659] Step 1:

[0660] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[0661] Step 2:

[0662] The server receives these inputs and stores them in a database.

[0663] Step 3:

[0664] The server displays a message to the user confirming registration.

[0665] Step 4:

[0666] The user enters their email address and password on the login page and clicks the login button.

[0667] Step 5:

[0668] The server compares the input information with a database and performs authentication.

[0669] Step 6:

[0670] If authentication is successful, the server displays the user's dashboard.

[0671] Lecture selection and attendance

[0672] Step 1:

[0673] The user selects the desired lecture from the list of lectures on the dashboard.

[0674] Step 2:

[0675] The server receives the selected lecture information and activates the generative artificial intelligence.

[0676] Step 3:

[0677] Generative artificial intelligence provides users with interactive lectures.

[0678] Step 4:

[0679] A chat box is provided for users to enter questions during the lecture.

[0680] Step 5:

[0681] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[0682] Step 6:

[0683] The server displays the generated answer to the user.

[0684] Language Selection

[0685] Step 1:

[0686] The user selects the language to use before the lecture begins.

[0687] Step 2:

[0688] The server transmits the selected language information to the generative artificial intelligence.

[0689] Step 3:

[0690] Configure the generative artificial intelligence to deliver lectures in the language of your choice.

[0691] Test administration and scoring

[0692] Step 1:

[0693] After the lecture, the user presses a button to start the section test.

[0694] Step 2:

[0695] The server requests the generative artificial intelligence to generate test questions.

[0696] Step 3:

[0697] Test questions generated by the generative artificial intelligence are displayed to the user via the server.

[0698] Step 4:

[0699] The user answers the test questions and presses the answer button.

[0700] Step 5:

[0701] The server receives the user's answer and sends it to the generative artificial intelligence.

[0702] Step 6:

[0703] Generative AI grades answers and generates results and feedback.

[0704] Step 7:

[0705] The server displays the score and feedback to the user.

[0706] Report submission and correction

[0707] Step 1:

[0708] A user creates a report in a specified format and accesses the upload page.

[0709] Step 2:

[0710] The user uploads the report to the server.

[0711] Step 3:

[0712] The server receives the submitted report and sends it to the generative artificial intelligence.

[0713] Step 4:

[0714] Generative AI corrects reports and generates feedback including errors and areas for improvement.

[0715] Step 5:

[0716] The server displays the feedback to the user.

[0717] Support for research activities

[0718] Step 1:

[0719] The user inputs a research topic into the research support function.

[0720] Step 2:

[0721] The server sends the input theme to the generative artificial intelligence.

[0722] Step 3:

[0723] The generative artificial intelligence searches for relevant literature and materials and provides them to users via a server.

[0724] Step 4:

[0725] The research will proceed based on the materials provided by the user, and if additional information is needed, the generative AI will be queried again.

[0726] Step 5:

[0727] The user completes the final product of the research and submits it to the system.

[0728] Step 6:

[0729] The server sends the final product to the generative artificial intelligence for review and feedback.

[0730] Step 7:

[0731] The server displays the review results to the user.

[0732] Specific examples

[0733] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server instructs the generative AI to prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[0734] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[0735] Example 1

[0736] 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."

[0737] Conventional online education systems have the problem of requiring time and effort to provide individual support for selecting lectures, conducting tests, correcting papers, and supporting research activities, which reduces the quality of the user experience. Furthermore, real-time responses to questions and the provision of lectures in multiple languages ​​are often insufficient, resulting in reduced learning efficiency. This has made it difficult to improve user satisfaction and learning outcomes.

[0738] 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.

[0739] In this invention, the server includes means for receiving user registration information and executing registration, means for launching a generative model that provides learning content based on a user request, means for selecting the language used in the lecture, means for accepting user tests and answers and grading them using the generative model, means for correcting reports submitted by the user using the generative model, means for research support using the generative model to support information gathering based on a research topic, and means for sending questions entered by the user during a lecture to the generative model and generating and returning answers. This improves the efficiency of the entire online education system, dramatically improves the user's learning experience, and enables multifaceted learning support.

[0740] "User registration information" is data used to identify a user and allow access to the system, such as name, email address, password, and desired field of study.

[0741] A "generative model" is an AI technology that uses natural language processing technology to generate content in response to user requests, and an example is a natural language generation model.

[0742] "Learning Content" means any information content provided for educational purposes, such as lectures, tests, reports, and research support.

[0743] The "language used in the lecture" is the language in which the lecture will be given, and can be selected by the user to enable lectures to be provided in multiple languages.

[0744] "Testing" refers to a test conducted to check the user's level of understanding, and includes processes such as asking questions, accepting answers, and grading.

[0745] An "answer" is an answer submitted by a user to a test question.

[0746] A "report" is a document or literature that a user creates and submits based on a specified assignment.

[0747] A "research theme" is a subject or issue that a user sets when conducting research activities.

[0748] "Information gathering" refers to the activity of collecting literature and materials related to a research topic and providing them to users.

[0749] "Questions from users" are points of uncertainty or doubt that users input in text format during a lecture and send to the system.

[0750] "Generating answers in real time" refers to the process in which a generative model receives a question from a user and immediately generates an answer and responds.

[0751] The present invention provides an online education system that utilizes a generative artificial intelligence model. This system is designed to enable users to efficiently access learning content. Specific embodiments of the system are described in detail below.

[0752] System Configuration

[0753] This system includes a user's device, a server, and a generative artificial intelligence model (generative model). The user's device is a device such as a computer, tablet, or smartphone, and communicates with the server via an internet connection. The server is located in a cloud environment or data center and manages and operates the user data and the generative model.

[0754] User Registration and Login

[0755] User registration: When a user uses the system for the first time, they enter their name, email address, password, and the field they want to study. The terminal sends this data to the server, which receives the user information and stores it in a database.

[0756] Login: Registered users log in by entering their email address and password. The server authenticates them by checking the information in its database. If authentication is successful, the user is redirected to the dashboard.

[0757] Lecture selection and attendance

[0758] Lecture selection: The user selects the lecture they want to take from the dashboard. The device sends the selected lecture information to the server. The server receives the lecture information and launches a generative model (e.g., GPT-4).

[0759] Attendance: The generative model provides the user with an interactive lecture. When the user enters a question during the lecture, the device sends the question to the server. The server sends the question as a prompt to the generative model, which then returns the generated answer to the user.

[0760] Language Selection

[0761] The user selects the desired language before the lecture begins. The terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture to be delivered in the selected language. This makes it possible to deliver lectures in multiple languages.

[0762] Test administration and scoring

[0763] Start test: The user selects a test from the dashboard and clicks the "Start test" button. The device sends a test start request to the server. The server asks the generative model to generate test questions and provides the generated questions to the user.

[0764] Test submission and scoring: The user enters their test answers and clicks the "Submit" button. The device sends the answer data to the server. The server then sends the answer data to the generative model and requests scoring. The scoring results are then fed back to the user.

[0765] Report submission and correction

[0766] The user creates a report based on a specified assignment and uploads it to the system from their device. The server sends the report to the generative model for correction. The generative model corrects the report and generates feedback pointing out errors and areas for improvement. The server returns this feedback to the user.

[0767] Support for research activities

[0768] The user enters a research topic and sends it from their device to the server. The server sends the topic information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates search results and provides them to the user via the server. The user can then continue their research based on the provided materials and ask the generative model additional questions.

[0769] Specific examples

[0770] For example, if a user selects the "Introduction to Python Programming" lecture, the device sends this information to the server. The server then prepares the first lecture of "Introduction to Python Programming" for the generative model. If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative model, which then generates and returns a specific answer, such as "Functions in Python are defined using the def keyword."

[0771] In this way, the present invention provides users with a highly efficient and multifunctional online learning environment through a series of operations.

[0772] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0773] Step 1: User Registration

[0774] Input: The user enters their name, email address, password, and the subject they want to learn about.

[0775] Specific operation: When the user clicks the "Register" button, the device sends this data to the server. The server receives the user data and saves it in the database. After saving is complete, the server returns a response to the user indicating successful registration.

[0776] Output: The user data is saved in the database and a registration success message is displayed to the user.

[0777] Step 2: Log in

[0778] Input: The user enters their email address and password.

[0779] Specific operation: When the user clicks the "Login" button, the device sends the entered data to the server, which then collates it with the user information stored in the database and performs authentication.

[0780] Output: If authentication is successful, the server returns the dashboard information to the terminal and the user is redirected to the dashboard.

[0781] Step 3: Select a course

[0782] Input: The user selects the course they want to take from the dashboard.

[0783] Specific operation: When the user clicks the "Select" button, the device sends the selected lecture information to the server. The server receives the lecture information and launches the corresponding generative model (e.g., GPT-4). It then asks the AI ​​to prepare the lecture and sends a command to the user device to start the lecture.

[0784] Output: The lecture start screen will be displayed on the user's device, and the AI ​​will be ready to provide the lecture.

[0785] Step 4: Language Selection

[0786] Input: The user selects the desired language before the lecture begins.

[0787] Specific operation: When the user selects a language, the terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture provision in the selected language.

[0788] Output: The user is now set up to receive lectures in the language of their choice.

[0789] Step 5: Answer questions during the lecture

[0790] Input: If a question arises during the lecture, the user inputs the question.

[0791] Specific operation: When a user clicks the "Ask" button, the device sends the question to the server. The server receives the question and sends it as a prompt to the generative model. The generative model generates an answer to the question and returns it to the server. The server receives the answer and sends it to the device.

[0792] Output: The answer generated by the generative model is displayed on the user's device.

[0793] Step 6: Testing

[0794] Input: User selects and starts a test from the dashboard.

[0795] Specific operation: When the user clicks the "Start Test" button, the device sends a test start request to the server. The server then requests the generative model to generate test questions and sends the generated questions to the user's device.

[0796] Output: The generated test questions are displayed on the user's terminal.

[0797] Step 7: Test submission and grading

[0798] Input: The user enters the test answers.

[0799] Specific operation: When the user clicks the "Submit" button, the device sends the answer data to the server. The server sends the answer data to the generative model and requests it to be graded. The generative model grades the answer and returns the results to the server. The server then sends the graded results to the device.

[0800] Output: The scoring results are displayed on the user's terminal.

[0801] Step 8: Submit your report and receive corrections

[0802] Input: The user creates and uploads a report based on the given assignment.

[0803] Specific operation: When a user uploads a report, the device sends the report data to the server. The server sends the report to the generative model and requests corrections. The generative model generates feedback pointing out errors and areas for improvement and returns it to the server. The server then sends the feedback to the device.

[0804] Output: The feedback generated by the generative model is displayed on the user's device.

[0805] Step 9: Enter your research topic and provide materials

[0806] Input: The user inputs the research topic.

[0807] Specific operation: When a user submits a theme, the device sends the theme information to the server. The server then sends the theme information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates related materials and returns them to the server. The server then sends the search results to the device.

[0808] Output: Related literature and materials are displayed on the user's terminal.

[0809] Step 10: Research work submission and feedback

[0810] Input: Users create and upload research artifacts.

[0811] Specific operation: When a user uploads an artifact, the device sends the artifact data to the server. The server sends the artifact to the generative model and requests review and feedback. The generative model generates reviews and feedback and returns them to the server. The server sends the feedback to the device.

[0812] Output: The feedback generated by the generative model is displayed on the user's device.

[0813] (Application example 1)

[0814] 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."

[0815] Modern education requires responding to the diverse needs of learners and providing optimal education for each individual. Traditional educational systems face challenges, such as difficulty providing real-time feedback and multilingual support. Furthermore, they lack the ability to provide an interactive learning experience using smart devices. A system is needed to solve these problems and provide an efficient and effective learning environment.

[0816] 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.

[0817] In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide educational content based on a user request, means for selecting the language of the educational content, means for accepting user tests and answers and grading them using the AI, means for correcting user-submitted reports using the AI, research support means using the AI ​​to support information gathering based on a research topic, means installed in a smartphone, smart glasses, a head-mounted display, or a robot, and response means using the AI ​​to support user learning through interactive dialogue. This allows for the provision of highly efficient and multifunctional education to individual learners and enables interactive learning experiences using a variety of devices.

[0818] "User registration information" refers to information such as name, email address, password, and desired field of study that a user provides when they start using the system.

[0819] "Educational content" refers to the lectures and teaching materials provided for learners to learn from.

[0820] "Generative artificial intelligence" is an AI model that can generate appropriate output based on input data.

[0821] "Language of Use" refers to the language in which the educational content is delivered.

[0822] A "test" is a confirmation test that learners take during or after a lecture to check their understanding.

[0823] "Answer" is the answer given by the learner to the test.

[0824] "Scoring" refers to the evaluation and assignment of scores to test answers.

[0825] A "report" is an assignment that a learner creates and submits in a specified format.

[0826] "Correction" refers to pointing out errors and areas for improvement in submitted reports and providing feedback.

[0827] A "research theme" is a specific subject that a learner sets for their research activities.

[0828] "Information gathering" means collecting materials and literature related to the research topic.

[0829] A "smartphone" is a highly functional mobile phone that can run a variety of applications in addition to making calls.

[0830] "Smart glasses" are a type of wearable device equipped with information display and communication functions.

[0831] A "head-mounted display" is a device worn on the head that displays visual information.

[0832] A "robot" is a machine that has a certain degree of autonomy and operates under human instructions.

[0833] "Interactive dialogue" refers to real-time, two-way communication between the learner and the system.

[0834] A "response mechanism" is a mechanism for providing answers to user questions.

[0835] The present invention provides a highly efficient and multifunctional educational system that utilizes generative artificial intelligence. This system allows users to register online, access educational content, ask questions, take tests, and submit reports. It also provides support for research activities. An embodiment of the system is described in detail below.

[0836] User Registration and Login

[0837] The server receives the user's name, email address, password, and desired field of study information and registers it in a database. After registering, the user logs in to the system using their email address and password. When logging in, the server compares the entered information with the information in the database and performs authentication. If authentication is successful, the user is redirected to the dashboard.

