System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a question input by a student during an online lesson; means for using generative artificial intelligence to analyze the received question and generate an optimal answer; means for providing the generated answer to the student; means for recording the question and the answer; means for analyzing a history of the recorded questions and identifying a field of difficulty of the student; means for automatically generating learning content based on the identified field of difficulty; and means for providing the generated learning content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the number of children not attending school has been increasing, resulting in an increase in the number of children missing out on learning opportunities. This issue is a serious challenge from the perspective of equality of educational opportunity. Furthermore, existing online learning systems have limited ways for students to resolve questions on the spot, and they may continue learning without fully understanding the material. Furthermore, there is no fully established system for identifying each student's weak areas and responding to them individually. This leads to problems such as a decrease in students' learning efficiency and a lack of motivation to continue learning. [Means for solving the problem]

[0005] This invention is a distance learning video lesson system using generative AI. It receives questions entered by students during online lessons in real time, analyzes them using generative AI, and generates optimal answers. This system includes a means for recording the received questions and generated answers, identifying students' weak areas by analyzing their question history, and automatically generating and providing learning content based on that information. Furthermore, it aims to maintain students' motivation to learn by analyzing their learning progress and generating and providing appropriate feedback. It also has a function for saving questions and generated answers in a database, allowing for detailed recording and management of students' learning history.

[0006] "Online classes" are an educational format in which students can receive lessons from teachers via the internet.

[0007] "Student" refers to a person receiving education, and generally includes children and students.

[0008] A "question" is a written expression of something that a student does not understand or wants to confirm while studying.

[0009] "Generative AI" refers to artificial intelligence that uses natural language processing and machine learning techniques to generate appropriate answers and information from input data.

[0010] An "answer" is the answer or explanation that the generative AI analyzes and arrives at in response to a student's question.

[0011] "Recording" refers to saving data such as questions and answers so that they can be referenced later.

[0012] "History" refers to the collection of records of questions and answers that a student has previously asked.

[0013] "Weak areas" refer to areas of learning that a student has difficulty understanding, as identified through historical analysis.

[0014] "Learning content" means content provided for educational purposes, including video courses, textbooks, etc.

[0015] "Automatic generation" means using AI or algorithms to generate content or information without human intervention.

[0016] "Feedback" refers to providing encouragement and advice based on students' learning progress and achievements.

[0017] A "database" is a system for systematically storing and managing data such as questions, answers, and history. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a distance learning video lesson system that utilizes generative AI, and is implemented as follows.

[0040] System Overview

[0041] The overall system consists of the following main components:

[0042] Devices used by students (PCs, tablets, etc.)

[0043] Server that processes queries and analyzes and stores data

[0044] Generation AI

[0045] Program processing

[0046] Receiving student questions

[0047] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[0048] The terminal receives the input question and transmits the question data to the server.

[0049] Handling and answering questions

[0050] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[0051] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[0052] Accumulation and analysis of question logs

[0053] The server stores the questions and answers in a database, which records the learning history.

[0054] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student frequently asks questions about "functions," it will identify functions as an area in which the student is weak.

[0055] Automatic generation and provision of individual learning content

[0056] The server automatically generates appropriate learning content based on weak areas. For example, it generates a video course on the domain of functions.

[0057] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[0058] Providing progress feedback

[0059] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving."

[0060] The server transmits the feedback to the terminal, and the terminal displays the feedback to provide it to the user.

[0061] Specific examples

[0062] Student question and answer process

[0063] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[0064] The terminal sends this question to the server.

[0065] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[0066] The device will display this answer, allowing the user to solve the problem on the spot.

[0067] Identifying weak areas and providing content

[0068] Example: The server analyzes the question log and discovers that a particular student frequently asks questions about "functions."

[0069] The server automatically generates learning content such as a "video course on the domain of functions."

[0070] The server transmits this content to the terminal, which then plays the video lecture, allowing the user to deepen their learning in areas they are weak at.

[0071] In this way, the distance learning video lesson system of the present invention can effectively support students' learning and provide equal educational opportunities.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[0075] Step 2:

[0076] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[0077] Step 3:

[0078] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[0079] Step 4:

[0080] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[0081] Step 5:

[0082] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[0083] Step 6:

[0084] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[0085] Step 7:

[0086] The server stores the questions and their answers in a database, which records the question history.

[0087] Step 8:

[0088] The server periodically analyzes the question log, which identifies each student's weak areas.

[0089] Step 9:

[0090] The server automatically generates appropriate learning content based on the identified weak areas, for example, a video course on the domain of functions.

[0091] Step 10:

[0092] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[0093] Step 11:

[0094] The device will display new learning content, allowing users to focus on areas they are weak in.

[0095] Step 12:

[0096] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[0097] Step 13:

[0098] The server generates appropriate feedback, for example, "You're making progress in understanding functions."

[0099] Step 14:

[0100] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[0101] Step 15:

[0102] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[0103] Through these steps, the online video lesson system using generative AI will enable students to ask and answer questions in real time and provide individually customized learning content.

[0104] Example 1

[0105] 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."

[0106] Conventional online teaching systems lack the ability to not only quickly and accurately answer students' questions, but also to identify each student's weak areas and provide personalized learning content. This makes it difficult to continuously monitor each student's level of understanding and progress and provide appropriate feedback. In addition, there is a lack of efficient means to accumulate question history data and provide automatically generated learning content. This makes it difficult to improve students' learning efficiency and enhance the quality of education.

[0107] 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.

[0108] In this invention, the server includes: means for receiving questions entered by students during online classes; means for analyzing the received questions and using a generative AI model to generate optimal answers; means for providing the generated answers to the students; means for recording questions and their answers; means for analyzing the recorded question history to identify the student's weak areas; means for automatically generating learning content based on the identified weak areas; means for providing the generated learning content; means for generating video courses to address the identified weak areas; and means for using prompt sentences in the generative AI model when analyzing the questions entered by the students. This not only enables prompt and accurate answers to students' questions, but also identifies individual weak areas and automatically generates and provides appropriate learning content. Furthermore, by accumulating and analyzing the question history, it becomes possible to grasp the student's learning progress and provide appropriate feedback, thereby improving the student's learning efficiency and enhancing the quality of education.

[0109] "Online classes" are classes that students take via the internet.

[0110] "Student" refers to a user who takes an online class and enters questions.

[0111] "Question" refers to a text inquiry entered by a student during an online class.

[0112] "Devices" refer to computers and tablets used by students, and are devices used to take online classes and input and send questions.

[0113] A "server" is a central processing unit that receives student questions, performs analysis, and generates and provides answers.

[0114] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate optimal answers to input questions.

[0115] "Prompt sentence" refers to the input-style sentence that a generative AI model uses to generate an answer to a question.

[0116] "Answer" refers to the answer information generated by the generative AI model in response to a question.

[0117] "Question history" refers to a record of questions previously entered by students and the answers generated.

[0118] "Weak areas" refer to areas of learning that a student has difficulty understanding, identified based on an analysis of their question history.

[0119] "Learning content" refers to teaching materials and information created to strengthen students' knowledge in areas of weakness.

[0120] "Video Course" refers to educational material in video format that is produced to explain specific learning content.

[0121] "Feedback" refers to the assessments and comments provided to students based on their learning progress.

[0122] "Database" refers to a recording device for storing the history of questions and answers and its management system.

[0123] This invention is a distance learning video lesson system that utilizes a generative AI model to provide quick and accurate answers to questions entered by students during online lessons, identify areas in which each student is weak, and support their understanding.

[0124] The system uses the following major hardware and software components:

[0125] Device: Student's computer, tablet, or other device

[0126] Server: A central processing unit that processes queries and analyzes and stores data.

[0127] Generative AI models: Algorithms and programs used to analyze questions and generate answers (e.g., GPT-4)

[0128] During online classes, users (students) enter questions in text format about points they do not understand. For example, they enter a question such as, "I don't understand the domain of a function." The device receives this question and sends it to the server using a format and protocol (e.g., HTTP, WebSocket).

[0129] The server receives the user's question and analyzes its contents. At this time, the question text is input as a prompt to the generative AI model, which generates the optimal answer. For example, it generates an answer such as "The domain of a function is the range of values ​​that are input to the function." The server sends the generated answer to the device, which displays it on the screen.

[0130] The server stores the user's questions and the generated answers in a database. This keeps a history of all questions and answers. At regular intervals, the server analyzes the log data and identifies the student's weak areas. For example, if a student asks many questions about "functions" multiple times, it will determine that functions are a weak area.

[0131] Based on the identified weak areas, the server uses a generative AI model to generate appropriate learning content. For example, it automatically generates a video lecture on the domain of functions. The server then sends the generated content to the device, which then displays it. This allows students to deepen their understanding of their weak areas.

[0132] The server also analyzes learning progress data and generates appropriate feedback. For example, it may generate feedback such as, "Your understanding of functions is improving." The server then sends the generated feedback to the device, which then displays it on the screen. This allows students to check their own learning progress and motivates them to continue learning.

[0133] Specific examples

[0134] Question and answer generation

[0135] If a user asks during an online class, "I don't know how to graph a linear function," the device sends this question to the server. The server uses a generative AI model to generate an answer: "The graph of a linear function has the form y = mx + b, where m is the slope and b is the y-intercept." The device displays this answer, allowing the user to solve the problem on the spot.

[0136] Identifying weak areas and providing content

[0137] If the server analyzes the question log and finds that a particular student frequently asks questions about "functions," it automatically generates learning content such as a "video lecture on the domain of functions." The server sends this content to the device, which then plays the video lecture, allowing the user to deepen their understanding of their weak areas.

[0138] In this way, the system of the present invention improves the quality of education by providing appropriate answers to students' questions and personalized learning support.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] During an online class, a user enters a question in text format. For example, they enter a question like, "I don't understand the domain of a function." The device receives this input and sends it to the server using a format and protocol (such as HTTP or WebSocket).

[0142] Input: The question text entered by the user

[0143] Output: The question data sent to the server

[0144] Specific behavior:

[0145] The user uses a web browser or dedicated application to enter a question into a text input field on the interface and press the submit button.

[0146] Step 2:

[0147] The server analyzes the received question. It inputs the question text as a prompt into a generative AI model (e.g., GPT-4) and generates the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[0148] Input: Query data sent to the server

[0149] Output: Answer text generated by the generative AI model

[0150] Specific behavior:

[0151] When the server receives the question text, it accesses the API of the generation AI, sends a prompt, and receives the generated answer.