[0838] Lecture selection and attendance

[0839] After logging in, a user can select the lecture they want to take from the dashboard. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4). This allows the generative AI to provide an interactive lecture. If a user has a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[0840] Language Selection

[0841] Before the lecture begins, users can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to provide learning content in multiple different languages.

[0842] Test administration and scoring

[0843] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[0844] Report submission and correction

[0845] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[0846] Support for research activities

[0847] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[0848] Interactive Dialogue

[0849] Generative AI supports learning through interactive dialogue with users. This dialogue can be conducted using a smartphone, smart glasses, a head-mounted display, or a robot. Specifically, when a user inputs a question while studying, the AI ​​provides an appropriate answer in real time. This allows users to immediately resolve their doubts and improves learning efficiency.

[0850] Specific examples

[0851] For example, if a user selects a lecture on "Introduction to Python Programming," and asks a question about "how to define a function" during the lecture, the AI ​​will immediately respond with a specific answer such as, "Functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. Furthermore, if the user submits a report assignment on "writing a Python script," the AI ​​will correct it and provide detailed feedback.

[0852] Prompt Sentence Examples

[0853] "What are some data preprocessing techniques for data science?"

[0854] In this way, the educational system based on the present invention provides an advanced educational experience for individual learners and provides an interactive learning experience utilizing a variety of devices.

[0855] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0856] Step 1:

[0857] The server receives the user registration information and stores it in a database. As input, it takes the user's name, email address, password, and desired learning area and stores that information in a database, providing the data to recognize and authenticate the user on future logins.

[0858] Step 2:

[0859] A user logs in with the registered email address and password. When the login request is sent to the server, the server checks the information in the database and authenticates the user. If the authentication is successful, the user is redirected to the dashboard. The input is the user's login information, and the output is the login success or failure status.

[0860] Step 3:

[0861] The user selects the lecture they wish to take from the dashboard. The selected lecture information is sent to the server, which then launches the corresponding generative AI. The input is the user's lecture selection information, and the output is the start of the lecture content.

[0862] Step 4:

[0863] The user selects the language to use before the lecture begins. The server sends the selected language information to the generative AI, which then provides the lecture in that language. The input is the user's language selection, and the output is the presentation of the lecture in the selected language.

[0864] Step 5:

[0865] During a lecture, a user enters a question and sends it to the server. The server then sends the question to a generative AI, which generates an answer in real time and returns it to the user. The input is the user's question, and the output is the answer provided by the generative AI.

[0866] Step 6:

[0867] During or after a lecture, the user starts a test. The server requests the generative AI to generate test questions and provides them to the user. When the user submits their answers, the server sends them to the AI, which then scores them. The input is the user's test answers, and the output is the scoring results and feedback.

[0868] Step 7:

[0869] Users create report assignments and upload them to the system. The server sends the submitted reports to a generative AI, which corrects them and generates feedback. The input is the user's report, and the output is the corrections and feedback.

[0870] Step 8:

[0871] Users input the topic of their research activities and send it to the server. The server then sends the topic information to a generative AI, which searches for related literature and materials and provides them to the user. Furthermore, when a research product is submitted, the server sends it to the AI ​​for review and feedback. The input is the user's research topic and product, and the output is related materials and feedback.

[0872] Step 9:

[0873] The user engages in interactive dialogue using a smartphone, smart glasses, a head-mounted display, or a robot. When the user inputs a question during learning, the generative AI responds in real time through the device. The input is the user's dialogue request, and the output is a real-time response from the generative AI.

[0874] Through the above steps, the educational system of the present invention provides users with an integrated educational experience and realizes efficient and interactive learning.

[0875] 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.

[0876] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[0877] User Registration and Login

[0878] User Registration

[0879] First, a user accesses the system and registers by entering their name, email address, password, and desired field of study. The server receives this information and stores it in a database. After registration is complete, the server displays a message to the user indicating successful registration.

[0880] Log in

[0881] The user enters their email address and password on the login page and presses the login button. The server verifies the entered information against the database, and if authentication is successful, displays the user's dashboard.

[0882] Lecture selection and attendance

[0883] Lecture selection

[0884] The user selects the lecture they wish to take from the dashboard. The server receives the selected lecture information and activates the corresponding generative artificial intelligence to prepare for providing the interactive lecture.

[0885] Lecture provision

[0886] The generative AI provides the user with a selected lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, which then generates an answer in real time and returns it to the user.

[0887] Language Selection

[0888] The user selects the language to use before the lecture begins. The server transmits the selected language information to the generative AI, and configures the generative AI to provide the lecture in that language.

[0889] Test administration and scoring

[0890] Users can take tests during or after a lecture. When a user starts a test, the server requests the generative AI to generate test questions and displays the generated questions to the user. When the user answers the questions and submits them, the server sends the answers to the generative AI, which then scores them and returns the results and feedback to the user.

[0891] Report submission and correction

[0892] When a user submits a report assignment, they create the report in the specified format and upload it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, generates feedback, and returns it to the user via the server.

[0893] Support for research activities

[0894] When a user sets a research topic and enters it into the server, the server sends the topic information to the generative AI. The generative AI searches for related literature and materials and provides that information to the user. The user can then conduct their research based on the provided materials, and if additional information is required, they can ask the generative AI again.

[0895] Finally, the user submits the research results to the system, and the server sends them to the generative artificial intelligence for review and feedback. The server then displays the review results to the user.

[0896] Introducing the Emotion Engine

[0897] To further personalize and enhance the user's learning experience, the present invention introduces an emotion engine.

[0898] emotion recognition

[0899] While the user is using the system, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone to recognize their emotions. The server receives this emotional information in real time.

[0900] Emotion-based lecture adjustment

[0901] The server then sends the recognized emotional information to the generative AI, which then dynamically adjusts the lecture delivery method and content to match the user's emotional state. For example, if the user is struggling to understand something, the generative AI can provide more detailed explanations or additional support.

[0902] Emotion regulation in question-answering

[0903] When a user asks a question, the emotion engine understands the user's emotional state, and the generative AI generates a response in a tone that corresponds to that. For example, if the user is judged to be irritated, the generative AI will respond in a more polite and calm tone.

[0904] Specific examples

[0905] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question during the lecture about "how to define a function." In this case, the server sends the question to the generative AI, which immediately responds with a specific answer: "Functions in Python are defined using the def keyword." At the same time, if the emotion engine analyzes the user's facial expression and determines that the user is having difficulty understanding, the generative AI will provide more detailed sample code and practice problems. Similarly, if a user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which then corrects it and provides detailed feedback.

[0906] In this way, the present invention can provide a highly efficient, multi-functional, and interactive online teaching system combined with emotion recognition functions.

[0907] The processing flow will be explained below.

[0908] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[0909] User Registration and Login

[0910] Step 1:

[0911] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[0912] Step 2:

[0913] The server receives these inputs and stores them in a database.

[0914] Step 3:

[0915] The server displays a message to the user confirming registration.

[0916] Step 4:

[0917] The user enters their email address and password on the login page and clicks the login button.

[0918] Step 5:

[0919] The server compares the input information with a database and performs authentication.

[0920] Step 6:

[0921] If authentication is successful, the server displays the user's dashboard.

[0922] Lecture selection and attendance

[0923] Step 1:

[0924] The user selects the desired lecture from the list of lectures on the dashboard.

[0925] Step 2:

[0926] The server receives the selected lecture information and activates the generative artificial intelligence.

[0927] Step 3:

[0928] Generative AI prepares to provide interactive lectures to users.

[0929] Step 4:

[0930] A chat box is provided for users to enter questions during the lecture.

[0931] Step 5:

[0932] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[0933] Step 6:

[0934] The server displays the generated answer to the user.

[0935] Language Selection

[0936] Step 1:

[0937] The user selects the language to use before the lecture begins.

[0938] Step 2:

[0939] The server transmits the selected language information to the generative artificial intelligence.

[0940] Step 3:

[0941] Configure the generative artificial intelligence to deliver lectures in the language of your choice.

[0942] Test administration and scoring

[0943] Step 1:

[0944] After the lecture, the user presses a button to start the section test.

[0945] Step 2:

[0946] The server requests the generative artificial intelligence to generate test questions.

[0947] Step 3:

[0948] Test questions generated by the generative artificial intelligence are displayed to the user via the server.

[0949] Step 4:

[0950] The user answers the test questions and presses the answer button.

[0951] Step 5:

[0952] The server receives the user's answer and sends it to the generative artificial intelligence.

[0953] Step 6:

[0954] Generative AI grades answers and generates results and feedback.

[0955] Step 7:

[0956] The server displays the score and feedback to the user.

[0957] Report submission and correction

[0958] Step 1:

[0959] A user creates a report in a specified format and accesses the upload page.

[0960] Step 2:

[0961] The user uploads the report to the server.

[0962] Step 3:

[0963] The server receives the submitted report and sends it to the generative artificial intelligence.

[0964] Step 4:

[0965] Generative AI corrects reports and generates feedback including errors and areas for improvement.

[0966] Step 5:

[0967] The server displays the feedback to the user.

[0968] Support for research activities

[0969] Step 1:

[0970] The user inputs a research topic into the research support function.

[0971] Step 2:

[0972] The server sends the input theme to the generative artificial intelligence.

[0973] Step 3:

[0974] The generative artificial intelligence searches for relevant literature and materials and provides them to users via a server.

[0975] Step 4:

[0976] The research will proceed based on the materials provided by the user, and if additional information is needed, the generative AI will be queried again.

[0977] Step 5:

[0978] The user completes the final product of the research and submits it to the system.

[0979] Step 6:

[0980] The server sends the final product to the generative artificial intelligence for review and feedback.

[0981] Step 7:

[0982] The server displays the review results to the user.

[0983] Introducing the Emotion Engine

[0984] emotion recognition

[0985] Step 1:

[0986] When a user uses the system, the device's built-in camera and microphone capture the user's facial expressions and tone of voice.

[0987] Step 2:

[0988] The server sends these capture data to the emotion engine.

[0989] Step 3:

[0990] The emotion engine analyzes the user's emotions and sends the recognition results to the server.

[0991] Emotion-based lecture adjustment

[0992] Step 1:

[0993] The server sends the recognized emotion information to the generative artificial intelligence.

[0994] Step 2:

[0995] Generative AI dynamically adjusts lecture content and delivery methods based on the user's emotional state.

[0996] Step 3:

[0997] The server provides the adjusted lecture content to the user.

[0998] Emotion regulation in question-answering

[0999] Step 1:

[1000] When a user asks a question, the device captures the user's tone of voice and facial expressions.

[1001] Step 2:

[1002] The server sends the captured data to the emotion engine to recognize the emotion.

[1003] Step 3:

[1004] The server sends the recognized emotional information to the generative artificial intelligence, which then generates a response in a corresponding tone.

[1005] Step 4:

[1006] The server displays the generated answer to the user.

[1007] Specific examples

[1008] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question about "how to define a function" during the lecture. In this case, the server sends the question to the generative AI, which immediately responds with a specific answer: "Functions in Python are defined using the def keyword." At the same time, if the emotion engine analyzes the user's facial expression and determines that the user is having difficulty understanding, the generative AI will provide more detailed sample code and practice problems. Similarly, if a user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which then corrects it and provides detailed feedback.

[1009] In this way, the present invention can provide a highly efficient, multi-functional, and interactive online teaching system combined with emotion recognition functions.

[1010] Example 2

[1011] 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."

[1012] Conventional online education systems lack the ability to customize to meet individual user needs or provide real-time question-and-answer functions. Furthermore, they are unable to adjust to take into account the user's level of understanding or emotional state, making it difficult to provide an effective learning experience. Furthermore, automation of the grading and correction of tests and reports submitted by users has not been fully realized. Furthermore, when it comes to supporting research activities, there is a problem of reduced learning efficiency because the information users need is not collected or feedback is not provided in real time. It is necessary to solve these issues and provide a more effective and customized online education system.

[1013] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1014] In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide learning content based on a user request, means for selecting the language used in lectures, means for accepting user test and answer submissions and grading them using the generative AI, means for correcting user-submitted reports using the generative AI, means for supporting research using the generative AI to support information gathering based on a research topic, means for recognizing user emotions and transmitting that information to the generative AI, and means for adjusting the tone of learning content and Q&A based on the recognized emotional information. This enables customization according to individual user needs, real-time Q&A, and the provision of an effective learning experience that takes into account the user's emotional state, automatic grading and correction of tests and reports, and support for research activities.

[1015] "User registration information" refers to information such as name, email address, password, and desired field of study that a user provides when registering with the system.

[1016] "Generative artificial intelligence" is an AI technology that generates answers and content in real time based on input text or questions.

[1017] "Language of lecture" refers to the language that the user can choose to use when taking a lecture, and the system will provide learning content in accordance with that language.

[1018] A "test" is an exam that a user takes to check what they have learned, and the answers are graded by generative artificial intelligence.

[1019] A "report" is a document that a user creates based on a specified assignment and submits to the system.

[1020] A "research theme" is a subject or issue that a user sets when conducting research activities.

[1021] "Emotion recognition" is a technology that uses the device's camera and microphone to analyze the user's facial expressions and tone of voice to determine their emotional state.

[1022] "Means of providing lectures" refers to the method by which generative artificial intelligence interactively provides lecture content to users.

[1023] "Question-answering tone" is a way for generative AI to adjust the tone and expression of its answers depending on the user's emotional state.

[1024] This invention is an online education system that combines generative artificial intelligence and an emotion engine to personalize the user's learning experience and provide a more effective educational experience. This system allows users to not only select learning content and attend lectures, but also answer questions in real time and adjust the content and tone of lectures according to their emotional state.

[1025] Hardware and software used

[1026] The system is implemented using the following hardware and software:

[1027] Server: Manages user information and communicates with generative AI. MySQL is used as the database.

[1028] Terminal: A device with a camera and microphone that allows a user to use the system.

[1029] Generative AI: For example, OpenAI's GPT-3 is used as a generative AI model.

[1030] Emotion engine: To analyze the user's emotional state, for example, using Microsoft Azure's Emotion API.

[1031] Data processing and calculation

[1032] 1. User Registration and Login:

[1033] Users register by entering their name, email address, password, and the field they want to study from their device. The server receives this information and stores it in a MySQL database. After registration, users log in using their email address and password. The server verifies the information entered against the database and performs authentication.

[1034] 2. Course selection and attendance:

[1035] After logging in, the user selects the lecture they wish to take from the dashboard. The server receives the selected lecture information and activates the generative AI model. During the lecture, when the user enters a question, the server sends the question to the generative AI and obtains an answer.

[1036] 3. Language Selection:

[1037] The user selects the language to use before the lecture begins, and the server sends the selected language information to the generative AI model, which then configures the AI ​​to provide the lecture in that language.

[1038] 4. Test Administration and Scoring:

[1039] When a user starts a test, the server requests the generative AI model to generate test questions and displays them to the user. When the user submits their answers, the server sends them to the generative AI model and returns the scoring results to the user.

[1040] 5. Report submission and correction:

[1041] Users create reports and upload them to the system from their devices. The server receives the submitted reports and sends them to the generative AI model for correction. The generative AI model then corrects the reports and returns feedback to the user via the server.

[1042] 6. Support for research activities:

[1043] Users set a research topic and input it into the server. The server sends that information to the generative AI model, which then provides relevant literature and materials. Users can use these as a basis for their research.

[1044] 7. Emotion recognition and response:

[1045] The emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone, and sends the emotional information to a server, which then sends it to a generative AI model, which then dynamically adjusts the content and tone of the lecture based on the recognized emotions.

[1046] Specific examples

[1047] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question during the lecture about "how to define a function." The server sends the question to the generative AI model, which immediately returns a specific answer: "Functions in Python are defined using the def keyword." At the same time, the emotion engine analyzes the user's facial expression, and if it determines that the user is having difficulty understanding, the generative AI model provides more detailed sample code and practice problems.

[1048] Prompt Sentence Examples

[1049] An example of a prompt for a user question is:

[1050] A user asked how to define a function in Python programming. Please explain it clearly with concrete code examples.

[1051] As described above, by combining generative artificial intelligence and an emotion engine, the system of the present invention is able to provide an effective and customized learning experience by customizing lessons to meet the individual needs of users, enabling real-time Q&A, and tailoring lectures based on emotions.

[1052] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1053] Step 1: User Registration

[1054] Specific explanation

[1055] A user enters their name, email address, password, and desired field of study into the registration form on a web page and presses the submit button.

[1056] concrete action

[1057] The terminal transmits the input user information to the server.

[1058] input

[1059] Enter your name, email address, password, and the field you want to study.

[1060] Data Processing

[1061] The server receives this information and stores it in a MySQL database using SQL queries.

[1062] output

[1063] A successful registration message such as "Registration complete" will be displayed on the user's screen.

[1064] Step 2: User Login

[1065] Specific explanation

[1066] The user enters their email address and password in the login form and clicks the submit button.

[1067] concrete action

[1068] The terminal transmits the input information to the server.

[1069] input

[1070] Entered email address and password

[1071] Data Processing

[1072] The server compares the received information with the user information stored in the MySQL database and performs authentication.

[1073] output

[1074] If authentication is successful, the server redirects the user to their dashboard, which displays a list of courses available for study.

[1075] Step 3: Select a course

[1076] Specific explanation

[1077] The user clicks on the desired lecture from the lecture list on the dashboard.

[1078] concrete action

[1079] The terminal transmits the selected lecture information to the server.

[1080] input

[1081] Lecture information selected by the user

[1082] Data Processing

[1083] The server records the lecture information, calls the generative AI model via an API, and prepares to start the lecture content.

[1084] output

[1085] The interactive lecture start screen is displayed to the user.

[1086] Step 4: Deliver the lecture

[1087] Specific explanation

[1088] A generative artificial intelligence provides selected lectures to users.

[1089] concrete action

[1090] The server displays lecture materials and content (text, images, code examples, etc.) generated by generative AI on the user's screen. The user enters a question in the question form and presses the submit button.

[1091] input

[1092] User Questions

[1093] Data Processing

[1094] The server sends the question to the generative AI as an API request, and the generative AI generates an answer in real time.

[1095] output

[1096] The server displays the generated answer on the user's screen.

[1097] Step 5: Language Selection

[1098] Specific explanation

[1099] The user selects the language to use before the lecture begins.

[1100] concrete action

[1101] The terminal transmits the selected language information to the server.

[1102] input

[1103] Selected Language

[1104] Data Processing

[1105] The server sends the language information to the generative AI as an API request, and configures the AI ​​to provide lectures in that language.

[1106] output

[1107] Lectures will be delivered in the language of your choice.

[1108] Step 6: Testing

[1109] Specific explanation

[1110] When a user starts a test, the server asks the generative AI model to generate test questions.

[1111] concrete action

[1112] The device presses the test start button.

[1113] input

[1114] Request to start testing

[1115] Data Processing

[1116] The server sends test questions to the generative AI model as an API request, and the generated test questions are displayed to the user. When the user enters and submits the answers, the server sends them to the generative AI model for grading, and the AI ​​grades them.

[1117] output

[1118] Scoring and feedback are displayed to the user.

[1119] Step 7: Submit your report

[1120] Specific explanation

[1121] The user creates a report and uploads it to the system from the terminal.

[1122] concrete action

[1123] The terminal sends the report file to the server via the upload form.

[1124] input

[1125] Uploaded report file

[1126] Data Processing

[1127] The server receives the report and sends it to the generative AI model as an API request to correct it. The generative AI model corrects the report and generates feedback.

[1128] output

[1129] Corrections and feedback are displayed to the user.

[1130] Step 8: Setting a research topic and gathering information

[1131] Specific explanation

[1132] The user sets a research topic and enters it into the server.

[1133] concrete action

[1134] The terminal transmits the entered research topic information to the server.

[1135] input

[1136] Research Theme

[1137] Data Processing

[1138] The server sends this information to the generative AI model, which then searches the web for relevant literature and materials and provides them.The same process is followed if the user requests additional information by entering a question again.

[1139] output

[1140] Related materials and bibliographic information is displayed to the user.

[1141] Step 9: Emotion Recognition

[1142] Specific explanation

[1143] Using the device's camera and microphone, the emotion engine analyzes the user's facial expressions and tone of voice.

[1144] concrete action

[1145] The facial expression and voice data collected by the device is sent to the server.

[1146] input

[1147] User facial and voice data

[1148] Data Processing

[1149] The server receives this data, analyzes the emotional state using an emotion engine (e.g., Emotion API), and sends the results to a generative AI model.

[1150] output

[1151] The results of the emotional state analysis are stored on a server and used to adjust the content and tone of the lecture.

[1152] Step 10: Emotionally Based Lecture Adjustments

[1153] Specific explanation

[1154] Based on the recognized emotional information, generative artificial intelligence dynamically adjusts the content and tone of the lecture.

[1155] concrete action

[1156] The server sends the emotional information to the generative artificial intelligence.

[1157] input

[1158] Emotional information from the emotion engine

[1159] Data Processing

[1160] The generative AI model uses that information to customize the content and tone of the lecture, offering more detailed explanations and additional support if the user is struggling to understand.

[1161] output

[1162] The adjusted content and tone of the lecture are displayed to the user.

[1163] Step 11: Emotional regulation of question responses

[1164] Specific explanation

[1165] When a user asks a question, the emotion engine grasps the user's emotional state, and the generative AI generates a response in a tone that corresponds to that.

[1166] concrete action

[1167] The terminal transmits the user's emotional state along with the question to the server.

[1168] input

[1169] User questions and sentiment information

[1170] Data Processing

[1171] The server sends this information to a generative artificial intelligence, which then generates responses in an emotional tone.

[1172] output

[1173] The generated answer is displayed to the user.

[1174] (Application example 2)

[1175] 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."

[1176] Conventional online education systems and shopping assistant systems provide uniform information and responses without considering the user's emotional state, making it difficult to provide a personalized experience. This makes it difficult to respond flexibly to the user's interests and level of understanding, resulting in a decline in the effectiveness of learning and the quality of the shopping experience.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide learning content based on a user request, means for selecting the language used in the lecture, means for accepting user test and answer submissions and grading them using the generative AI, means for correcting user-submitted reports using the generative AI, means for supporting research using the generative AI to support information gathering based on a research topic, means for recognizing the user's emotional state in real time using an emotion engine, means for dynamically adjusting the method for providing learning content based on the user's emotional information, and means for providing answers to user questions in a tone that reflects the user's emotional information. This allows for a personalized experience tailored to the user's emotional state, improving the quality of learning outcomes and shopping experiences.

[1178] "User registration information" is basic information such as the user's name, email address, password, and areas of interest.

[1179] "Generative AI" is an AI system that generates and provides learning content and real-time answers based on user requests.

[1180] The "language used in the lecture" refers to the language used when the user attends the lecture.

[1181] "Test administration and answers" refers to an evaluation test that the user takes to check what they have learned, and the user's answers to that test.

[1182] "Report correction" is the process in which a generative AI reviews a report submitted by a user and provides corrections and feedback.

[1183] A "research theme" refers to the theme or issue that the user is researching.

[1184] The "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state in real time.

[1185] "Affective tailoring" is the process of dynamically adjusting the tone of learning content and responses based on the perceived emotional state of the user.

[1186] "Means for providing answers in real time" refers to means for generating and providing answers immediately when a user asks a question.

[1187] This invention is an online education system that combines generative artificial intelligence and an emotion engine to provide a personalized learning experience based on the user's emotional state. The same technology can also be applied to smart shopping assistant systems in physical stores. The system for implementing this invention consists of hardware such as a smartphone, smart glasses, and a head-mounted display, as well as the following software modules:

[1188] System Program

[1189] The system first receives user registration information and stores it in a database on the backend, including the user's name, email address, password, interests, etc. Then, when the user logs in, a customized dashboard based on their interests is displayed.

[1190] Providing learning content

[1191] When a user selects a learning content, the generative AI is activated to generate interactive lectures and explanations for that specific content. The user can pre-select the corresponding language, and the AI ​​will provide the content based on the selected language.

[1192] Emotion Recognition and Dynamic Regulation

[1193] The system uses the cameras and microphones of smart glasses or head-mounted displays to recognize the user's emotional state in real time. Examples of technologies used include image processing libraries such as OpenCV and the emotion_recognition library. This analyzes the user's facial expressions and tone of voice, and transmits the user's emotional information to the server in real time. The server then sends this information to a generative artificial intelligence (AI) system, which dynamically adjusts the content delivery method based on the user's emotional state.

[1194] Real-time answers

[1195] When a user asks a question, the generative AI generates a real-time answer to the question, taking into account the emotional information from the emotion engine. If the user is frustrated, the AI ​​will use a polite tone, and if the user is interested, it will provide a detailed explanation.

[1196] Specific examples

[1197] For example, consider a scenario where a user is wearing smart glasses and walking through a store. When the user shows interest in a TV, the camera captures the user's facial expression, and the emotion engine recognizes it as "interested." As a result, the generative AI provides a detailed description, such as "This TV has 4K resolution and is equipped with the latest video technology." In this scenario, an example of a prompt sentence to be input to the generative AI model is as follows:

[1198] "Please enter a prompt to generate detailed descriptions of products that the user is interested in. User sentiment is interesting."

[1199] This allows users to receive appropriate information according to their emotional state, improving their purchasing and learning experiences. Dynamic adjustments based on emotional information maximize user satisfaction and effectiveness.

[1200] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1201] Step 1:

[1202] A user accesses the system using a terminal. As input, the user enters user information such as name, email address, password, and areas of interest. The server receives this information and stores it in a database. As output, it displays a message that the user registration was successful.

[1203] Step 2:

[1204] The user enters their email address and password on the login page and presses the login button. The server verifies the information entered against the database and performs authentication. If authentication is successful, the user's dashboard is displayed. The output is the dashboard page indicating successful authentication.

[1205] Step 3:

[1206] A user selects learning content from a dashboard. As input, a request for the learning content is sent to the server. The server receives the information of the selected learning content and launches the generative AI. As output, the AI ​​is ready to provide an interactive lecture.

[1207] Step 4:

[1208] The user selects the language to use before the lecture begins. The selected language information is sent to the server as input. The server then sends this information to the generative artificial intelligence, which configures the lecture to be provided in the selected language. The configured language information is displayed as output.

[1209] Step 5:

[1210] When a user has a question during a lecture, they input it using a terminal. The question is sent as input to the server. The server then sends it to a generative artificial intelligence, which generates an answer in real time. The generated answer is then displayed to the user as output.

[1211] Step 6:

[1212] During or after a lecture, the user takes a test. As input, a request to start the test is sent to the server. The server asks the generative AI to generate test questions and displays the generated questions to the user. When the user answers the questions and submits them, the answer data is sent to the server, and the AI ​​scores them. As output, the test results and feedback are displayed to the user.

[1213] Step 7:

[1214] When a user submits a report assignment, they create the report in the specified format and upload it to the system. The report upload data is sent to the server as input. The server then sends the submitted report to a generative AI, which then corrects the report. The correction results and feedback are displayed to the user as output.

[1215] Step 8:

[1216] The user sets a research topic and inputs it into the server. Information about the research topic is sent to the server as input. The server sends this information to a generative artificial intelligence, which searches for related literature and materials. As output, related information is provided to the user. The user then conducts their research based on the provided materials.

[1217] Step 9:

[1218] The system uses the device's built-in camera and microphone to recognize the user's emotional state in real time. As input, camera footage and audio data are sent to the server. The server processes this data using an emotion engine to recognize the user's emotional information. As output, the recognized emotional information is sent to the generative artificial intelligence.

[1219] Step 10:

[1220] The system dynamically adjusts the delivery method of learning content and the tone of answers based on the user's emotional information. As input, the emotional information and the user's request are sent to the server. The server then communicates this information to the generative AI, which then adjusts the delivery method. As output, the adjusted content and answers are displayed to the user.

[1221] Example prompt sentence:

[1222] "Please enter a prompt to generate detailed descriptions of products that the user is interested in. User sentiment is interesting."