[0152] Step 3:

[0153] The server sends the generated answer to the terminal, which then displays the answer on its screen, allowing the user to obtain the answer to their question.

[0154] Input: Answer text generated by the generative AI model

[0155] Output: Answer information displayed on the terminal

[0156] Specific behavior:

[0157] The server generates an answer and sends it back to the device as an HTTP response, which the device parses and displays in a browser or application.

[0158] Step 4:

[0159] The server stores the user's questions and generated answers in a database, thus maintaining a history of all questions and answers.

[0160] Input: Question text and generated answer text

[0161] Output: Question and answer history stored in a database

[0162] Specific behavior:

[0163] The server stores the questions and answers in a database such as MySQL or PostgreSQL using an INSERT query.

[0164] Step 5:

[0165] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student repeatedly asks questions about "functions," it will determine that functions are a weak area.

[0166] Input: A history of questions and answers stored in a database

[0167] Output: Information on identified weaknesses

[0168] Specific behavior:

[0169] Scheduled tasks and periodic batch processes are run to aggregate and analyze database logs and identify weak areas based on specific keywords and frequencies.

[0170] Step 6:

[0171] Based on the weak areas identified by the server, appropriate learning content is generated using generative AI. For example, a video course on the domain of functions is automatically generated.

[0172] Input: Information about identified weaknesses

[0173] Output: Generated learning content

[0174] Specific behavior:

[0175] Use generative AI to generate text content and quiz questions, and optionally integrate with video generation tools (e.g., FFmpeg) to generate video content.

[0176] Step 7:

[0177] The server sends the generated learning content to the device, which then displays it, allowing the user to deepen their understanding of weak areas.

[0178] Input: Generated learning content

[0179] Output: Learning content displayed on the device

[0180] Specific behavior:

[0181] The generated content is delivered to the terminal in the form of HTTP streaming or file download, and the terminal displays it.

[0182] Step 8:

[0183] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving," which is sent to the device and displayed.

[0184] Input: Learning progress data

[0185] Output: Generated feedback

[0186] Specific behavior:

[0187] The server analyzes the learning progress data and generates feedback messages using a generation AI. The generated feedback is sent to the device in JSON format, and the device formats it for display on the screen and presents it to the user.

[0188] (Application example 1)

[0189] 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."

[0190] Today's students have a strong desire to make effective use of their time, such as while traveling, for studying. However, conventional online class systems are mainly used in fixed locations, and do not provide sufficient support for learning while traveling. There is also a lack of systems that can quickly answer questions students have during online classes and provide learning content specialized for weak areas. For this reason, there is a need to develop a system that can effectively support learning even while traveling.

[0191] 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.

[0192] In this invention, the server includes means for receiving questions entered by students during online classes, means using artificial intelligence to analyze the received questions and generate optimal answers, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history to identify weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for supporting the students' learning through a terminal installed in the autonomous vehicle, and means for analyzing questions entered by students during learning in real time and providing answers, thereby enabling effective support for students' learning even while on the move.

[0193] "Means for receiving questions entered by students during online classes" refers to a system for receiving questions sent from the devices used by students.

[0194] "Methods using generative artificial intelligence to analyze received questions and generate optimal answers" refers to a method that uses generative AI to understand the content of questions and generate appropriate answers.

[0195] The "means for providing the generated answers to the students" is a mechanism for displaying the generated answers on the students' terminals.

[0196] The "means for recording questions and their answers" is a mechanism for storing received questions and their answers in a database.

[0197] The "means of analyzing the recorded question history to identify areas in which students are weak" is a method of analyzing saved question and answer data to identify areas in which students are weak.

[0198] "Means for automatically generating learning content based on identified areas of weakness" refers to a method for using generative AI to create appropriate learning materials tailored to areas of weakness.

[0199] The "means for providing the generated learning content" is a mechanism for transmitting the generated learning materials to the student's terminal and displaying them.

[0200] "Means to support students' learning through terminals installed inside self-driving vehicles" refers to a system that utilizes terminals installed inside self-driving vehicles to enable students to study while on the move.

[0201] "Means for analyzing questions entered by students in real time while studying and providing answers" refers to a method for instantly analyzing questions entered in real time while studying and generating and providing appropriate answers.

[0202] This invention is a system that allows students to study effectively even while on the move. This system provides the functionality to analyze questions entered during online classes and generate appropriate answers. Furthermore, it can analyze students' learning histories and automatically generate individual learning content. To achieve this, the following specific components and processes are required.

[0203] System configuration

[0204] The overall system consists of the following main components:

[0205] Devices used by students (computers, tablets, devices installed in autonomous vehicles, etc.)

[0206] Server (query processing, data analysis and storage)

[0207] Generative AI (understands the meaning of the question and generates the best answer)

[0208] Hardware and Software

[0209] 1. Terminal

[0210] A terminal is a device used by a student, including a computer, tablet, or learning tablet installed in an autonomous vehicle. The terminal is connected to the internet and has an interface for entering questions and a display for viewing the generated answers.

[0211] 2. Server

[0212] The server is a central management system that receives questions, analyzes them, generates answers, manages the database, etc. This server is operated on a cloud basis and has various software installed for query processing and automatic generation of learning content.

[0213] 3. Generation AI

[0214] The generative AI running on the server analyzes the meaning of the question based on natural language processing and generates appropriate answers. The AI ​​models used include GPT-3 and similar advanced generative AI.

[0215] Program processing

[0216] 1. Receiving Questions

[0217] When a student enters a question in text format during an online class, the device sends the question to the server.

[0218] 2. Question Analysis and Answer Generation

[0219] The server sends the received question to the AI ​​generator, which then generates the best answer. At this time, the AI ​​is given the following prompt as input:

[0220] Q: I don't know the domain of a function.

[0221] A:

[0222] The AI ​​generates the following response to this prompt:

[0223] The domain of a function is the range of values ​​that are input to the function.

[0224] 3. Providing and recording responses

[0225] The server sends the generated answers to the terminal, which displays them to the student, and simultaneously stores the questions and answers in a database.

[0226] Specific examples

[0227] Consider the case where a user (student) asks a question during an online class, "I don't know how to graph a linear function." At this time, the device receives the question and sends it to the server. The server uses generative AI to generate the following answer:

[0228] The graph of a linear function is of the form y = mx + b, where m is the slope and b is the y-intercept.

[0229] As soon as this answer is generated, it is sent to the device and displayed to the student.

[0230] This system can also automatically generate and provide learning content based on identified areas of weakness. For example, if the server identifies a student's weak area as "functions," it can generate a video course on the domain of functions, send it to the student's device, and display it to the student. In this way, it is possible to effectively support students' learning even while they are on the move.

[0231] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0232] Step 1:

[0233] The terminal receives questions from students. When a student enters a question in text format into the terminal, the question is sent to the terminal's receiving interface. For example, the text may be "I don't understand the domain of a function."

[0234] Input: Student question text

[0235] Output: Question data received by the device

[0236] Step 2:

[0237] The terminal sends the received query to the server. The query data is sent to the server via the network. For example, the following data is sent:

[0238] Q: I don't know the domain of a function.

[0239] Input: Received question data

[0240] Output: The query data sent to the server

[0241] Step 3:

[0242] The server sends the received question to the generation AI. The received question data is input into the generation AI model to construct a prompt sentence. For example, the following prompt sentence is generated:

[0243] Q: I don't know the domain of a function.

[0244] A:

[0245] Input: Received question data

[0246] Output: The prompt sent to the generation AI

[0247] Step 4:

[0248] The generation AI analyzes the prompt and generates the most appropriate answer. The generation AI uses natural language processing technology to understand the content of the question and generate an appropriate answer. For example, the following answer may be generated:

[0249] The domain of a function is the range of values ​​that are input to the function.

[0250] Input: prompt statement

[0251] Output: Generated answer text

[0252] Step 5:

[0253] The server sends the generated response to the terminal. The generated response data is returned to the terminal. For example, the following data is sent:

[0254] Answer: The domain of a function is the range of values ​​that are input to the function.

[0255] Input: Generated answer text

[0256] Output: Response data sent to the device

[0257] Step 6:

[0258] The device receives the answer and displays it to the student. The generated answer is displayed on the device's display. For example, the following text might be displayed:

[0259] The domain of a function is the range of values ​​that are input to the function.

[0260] Input: Response data from the server

[0261] Output: Answer text displayed on the screen

[0262] Step 7:

[0263] The server records the question and answer in a database. The question and its answer are stored in a database. For example, the following records are stored:

[0264] Q: I don't know the domain of a function.

[0265] Answer: The domain of a function is the range of values ​​that are input to the function.

[0266] Input: Question and answer data

[0267] Output: Records saved in the database

[0268] Step 8:

[0269] The server periodically analyzes the question history to identify areas where students are weak. The stored data is analyzed to identify frequently asked question categories. For example, if there are many questions about "functions," it will identify that area as a weak area.

[0270] Input: Question history stored in the database

[0271] Output: Identified weak areas

[0272] Step 9:

[0273] The server automatically generates learning content based on weak areas. Generative AI is used to create appropriate learning content (e.g., video courses). For example, the following video courses are generated:

[0274] Video content: Detailed explanation of the domain of a function

[0275] Input: Identified weak areas information

[0276] Output: Generated learning content

[0277] Step 10:

[0278] The server transmits the generated learning content to the terminal, which provides it to the student. The server transmits the generated learning content to the terminal, which plays the content and displays it to the student.

[0279] Input: Generated learning content

[0280] Output: Learning content sent to the device, learning content played on the device

[0281] The above are the specific processing steps for carrying out the present invention.

[0282] 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.

[0283] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[0284] System Overview

[0285] The entire system consists of the following main components:

[0286] Devices used by students (PCs, tablets, etc.)

[0287] Server that processes queries and analyzes and stores data

[0288] Generation AI

[0289] Emotion Engine

[0290] Program processing

[0291] Receiving student questions

[0292] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[0293] The terminal receives the input question and transmits the question data to the server.

[0294] Handling and answering questions

[0295] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[0296] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[0297] Accumulation and analysis of question logs

[0298] The server stores the questions and answers in a database, which records the learning history.

[0299] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," it identifies functions as a weak area.

[0300] Emotion recognition and response using emotion engine

[0301] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize the user's emotional state. For example, it can determine whether the student is confused or happy through the camera and microphone.

[0302] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[0303] Automatic generation and provision of individual learning content based on weak areas and emotional data

[0304] The server combines and analyzes question history and emotional data to identify areas in which students are weak and areas that cause them emotional stress.