[1223] The above are the specific processing steps for carrying out the invention, which allows for flexible responses to the user's emotional state, providing a personalized learning or shopping experience.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] [Third embodiment]

[1228] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1229] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1230] 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).

[1231] 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.

[1232] 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.

[1233] 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).

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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."

[1240] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[1241] User Registration and Login

[1242] When a user uses the system for the first time, they must first register. The user registers by entering their name, email address, password, and the field they wish to study. The server receives this information and stores it in a database. Once registration is complete, the user logs in using the email address and password they used when registering. When logging in, the server compares the information in the database with the information they entered and authenticates them. If authentication is successful, the user is redirected to the dashboard.

[1243] Lecture selection and attendance

[1244] After logging in, users can select the lectures they wish to take from the dashboard. Lectures are categorized by field and can be freely selected according to the user's interests and needs. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4) corresponding to that lecture. This allows the generative AI to provide the user with an interactive lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[1245] Language Selection

[1246] Before the lecture begins, the user can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to deliver lectures in multiple languages.

[1247] Test administration and scoring

[1248] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[1249] Report submission and correction

[1250] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[1251] Support for research activities

[1252] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[1253] Specific examples

[1254] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server has the generative AI prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[1255] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[1256] The processing flow will be explained below.

[1257] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[1258] User Registration and Login

[1259] Step 1:

[1260] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[1261] Step 2:

[1262] The server receives these inputs and stores them in a database.

[1263] Step 3:

[1264] The server displays a message to the user confirming registration.

[1265] Step 4:

[1266] The user enters their email address and password on the login page and clicks the login button.

[1267] Step 5:

[1268] The server compares the input information with a database and performs authentication.

[1269] Step 6:

[1270] If authentication is successful, the server displays the user's dashboard.

[1271] Lecture selection and attendance

[1272] Step 1:

[1273] The user selects the desired lecture from the list of lectures on the dashboard.

[1274] Step 2:

[1275] The server receives the selected lecture information and activates the generative artificial intelligence.

[1276] Step 3:

[1277] Generative artificial intelligence provides users with interactive lectures.

[1278] Step 4:

[1279] A chat box is provided for users to enter questions during the lecture.

[1280] Step 5:

[1281] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[1282] Step 6:

[1283] The server displays the generated answer to the user.

[1284] Language Selection

[1285] Step 1:

[1286] The user selects the language to use before the lecture begins.

[1287] Step 2:

[1288] The server transmits the selected language information to the generative artificial intelligence.

[1289] Step 3:

[1290] Configure the generative artificial intelligence to deliver lectures in the language of your choice.

[1291] Test administration and scoring

[1292] Step 1:

[1293] After the lecture, the user presses a button to start the section test.

[1294] Step 2:

[1295] The server requests the generative artificial intelligence to generate test questions.

[1296] Step 3:

[1297] Test questions generated by the generative artificial intelligence are displayed to the user via the server.

[1298] Step 4:

[1299] The user answers the test questions and presses the answer button.

[1300] Step 5:

[1301] The server receives the user's answer and sends it to the generative artificial intelligence.

[1302] Step 6:

[1303] Generative AI grades answers and generates results and feedback.

[1304] Step 7:

[1305] The server displays the score and feedback to the user.

[1306] Report submission and correction

[1307] Step 1:

[1308] A user creates a report in a specified format and accesses the upload page.

[1309] Step 2:

[1310] The user uploads the report to the server.

[1311] Step 3:

[1312] The server receives the submitted report and sends it to the generative artificial intelligence.

[1313] Step 4:

[1314] Generative AI corrects reports and generates feedback including errors and areas for improvement.

[1315] Step 5:

[1316] The server displays the feedback to the user.

[1317] Support for research activities

[1318] Step 1:

[1319] The user inputs a research topic into the research support function.

[1320] Step 2:

[1321] The server sends the input theme to the generative artificial intelligence.

[1322] Step 3:

[1323] The generative artificial intelligence searches for relevant literature and materials and provides them to users via a server.

[1324] Step 4:

[1325] The research will proceed based on the materials provided by the user, and if additional information is needed, the generative AI will be queried again.

[1326] Step 5:

[1327] The user completes the final product of the research and submits it to the system.

[1328] Step 6:

[1329] The server sends the final product to the generative artificial intelligence for review and feedback.

[1330] Step 7:

[1331] The server displays the review results to the user.

[1332] Specific examples

[1333] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server instructs the generative AI to prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[1334] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[1335] Example 1

[1336] 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."

[1337] Conventional online education systems have the problem of requiring time and effort to provide individual support for selecting lectures, conducting tests, correcting papers, and supporting research activities, which reduces the quality of the user experience. Furthermore, real-time responses to questions and the provision of lectures in multiple languages ​​are often insufficient, resulting in reduced learning efficiency. This has made it difficult to improve user satisfaction and learning outcomes.

[1338] 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.

[1339] In this invention, the server includes means for receiving user registration information and executing registration, means for launching a generative model that provides learning content based on a user request, means for selecting the language used in the lecture, means for accepting user tests and answers and grading them using the generative model, means for correcting reports submitted by the user using the generative model, means for research support using the generative model to support information gathering based on a research topic, and means for sending questions entered by the user during a lecture to the generative model and generating and returning answers. This improves the efficiency of the entire online education system, dramatically improves the user's learning experience, and enables multifaceted learning support.

[1340] "User registration information" is data used to identify a user and allow access to the system, such as name, email address, password, and desired field of study.

[1341] A "generative model" is an AI technology that uses natural language processing technology to generate content in response to user requests, and an example is a natural language generation model.

[1342] "Learning Content" means any information content provided for educational purposes, such as lectures, tests, reports, and research support.

[1343] The "language used in the lecture" is the language in which the lecture will be given, and can be selected by the user to enable lectures to be provided in multiple languages.

[1344] "Testing" refers to a test conducted to check the user's level of understanding, and includes processes such as asking questions, accepting answers, and grading.

[1345] An "answer" is an answer submitted by a user to a test question.

[1346] A "report" is a document or literature that a user creates and submits based on a specified assignment.

[1347] A "research theme" is a subject or issue that a user sets when conducting research activities.

[1348] "Information gathering" refers to the activity of collecting literature and materials related to a research topic and providing them to users.

[1349] "Questions from users" are points of uncertainty or doubt that users input in text format during a lecture and send to the system.

[1350] "Generating answers in real time" refers to the process in which a generative model receives a question from a user and immediately generates an answer and responds.

[1351] The present invention provides an online education system that utilizes a generative artificial intelligence model. This system is designed to enable users to efficiently access learning content. Specific embodiments of the system are described in detail below.

[1352] System Configuration

[1353] This system includes a user's device, a server, and a generative artificial intelligence model (generative model). The user's device is a device such as a computer, tablet, or smartphone, and communicates with the server via an internet connection. The server is located in a cloud environment or data center and manages and operates the user data and the generative model.

[1354] User Registration and Login

[1355] User registration: When a user uses the system for the first time, they enter their name, email address, password, and the field they want to study. The terminal sends this data to the server, which receives the user information and stores it in a database.

[1356] Login: Registered users log in by entering their email address and password. The server authenticates them by checking the information in its database. If authentication is successful, the user is redirected to the dashboard.

[1357] Lecture selection and attendance

[1358] Lecture selection: The user selects the lecture they want to take from the dashboard. The device sends the selected lecture information to the server. The server receives the lecture information and launches a generative model (e.g., GPT-4).

[1359] Attendance: The generative model provides the user with an interactive lecture. When the user enters a question during the lecture, the device sends the question to the server. The server sends the question as a prompt to the generative model, which then returns the generated answer to the user.

[1360] Language Selection

[1361] The user selects the desired language before the lecture begins. The terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture to be delivered in the selected language. This makes it possible to deliver lectures in multiple languages.

[1362] Test administration and scoring

[1363] Start test: The user selects a test from the dashboard and clicks the "Start test" button. The device sends a test start request to the server. The server asks the generative model to generate test questions and provides the generated questions to the user.

[1364] Test submission and scoring: The user enters their test answers and clicks the "Submit" button. The device sends the answer data to the server. The server then sends the answer data to the generative model and requests scoring. The scoring results are then fed back to the user.

[1365] Report submission and correction

[1366] The user creates a report based on a specified assignment and uploads it to the system from their device. The server sends the report to the generative model for correction. The generative model corrects the report and generates feedback pointing out errors and areas for improvement. The server returns this feedback to the user.

[1367] Support for research activities

[1368] The user enters a research topic and sends it from their device to the server. The server sends the topic information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates search results and provides them to the user via the server. The user can then continue their research based on the provided materials and ask the generative model additional questions.

[1369] Specific examples

[1370] For example, if a user selects the "Introduction to Python Programming" lecture, the device sends this information to the server. The server then prepares the first lecture of "Introduction to Python Programming" for the generative model. If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative model, which then generates and returns a specific answer, such as "Functions in Python are defined using the def keyword."

[1371] In this way, the present invention provides users with a highly efficient and multifunctional online learning environment through a series of operations.

[1372] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1373] Step 1: User Registration

[1374] Input: The user enters their name, email address, password, and the subject they want to learn about.

[1375] Specific operation: When the user clicks the "Register" button, the device sends this data to the server. The server receives the user data and saves it in the database. After saving is complete, the server returns a response to the user indicating successful registration.

[1376] Output: The user data is saved in the database and a registration success message is displayed to the user.

[1377] Step 2: Log in

[1378] Input: The user enters their email address and password.

[1379] Specific operation: When the user clicks the "Login" button, the device sends the entered data to the server, which then collates it with the user information stored in the database and performs authentication.

[1380] Output: If authentication is successful, the server returns the dashboard information to the terminal and the user is redirected to the dashboard.

[1381] Step 3: Select a course

[1382] Input: The user selects the course they want to take from the dashboard.

[1383] Specific operation: When the user clicks the "Select" button, the device sends the selected lecture information to the server. The server receives the lecture information and launches the corresponding generative model (e.g., GPT-4). It then asks the AI ​​to prepare the lecture and sends a command to the user device to start the lecture.

[1384] Output: The lecture start screen will be displayed on the user's device, and the AI ​​will be ready to provide the lecture.

[1385] Step 4: Language Selection

[1386] Input: The user selects the desired language before the lecture begins.

[1387] Specific operation: When the user selects a language, the terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture provision in the selected language.

[1388] Output: The user is now set up to receive lectures in the language of their choice.

[1389] Step 5: Answer questions during the lecture

[1390] Input: If a question arises during the lecture, the user inputs the question.

[1391] Specific operation: When a user clicks the "Ask" button, the device sends the question to the server. The server receives the question and sends it as a prompt to the generative model. The generative model generates an answer to the question and returns it to the server. The server receives the answer and sends it to the device.

[1392] Output: The answer generated by the generative model is displayed on the user's device.

[1393] Step 6: Testing

[1394] Input: User selects and starts a test from the dashboard.

[1395] Specific operation: When the user clicks the "Start Test" button, the device sends a test start request to the server. The server then requests the generative model to generate test questions and sends the generated questions to the user's device.

[1396] Output: The generated test questions are displayed on the user's terminal.

[1397] Step 7: Test submission and grading

[1398] Input: The user enters the test answers.

[1399] Specific operation: When the user clicks the "Submit" button, the device sends the answer data to the server. The server sends the answer data to the generative model and requests it to be graded. The generative model grades the answer and returns the results to the server. The server then sends the graded results to the device.

[1400] Output: The scoring results are displayed on the user's terminal.

[1401] Step 8: Submit your report and receive corrections

[1402] Input: The user creates and uploads a report based on the given assignment.

[1403] Specific operation: When a user uploads a report, the device sends the report data to the server. The server sends the report to the generative model and requests corrections. The generative model generates feedback pointing out errors and areas for improvement and returns it to the server. The server then sends the feedback to the device.

[1404] Output: The feedback generated by the generative model is displayed on the user's device.

[1405] Step 9: Enter your research topic and provide materials

[1406] Input: The user inputs the research topic.

[1407] Specific operation: When a user submits a theme, the device sends the theme information to the server. The server then sends the theme information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates related materials and returns them to the server. The server then sends the search results to the device.

[1408] Output: Related literature and materials are displayed on the user's terminal.

[1409] Step 10: Research work submission and feedback

[1410] Input: Users create and upload research artifacts.

[1411] Specific operation: When a user uploads an artifact, the device sends the artifact data to the server. The server sends the artifact to the generative model and requests review and feedback. The generative model generates reviews and feedback and returns them to the server. The server sends the feedback to the device.

[1412] Output: The feedback generated by the generative model is displayed on the user's device.

[1413] (Application example 1)

[1414] 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."

[1415] Modern education requires responding to the diverse needs of learners and providing optimal education for each individual. Traditional educational systems face challenges, such as difficulty providing real-time feedback and multilingual support. Furthermore, they lack the ability to provide an interactive learning experience using smart devices. A system is needed to solve these problems and provide an efficient and effective learning environment.

[1416] 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.

[1417] In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide educational content based on a user request, means for selecting the language of the educational content, means for accepting user tests and answers and grading them using the AI, means for correcting user-submitted reports using the AI, research support means using the AI ​​to support information gathering based on a research topic, means installed in a smartphone, smart glasses, a head-mounted display, or a robot, and response means using the AI ​​to support user learning through interactive dialogue. This allows for the provision of highly efficient and multifunctional education to individual learners and enables interactive learning experiences using a variety of devices.

[1418] "User registration information" refers to information such as name, email address, password, and desired field of study that a user provides when they start using the system.

[1419] "Educational content" refers to the lectures and teaching materials provided for learners to learn from.

[1420] "Generative artificial intelligence" is an AI model that can generate appropriate output based on input data.

[1421] "Language of Use" refers to the language in which the educational content is delivered.

[1422] A "test" is a confirmation test that learners take during or after a lecture to check their understanding.

[1423] "Answer" is the answer given by the learner to the test.

[1424] "Scoring" refers to the evaluation and assignment of scores to test answers.

[1425] A "report" is an assignment that a learner creates and submits in a specified format.

[1426] "Correction" refers to pointing out errors and areas for improvement in submitted reports and providing feedback.

[1427] A "research theme" is a specific subject that a learner sets for their research activities.

[1428] "Information gathering" means collecting materials and literature related to the research topic.

[1429] A "smartphone" is a highly functional mobile phone that can run a variety of applications in addition to making calls.

[1430] "Smart glasses" are a type of wearable device equipped with information display and communication functions.

[1431] A "head-mounted display" is a device worn on the head that displays visual information.

[1432] A "robot" is a machine that has a certain degree of autonomy and operates under human instructions.

[1433] "Interactive dialogue" refers to real-time, two-way communication between the learner and the system.

[1434] A "response mechanism" is a mechanism for providing answers to user questions.

[1435] The present invention provides a highly efficient and multifunctional educational system that utilizes generative artificial intelligence. This system allows users to register online, access educational content, ask questions, take tests, and submit reports. It also provides support for research activities. An embodiment of the system is described in detail below.

[1436] User Registration and Login

[1437] The server receives the user's name, email address, password, and desired field of study information and registers it in a database. After registering, the user logs in to the system using their email address and password. When logging in, the server compares the entered information with the information in the database and performs authentication. If authentication is successful, the user is redirected to the dashboard.

[1438] Lecture selection and attendance

[1439] After logging in, a user can select the lecture they want to take from the dashboard. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4). This allows the generative AI to provide an interactive lecture. If a user has a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[1440] Language Selection

[1441] Before the lecture begins, users can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to provide learning content in multiple different languages.

[1442] Test administration and scoring

[1443] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[1444] Report submission and correction

[1445] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[1446] Support for research activities

[1447] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[1448] Interactive Dialogue

[1449] Generative AI supports learning through interactive dialogue with users. This dialogue can be conducted using a smartphone, smart glasses, a head-mounted display, or a robot. Specifically, when a user inputs a question while studying, the AI ​​provides an appropriate answer in real time. This allows users to immediately resolve their doubts and improves learning efficiency.

[1450] Specific examples

[1451] For example, if a user selects a lecture on "Introduction to Python Programming," and asks a question about "how to define a function" during the lecture, the AI ​​will immediately respond with a specific answer such as, "Functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. Furthermore, if the user submits a report assignment on "writing a Python script," the AI ​​will correct it and provide detailed feedback.

[1452] Prompt Sentence Examples

[1453] "What are some data preprocessing techniques for data science?"

[1454] In this way, the educational system based on the present invention provides an advanced educational experience for individual learners and provides an interactive learning experience utilizing a variety of devices.

[1455] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1456] Step 1:

[1457] The server receives the user registration information and stores it in a database. As input, it takes the user's name, email address, password, and desired learning area and stores that information in a database, providing the data to recognize and authenticate the user on future logins.

[1458] Step 2:

[1459] A user logs in with the registered email address and password. When the login request is sent to the server, the server checks the information in the database and authenticates the user. If the authentication is successful, the user is redirected to the dashboard. The input is the user's login information, and the output is the login success or failure status.

[1460] Step 3:

[1461] The user selects the lecture they wish to take from the dashboard. The selected lecture information is sent to the server, which then launches the corresponding generative AI. The input is the user's lecture selection information, and the output is the start of the lecture content.

[1462] Step 4:

[1463] The user selects the language to use before the lecture begins. The server sends the selected language information to the generative AI, which then provides the lecture in that language. The input is the user's language selection, and the output is the presentation of the lecture in the selected language.

[1464] Step 5:

[1465] During a lecture, a user enters a question and sends it to the server. The server then sends the question to a generative AI, which generates an answer in real time and returns it to the user. The input is the user's question, and the output is the answer provided by the generative AI.

[1466] Step 6:

[1467] During or after a lecture, the user starts a test. The server requests the generative AI to generate test questions and provides them to the user. When the user submits their answers, the server sends them to the AI, which then scores them. The input is the user's test answers, and the output is the scoring results and feedback.

[1468] Step 7:

[1469] Users create report assignments and upload them to the system. The server sends the submitted reports to a generative AI, which corrects them and generates feedback. The input is the user's report, and the output is the corrections and feedback.

[1470] Step 8:

[1471] Users input the topic of their research activities and send it to the server. The server then sends the topic information to a generative AI, which searches for related literature and materials and provides them to the user. Furthermore, when a research product is submitted, the server sends it to the AI ​​for review and feedback. The input is the user's research topic and product, and the output is related materials and feedback.

[1472] Step 9:

[1473] The user engages in interactive dialogue using a smartphone, smart glasses, a head-mounted display, or a robot. When the user inputs a question during learning, the generative AI responds in real time through the device. The input is the user's dialogue request, and the output is a real-time response from the generative AI.

[1474] Through the above steps, the educational system of the present invention provides users with an integrated educational experience and realizes efficient and interactive learning.

[1475] 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.

[1476] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[1477] User Registration and Login

[1478] User Registration

[1479] First, a user accesses the system and registers by entering their name, email address, password, and desired field of study. The server receives this information and stores it in a database. After registration is complete, the server displays a message to the user indicating successful registration.

[1480] Log in

[1481] The user enters their email address and password on the login page and presses the login button. The server verifies the entered information against the database, and if authentication is successful, displays the user's dashboard.

[1482] Lecture selection and attendance

[1483] Lecture selection

[1484] The user selects the lecture they wish to take from the dashboard. The server receives the selected lecture information and activates the corresponding generative artificial intelligence to prepare for providing the interactive lecture.

[1485] Lecture provision

[1486] The generative AI provides the user with a selected lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, which then generates an answer in real time and returns it to the user.

[1487] Language Selection

[1488] The user selects the language to use before the lecture begins. The server transmits the selected language information to the generative AI, and configures the generative AI to provide the lecture in that language.

[1489] Test administration and scoring

[1490] Users can take tests during or after a lecture. When a user starts a test, the server requests the generative AI to generate test questions and displays the generated questions to the user. When the user answers the questions and submits them, the server sends the answers to the generative AI, which then scores them and returns the results and feedback to the user.

[1491] Report submission and correction

[1492] When a user submits a report assignment, they create the report in the specified format and upload it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, generates feedback, and returns it to the user via the server.

[1493] Support for research activities

[1494] When a user sets a research topic and enters it into the server, the server sends the topic information to the generative AI. The generative AI searches for related literature and materials and provides that information to the user. The user can then conduct their research based on the provided materials, and if additional information is required, they can ask the generative AI again.

[1495] Finally, the user submits the research results to the system, and the server sends them to the generative artificial intelligence for review and feedback. The server then displays the review results to the user.

[1496] Introducing the Emotion Engine

[1497] To further personalize and enhance the user's learning experience, the present invention introduces an emotion engine.

[1498] emotion recognition

[1499] While the user is using the system, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone to recognize their emotions. The server receives this emotional information in real time.

[1500] Emotion-based lecture adjustment

[1501] The server then sends the recognized emotional information to the generative AI, which then dynamically adjusts the lecture delivery method and content to match the user's emotional state. For example, if the user is struggling to understand something, the generative AI can provide more detailed explanations or additional support.

[1502] Emotion regulation in question-answering

[1503] When a user asks a question, the emotion engine understands the user's emotional state, and the generative AI generates a response in a tone that corresponds to that. For example, if the user is judged to be irritated, the generative AI will respond in a more polite and calm tone.

[1504] Specific examples

[1505] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question during the lecture about "how to define a function." In this case, the server sends the question to the generative AI, which immediately responds with a specific answer: "Functions in Python are defined using the def keyword." At the same time, if the emotion engine analyzes the user's facial expression and determines that the user is having difficulty understanding, the generative AI will provide more detailed sample code and practice problems. Similarly, if a user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which then corrects it and provides detailed feedback.

[1506] In this way, the present invention can provide a highly efficient, multi-functional, and interactive online teaching system combined with emotion recognition functions.

[1507] The processing flow will be explained below.

[1508] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[1509] User Registration and Login

[1510] Step 1:

[1511] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[1512] Step 2:

[1513] The server receives these inputs and stores them in a database.

[1514] Step 3:

[1515] The server displays a message to the user confirming registration.

[1516] Step 4:

[1517] The user enters their email address and password on the login page and clicks the login button.

[1518] Step 5:

[1519] The server compares the input information with a database and performs authentication.

[1520] Step 6:

[1521] If authentication is successful, the server displays the user's dashboard.

[1522] Lecture selection and attendance

[1523] Step 1:

[1524] The user selects the desired lecture from the list of lectures on the dashboard.

[1525] Step 2:

[1526] The server receives the selected lecture information and activates the generative artificial intelligence.

[1527] Step 3:

[1528] Generative AI prepares to provide interactive lectures to users.

[1529] Step 4:

[1530] A chat box is provided for users to enter questions during the lecture.

[1531] Step 5:

[1532] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[1533] Step 6:

[1534] The server displays the generated answer to the user.

[1535] Language Selection

[1536] Step 1:

[1537] The user selects the language to use before the lecture begins.

[1538] Step 2:

[1539] The server transmits the selected language information to the generative artificial intelligence.

[1540] Step 3:

[1541] Configure the generative artificial intelligence to deliver lectures in the language of your choice.

[1542] Test administration and scoring

[1543] Step 1:

[1544] After the lecture, the user presses a button to start the section test.

[1545] Step 2:

[1546] The server requests the generative artificial intelligence to generate test questions.

[1547] Step 3:

[1548] Test questions generated by the generative artificial intelligence are displayed to the user via the server.

[1549] Step 4:

[1550] The user answers the test questions and presses the answer button.

[1551] Step 5:

[1552] The server receives the user's answer and sends it to the generative artificial intelligence.

[1553] Step 6:

[1554] Generative AI grades answers and generates results and feedback.

[1555] Step 7:

[1556] The server displays the score and feedback to the user.

[1557] Report submission and correction

[1558] Step 1:

[1559] A user creates a report in a specified format and accesses the upload page.

[1560] Step 2:

[1561] The user uploads the report to the server.

[1562] Step 3:

[1563] The server receives the submitted report and sends it to the generative artificial intelligence.

[1564] Step 4:

[1565] Generative AI corrects reports and generates feedback including errors and areas for improvement.

[1566] Step 5:

[1567] The server displays the feedback to the user.

[1568] Support for research activities

[1569] Step 1:

[1570] The user inputs a research topic into the research support function.

[1571] Step 2:

[1572] The server sends the input theme to the generative artificial intelligence.

[1573] Step 3:

[1574] The generative artificial intelligence searches for relevant literature and materials and provides them to users via a server.

[1575] Step 4:

[1576] The research will proceed based on the materials provided by the user, and if additional information is needed, the generative AI will be queried again.

[1577] Step 5:

[1578] The user completes the final product of the research and submits it to the system.

[1579] Step 6:

[1580] The server sends the final product to the generative artificial intelligence for review and feedback.

[1581] Step 7:

[1582] The server displays the review results to the user.

[1583] Introducing the Emotion Engine

[1584] emotion recognition

[1585] Step 1:

[1586] When a user uses the system, the device's built-in camera and microphone capture the user's facial expressions and tone of voice.

[1587] Step 2:

[1588] The server sends these capture data to the emotion engine.

[1589] Step 3:

[1590] The emotion engine analyzes the user's emotions and sends the recognition results to the server.

[1591] Emotion-based lecture adjustment

[1592] Step 1:

[1593] The server sends the recognized emotion information to the generative artificial intelligence.

[1594] Step 2:

[1595] Generative AI dynamically adjusts lecture content and delivery methods based on the user's emotional state.

[1596] Step 3:

[1597] The server provides the adjusted lecture content to the user.

[1598] Emotion regulation in question-answering

[1599] Step 1:

[1600] When a user asks a question, the device captures the user's tone of voice and facial expressions.

[1601] Step 2:

[1602] The server sends the captured data to the emotion engine to recognize the emotion.

[1603] Step 3:

[1604] The server sends the recognized emotional information to the generative artificial intelligence, which then generates a response in a corresponding tone.

[1605] Step 4:

[1606] The server displays the generated answer to the user.

[1607] Specific examples

[1608] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question about "how to define a function" during the lecture. In this case, the server sends the question to the generative AI, which immediately responds with a specific answer: "Functions in Python are defined using the def keyword." At the same time, if the emotion engine analyzes the user's facial expression and determines that the user is having difficulty understanding, the generative AI will provide more detailed sample code and practice problems. Similarly, if a user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which then corrects it and provides detailed feedback.

[1609] In this way, the present invention can provide a highly efficient, multi-functional, and interactive online teaching system combined with emotion recognition functions.

[1610] Example 2

[1611] 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."

[1612] Conventional online education systems lack the ability to customize to meet individual user needs or provide real-time question-and-answer functions. Furthermore, they are unable to adjust to take into account the user's level of understanding or emotional state, making it difficult to provide an effective learning experience. Furthermore, automation of the grading and correction of tests and reports submitted by users has not been fully realized. Furthermore, when it comes to supporting research activities, there is a problem of reduced learning efficiency because the information users need is not collected or feedback is not provided in real time. It is necessary to solve these issues and provide a more effective and customized online education system.

[1613] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1614] In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide learning content based on a user request, means for selecting the language used in lectures, means for accepting user test and answer submissions and grading them using the generative AI, means for correcting user-submitted reports using the generative AI, means for supporting research using the generative AI to support information gathering based on a research topic, means for recognizing user emotions and transmitting that information to the generative AI, and means for adjusting the tone of learning content and Q&A based on the recognized emotional information. This enables customization according to individual user needs, real-time Q&A, and the provision of an effective learning experience that takes into account the user's emotional state, automatic grading and correction of tests and reports, and support for research activities.

[1615] "User registration information" refers to information such as name, email address, password, and desired field of study that a user provides when registering with the system.

[1616] "Generative artificial intelligence" is an AI technology that generates answers and content in real time based on input text or questions.

[1617] "Language of lecture" refers to the language that the user can choose to use when taking a lecture, and the system will provide learning content in accordance with that language.