[0305] The server automatically generates appropriate learning content based on the identified weaknesses, such as a video course on the domain of functions or emotional support.

[0306] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[0307] Providing progress feedback

[0308] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[0309] The server transmits the feedback to the terminal, and the terminal displays the feedback on the screen to provide it to the user.

[0310] Specific examples

[0311] Student question and answer process

[0312] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[0313] The terminal sends this question to the server.

[0314] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[0315] The device will display this answer, allowing the user to solve the problem on the spot.

[0316] Emotion recognition and response adjustment

[0317] Example: A user asks with a confused look, "I still don't understand the domain of a function."

[0318] The device uses an emotion engine to recognize confusion and transmits this information to the server.

[0319] The server generates an answer with detailed diagrams and additional explanations, specifically answering, "The domain of a function indicates the range of values ​​of x. For example, the function f(x) = √x has x ≥ 0."

[0320] The terminal displays this detailed response and provides it to the user.

[0321] Identifying weak areas and utilizing emotional data

[0322] Example: The server analyzes the question log and sentiment data and discovers that users often ask questions about functions, but that they express confusion and tension each time.

[0323] The server automatically generates learning content including a "video lecture on the domain of functions" and emotional support messages.

[0324] The server sends this content to the terminal, which displays it and provides it to the user.

[0325] The processing flow will be explained below.

[0326] Step 1:

[0327] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[0328] Step 2:

[0329] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[0330] Step 3:

[0331] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[0332] Step 4:

[0333] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[0334] Step 5:

[0335] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[0336] Step 6:

[0337] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[0338] Step 7:

[0339] The server stores the questions and their answers in a database, which records the question history.

[0340] Step 8:

[0341] The server periodically analyzes the question log, which identifies each student's weak areas.

[0342] Step 9:

[0343] The device analyzes the user's facial expressions and voice through an emotion engine to recognize the user's emotional state. For example, it can determine whether a student is confused or happy through a camera or microphone.

[0344] Step 10:

[0345] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[0346] Step 11:

[0347] The server automatically generates appropriate learning content based on the identified weak areas, such as a video course on the domain of functions.

[0348] Step 12:

[0349] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[0350] Step 13:

[0351] The device will display new learning content, allowing users to focus on areas they are weak in.

[0352] Step 14:

[0353] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[0354] Step 15:

[0355] The server generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[0356] Step 16:

[0357] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[0358] Step 17:

[0359] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[0360] Through these steps, the online video lesson system using generative AI and an emotion engine will enable students to answer questions in real time, provide individually customized learning content, and provide emotional support.

[0361] Example 2

[0362] 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."

[0363] With conventional online class systems, it was difficult to provide quick and accurate answers to questions entered by students, and it was also difficult to properly grasp students' learning progress and provide individual support. Furthermore, because it was not possible to respond to students' emotional states, there were problems such as not being able to provide appropriate support when students felt confused or stressed. This limited the effectiveness of online classes and created the issue of not being able to fully motivate students to learn.

[0364] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students, means for transmitting the received questions to the server, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for saving the questions and their answers in a database, means for analyzing the saved question history and identifying the student's weak areas, means for analyzing the student's facial expressions and voice tone on the terminal and recognizing the student's emotional state using an emotion engine, means for adjusting the response content based on the student's emotional state, means for automatically generating learning content based on the identified weak areas, and means for providing the generated learning content. This enables quick and accurate answers to questions entered by students and allows individualized instruction that takes into account the student's learning progress and emotional state, thereby maximizing the effectiveness of online classes.

[0365] A "server" is a computer system that receives requests from clients via a network, processes the requests, and returns the results to the clients.

[0366] A "terminal" is a device that is connected to a network and that a user operates to input and display information, and specifically refers to a personal computer or tablet.

[0367] "Student" refers to a learner who takes an online class and inputs questions through the system.

[0368] "Question data" refers to text data containing questions entered by students during online classes.

[0369] "Generative AI" refers to an algorithm or system that uses natural language processing technology to analyze input questions and generate optimal answers.

[0370] A "database" is a data structure or system that stores data in an organized manner and allows efficient searching and manipulation of that data.

[0371] "Question history" refers to a collection of data that records questions that students have previously asked through the system and the answers to those questions.

[0372] "Weak areas" refer to areas of the learning content that students repeatedly ask about but find difficult to understand.

[0373] An "emotion engine" is an algorithm or system that analyzes and recognizes a user's emotional state based on input information such as the user's facial expressions and voice.

[0374] The "emotional state" refers to the emotion the user is currently feeling, and indicates a psychological state such as confusion or joy.

[0375] "Learning Content" means learning materials in digital form, including teaching materials, lectures, video courses, etc., provided for educational purposes.

[0376] "Feedback" refers to responses such as encouragement, advice, and other messages that are generated based on a student's learning progress.

[0377] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[0378] System Overview

[0379] The entire system consists of the following main components:

[0380] Devices used by students (PCs, tablets, etc.)

[0381] Server that processes queries and analyzes and stores data

[0382] Generation AI

[0383] Emotion Engine

[0384] System Operation

[0385] Receiving student questions

[0386] During online classes, users (students) enter questions in text format. For example, they might enter a question like, "I don't know how to draw the graph of a linear function." This question data is received by the device and sent to the server. The commonly used HTTP protocol is used for communication.

[0387] Handling and answering questions

[0388] The server analyzes the received question data. Specifically, it analyzes the received data in JSON format and extracts the question content. Next, the question content is input into the generation AI, which generates the optimal answer. A high-performance natural language processing model is used as the generation AI model. For example, it generates an answer such as, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The server sends the generated answer to the device, which displays it on the screen and provides it to the user.

[0389] Accumulation and analysis of question logs

[0390] The server stores questions and their answers in a database. Specifically, it records information such as the question content, answer content, question date and time, and answer date and time. Periodically, the server analyzes the question log to identify each student's weak areas. This analysis is performed using database queries and machine learning algorithms. For example, if a student repeatedly asks questions about "functions," it can determine that the student is weak in functions.

[0391] Emotion recognition and response using emotion engine

[0392] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize their emotional state. For example, it can identify a student's confusion, joy, or nervousness through a camera or microphone. The server then analyzes the student's emotional state and adjusts the response accordingly. For example, if the student is confused, it will generate a response with more detailed explanations and illustrations and send it back to the device.

[0393] Automatic generation and provision of individual learning content based on weak areas and emotional data

[0394] The server combines and analyzes the question history and emotional data to identify areas where the student is weak or stressed. Based on the identified areas of weakness, the server automatically generates appropriate learning content. For example, it might create a "video course on the domain of functions." The generated learning content is then sent back to the device and provided to the user.

[0395] Providing progress feedback

[0396] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions" or "You seem a little confused, but keep trying." This feedback is sent to the device, which displays it on the screen for the user.

[0397] Specific operation example

[0398] During an online class, a user might type a question like, "I don't know how to graph a linear function." The device then sends the question to the server. The server uses generative AI to generate an answer, saying, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The device then displays this answer, allowing the user to solve the problem on the spot. Similarly, if a user asks with a confused expression, "I still don't understand the domain of a function," the device uses its emotion engine to recognize the confusion and sends this information to the server. The server then generates an answer with detailed diagrams and additional explanations, specifically stating, "The domain of a function indicates the extent to which the value of x extends. For example, for the function f(x)=√x, x≥0." The device then displays this detailed answer and provides it to the user.

[0399] This system will enable quick and accurate resolution of problems students encounter during online classes, and will also enable optimal support tailored to individual learning situations.

[0400] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0401] Step 1: Student Question Input

[0402] During an online class, a user (student) inputs a question in text format. For example, a question might be, "I don't know how to draw a graph of a linear function." This input is received by the terminal.

[0403] Input: Student entered text question

[0404] Output: Question data saved on the device

[0405] Step 2: Sending query data via device

[0406] The device sends the received question data to the server using the HTTP protocol, and the data is sent in JSON format.

[0407] Input: Question data saved on the device

[0408] Output: The query data sent to the server

[0409] Step 3: Query data received and parsed by the server

[0410] The server analyzes the received question data. Specifically, it analyzes the received JSON data and extracts the question content.

[0411] Input: JSON-formatted question data sent from the terminal

[0412] Output: Parsed question

[0413] Step 4: Generative AI generates answers

[0414] The server inputs the analyzed question content into the generation AI, which generates the optimal answer. The generation AI model uses natural language processing technology.

[0415] Input: Parsed question content

[0416] Output: Generated answer text

[0417] Step 5: Server sends response

[0418] The server sends the generated response text to the device, again using the HTTP protocol and JSON format.

[0419] Input: Generated answer text

[0420] Output: Answer text sent to the terminal

[0421] Step 6: Displaying the Answer via Terminal

[0422] The terminal displays the received answer text on the screen and provides it to the user. Specifically, the answer is displayed in a text box.

[0423] Input: Answer text sent from the server

[0424] Output: Answer displayed on the device screen

[0425] Step 7: Storing the Questions and Answers in a Database

[0426] The server stores the questions and their answers in a database, which includes the question, the answer, the date and time of the question, and the date and time of the answer.

[0427] Input: Question and answer

[0428] Output: Questions and answers stored in a database

[0429] Step 8: Server Analysis of Question Log

[0430] The server periodically analyzes the database and identifies weak areas based on each student's question history, using machine learning algorithms and database queries.

[0431] Input: Question logs stored in the database

[0432] Output: Identified student weaknesses

[0433] Step 9: Emotion Recognition with the Emotion Engine

[0434] The device analyzes students' facial expressions and voices and uses an emotion engine to recognize their emotional state, for example identifying confusion or joy through the camera and microphone.

[0435] Input: Student facial expressions and voices from the camera and microphone

[0436] Output: Perceived student emotional state

[0437] Step 10: Server Adjustment of Response

[0438] The server analyzes the perceived emotional state and adjusts the response accordingly, for example by adding detailed explanations or using illustrations.

[0439] Input: Perceived emotional state

[0440] Output: Adjusted response content

[0441] Step 11: Automatic generation of personalized learning content

[0442] The server combines and analyzes question history and emotional data, and automatically generates appropriate learning content based on the student's weak areas.

[0443] Input: Question history and sentiment data

[0444] Output: Generated learning content

[0445] Step 12: Sending learning content from the server

[0446] The server sends the generated learning content to the device using the HTTP protocol, with the data sent in JSON format.