[1618] A "test" is an exam that a user takes to check what they have learned, and the answers are graded by generative artificial intelligence.

[1619] A "report" is a document that a user creates based on a specified assignment and submits to the system.

[1620] A "research theme" is a subject or issue that a user sets when conducting research activities.

[1621] "Emotion recognition" is a technology that uses the device's camera and microphone to analyze the user's facial expressions and tone of voice to determine their emotional state.

[1622] "Means of providing lectures" refers to the method by which generative artificial intelligence interactively provides lecture content to users.

[1623] "Question-answering tone" is a way for generative AI to adjust the tone and expression of its answers depending on the user's emotional state.

[1624] This invention is an online education system that combines generative artificial intelligence and an emotion engine to personalize the user's learning experience and provide a more effective educational experience. This system allows users to not only select learning content and attend lectures, but also answer questions in real time and adjust the content and tone of lectures according to their emotional state.

[1625] Hardware and software used

[1626] The system is implemented using the following hardware and software:

[1627] Server: Manages user information and communicates with generative AI. MySQL is used as the database.

[1628] Terminal: A device with a camera and microphone that allows a user to use the system.

[1629] Generative AI: For example, OpenAI's GPT-3 is used as a generative AI model.

[1630] Emotion engine: To analyze the user's emotional state, for example, using Microsoft Azure's Emotion API.

[1631] Data processing and calculation

[1632] 1. User Registration and Login:

[1633] Users register by entering their name, email address, password, and the field they want to study from their device. The server receives this information and stores it in a MySQL database. After registration, users log in using their email address and password. The server verifies the information entered against the database and performs authentication.

[1634] 2. Course selection and attendance:

[1635] After logging in, the user selects the lecture they wish to take from the dashboard. The server receives the selected lecture information and activates the generative AI model. During the lecture, when the user enters a question, the server sends the question to the generative AI and obtains an answer.

[1636] 3. Language Selection:

[1637] The user selects the language to use before the lecture begins, and the server sends the selected language information to the generative AI model, which then configures the AI ​​to provide the lecture in that language.

[1638] 4. Test Administration and Scoring:

[1639] When a user starts a test, the server requests the generative AI model to generate test questions and displays them to the user. When the user submits their answers, the server sends them to the generative AI model and returns the scoring results to the user.

[1640] 5. Report submission and correction:

[1641] Users create reports and upload them to the system from their devices. The server receives the submitted reports and sends them to the generative AI model for correction. The generative AI model then corrects the reports and returns feedback to the user via the server.

[1642] 6. Support for research activities:

[1643] Users set a research topic and input it into the server. The server sends that information to the generative AI model, which then provides relevant literature and materials. Users can use these as a basis for their research.

[1644] 7. Emotion recognition and response:

[1645] The emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone, and sends the emotional information to a server, which then sends it to a generative AI model, which then dynamically adjusts the content and tone of the lecture based on the recognized emotions.

[1646] Specific examples

[1647] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question during the lecture about "how to define a function." The server sends the question to the generative AI model, which immediately returns a specific answer: "Functions in Python are defined using the def keyword." At the same time, the emotion engine analyzes the user's facial expression, and if it determines that the user is having difficulty understanding, the generative AI model provides more detailed sample code and practice problems.

[1648] Prompt Sentence Examples

[1649] An example of a prompt for a user question is:

[1650] A user asked how to define a function in Python programming. Please explain it clearly with concrete code examples.

[1651] As described above, by combining generative artificial intelligence and an emotion engine, the system of the present invention is able to provide an effective and customized learning experience by customizing lessons to meet the individual needs of users, enabling real-time Q&A, and tailoring lectures based on emotions.

[1652] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1653] Step 1: User Registration

[1654] Specific explanation

[1655] A user enters their name, email address, password, and desired field of study into the registration form on a web page and presses the submit button.

[1656] concrete action

[1657] The terminal transmits the input user information to the server.

[1658] input

[1659] Enter your name, email address, password, and the field you want to study.

[1660] Data Processing

[1661] The server receives this information and stores it in a MySQL database using SQL queries.

[1662] output

[1663] A successful registration message such as "Registration complete" will be displayed on the user's screen.

[1664] Step 2: User Login

[1665] Specific explanation

[1666] The user enters their email address and password in the login form and clicks the submit button.

[1667] concrete action

[1668] The terminal transmits the input information to the server.

[1669] input

[1670] Entered email address and password

[1671] Data Processing

[1672] The server compares the received information with the user information stored in the MySQL database and performs authentication.

[1673] output

[1674] If authentication is successful, the server redirects the user to their dashboard, which displays a list of courses available for study.

[1675] Step 3: Select a course

[1676] Specific explanation

[1677] The user clicks on the desired lecture from the lecture list on the dashboard.

[1678] concrete action

[1679] The terminal transmits the selected lecture information to the server.

[1680] input

[1681] Lecture information selected by the user

[1682] Data Processing

[1683] The server records the lecture information, calls the generative AI model via an API, and prepares to start the lecture content.

[1684] output

[1685] The interactive lecture start screen is displayed to the user.

[1686] Step 4: Deliver the lecture

[1687] Specific explanation

[1688] A generative artificial intelligence provides selected lectures to users.

[1689] concrete action

[1690] The server displays lecture materials and content (text, images, code examples, etc.) generated by generative AI on the user's screen. The user enters a question in the question form and presses the submit button.

[1691] input

[1692] User Questions

[1693] Data Processing

[1694] The server sends the question to the generative AI as an API request, and the generative AI generates an answer in real time.

[1695] output

[1696] The server displays the generated answer on the user's screen.

[1697] Step 5: Language Selection

[1698] Specific explanation

[1699] The user selects the language to use before the lecture begins.

[1700] concrete action

[1701] The terminal transmits the selected language information to the server.

[1702] input

[1703] Selected Language

[1704] Data Processing

[1705] The server sends the language information to the generative AI as an API request, and configures the AI ​​to provide lectures in that language.

[1706] output

[1707] Lectures will be delivered in the language of your choice.

[1708] Step 6: Testing

[1709] Specific explanation

[1710] When a user starts a test, the server asks the generative AI model to generate test questions.

[1711] concrete action

[1712] The device presses the test start button.

[1713] input

[1714] Request to start testing

[1715] Data Processing

[1716] The server sends test questions to the generative AI model as an API request, and the generated test questions are displayed to the user. When the user enters and submits the answers, the server sends them to the generative AI model for grading, and the AI ​​grades them.

[1717] output

[1718] Scoring and feedback are displayed to the user.

[1719] Step 7: Submit your report

[1720] Specific explanation

[1721] The user creates a report and uploads it to the system from the terminal.

[1722] concrete action

[1723] The terminal sends the report file to the server via the upload form.

[1724] input

[1725] Uploaded report file

[1726] Data Processing

[1727] The server receives the report and sends it to the generative AI model as an API request to correct it. The generative AI model corrects the report and generates feedback.

[1728] output

[1729] Corrections and feedback are displayed to the user.

[1730] Step 8: Setting a research topic and gathering information

[1731] Specific explanation

[1732] The user sets a research topic and enters it into the server.

[1733] concrete action

[1734] The terminal transmits the entered research topic information to the server.

[1735] input

[1736] Research Theme

[1737] Data Processing

[1738] The server sends this information to the generative AI model, which then searches the web for relevant literature and materials and provides them.The same process is followed if the user requests additional information by entering a question again.

[1739] output

[1740] Related materials and bibliographic information is displayed to the user.

[1741] Step 9: Emotion Recognition

[1742] Specific explanation

[1743] Using the device's camera and microphone, the emotion engine analyzes the user's facial expressions and tone of voice.

[1744] concrete action

[1745] The facial expression and voice data collected by the device is sent to the server.

[1746] input

[1747] User facial and voice data

[1748] Data Processing

[1749] The server receives this data, analyzes the emotional state using an emotion engine (e.g., Emotion API), and sends the results to a generative AI model.

[1750] output

[1751] The results of the emotional state analysis are stored on a server and used to adjust the content and tone of the lecture.

[1752] Step 10: Emotionally Based Lecture Adjustments

[1753] Specific explanation

[1754] Based on the recognized emotional information, generative artificial intelligence dynamically adjusts the content and tone of the lecture.

[1755] concrete action

[1756] The server sends the emotional information to the generative artificial intelligence.

[1757] input

[1758] Emotional information from the emotion engine

[1759] Data Processing

[1760] The generative AI model uses that information to customize the content and tone of the lecture, offering more detailed explanations and additional support if the user is struggling to understand.

[1761] output

[1762] The adjusted content and tone of the lecture are displayed to the user.

[1763] Step 11: Emotional regulation of question responses

[1764] Specific explanation

[1765] When a user asks a question, the emotion engine grasps the user's emotional state, and the generative AI generates a response in a tone that corresponds to that.

[1766] concrete action

[1767] The terminal transmits the user's emotional state along with the question to the server.

[1768] input

[1769] User questions and sentiment information

[1770] Data Processing

[1771] The server sends this information to a generative artificial intelligence, which then generates responses in an emotional tone.

[1772] output

[1773] The generated answer is displayed to the user.

[1774] (Application example 2)

[1775] 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."

[1776] Conventional online education systems and shopping assistant systems provide uniform information and responses without considering the user's emotional state, making it difficult to provide a personalized experience. This makes it difficult to respond flexibly to the user's interests and level of understanding, resulting in a decline in the effectiveness of learning and the quality of the shopping experience.

[1777] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide learning content based on a user request, means for selecting the language used in the lecture, means for accepting user test and answer submissions and grading them using the generative AI, means for correcting user-submitted reports using the generative AI, means for supporting research using the generative AI to support information gathering based on a research topic, means for recognizing the user's emotional state in real time using an emotion engine, means for dynamically adjusting the method for providing learning content based on the user's emotional information, and means for providing answers to user questions in a tone that reflects the user's emotional information. This allows for a personalized experience tailored to the user's emotional state, improving the quality of learning outcomes and shopping experiences.

[1778] "User registration information" is basic information such as the user's name, email address, password, and areas of interest.

[1779] "Generative AI" is an AI system that generates and provides learning content and real-time answers based on user requests.

[1780] The "language used in the lecture" refers to the language used when the user attends the lecture.

[1781] "Test administration and answers" refers to an evaluation test that the user takes to check what they have learned, and the user's answers to that test.

[1782] "Report correction" is the process in which a generative AI reviews a report submitted by a user and provides corrections and feedback.

[1783] A "research theme" refers to the theme or issue that the user is researching.

[1784] The "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state in real time.

[1785] "Affective tailoring" is the process of dynamically adjusting the tone of learning content and responses based on the perceived emotional state of the user.

[1786] "Means for providing answers in real time" refers to means for generating and providing answers immediately when a user asks a question.

[1787] This invention is an online education system that combines generative artificial intelligence and an emotion engine to provide a personalized learning experience based on the user's emotional state. The same technology can also be applied to smart shopping assistant systems in physical stores. The system for implementing this invention consists of hardware such as a smartphone, smart glasses, and a head-mounted display, as well as the following software modules:

[1788] System Program

[1789] The system first receives user registration information and stores it in a database on the backend, including the user's name, email address, password, interests, etc. Then, when the user logs in, a customized dashboard based on their interests is displayed.

[1790] Providing learning content

[1791] When a user selects a learning content, the generative AI is activated to generate interactive lectures and explanations for that specific content. The user can pre-select the corresponding language, and the AI ​​will provide the content based on the selected language.

[1792] Emotion Recognition and Dynamic Regulation

[1793] The system uses the cameras and microphones of smart glasses or head-mounted displays to recognize the user's emotional state in real time. Examples of technologies used include image processing libraries such as OpenCV and the emotion_recognition library. This analyzes the user's facial expressions and tone of voice, and transmits the user's emotional information to the server in real time. The server then sends this information to a generative artificial intelligence (AI) system, which dynamically adjusts the content delivery method based on the user's emotional state.

[1794] Real-time answers

[1795] When a user asks a question, the generative AI generates a real-time answer to the question, taking into account the emotional information from the emotion engine. If the user is frustrated, the AI ​​will use a polite tone, and if the user is interested, it will provide a detailed explanation.

[1796] Specific examples

[1797] For example, consider a scenario where a user is wearing smart glasses and walking through a store. When the user shows interest in a TV, the camera captures the user's facial expression, and the emotion engine recognizes it as "interested." As a result, the generative AI provides a detailed description, such as "This TV has 4K resolution and is equipped with the latest video technology." In this scenario, an example of a prompt sentence to be input to the generative AI model is as follows:

[1798] "Please enter a prompt to generate detailed descriptions of products that the user is interested in. User sentiment is interesting."

[1799] This allows users to receive appropriate information according to their emotional state, improving their purchasing and learning experiences. Dynamic adjustments based on emotional information maximize user satisfaction and effectiveness.

[1800] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1801] Step 1:

[1802] A user accesses the system using a terminal. As input, the user enters user information such as name, email address, password, and areas of interest. The server receives this information and stores it in a database. As output, it displays a message that the user registration was successful.

[1803] Step 2:

[1804] The user enters their email address and password on the login page and presses the login button. The server verifies the information entered against the database and performs authentication. If authentication is successful, the user's dashboard is displayed. The output is the dashboard page indicating successful authentication.

[1805] Step 3:

[1806] A user selects learning content from a dashboard. As input, a request for the learning content is sent to the server. The server receives the information of the selected learning content and launches the generative AI. As output, the AI ​​is ready to provide an interactive lecture.

[1807] Step 4:

[1808] The user selects the language to use before the lecture begins. The selected language information is sent to the server as input. The server then sends this information to the generative artificial intelligence, which configures the lecture to be provided in the selected language. The configured language information is displayed as output.

[1809] Step 5:

[1810] When a user has a question during a lecture, they input it using a terminal. The question is sent as input to the server. The server then sends it to a generative artificial intelligence, which generates an answer in real time. The generated answer is then displayed to the user as output.

[1811] Step 6:

[1812] During or after a lecture, the user takes a test. As input, a request to start the test is sent to the server. The server asks the generative AI to generate test questions and displays the generated questions to the user. When the user answers the questions and submits them, the answer data is sent to the server, and the AI ​​scores them. As output, the test results and feedback are displayed to the user.