[0447] Input: Generated learning content

[0448] Output: Learning content sent to your device

[0449] Step 13: Viewing learning content on your device

[0450] The device displays the received learning content on the screen and provides it to the user, for example, by playing a video lecture or text content.

[0451] Input: Learning content sent from the server

[0452] Output: Learning content displayed on the device screen

[0453] Step 14: Server analyzes progress and generates feedback

[0454] The server analyzes the student's learning progress and generates appropriate feedback, such as a message saying, "You're making progress in understanding functions."

[0455] Input: Student progress data

[0456] Output: The generated feedback message

[0457] Step 15: Displaying feedback via terminal

[0458] The device displays feedback messages on the screen and provides them to the user, allowing the student to understand their own learning progress.

[0459] Input: Feedback message sent by the server

[0460] Output: Feedback displayed on the device screen

[0461] (Application example 2)

[0462] 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."

[0463] Conventional online teaching systems are required to not only provide answers to questions, but also to recognize the emotional state of each student and respond based on that state. Furthermore, they lack the ability to tailor responses based on emotional states and provide individual learning content for weak areas. As a result, students may feel stressed and their learning efficiency may decrease.

[0464] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0465] In this invention, the server includes means for receiving questions entered by students during online classes, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history and identifying weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for recognizing the emotional state of the students, and means for adjusting answers based on the recognized emotional state, thereby enabling flexible learning support tailored to the emotional state of each individual student.

[0466] "Online classes" are a form of education conducted via the Internet, allowing students to participate in classes from remote locations.

[0467] "Means for receiving questions" refers to the functionality of a device or software for obtaining text questions entered by students during online classes.

[0468] "Generative AI" is a technology for analyzing input question data and generating appropriate answers, such as a natural language processing model.

[0469] The "means for providing an answer" refers to the functionality of the device or software for presenting the generated answer to the student.

[0470] A "recording means" is a technique for saving questions and their answers in a format that can be analyzed later.

[0471] "Means for analyzing history" is a function that analyzes recorded question and answer data and extracts specific patterns.

[0472] "Means for identifying weak areas" refers to technology for identifying the content or areas in which a student is weak based on historical data.

[0473] "Means for automatically generating learning content" refers to a function that automatically creates appropriate educational materials and assignments based on students' weak areas.

[0474] "Means of provision" refers to the functions of the device or software used to present the generated learning content and answers to students.

[0475] The "means of recognizing emotional states" is a technology that uses cameras and microphones to analyze and recognize emotions from students' facial expressions and voices.

[0476] "Means for tailoring responses" refers to the ability to change the level of detail and content of generated responses based on the perceived emotional state.

[0477] The present invention is a distance learning video teaching system that uses both a generative AI and an emotion engine, and is implemented with the following configuration.

[0478] System configuration

[0479] The entire system consists of the following main components:

[0480] Device: The device used by students for online classes (computer, tablet, etc.)

[0481] Server: A computing device that processes queries and analyzes and stores data.

[0482] Generative AI: An artificial intelligence engine that analyzes questions and generates answers

[0483] Emotion Engine: An analytical engine for recognizing students' emotional states

[0484] System Program

[0485] Receiving and processing questions

[0486] During online classes, users (students) use their devices to input questions in text format. The input questions are received by the devices and sent to the server.

[0487] Answer generation and provision

[0488] The server analyzes the received question and generates the optimal answer using a generation AI. The generated answer is then sent from the server to the device, which displays the answer on the screen for the user.

[0489] Recording and analyzing questions and answers

[0490] The server stores the questions and answers in a database, which records the learning history.

[0491] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," the server identifies functions as a weak area.

[0492] Emotion recognition and response regulation

[0493] The device uses a camera and microphone to analyze the student's facial expressions and voice, and an emotion engine to recognize the user's emotional state. The server then adjusts the response based on the user's emotional state. For example, if the student is confused, it adds detailed explanations and illustrations. If the student is happy, it provides a concise answer.

[0494] Creation and provision of personalized learning content

[0495] The server combines and analyzes question history and emotional data to identify areas where students are weak or experiencing emotional stress. The server then automatically generates appropriate learning content based on this data. The generated learning content is then sent to the device, which displays it and provides it to the user.

[0496] Hardware and software used

[0497] Hardware: smartphone, tablet, computer, camera, microphone

[0498] software:

[0499] Generative AI: Natural language processing models such as GPT-3

[0500] Emotion engine: Analysis engines such as EmotionEngine

[0501] Database: Data management system such as PostgreSQL

[0502] Server: A web framework such as Flask

[0503] Specific examples

[0504] 1. A user (student) types, "I don't understand the basic concept of differentiation" during an online class.

[0505] 2. The device sends this question to the server.

[0506] 3. The server uses a generative AI to generate the answer: "The basic concept of differentiation is that it shows the instantaneous rate of change of a function."

[0507] 4. The emotion engine recognizes the user's confused facial expression through the camera and microphone.

[0508] 5. The server adds a more detailed response: "The derivative shows the rate of change of a function at each point."

[0509] 6. The device displays this detailed response and provides it to the user.

[0510] Example prompt sentence:

[0511] text

[0512] "Student typed question: I don't understand the basic concept of differentiation.

[0513] Emotion recognition result: Confused.

[0514] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[0515] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0516] Processing Steps

[0517] Step 1:

[0518] A user enters a question in text format during an online class.

[0519] Input: Students type in their questions in text format.

[0520] Action: The user enters a question using the device's input device (keyboard or touchscreen).

[0521] Output: The question text is saved on the device and sent to the server.

[0522] Step 2:

[0523] The terminal receives the entered question and transmits it to the server.

[0524] Input: The question text entered in step 1.

[0525] How it works: The device sends the question text to the server via an HTTP request.

[0526] Output: The question text arrives at the server and is queued for analysis.

[0527] Step 3:

[0528] The server analyzes the received question and uses a generative AI to generate the optimal answer.

[0529] Input: The question text stored on the server.

[0530] How it works: The server provides the generative AI model with question text and prompts to generate an answer. The generative AI model analyzes the meaning of the question and generates an appropriate answer.

[0531] Output: The generated answer text.

[0532] Step 4:

[0533] The generated answer is sent to the terminal, which displays the answer on the screen.

[0534] Input: The answer text generated by the server.

[0535] Operation: The server sends the answer text to the device as an HTTP response. The device displays the received answer on the screen.

[0536] Output: The answer text displayed on the terminal.

[0537] Step 5:

[0538] Store the questions and their answers in a database.

[0539] Input: Question text and answer text.

[0540] How it works: The server stores question and answer data pairs in a database.

[0541] Output: A new question and answer entry is saved in the database.

[0542] Step 6:

[0543] The server analyzes the question log and identifies areas in which students are weak.

[0544] Input: A history of questions and answers stored in a database.

[0545] How it works: The server uses data mining techniques to analyze the question log and find out whether questions in specific areas are common. An algorithm is then applied to identify areas of weakness.

[0546] Output: Identified weak areas.

[0547] Step 7:

[0548] Automatically generate learning content based on identified areas of weakness.

[0549] Input: Information about identified weaknesses.

[0550] How it works: The server inputs information about weak areas as prompts into the generative AI model, which then generates learning content based on that information. The generative AI model then generates appropriate video courses and text materials.

[0551] Output: The generated learning content.

[0552] Step 8:

[0553] The generated learning content is sent to the terminal, which displays it.

[0554] Input: Generated learning content.

[0555] Operation: The server sends the learning content to the device via HTTP response, and the device displays the received content on the screen.

[0556] Output: The learning content displayed on your device.

[0557] Step 9:

[0558] The device analyzes the student's facial expressions and voice to recognize their emotional state.

[0559] Input: Facial expression and vocal data captured by a camera or microphone.

[0560] How it works: The device uses an emotion engine to analyze facial expressions and voice to recognize the student's emotional state.

[0561] Output: Recognized emotional state data.

[0562] Step 10:

[0563] The server tailors the response based on the perceived emotional state.

[0564] Input: Recognized emotional state data and generated answer text.

[0565] How it works: The server regenerates or adjusts the answer text based on the emotional state, providing a tailored response, such as adding detailed explanations or illustrations.

[0566] Output: The adjusted answer text.

[0567] Example prompt sentence:

[0568] text

[0569] "Student typed question: I don't understand the basic concept of differentiation.

[0570] Emotion recognition result: Confused.

[0571] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] [Second embodiment]

[0576] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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).

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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."

[0588] The present invention is a distance learning video lesson system that utilizes generative AI, and is implemented as follows.

[0589] System Overview

[0590] The overall system consists of the following main components:

[0591] Devices used by students (PCs, tablets, etc.)

[0592] Server that processes queries and analyzes and stores data

[0593] Generation AI

[0594] Program processing

[0595] Receiving student questions

[0596] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[0597] The terminal receives the input question and transmits the question data to the server.

[0598] Handling and answering questions

[0599] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[0600] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[0601] Accumulation and analysis of question logs

[0602] The server stores the questions and answers in a database, which records the learning history.

[0603] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student frequently asks questions about "functions," it will identify functions as an area in which the student is weak.

[0604] Automatic generation and provision of individual learning content

[0605] The server automatically generates appropriate learning content based on weak areas. For example, it generates a video course on the domain of functions.

[0606] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[0607] Providing progress feedback

[0608] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving."

[0609] The server transmits the feedback to the terminal, and the terminal displays the feedback to provide it to the user.

[0610] Specific examples

[0611] Student question and answer process

[0612] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[0613] The terminal sends this question to the server.

[0614] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[0615] The device will display this answer, allowing the user to solve the problem on the spot.

[0616] Identifying weak areas and providing content

[0617] Example: The server analyzes the question log and discovers that a particular student frequently asks questions about "functions."

[0618] The server automatically generates learning content such as a "video course on the domain of functions."

[0619] The server transmits this content to the terminal, which then plays the video lecture, allowing the user to deepen their learning in areas they are weak at.

[0620] In this way, the distance learning video lesson system of the present invention can effectively support students' learning and provide equal educational opportunities.

[0621] The processing flow will be explained below.

[0622] Step 1:

[0623] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[0624] Step 2:

[0625] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[0626] Step 3:

[0627] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[0628] Step 4:

[0629] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[0630] Step 5:

[0631] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[0632] Step 6:

[0633] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[0634] Step 7:

[0635] The server stores the questions and their answers in a database, which records the question history.

[0636] Step 8:

[0637] The server periodically analyzes the question log, which identifies each student's weak areas.

[0638] Step 9:

[0639] The server automatically generates appropriate learning content based on the identified weak areas, for example, a video course on the domain of functions.

[0640] Step 10:

[0641] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[0642] Step 11:

[0643] The device will display new learning content, allowing users to focus on areas they are weak in.

[0644] Step 12:

[0645] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[0646] Step 13:

[0647] The server generates appropriate feedback, for example, "You're making progress in understanding functions."

[0648] Step 14:

[0649] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[0650] Step 15:

[0651] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[0652] Through these steps, the online video lesson system using generative AI will enable students to ask and answer questions in real time and provide individually customized learning content.

[0653] Example 1

[0654] 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."

[0655] Conventional online teaching systems lack the ability to not only quickly and accurately answer students' questions, but also to identify each student's weak areas and provide personalized learning content. This makes it difficult to continuously monitor each student's level of understanding and progress and provide appropriate feedback. In addition, there is a lack of efficient means to accumulate question history data and provide automatically generated learning content. This makes it difficult to improve students' learning efficiency and enhance the quality of education.

[0656] 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.

[0657] In this invention, the server includes: means for receiving questions entered by students during online classes; means for analyzing the received questions and using a generative AI model to generate optimal answers; means for providing the generated answers to the students; means for recording questions and their answers; means for analyzing the recorded question history to identify the student's weak areas; means for automatically generating learning content based on the identified weak areas; means for providing the generated learning content; means for generating video courses to address the identified weak areas; and means for using prompt sentences in the generative AI model when analyzing the questions entered by the students. This not only enables prompt and accurate answers to students' questions, but also identifies individual weak areas and automatically generates and provides appropriate learning content. Furthermore, by accumulating and analyzing the question history, it becomes possible to grasp the student's learning progress and provide appropriate feedback, thereby improving the student's learning efficiency and enhancing the quality of education.

[0658] "Online classes" are classes that students take via the internet.

[0659] "Student" refers to a user who takes an online class and enters questions.

[0660] "Question" refers to a text inquiry entered by a student during an online class.

[0661] "Devices" refer to computers and tablets used by students, and are devices used to take online classes and input and send questions.

[0662] A "server" is a central processing unit that receives student questions, performs analysis, and generates and provides answers.

[0663] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate optimal answers to input questions.

[0664] "Prompt sentence" refers to the input-style sentence that a generative AI model uses to generate an answer to a question.

[0665] "Answer" refers to the answer information generated by the generative AI model in response to a question.

[0666] "Question history" refers to a record of questions previously entered by students and the answers generated.

[0667] "Weak areas" refer to areas of learning that a student has difficulty understanding, identified based on an analysis of their question history.

[0668] "Learning content" refers to teaching materials and information created to strengthen students' knowledge in areas of weakness.

[0669] "Video Course" refers to educational material in video format that is produced to explain specific learning content.

[0670] "Feedback" refers to the assessments and comments provided to students based on their learning progress.

[0671] "Database" refers to a recording device for storing the history of questions and answers and its management system.

[0672] This invention is a distance learning video lesson system that utilizes a generative AI model to provide quick and accurate answers to questions entered by students during online lessons, identify areas in which each student is weak, and support their understanding.

[0673] The system uses the following major hardware and software components:

[0674] Device: Student's computer, tablet, or other device

[0675] Server: A central processing unit that processes queries and analyzes and stores data.

[0676] Generative AI models: Algorithms and programs used to analyze questions and generate answers (e.g., GPT-4)

[0677] During online classes, users (students) enter questions in text format about points they do not understand. For example, they enter a question such as, "I don't understand the domain of a function." The device receives this question and sends it to the server using a format and protocol (e.g., HTTP, WebSocket).

[0678] The server receives the user's question and analyzes its contents. At this time, the question text is input as a prompt to the generative AI model, which generates the optimal answer. For example, it generates an answer such as "The domain of a function is the range of values ​​that are input to the function." The server sends the generated answer to the device, which displays it on the screen.

[0679] The server stores the user's questions and the generated answers in a database. This keeps a history of all questions and answers. At regular intervals, the server analyzes the log data and identifies the student's weak areas. For example, if a student asks many questions about "functions" multiple times, it will determine that functions are a weak area.

[0680] Based on the identified weak areas, the server uses a generative AI model to generate appropriate learning content. For example, it automatically generates a video lecture on the domain of functions. The server then sends the generated content to the device, which then displays it. This allows students to deepen their understanding of their weak areas.

[0681] The server also analyzes learning progress data and generates appropriate feedback. For example, it may generate feedback such as, "Your understanding of functions is improving." The server then sends the generated feedback to the device, which then displays it on the screen. This allows students to check their own learning progress and motivates them to continue learning.

[0682] Specific examples

[0683] Question and answer generation

[0684] If a user asks during an online class, "I don't know how to graph a linear function," the device sends this question to the server. The server uses a generative AI model to generate an answer: "The graph of a linear function has the form y = mx + b, where m is the slope and b is the y-intercept." The device displays this answer, allowing the user to solve the problem on the spot.

[0685] Identifying weak areas and providing content

[0686] If the server analyzes the question log and finds that a particular student frequently asks questions about "functions," it automatically generates learning content such as a "video lecture on the domain of functions." The server sends this content to the device, which then plays the video lecture, allowing the user to deepen their understanding of their weak areas.

[0687] In this way, the system of the present invention improves the quality of education by providing appropriate answers to students' questions and personalized learning support.

[0688] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0689] Step 1:

[0690] During an online class, a user enters a question in text format. For example, they enter a question like, "I don't understand the domain of a function." The device receives this input and sends it to the server using a format and protocol (such as HTTP or WebSocket).

[0691] Input: The question text entered by the user

[0692] Output: The question data sent to the server

[0693] Specific behavior:

[0694] The user uses a web browser or dedicated application to enter a question into a text input field on the interface and press the submit button.

[0695] Step 2:

[0696] The server analyzes the received question. It inputs the question text as a prompt into a generative AI model (e.g., GPT-4) and generates the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[0697] Input: Query data sent to the server

[0698] Output: Answer text generated by the generative AI model

[0699] Specific behavior:

[0700] When the server receives the question text, it accesses the API of the generation AI, sends a prompt, and receives the generated answer.

[0701] Step 3:

[0702] The server sends the generated answer to the terminal, which then displays the answer on its screen, allowing the user to obtain the answer to their question.

[0703] Input: Answer text generated by the generative AI model

[0704] Output: Answer information displayed on the terminal

[0705] Specific behavior:

[0706] The server generates an answer and sends it back to the device as an HTTP response, which the device parses and displays in a browser or application.

[0707] Step 4:

[0708] The server stores the user's questions and generated answers in a database, thus maintaining a history of all questions and answers.

[0709] Input: Question text and generated answer text

[0710] Output: Question and answer history stored in a database

[0711] Specific behavior:

[0712] The server stores the questions and answers in a database such as MySQL or PostgreSQL using an INSERT query.

[0713] Step 5:

[0714] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student repeatedly asks questions about "functions," it will determine that functions are a weak area.

[0715] Input: A history of questions and answers stored in a database

[0716] Output: Information on identified weaknesses

[0717] Specific behavior:

[0718] Scheduled tasks and periodic batch processes are run to aggregate and analyze database logs and identify weak areas based on specific keywords and frequencies.

[0719] Step 6:

[0720] Based on the weak areas identified by the server, appropriate learning content is generated using generative AI. For example, a video course on the domain of functions is automatically generated.

[0721] Input: Information about identified weaknesses

[0722] Output: Generated learning content

[0723] Specific behavior:

[0724] Use generative AI to generate text content and quiz questions, and optionally integrate with video generation tools (e.g., FFmpeg) to generate video content.

[0725] Step 7:

[0726] The server sends the generated learning content to the device, which then displays it, allowing the user to deepen their understanding of weak areas.

[0727] Input: Generated learning content

[0728] Output: Learning content displayed on the device

[0729] Specific behavior:

[0730] The generated content is delivered to the terminal in the form of HTTP streaming or file download, and the terminal displays it.

[0731] Step 8:

[0732] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving," which is sent to the device and displayed.

[0733] Input: Learning progress data

[0734] Output: Generated feedback

[0735] Specific behavior:

[0736] The server analyzes the learning progress data and generates feedback messages using a generation AI. The generated feedback is sent to the device in JSON format, and the device formats it for display on the screen and presents it to the user.

[0737] (Application example 1)

[0738] 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."

[0739] Today's students have a strong desire to make effective use of their time, such as while traveling, for studying. However, conventional online class systems are mainly used in fixed locations, and do not provide sufficient support for learning while traveling. There is also a lack of systems that can quickly answer questions students have during online classes and provide learning content specialized for weak areas. For this reason, there is a need to develop a system that can effectively support learning even while traveling.

[0740] 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.

[0741] In this invention, the server includes means for receiving questions entered by students during online classes, means using artificial intelligence to analyze the received questions and generate optimal answers, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history to identify weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for supporting the students' learning through a terminal installed in the autonomous vehicle, and means for analyzing questions entered by students during learning in real time and providing answers, thereby enabling effective support for students' learning even while on the move.

[0742] "Means for receiving questions entered by students during online classes" refers to a system for receiving questions sent from the devices used by students.

[0743] "Methods using generative artificial intelligence to analyze received questions and generate optimal answers" refers to a method that uses generative AI to understand the content of questions and generate appropriate answers.

[0744] The "means for providing the generated answers to the students" is a mechanism for displaying the generated answers on the students' terminals.

[0745] The "means for recording questions and their answers" is a mechanism for storing received questions and their answers in a database.

[0746] The "means of analyzing the recorded question history to identify areas in which students are weak" is a method of analyzing saved question and answer data to identify areas in which students are weak.

[0747] "Means for automatically generating learning content based on identified areas of weakness" refers to a method for using generative AI to create appropriate learning materials tailored to areas of weakness.

[0748] The "means for providing the generated learning content" is a mechanism for transmitting the generated learning materials to the student's terminal and displaying them.

[0749] "Means to support students' learning through terminals installed inside self-driving vehicles" refers to a system that utilizes terminals installed inside self-driving vehicles to enable students to study while on the move.

[0750] "Means for analyzing questions entered by students in real time while studying and providing answers" refers to a method for instantly analyzing questions entered in real time while studying and generating and providing appropriate answers.

[0751] This invention is a system that allows students to study effectively even while on the move. This system provides the functionality to analyze questions entered during online classes and generate appropriate answers. Furthermore, it can analyze students' learning histories and automatically generate individual learning content. To achieve this, the following specific components and processes are required.