[1813] Step 7:

[1814] When a user submits a report assignment, they create the report in the specified format and upload it to the system. The report upload data is sent to the server as input. The server then sends the submitted report to a generative AI, which then corrects the report. The correction results and feedback are displayed to the user as output.

[1815] Step 8:

[1816] The user sets a research topic and inputs it into the server. Information about the research topic is sent to the server as input. The server sends this information to a generative artificial intelligence, which searches for related literature and materials. As output, related information is provided to the user. The user then conducts their research based on the provided materials.

[1817] Step 9:

[1818] The system uses the device's built-in camera and microphone to recognize the user's emotional state in real time. As input, camera footage and audio data are sent to the server. The server processes this data using an emotion engine to recognize the user's emotional information. As output, the recognized emotional information is sent to the generative artificial intelligence.

[1819] Step 10:

[1820] The system dynamically adjusts the delivery method of learning content and the tone of answers based on the user's emotional information. As input, the emotional information and the user's request are sent to the server. The server then communicates this information to the generative AI, which then adjusts the delivery method. As output, the adjusted content and answers are displayed to the user.

[1821] Example prompt sentence:

[1822] "Please enter a prompt to generate detailed descriptions of products that the user is interested in. User sentiment is interesting."

[1823] The above are the specific processing steps for carrying out the invention, which allows for flexible responses to the user's emotional state, providing a personalized learning or shopping experience.

[1824] 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.

[1825] 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.

[1826] 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.

[1827] [Fourth embodiment]

[1828] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1829] 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.

[1830] 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).

[1831] 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.

[1832] 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.

[1833] 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).

[1834] 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.

[1835] 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.

[1836] 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.

[1837] 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.

[1838] 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.

[1839] 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.

[1840] 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."

[1841] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[1842] User Registration and Login

[1843] When a user uses the system for the first time, they must first register. The user registers by entering their name, email address, password, and the field they wish to study. The server receives this information and stores it in a database. Once registration is complete, the user logs in using the email address and password they used when registering. When logging in, the server compares the information in the database with the information they entered and authenticates them. If authentication is successful, the user is redirected to the dashboard.

[1844] Lecture selection and attendance

[1845] After logging in, users can select the lectures they wish to take from the dashboard. Lectures are categorized by field and can be freely selected according to the user's interests and needs. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4) corresponding to that lecture. This allows the generative AI to provide the user with an interactive lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[1846] Language Selection

[1847] Before the lecture begins, the user can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to deliver lectures in multiple languages.

[1848] Test administration and scoring

[1849] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[1850] Report submission and correction

[1851] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[1852] Support for research activities

[1853] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[1854] Specific examples

[1855] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server has the generative AI prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[1856] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[1857] The processing flow will be explained below.

[1858] The present invention provides a method for specifically implementing an online education system that utilizes generative artificial intelligence.

[1859] User Registration and Login

[1860] Step 1:

[1861] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[1862] Step 2:

[1863] The server receives these inputs and stores them in a database.

[1864] Step 3:

[1865] The server displays a message to the user confirming registration.

[1866] Step 4:

[1867] The user enters their email address and password on the login page and clicks the login button.

[1868] Step 5:

[1869] The server compares the input information with a database and performs authentication.

[1870] Step 6:

[1871] If authentication is successful, the server displays the user's dashboard.

[1872] Lecture selection and attendance

[1873] Step 1:

[1874] The user selects the desired lecture from the list of lectures on the dashboard.

[1875] Step 2:

[1876] The server receives the selected lecture information and activates the generative artificial intelligence.

[1877] Step 3:

[1878] Generative artificial intelligence provides users with interactive lectures.

[1879] Step 4:

[1880] A chat box is provided for users to enter questions during the lecture.

[1881] Step 5:

[1882] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[1883] Step 6:

[1884] The server displays the generated answer to the user.

[1885] Language Selection

[1886] Step 1:

[1887] The user selects the language to use before the lecture begins.

[1888] Step 2:

[1889] The server transmits the selected language information to the generative artificial intelligence.

[1890] Step 3:

[1891] Configure the generative artificial intelligence to deliver lectures in the language of your choice.

[1892] Test administration and scoring

[1893] Step 1:

[1894] After the lecture, the user presses a button to start the section test.

[1895] Step 2:

[1896] The server requests the generative artificial intelligence to generate test questions.

[1897] Step 3:

[1898] Test questions generated by the generative artificial intelligence are displayed to the user via the server.

[1899] Step 4:

[1900] The user answers the test questions and presses the answer button.

[1901] Step 5:

[1902] The server receives the user's answer and sends it to the generative artificial intelligence.

[1903] Step 6:

[1904] Generative AI grades answers and generates results and feedback.

[1905] Step 7:

[1906] The server displays the score and feedback to the user.

[1907] Report submission and correction

[1908] Step 1:

[1909] A user creates a report in a specified format and accesses the upload page.

[1910] Step 2:

[1911] The user uploads the report to the server.

[1912] Step 3:

[1913] The server receives the submitted report and sends it to the generative artificial intelligence.

[1914] Step 4:

[1915] Generative AI corrects reports and generates feedback including errors and areas for improvement.

[1916] Step 5:

[1917] The server displays the feedback to the user.

[1918] Support for research activities

[1919] Step 1:

[1920] The user inputs a research topic into the research support function.

[1921] Step 2:

[1922] The server sends the input theme to the generative artificial intelligence.

[1923] Step 3:

[1924] The generative artificial intelligence searches for relevant literature and materials and provides them to users via a server.

[1925] Step 4:

[1926] The research will proceed based on the materials provided by the user, and if additional information is needed, the generative AI will be queried again.

[1927] Step 5:

[1928] The user completes the final product of the research and submits it to the system.

[1929] Step 6:

[1930] The server sends the final product to the generative artificial intelligence for review and feedback.

[1931] Step 7:

[1932] The server displays the review results to the user.

[1933] Specific examples

[1934] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming." In this case, when the user selects a lecture, the server instructs the generative AI to prepare the first lecture of "Introduction to Python Programming." If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative AI, which immediately returns a specific answer such as "functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. If the user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which corrects it and provides detailed feedback.

[1935] In this way, the present invention can provide a highly efficient and multifunctional online education system.

[1936] Example 1

[1937] 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."

[1938] Conventional online education systems have the problem of requiring time and effort to provide individual support for selecting lectures, conducting tests, correcting papers, and supporting research activities, which reduces the quality of the user experience. Furthermore, real-time responses to questions and the provision of lectures in multiple languages ​​are often insufficient, resulting in reduced learning efficiency. This has made it difficult to improve user satisfaction and learning outcomes.

[1939] 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.

[1940] In this invention, the server includes means for receiving user registration information and executing registration, means for launching a generative model that provides learning content based on a user request, means for selecting the language used in the lecture, means for accepting user tests and answers and grading them using the generative model, means for correcting reports submitted by the user using the generative model, means for research support using the generative model to support information gathering based on a research topic, and means for sending questions entered by the user during a lecture to the generative model and generating and returning answers. This improves the efficiency of the entire online education system, dramatically improves the user's learning experience, and enables multifaceted learning support.

[1941] "User registration information" is data used to identify a user and allow access to the system, such as name, email address, password, and desired field of study.

[1942] A "generative model" is an AI technology that uses natural language processing technology to generate content in response to user requests, and an example is a natural language generation model.

[1943] "Learning Content" means any information content provided for educational purposes, such as lectures, tests, reports, and research support.

[1944] The "language used in the lecture" is the language in which the lecture will be given, and can be selected by the user to enable lectures to be provided in multiple languages.

[1945] "Testing" refers to a test conducted to check the user's level of understanding, and includes processes such as asking questions, accepting answers, and grading.

[1946] An "answer" is an answer submitted by a user to a test question.

[1947] A "report" is a document or literature that a user creates and submits based on a specified assignment.

[1948] A "research theme" is a subject or issue that a user sets when conducting research activities.

[1949] "Information gathering" refers to the activity of collecting literature and materials related to a research topic and providing them to users.

[1950] "Questions from users" are points of uncertainty or doubt that users input in text format during a lecture and send to the system.

[1951] "Generating answers in real time" refers to the process in which a generative model receives a question from a user and immediately generates an answer and responds.

[1952] The present invention provides an online education system that utilizes a generative artificial intelligence model. This system is designed to enable users to efficiently access learning content. Specific embodiments of the system are described in detail below.

[1953] System Configuration

[1954] This system includes a user's device, a server, and a generative artificial intelligence model (generative model). The user's device is a device such as a computer, tablet, or smartphone, and communicates with the server via an internet connection. The server is located in a cloud environment or data center and manages and operates the user data and the generative model.

[1955] User Registration and Login

[1956] User registration: When a user uses the system for the first time, they enter their name, email address, password, and the field they want to study. The terminal sends this data to the server, which receives the user information and stores it in a database.

[1957] Login: Registered users log in by entering their email address and password. The server authenticates them by checking the information in its database. If authentication is successful, the user is redirected to the dashboard.

[1958] Lecture selection and attendance

[1959] Lecture selection: The user selects the lecture they want to take from the dashboard. The device sends the selected lecture information to the server. The server receives the lecture information and launches a generative model (e.g., GPT-4).

[1960] Attendance: The generative model provides the user with an interactive lecture. When the user enters a question during the lecture, the device sends the question to the server. The server sends the question as a prompt to the generative model, which then returns the generated answer to the user.

[1961] Language Selection

[1962] The user selects the desired language before the lecture begins. The terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture to be delivered in the selected language. This makes it possible to deliver lectures in multiple languages.

[1963] Test administration and scoring

[1964] Start test: The user selects a test from the dashboard and clicks the "Start test" button. The device sends a test start request to the server. The server asks the generative model to generate test questions and provides the generated questions to the user.

[1965] Test submission and scoring: The user enters their test answers and clicks the "Submit" button. The device sends the answer data to the server. The server then sends the answer data to the generative model and requests scoring. The scoring results are then fed back to the user.

[1966] Report submission and correction

[1967] The user creates a report based on a specified assignment and uploads it to the system from their device. The server sends the report to the generative model for correction. The generative model corrects the report and generates feedback pointing out errors and areas for improvement. The server returns this feedback to the user.

[1968] Support for research activities

[1969] The user enters a research topic and sends it from their device to the server. The server sends the topic information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates search results and provides them to the user via the server. The user can then continue their research based on the provided materials and ask the generative model additional questions.

[1970] Specific examples

[1971] For example, if a user selects the "Introduction to Python Programming" lecture, the device sends this information to the server. The server then prepares the first lecture of "Introduction to Python Programming" for the generative model. If the user asks a question about "how to define a function" during the lecture, the server sends the question to the generative model, which then generates and returns a specific answer, such as "Functions in Python are defined using the def keyword."

[1972] In this way, the present invention provides users with a highly efficient and multifunctional online learning environment through a series of operations.

[1973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1974] Step 1: User Registration

[1975] Input: The user enters their name, email address, password, and the subject they want to learn about.

[1976] Specific operation: When the user clicks the "Register" button, the device sends this data to the server. The server receives the user data and saves it in the database. After saving is complete, the server returns a response to the user indicating successful registration.

[1977] Output: The user data is saved in the database and a registration success message is displayed to the user.

[1978] Step 2: Log in

[1979] Input: The user enters their email address and password.

[1980] Specific operation: When the user clicks the "Login" button, the device sends the entered data to the server, which then collates it with the user information stored in the database and performs authentication.

[1981] Output: If authentication is successful, the server returns the dashboard information to the terminal and the user is redirected to the dashboard.

[1982] Step 3: Select a course

[1983] Input: The user selects the course they want to take from the dashboard.

[1984] Specific operation: When the user clicks the "Select" button, the device sends the selected lecture information to the server. The server receives the lecture information and launches the corresponding generative model (e.g., GPT-4). It then asks the AI ​​to prepare the lecture and sends a command to the user device to start the lecture.

[1985] Output: The lecture start screen will be displayed on the user's device, and the AI ​​will be ready to provide the lecture.

[1986] Step 4: Language Selection

[1987] Input: The user selects the desired language before the lecture begins.

[1988] Specific operation: When the user selects a language, the terminal sends the selected language information to the server. The server then sends the language information to the generative model and sets up the lecture provision in the selected language.

[1989] Output: The user is now set up to receive lectures in the language of their choice.

[1990] Step 5: Answer questions during the lecture

[1991] Input: If a question arises during the lecture, the user inputs the question.

[1992] Specific operation: When a user clicks the "Ask" button, the device sends the question to the server. The server receives the question and sends it as a prompt to the generative model. The generative model generates an answer to the question and returns it to the server. The server receives the answer and sends it to the device.

[1993] Output: The answer generated by the generative model is displayed on the user's device.

[1994] Step 6: Testing

[1995] Input: User selects and starts a test from the dashboard.

[1996] Specific operation: When the user clicks the "Start Test" button, the device sends a test start request to the server. The server then requests the generative model to generate test questions and sends the generated questions to the user's device.

[1997] Output: The generated test questions are displayed on the user's terminal.

[1998] Step 7: Test submission and grading

[1999] Input: The user enters the test answers.

[2000] Specific operation: When the user clicks the "Submit" button, the device sends the answer data to the server. The server sends the answer data to the generative model and requests it to be graded. The generative model grades the answer and returns the results to the server. The server then sends the graded results to the device.

[2001] Output: The scoring results are displayed on the user's terminal.

[2002] Step 8: Submit your report and receive corrections

[2003] Input: The user creates and uploads a report based on the given assignment.

[2004] Specific operation: When a user uploads a report, the device sends the report data to the server. The server sends the report to the generative model and requests corrections. The generative model generates feedback pointing out errors and areas for improvement and returns it to the server. The server then sends the feedback to the device.

[2005] Output: The feedback generated by the generative model is displayed on the user's device.

[2006] Step 9: Enter your research topic and provide materials

[2007] Input: The user inputs the research topic.