[0752] System configuration

[0753] The overall system consists of the following main components:

[0754] Devices used by students (computers, tablets, devices installed in autonomous vehicles, etc.)

[0755] Server (query processing, data analysis and storage)

[0756] Generative AI (understands the meaning of the question and generates the best answer)

[0757] Hardware and Software

[0758] 1. Terminal

[0759] A terminal is a device used by a student, including a computer, tablet, or learning tablet installed in an autonomous vehicle. The terminal is connected to the internet and has an interface for entering questions and a display for viewing the generated answers.

[0760] 2. Server

[0761] The server is a central management system that receives questions, analyzes them, generates answers, manages the database, etc. This server is operated on a cloud basis and has various software installed for query processing and automatic generation of learning content.

[0762] 3. Generation AI

[0763] The generative AI running on the server analyzes the meaning of the question based on natural language processing and generates appropriate answers. The AI ​​models used include GPT-3 and similar advanced generative AI.

[0764] Program processing

[0765] 1. Receiving Questions

[0766] When a student enters a question in text format during an online class, the device sends the question to the server.

[0767] 2. Question Analysis and Answer Generation

[0768] The server sends the received question to the AI ​​generator, which then generates the best answer. At this time, the AI ​​is given the following prompt as input:

[0769] Q: I don't know the domain of a function.

[0770] A:

[0771] The AI ​​generates the following response to this prompt:

[0772] The domain of a function is the range of values ​​that are input to the function.

[0773] 3. Providing and recording responses

[0774] The server sends the generated answers to the terminal, which displays them to the student, and simultaneously stores the questions and answers in a database.

[0775] Specific examples

[0776] Consider the case where a user (student) asks a question during an online class, "I don't know how to graph a linear function." At this time, the device receives the question and sends it to the server. The server uses generative AI to generate the following answer:

[0777] The graph of a linear function is of the form y = mx + b, where m is the slope and b is the y-intercept.

[0778] As soon as this answer is generated, it is sent to the device and displayed to the student.

[0779] This system can also automatically generate and provide learning content based on identified areas of weakness. For example, if the server identifies a student's weak area as "functions," it can generate a video course on the domain of functions, send it to the student's device, and display it to the student. In this way, it is possible to effectively support students' learning even while they are on the move.

[0780] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0781] Step 1:

[0782] The terminal receives questions from students. When a student enters a question in text format into the terminal, the question is sent to the terminal's receiving interface. For example, the text may be "I don't understand the domain of a function."

[0783] Input: Student question text

[0784] Output: Question data received by the device

[0785] Step 2:

[0786] The terminal sends the received query to the server. The query data is sent to the server via the network. For example, the following data is sent:

[0787] Q: I don't know the domain of a function.

[0788] Input: Received question data

[0789] Output: The query data sent to the server

[0790] Step 3:

[0791] The server sends the received question to the generation AI. The received question data is input into the generation AI model to construct a prompt sentence. For example, the following prompt sentence is generated:

[0792] Q: I don't know the domain of a function.

[0793] A:

[0794] Input: Received question data

[0795] Output: The prompt sent to the generation AI

[0796] Step 4:

[0797] The generation AI analyzes the prompt and generates the most appropriate answer. The generation AI uses natural language processing technology to understand the content of the question and generate an appropriate answer. For example, the following answer may be generated:

[0798] The domain of a function is the range of values ​​that are input to the function.

[0799] Input: prompt statement

[0800] Output: Generated answer text

[0801] Step 5:

[0802] The server sends the generated response to the terminal. The generated response data is returned to the terminal. For example, the following data is sent:

[0803] Answer: The domain of a function is the range of values ​​that are input to the function.

[0804] Input: Generated answer text

[0805] Output: Response data sent to the device

[0806] Step 6:

[0807] The device receives the answer and displays it to the student. The generated answer is displayed on the device's display. For example, the following text might be displayed:

[0808] The domain of a function is the range of values ​​that are input to the function.

[0809] Input: Response data from the server

[0810] Output: Answer text displayed on the screen

[0811] Step 7:

[0812] The server records the question and answer in a database. The question and its answer are stored in a database. For example, the following records are stored:

[0813] Q: I don't know the domain of a function.

[0814] Answer: The domain of a function is the range of values ​​that are input to the function.

[0815] Input: Question and answer data

[0816] Output: Records saved in the database

[0817] Step 8:

[0818] The server periodically analyzes the question history to identify areas where students are weak. The stored data is analyzed to identify frequently asked question categories. For example, if there are many questions about "functions," it will identify that area as a weak area.

[0819] Input: Question history stored in the database

[0820] Output: Identified weak areas

[0821] Step 9:

[0822] The server automatically generates learning content based on weak areas. Generative AI is used to create appropriate learning content (e.g., video courses). For example, the following video courses are generated:

[0823] Video content: Detailed explanation of the domain of a function

[0824] Input: Identified weak areas information

[0825] Output: Generated learning content

[0826] Step 10:

[0827] The server transmits the generated learning content to the terminal, which provides it to the student. The server transmits the generated learning content to the terminal, which plays the content and displays it to the student.

[0828] Input: Generated learning content

[0829] Output: Learning content sent to the device, learning content played on the device

[0830] The above are the specific processing steps for carrying out the present invention.

[0831] 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.

[0832] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[0833] System Overview

[0834] The entire system consists of the following main components:

[0835] Devices used by students (PCs, tablets, etc.)

[0836] Server that processes queries and analyzes and stores data

[0837] Generation AI

[0838] Emotion Engine

[0839] Program processing

[0840] Receiving student questions

[0841] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[0842] The terminal receives the input question and transmits the question data to the server.

[0843] Handling and answering questions

[0844] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[0845] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[0846] Accumulation and analysis of question logs

[0847] The server stores the questions and answers in a database, which records the learning history.

[0848] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," it identifies functions as a weak area.

[0849] Emotion recognition and response using emotion engine

[0850] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize the user's emotional state. For example, it can determine whether the student is confused or happy through the camera and microphone.

[0851] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[0852] Automatic generation and provision of individual learning content based on weak areas and emotional data

[0853] The server combines and analyzes question history and emotional data to identify areas in which students are weak and areas that cause them emotional stress.

[0854] The server automatically generates appropriate learning content based on the identified weaknesses, such as a video course on the domain of functions or emotional support.

[0855] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[0856] Providing progress feedback

[0857] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[0858] The server transmits the feedback to the terminal, and the terminal displays the feedback on the screen to provide it to the user.

[0859] Specific examples

[0860] Student question and answer process

[0861] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[0862] The terminal sends this question to the server.

[0863] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[0864] The device will display this answer, allowing the user to solve the problem on the spot.

[0865] Emotion recognition and response adjustment

[0866] Example: A user asks with a confused look, "I still don't understand the domain of a function."

[0867] The device uses an emotion engine to recognize confusion and transmits this information to the server.

[0868] The server generates an answer with detailed diagrams and additional explanations, specifically answering, "The domain of a function indicates the range of values ​​of x. For example, the function f(x) = √x has x ≥ 0."

[0869] The terminal displays this detailed response and provides it to the user.

[0870] Identifying weak areas and utilizing emotional data

[0871] Example: The server analyzes the question log and sentiment data and discovers that users often ask questions about functions, but that they express confusion and tension each time.

[0872] The server automatically generates learning content including a "video lecture on the domain of functions" and emotional support messages.

[0873] The server sends this content to the terminal, which displays it and provides it to the user.

[0874] The processing flow will be explained below.

[0875] Step 1:

[0876] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[0877] Step 2:

[0878] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[0879] Step 3:

[0880] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[0881] Step 4:

[0882] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[0883] Step 5:

[0884] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[0885] Step 6:

[0886] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[0887] Step 7:

[0888] The server stores the questions and their answers in a database, which records the question history.

[0889] Step 8:

[0890] The server periodically analyzes the question log, which identifies each student's weak areas.

[0891] Step 9:

[0892] The device analyzes the user's facial expressions and voice through an emotion engine to recognize the user's emotional state. For example, it can determine whether a student is confused or happy through a camera or microphone.

[0893] Step 10:

[0894] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[0895] Step 11:

[0896] The server automatically generates appropriate learning content based on the identified weak areas, such as a video course on the domain of functions.

[0897] Step 12:

[0898] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[0899] Step 13:

[0900] The device will display new learning content, allowing users to focus on areas they are weak in.

[0901] Step 14:

[0902] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[0903] Step 15:

[0904] The server generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[0905] Step 16:

[0906] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[0907] Step 17:

[0908] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[0909] Through these steps, the online video lesson system using generative AI and an emotion engine will enable students to answer questions in real time, provide individually customized learning content, and provide emotional support.

[0910] Example 2

[0911] 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."

[0912] With conventional online class systems, it was difficult to provide quick and accurate answers to questions entered by students, and it was also difficult to properly grasp students' learning progress and provide individual support. Furthermore, because it was not possible to respond to students' emotional states, there were problems such as not being able to provide appropriate support when students felt confused or stressed. This limited the effectiveness of online classes and created the issue of not being able to fully motivate students to learn.

[0913] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students, means for transmitting the received questions to the server, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for saving the questions and their answers in a database, means for analyzing the saved question history and identifying the student's weak areas, means for analyzing the student's facial expressions and voice tone on the terminal and recognizing the student's emotional state using an emotion engine, means for adjusting the response content based on the student's emotional state, means for automatically generating learning content based on the identified weak areas, and means for providing the generated learning content. This enables quick and accurate answers to questions entered by students and allows individualized instruction that takes into account the student's learning progress and emotional state, thereby maximizing the effectiveness of online classes.

[0914] A "server" is a computer system that receives requests from clients via a network, processes the requests, and returns the results to the clients.

[0915] A "terminal" is a device that is connected to a network and that a user operates to input and display information, and specifically refers to a personal computer or tablet.

[0916] "Student" refers to a learner who takes an online class and inputs questions through the system.

[0917] "Question data" refers to text data containing questions entered by students during online classes.

[0918] "Generative AI" refers to an algorithm or system that uses natural language processing technology to analyze input questions and generate optimal answers.

[0919] A "database" is a data structure or system that stores data in an organized manner and allows efficient searching and manipulation of that data.

[0920] "Question history" refers to a collection of data that records questions that students have previously asked through the system and the answers to those questions.

[0921] "Weak areas" refer to areas of the learning content that students repeatedly ask about but find difficult to understand.

[0922] An "emotion engine" is an algorithm or system that analyzes and recognizes a user's emotional state based on input information such as the user's facial expressions and voice.

[0923] The "emotional state" refers to the emotion the user is currently feeling, and indicates a psychological state such as confusion or joy.

[0924] "Learning Content" means learning materials in digital form, including teaching materials, lectures, video courses, etc., provided for educational purposes.

[0925] "Feedback" refers to responses such as encouragement, advice, and other messages that are generated based on a student's learning progress.

[0926] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[0927] System Overview

[0928] The entire system consists of the following main components:

[0929] Devices used by students (PCs, tablets, etc.)

[0930] Server that processes queries and analyzes and stores data

[0931] Generation AI

[0932] Emotion Engine

[0933] System Operation

[0934] Receiving student questions

[0935] During online classes, users (students) enter questions in text format. For example, they might enter a question like, "I don't know how to draw the graph of a linear function." This question data is received by the device and sent to the server. The commonly used HTTP protocol is used for communication.

[0936] Handling and answering questions

[0937] The server analyzes the received question data. Specifically, it analyzes the received data in JSON format and extracts the question content. Next, the question content is input into the generation AI, which generates the optimal answer. A high-performance natural language processing model is used as the generation AI model. For example, it generates an answer such as, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The server sends the generated answer to the device, which displays it on the screen and provides it to the user.

[0938] Accumulation and analysis of question logs

[0939] The server stores questions and their answers in a database. Specifically, it records information such as the question content, answer content, question date and time, and answer date and time. Periodically, the server analyzes the question log to identify each student's weak areas. This analysis is performed using database queries and machine learning algorithms. For example, if a student repeatedly asks questions about "functions," it can determine that the student is weak in functions.

[0940] Emotion recognition and response using emotion engine

[0941] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize their emotional state. For example, it can identify a student's confusion, joy, or nervousness through a camera or microphone. The server then analyzes the student's emotional state and adjusts the response accordingly. For example, if the student is confused, it will generate a response with more detailed explanations and illustrations and send it back to the device.

[0942] Automatic generation and provision of individual learning content based on weak areas and emotional data

[0943] The server combines and analyzes the question history and emotional data to identify areas where the student is weak or stressed. Based on the identified areas of weakness, the server automatically generates appropriate learning content. For example, it might create a "video course on the domain of functions." The generated learning content is then sent back to the device and provided to the user.

[0944] Providing progress feedback

[0945] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions" or "You seem a little confused, but keep trying." This feedback is sent to the device, which displays it on the screen for the user.

[0946] Specific operation example

[0947] During an online class, a user might type a question like, "I don't know how to graph a linear function." The device then sends the question to the server. The server uses generative AI to generate an answer, saying, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The device then displays this answer, allowing the user to solve the problem on the spot. Similarly, if a user asks with a confused expression, "I still don't understand the domain of a function," the device uses its emotion engine to recognize the confusion and sends this information to the server. The server then generates an answer with detailed diagrams and additional explanations, specifically stating, "The domain of a function indicates the extent to which the value of x extends. For example, for the function f(x)=√x, x≥0." The device then displays this detailed answer and provides it to the user.

[0948] This system will enable quick and accurate resolution of problems students encounter during online classes, and will also enable optimal support tailored to individual learning situations.

[0949] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0950] Step 1: Student Question Input

[0951] During an online class, a user (student) inputs a question in text format. For example, a question might be, "I don't know how to draw a graph of a linear function." This input is received by the terminal.

[0952] Input: Student entered text question

[0953] Output: Question data saved on the device

[0954] Step 2: Sending query data via device

[0955] The device sends the received question data to the server using the HTTP protocol, and the data is sent in JSON format.

[0956] Input: Question data saved on the device

[0957] Output: The query data sent to the server

[0958] Step 3: Query data received and parsed by the server

[0959] The server analyzes the received question data. Specifically, it analyzes the received JSON data and extracts the question content.

[0960] Input: JSON-formatted question data sent from the terminal

[0961] Output: Parsed question

[0962] Step 4: Generative AI generates answers

[0963] The server inputs the analyzed question content into the generation AI, which generates the optimal answer. The generation AI model uses natural language processing technology.

[0964] Input: Parsed question content

[0965] Output: Generated answer text

[0966] Step 5: Server sends response

[0967] The server sends the generated response text to the device, again using the HTTP protocol and JSON format.

[0968] Input: Generated answer text

[0969] Output: Answer text sent to the terminal

[0970] Step 6: Displaying the Answer via Terminal

[0971] The terminal displays the received answer text on the screen and provides it to the user. Specifically, the answer is displayed in a text box.

[0972] Input: Answer text sent from the server

[0973] Output: Answer displayed on the device screen

[0974] Step 7: Storing the Questions and Answers in a Database

[0975] The server stores the questions and their answers in a database, which includes the question, the answer, the date and time of the question, and the date and time of the answer.

[0976] Input: Question and answer

[0977] Output: Questions and answers stored in a database

[0978] Step 8: Server Analysis of Question Log

[0979] The server periodically analyzes the database and identifies weak areas based on each student's question history, using machine learning algorithms and database queries.

[0980] Input: Question logs stored in the database

[0981] Output: Identified student weaknesses

[0982] Step 9: Emotion Recognition with the Emotion Engine

[0983] The device analyzes students' facial expressions and voices and uses an emotion engine to recognize their emotional state, for example identifying confusion or joy through the camera and microphone.

[0984] Input: Student facial expressions and voices from the camera and microphone

[0985] Output: Perceived student emotional state

[0986] Step 10: Server Adjustment of Response

[0987] The server analyzes the perceived emotional state and adjusts the response accordingly, for example by adding detailed explanations or using illustrations.

[0988] Input: Perceived emotional state

[0989] Output: Adjusted response content

[0990] Step 11: Automatic generation of personalized learning content

[0991] The server combines and analyzes question history and emotional data, and automatically generates appropriate learning content based on the student's weak areas.

[0992] Input: Question history and sentiment data

[0993] Output: Generated learning content

[0994] Step 12: Sending learning content from the server

[0995] The server sends the generated learning content to the device using the HTTP protocol, with the data sent in JSON format.

[0996] Input: Generated learning content

[0997] Output: Learning content sent to your device

[0998] Step 13: Viewing learning content on your device

[0999] The device displays the received learning content on the screen and provides it to the user, for example, by playing a video lecture or text content.

[1000] Input: Learning content sent from the server

[1001] Output: Learning content displayed on the device screen

[1002] Step 14: Server analyzes progress and generates feedback

[1003] The server analyzes the student's learning progress and generates appropriate feedback, such as a message saying, "You're making progress in understanding functions."

[1004] Input: Student progress data

[1005] Output: The generated feedback message

[1006] Step 15: Displaying feedback via terminal

[1007] The device displays feedback messages on the screen and provides them to the user, allowing the student to understand their own learning progress.

[1008] Input: Feedback message sent by the server

[1009] Output: Feedback displayed on the device screen

[1010] (Application example 2)

[1011] 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."

[1012] Conventional online teaching systems are required to not only provide answers to questions, but also to recognize the emotional state of each student and respond based on that state. Furthermore, they lack the ability to tailor responses based on emotional states and provide individual learning content for weak areas. As a result, students may feel stressed and their learning efficiency may decrease.

[1013] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1014] In this invention, the server includes means for receiving questions entered by students during online classes, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history and identifying weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for recognizing the emotional state of the students, and means for adjusting answers based on the recognized emotional state, thereby enabling flexible learning support tailored to the emotional state of each individual student.

[1015] "Online classes" are a form of education conducted via the Internet, allowing students to participate in classes from remote locations.

[1016] "Means for receiving questions" refers to the functionality of a device or software for obtaining text questions entered by students during online classes.

[1017] "Generative AI" is a technology for analyzing input question data and generating appropriate answers, such as a natural language processing model.

[1018] The "means for providing an answer" refers to the functionality of the device or software for presenting the generated answer to the student.

[1019] A "recording means" is a technique for saving questions and their answers in a format that can be analyzed later.

[1020] "Means for analyzing history" is a function that analyzes recorded question and answer data and extracts specific patterns.

[1021] "Means for identifying weak areas" refers to technology for identifying the content or areas in which a student is weak based on historical data.

[1022] "Means for automatically generating learning content" refers to a function that automatically creates appropriate educational materials and assignments based on students' weak areas.

[1023] "Means of provision" refers to the functions of the device or software used to present the generated learning content and answers to students.

[1024] The "means of recognizing emotional states" is a technology that uses cameras and microphones to analyze and recognize emotions from students' facial expressions and voices.

[1025] "Means for tailoring responses" refers to the ability to change the level of detail and content of generated responses based on the perceived emotional state.

[1026] The present invention is a distance learning video teaching system that uses both a generative AI and an emotion engine, and is implemented with the following configuration.

[1027] System configuration

[1028] The entire system consists of the following main components:

[1029] Device: The device used by students for online classes (computer, tablet, etc.)

[1030] Server: A computing device that processes queries and analyzes and stores data.

[1031] Generative AI: An artificial intelligence engine that analyzes questions and generates answers

[1032] Emotion Engine: An analytical engine for recognizing students' emotional states

[1033] System Program

[1034] Receiving and processing questions

[1035] During online classes, users (students) use their devices to input questions in text format. The input questions are received by the devices and sent to the server.

[1036] Answer generation and provision

[1037] The server analyzes the received question and generates the optimal answer using a generation AI. The generated answer is then sent from the server to the device, which displays the answer on the screen for the user.

[1038] Recording and analyzing questions and answers

[1039] The server stores the questions and answers in a database, which records the learning history.

[1040] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," the server identifies functions as a weak area.

[1041] Emotion recognition and response regulation

[1042] The device uses a camera and microphone to analyze the student's facial expressions and voice, and an emotion engine to recognize the user's emotional state. The server then adjusts the response based on the user's emotional state. For example, if the student is confused, it adds detailed explanations and illustrations. If the student is happy, it provides a concise answer.

[1043] Creation and provision of personalized learning content

[1044] The server combines and analyzes question history and emotional data to identify areas where students are weak or experiencing emotional stress. The server then automatically generates appropriate learning content based on this data. The generated learning content is then sent to the device, which displays it and provides it to the user.

[1045] Hardware and software used

[1046] Hardware: smartphone, tablet, computer, camera, microphone

[1047] software:

[1048] Generative AI: Natural language processing models such as GPT-3

[1049] Emotion engine: Analysis engines such as EmotionEngine

[1050] Database: Data management system such as PostgreSQL

[1051] Server: A web framework such as Flask

[1052] Specific examples

[1053] 1. A user (student) types, "I don't understand the basic concept of differentiation" during an online class.