[2008] Specific operation: When a user submits a theme, the device sends the theme information to the server. The server then sends the theme information to the generative model, requesting it to search for and provide related literature and materials. The generative model generates related materials and returns them to the server. The server then sends the search results to the device.

[2009] Output: Related literature and materials are displayed on the user's terminal.

[2010] Step 10: Research work submission and feedback

[2011] Input: Users create and upload research artifacts.

[2012] Specific operation: When a user uploads an artifact, the device sends the artifact data to the server. The server sends the artifact to the generative model and requests review and feedback. The generative model generates reviews and feedback and returns them to the server. The server sends the feedback to the device.

[2013] Output: The feedback generated by the generative model is displayed on the user's device.

[2014] (Application example 1)

[2015] 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."

[2016] Modern education requires responding to the diverse needs of learners and providing optimal education for each individual. Traditional educational systems face challenges, such as difficulty providing real-time feedback and multilingual support. Furthermore, they lack the ability to provide an interactive learning experience using smart devices. A system is needed to solve these problems and provide an efficient and effective learning environment.

[2017] 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.

[2018] In this invention, the server includes means for receiving user registration information and executing registration, means for activating a generative artificial intelligence (AI) to provide educational content based on a user request, means for selecting the language of the educational content, means for accepting user tests and answers and grading them using the AI, means for correcting user-submitted reports using the AI, research support means using the AI ​​to support information gathering based on a research topic, means installed in a smartphone, smart glasses, a head-mounted display, or a robot, and response means using the AI ​​to support user learning through interactive dialogue. This allows for the provision of highly efficient and multifunctional education to individual learners and enables interactive learning experiences using a variety of devices.

[2019] "User registration information" refers to information such as name, email address, password, and desired field of study that a user provides when they start using the system.

[2020] "Educational content" refers to the lectures and teaching materials provided for learners to learn from.

[2021] "Generative artificial intelligence" is an AI model that can generate appropriate output based on input data.

[2022] "Language of Use" refers to the language in which the educational content is delivered.

[2023] A "test" is a confirmation test that learners take during or after a lecture to check their understanding.

[2024] "Answer" is the answer given by the learner to the test.

[2025] "Scoring" refers to the evaluation and assignment of scores to test answers.

[2026] A "report" is an assignment that a learner creates and submits in a specified format.

[2027] "Correction" refers to pointing out errors and areas for improvement in submitted reports and providing feedback.

[2028] A "research theme" is a specific subject that a learner sets for their research activities.

[2029] "Information gathering" means collecting materials and literature related to the research topic.

[2030] A "smartphone" is a highly functional mobile phone that can run a variety of applications in addition to making calls.

[2031] "Smart glasses" are a type of wearable device equipped with information display and communication functions.

[2032] A "head-mounted display" is a device worn on the head that displays visual information.

[2033] A "robot" is a machine that has a certain degree of autonomy and operates under human instructions.

[2034] "Interactive dialogue" refers to real-time, two-way communication between the learner and the system.

[2035] A "response mechanism" is a mechanism for providing answers to user questions.

[2036] The present invention provides a highly efficient and multifunctional educational system that utilizes generative artificial intelligence. This system allows users to register online, access educational content, ask questions, take tests, and submit reports. It also provides support for research activities. An embodiment of the system is described in detail below.

[2037] User Registration and Login

[2038] The server receives the user's name, email address, password, and desired field of study information and registers it in a database. After registering, the user logs in to the system using their email address and password. When logging in, the server compares the entered information with the information in the database and performs authentication. If authentication is successful, the user is redirected to the dashboard.

[2039] Lecture selection and attendance

[2040] After logging in, a user can select the lecture they want to take from the dashboard. The server receives the selected lecture information and launches a generative AI (e.g., GPT-4). This allows the generative AI to provide an interactive lecture. If a user has a question during the lecture, the server sends the question to the generative AI, and the AI ​​generates an answer in real time and returns it to the user.

[2041] Language Selection

[2042] Before the lecture begins, users can select the language in which the lecture will be delivered. The server then sends the selected language information to the generative AI, which then configures the AI ​​to deliver the lecture in the selected language. This makes it possible to provide learning content in multiple different languages.

[2043] Test administration and scoring

[2044] During or after a lecture, users can take a test to check their understanding. When a user starts a test, the server requests the generative AI to generate test questions. When the user answers the questions and presses the submit button, the server receives the answers and sends them to the generative AI. The AI ​​immediately grades the questions and returns the results and feedback to the user via the server.

[2045] Report submission and correction

[2046] After receiving a report assignment, the user creates the report in the specified format and uploads it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, identifies errors and areas for improvement, and generates feedback. The server returns this feedback to the user, allowing the user to improve the quality of their report.

[2047] Support for research activities

[2048] When a user conducts research, they first enter their research topic. The server sends this topic information to the generative AI. The AI ​​then searches for relevant literature and materials based on the topic and provides them to the user. The user can then conduct their research based on the provided materials and ask the generative AI additional questions as needed. Finally, the user submits their research results to the system, and the server sends them back to the generative AI for review and feedback.

[2049] Interactive Dialogue

[2050] Generative AI supports learning through interactive dialogue with users. This dialogue can be conducted using a smartphone, smart glasses, a head-mounted display, or a robot. Specifically, when a user inputs a question while studying, the AI ​​provides an appropriate answer in real time. This allows users to immediately resolve their doubts and improves learning efficiency.

[2051] Specific examples

[2052] For example, if a user selects a lecture on "Introduction to Python Programming," and asks a question about "how to define a function" during the lecture, the AI ​​will immediately respond with a specific answer such as, "Functions in Python are defined using the def keyword." After the lecture, the user takes a test, and the server asks the generative AI to grade it and provides feedback on the results to the user. Furthermore, if the user submits a report assignment on "writing a Python script," the AI ​​will correct it and provide detailed feedback.

[2053] Prompt Sentence Examples

[2054] "What are some data preprocessing techniques for data science?"

[2055] In this way, the educational system based on the present invention provides an advanced educational experience for individual learners and provides an interactive learning experience utilizing a variety of devices.

[2056] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2057] Step 1:

[2058] The server receives the user registration information and stores it in a database. As input, it takes the user's name, email address, password, and desired learning area and stores that information in a database, providing the data to recognize and authenticate the user on future logins.

[2059] Step 2:

[2060] A user logs in with the registered email address and password. When the login request is sent to the server, the server checks the information in the database and authenticates the user. If the authentication is successful, the user is redirected to the dashboard. The input is the user's login information, and the output is the login success or failure status.

[2061] Step 3:

[2062] The user selects the lecture they wish to take from the dashboard. The selected lecture information is sent to the server, which then launches the corresponding generative AI. The input is the user's lecture selection information, and the output is the start of the lecture content.

[2063] Step 4:

[2064] The user selects the language to use before the lecture begins. The server sends the selected language information to the generative AI, which then provides the lecture in that language. The input is the user's language selection, and the output is the presentation of the lecture in the selected language.

[2065] Step 5:

[2066] During a lecture, a user enters a question and sends it to the server. The server then sends the question to a generative AI, which generates an answer in real time and returns it to the user. The input is the user's question, and the output is the answer provided by the generative AI.

[2067] Step 6:

[2068] During or after a lecture, the user starts a test. The server requests the generative AI to generate test questions and provides them to the user. When the user submits their answers, the server sends them to the AI, which then scores them. The input is the user's test answers, and the output is the scoring results and feedback.

[2069] Step 7:

[2070] Users create report assignments and upload them to the system. The server sends the submitted reports to a generative AI, which corrects them and generates feedback. The input is the user's report, and the output is the corrections and feedback.

[2071] Step 8:

[2072] Users input the topic of their research activities and send it to the server. The server then sends the topic information to a generative AI, which searches for related literature and materials and provides them to the user. Furthermore, when a research product is submitted, the server sends it to the AI ​​for review and feedback. The input is the user's research topic and product, and the output is related materials and feedback.

[2073] Step 9:

[2074] The user engages in interactive dialogue using a smartphone, smart glasses, a head-mounted display, or a robot. When the user inputs a question during learning, the generative AI responds in real time through the device. The input is the user's dialogue request, and the output is a real-time response from the generative AI.

[2075] Through the above steps, the educational system of the present invention provides users with an integrated educational experience and realizes efficient and interactive learning.

[2076] 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.

[2077] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[2078] User Registration and Login

[2079] User Registration

[2080] First, a user accesses the system and registers by entering their name, email address, password, and desired field of study. The server receives this information and stores it in a database. After registration is complete, the server displays a message to the user indicating successful registration.

[2081] Log in

[2082] The user enters their email address and password on the login page and presses the login button. The server verifies the entered information against the database, and if authentication is successful, displays the user's dashboard.

[2083] Lecture selection and attendance

[2084] Lecture selection

[2085] The user selects the lecture they wish to take from the dashboard. The server receives the selected lecture information and activates the corresponding generative artificial intelligence to prepare for providing the interactive lecture.

[2086] Lecture provision

[2087] The generative AI provides the user with a selected lecture. If the user asks a question during the lecture, the server sends the question to the generative AI, which then generates an answer in real time and returns it to the user.

[2088] Language Selection

[2089] The user selects the language to use before the lecture begins. The server transmits the selected language information to the generative AI, and configures the generative AI to provide the lecture in that language.

[2090] Test administration and scoring

[2091] Users can take tests during or after a lecture. When a user starts a test, the server requests the generative AI to generate test questions and displays the generated questions to the user. When the user answers the questions and submits them, the server sends the answers to the generative AI, which then scores them and returns the results and feedback to the user.

[2092] Report submission and correction

[2093] When a user submits a report assignment, they create the report in the specified format and upload it to the system. The server receives the submitted report and sends it to a generative AI. The AI ​​corrects the report, generates feedback, and returns it to the user via the server.

[2094] Support for research activities

[2095] When a user sets a research topic and enters it into the server, the server sends the topic information to the generative AI. The generative AI searches for related literature and materials and provides that information to the user. The user can then conduct their research based on the provided materials, and if additional information is required, they can ask the generative AI again.

[2096] Finally, the user submits the research results to the system, and the server sends them to the generative artificial intelligence for review and feedback. The server then displays the review results to the user.

[2097] Introducing the Emotion Engine

[2098] To further personalize and enhance the user's learning experience, the present invention introduces an emotion engine.

[2099] emotion recognition

[2100] While the user is using the system, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone to recognize their emotions. The server receives this emotional information in real time.

[2101] Emotion-based lecture adjustment

[2102] The server then sends the recognized emotional information to the generative AI, which then dynamically adjusts the lecture delivery method and content to match the user's emotional state. For example, if the user is struggling to understand something, the generative AI can provide more detailed explanations or additional support.

[2103] Emotion regulation in question-answering

[2104] When a user asks a question, the emotion engine understands the user's emotional state, and the generative AI generates a response in a tone that corresponds to that. For example, if the user is judged to be irritated, the generative AI will respond in a more polite and calm tone.

[2105] Specific examples

[2106] As a concrete example, consider the case where a user selects a lecture titled "Introduction to Python Programming" and asks a question during the lecture about "how to define a function." In this case, the server sends the question to the generative AI, which immediately responds with a specific answer: "Functions in Python are defined using the def keyword." At the same time, if the emotion engine analyzes the user's facial expression and determines that the user is having difficulty understanding, the generative AI will provide more detailed sample code and practice problems. Similarly, if a user submits a report assignment titled "Writing a Python Script," the server sends it to the generative AI, which then corrects it and provides detailed feedback.

[2107] In this way, the present invention can provide a highly efficient, multi-functional, and interactive online teaching system combined with emotion recognition functions.

[2108] The processing flow will be explained below.

[2109] The present invention provides a concrete method for implementing an online education system that combines generative artificial intelligence and an emotion engine, with the aim of personalizing the user's learning experience and providing more effective education.

[2110] User Registration and Login

[2111] Step 1:

[2112] A user accesses the system and fills in the registration form with their name, email address, password, and the field they wish to study.

[2113] Step 2:

[2114] The server receives these inputs and stores them in a database.

[2115] Step 3:

[2116] The server displays a message to the user confirming registration.

[2117] Step 4:

[2118] The user enters their email address and password on the login page and clicks the login button.

[2119] Step 5:

[2120] The server compares the input information with a database and performs authentication.

[2121] Step 6:

[2122] If authentication is successful, the server displays the user's dashboard.

[2123] Lecture selection and attendance

[2124] Step 1:

[2125] The user selects the desired lecture from the list of lectures on the dashboard.

[2126] Step 2:

[2127] The server receives the selected lecture information and activates the generative artificial intelligence.

[2128] Step 3:

[2129] Generative AI prepares to provide interactive lectures to users.

[2130] Step 4:

[2131] A chat box is provided for users to enter questions during the lecture.

[2132] Step 5:

[2133] The server sends the question to a generative artificial intelligence, which then generates an answer to the question.

[2134] Step 6:

[2135] The server displays the generated answer to the user.

[2136] Language Selection

[2137] Step 1:

[2138] The user selects the language to use before the lecture begins.

[2139] Step 2:

[2140] The server transmits the selected language information to the generative artificial intelligence.

[2141] Step 3:

[2142] Configure the generative artificial intelligence to deliver lectures in the language of your choice. 【2143...

Claims

1. means for receiving user registration information and performing registration; means for invoking a generative artificial intelligence to provide learning content based on requests from a user; A means to choose the language in which lectures are delivered; A means for accepting user test takers and answers and scoring them using generative artificial intelligence; A means for correcting reports submitted by users using generative artificial intelligence; A research support method using generative artificial intelligence to support information gathering based on research themes, and A system including:

2. The system according to claim 1, further comprising a response means using generative artificial intelligence to generate and provide answers to questions from users in real time.

3. The system of claim 1 , wherein the learning content provided by the generative artificial intelligence is provided in a plurality of different languages.

4. 10. The system of claim 1, further comprising an instantaneous evaluation means using generative artificial intelligence to provide test results and feedback to the user immediately.

5. 2. The system according to claim 1, further comprising specialized information retrieval means for causing the generative artificial intelligence to search for and recommend related literature in response to a user inputting a research topic.

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