[1054] 2. The device sends this question to the server.

[1055] 3. The server uses a generative AI to generate the answer: "The basic concept of differentiation is that it shows the instantaneous rate of change of a function."

[1056] 4. The emotion engine recognizes the user's confused facial expression through the camera and microphone.

[1057] 5. The server adds a more detailed response: "The derivative shows the rate of change of a function at each point."

[1058] 6. The device displays this detailed response and provides it to the user.

[1059] Example prompt sentence:

[1060] text

[1061] "Student typed question: I don't understand the basic concept of differentiation.

[1062] Emotion recognition result: Confused.

[1063] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[1064] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1065] Processing Steps

[1066] Step 1:

[1067] A user enters a question in text format during an online class.

[1068] Input: Students type in their questions in text format.

[1069] Action: The user enters a question using the device's input device (keyboard or touchscreen).

[1070] Output: The question text is saved on the device and sent to the server.

[1071] Step 2:

[1072] The terminal receives the entered question and transmits it to the server.

[1073] Input: The question text entered in step 1.

[1074] How it works: The device sends the question text to the server via an HTTP request.

[1075] Output: The question text arrives at the server and is queued for analysis.

[1076] Step 3:

[1077] The server analyzes the received question and uses a generative AI to generate the optimal answer.

[1078] Input: The question text stored on the server.

[1079] How it works: The server provides the generative AI model with question text and prompts to generate an answer. The generative AI model analyzes the meaning of the question and generates an appropriate answer.

[1080] Output: The generated answer text.

[1081] Step 4:

[1082] The generated answer is sent to the terminal, which displays the answer on the screen.

[1083] Input: The answer text generated by the server.

[1084] Operation: The server sends the answer text to the device as an HTTP response. The device displays the received answer on the screen.

[1085] Output: The answer text displayed on the terminal.

[1086] Step 5:

[1087] Store the questions and their answers in a database.

[1088] Input: Question text and answer text.

[1089] How it works: The server stores question and answer data pairs in a database.

[1090] Output: A new question and answer entry is saved in the database.

[1091] Step 6:

[1092] The server analyzes the question log and identifies areas in which students are weak.

[1093] Input: A history of questions and answers stored in a database.

[1094] How it works: The server uses data mining techniques to analyze the question log and find out whether questions in specific areas are common. An algorithm is then applied to identify areas of weakness.

[1095] Output: Identified weak areas.

[1096] Step 7:

[1097] Automatically generate learning content based on identified areas of weakness.

[1098] Input: Information about identified weaknesses.

[1099] How it works: The server inputs information about weak areas as prompts into the generative AI model, which then generates learning content based on that information. The generative AI model then generates appropriate video courses and text materials.

[1100] Output: The generated learning content.

[1101] Step 8:

[1102] The generated learning content is sent to the terminal, which displays it.

[1103] Input: Generated learning content.

[1104] Operation: The server sends the learning content to the device via HTTP response, and the device displays the received content on the screen.

[1105] Output: The learning content displayed on your device.

[1106] Step 9:

[1107] The device analyzes the student's facial expressions and voice to recognize their emotional state.

[1108] Input: Facial expression and vocal data captured by a camera or microphone.

[1109] How it works: The device uses an emotion engine to analyze facial expressions and voice to recognize the student's emotional state.

[1110] Output: Recognized emotional state data.

[1111] Step 10:

[1112] The server tailors the response based on the perceived emotional state.

[1113] Input: Recognized emotional state data and generated answer text.

[1114] How it works: The server regenerates or adjusts the answer text based on the emotional state, providing a tailored response, such as adding detailed explanations or illustrations.

[1115] Output: The adjusted answer text.

[1116] Example prompt sentence:

[1117] text

[1118] "Student typed question: I don't understand the basic concept of differentiation.

[1119] Emotion recognition result: Confused.

[1120] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[1121] 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.

[1122] 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.

[1123] 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.

[1124] [Third embodiment]

[1125] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1127] 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).

[1128] 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.

[1129] 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.

[1130] 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).

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] 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.

[1136] 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."

[1137] The present invention is a distance learning video lesson system that utilizes generative AI, and is implemented as follows.

[1138] System Overview

[1139] The overall system consists of the following main components:

[1140] Devices used by students (PCs, tablets, etc.)

[1141] Server that processes queries and analyzes and stores data

[1142] Generation AI

[1143] Program processing

[1144] Receiving student questions

[1145] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[1146] The terminal receives the input question and transmits the question data to the server.

[1147] Handling and answering questions

[1148] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[1149] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[1150] Accumulation and analysis of question logs

[1151] The server stores the questions and answers in a database, which records the learning history.

[1152] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student frequently asks questions about "functions," it will identify functions as an area in which the student is weak.

[1153] Automatic generation and provision of individual learning content

[1154] The server automatically generates appropriate learning content based on weak areas. For example, it generates a video course on the domain of functions.

[1155] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[1156] Providing progress feedback

[1157] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving."

[1158] The server transmits the feedback to the terminal, and the terminal displays the feedback to provide it to the user.

[1159] Specific examples

[1160] Student question and answer process

[1161] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[1162] The terminal sends this question to the server.

[1163] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[1164] The device will display this answer, allowing the user to solve the problem on the spot.

[1165] Identifying weak areas and providing content

[1166] Example: The server analyzes the question log and discovers that a particular student frequently asks questions about "functions."

[1167] The server automatically generates learning content such as a "video course on the domain of functions."

[1168] The server transmits this content to the terminal, which then plays the video lecture, allowing the user to deepen their learning in areas they are weak at.

[1169] In this way, the distance learning video lesson system of the present invention can effectively support students' learning and provide equal educational opportunities.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[1173] Step 2:

[1174] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[1175] Step 3:

[1176] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[1177] Step 4:

[1178] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[1179] Step 5:

[1180] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[1181] Step 6:

[1182] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[1183] Step 7:

[1184] The server stores the questions and their answers in a database, which records the question history.

[1185] Step 8:

[1186] The server periodically analyzes the question log, which identifies each student's weak areas.

[1187] Step 9:

[1188] The server automatically generates appropriate learning content based on the identified weak areas, for example, a video course on the domain of functions.

[1189] Step 10:

[1190] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[1191] Step 11:

[1192] The device will display new learning content, allowing users to focus on areas they are weak in.

[1193] Step 12:

[1194] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[1195] Step 13:

[1196] The server generates appropriate feedback, for example, "You're making progress in understanding functions."

[1197] Step 14:

[1198] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[1199] Step 15:

[1200] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[1201] Through these steps, the online video lesson system using generative AI will enable students to ask and answer questions in real time and provide individually customized learning content.

[1202] Example 1

[1203] 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."

[1204] Conventional online teaching systems lack the ability to not only quickly and accurately answer students' questions, but also to identify each student's weak areas and provide personalized learning content. This makes it difficult to continuously monitor each student's level of understanding and progress and provide appropriate feedback. In addition, there is a lack of efficient means to accumulate question history data and provide automatically generated learning content. This makes it difficult to improve students' learning efficiency and enhance the quality of education.

[1205] 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.

[1206] In this invention, the server includes: means for receiving questions entered by students during online classes; means for analyzing the received questions and using a generative AI model to generate optimal answers; means for providing the generated answers to the students; means for recording questions and their answers; means for analyzing the recorded question history to identify the student's weak areas; means for automatically generating learning content based on the identified weak areas; means for providing the generated learning content; means for generating video courses to address the identified weak areas; and means for using prompt sentences in the generative AI model when analyzing the questions entered by the students. This not only enables prompt and accurate answers to students' questions, but also identifies individual weak areas and automatically generates and provides appropriate learning content. Furthermore, by accumulating and analyzing the question history, it becomes possible to grasp the student's learning progress and provide appropriate feedback, thereby improving the student's learning efficiency and enhancing the quality of education.

[1207] "Online classes" are classes that students take via the internet.

[1208] "Student" refers to a user who takes an online class and enters questions.

[1209] "Question" refers to a text inquiry entered by a student during an online class.

[1210] "Devices" refer to computers and tablets used by students, and are devices used to take online classes and input and send questions.

[1211] A "server" is a central processing unit that receives student questions, performs analysis, and generates and provides answers.

[1212] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate optimal answers to input questions.

[1213] "Prompt sentence" refers to the input-style sentence that a generative AI model uses to generate an answer to a question.

[1214] "Answer" refers to the answer information generated by the generative AI model in response to a question.

[1215] "Question history" refers to a record of questions previously entered by students and the answers generated.

[1216] "Weak areas" refer to areas of learning that a student has difficulty understanding, identified based on an analysis of their question history.

[1217] "Learning content" refers to teaching materials and information created to strengthen students' knowledge in areas of weakness.

[1218] "Video Course" refers to educational material in video format that is produced to explain specific learning content.

[1219] "Feedback" refers to the assessments and comments provided to students based on their learning progress.

[1220] "Database" refers to a recording device for storing the history of questions and answers and its management system.

[1221] This invention is a distance learning video lesson system that utilizes a generative AI model to provide quick and accurate answers to questions entered by students during online lessons, identify areas in which each student is weak, and support their understanding.

[1222] The system uses the following major hardware and software components:

[1223] Device: Student's computer, tablet, or other device

[1224] Server: A central processing unit that processes queries and analyzes and stores data.

[1225] Generative AI models: Algorithms and programs used to analyze questions and generate answers (e.g., GPT-4)

[1226] During online classes, users (students) enter questions in text format about points they do not understand. For example, they enter a question such as, "I don't understand the domain of a function." The device receives this question and sends it to the server using a format and protocol (e.g., HTTP, WebSocket).

[1227] The server receives the user's question and analyzes its contents. At this time, the question text is input as a prompt to the generative AI model, which generates the optimal answer. For example, it generates an answer such as "The domain of a function is the range of values ​​that are input to the function." The server sends the generated answer to the device, which displays it on the screen.

[1228] The server stores the user's questions and the generated answers in a database. This keeps a history of all questions and answers. At regular intervals, the server analyzes the log data and identifies the student's weak areas. For example, if a student asks many questions about "functions" multiple times, it will determine that functions are a weak area.

[1229] Based on the identified weak areas, the server uses a generative AI model to generate appropriate learning content. For example, it automatically generates a video lecture on the domain of functions. The server then sends the generated content to the device, which then displays it. This allows students to deepen their understanding of their weak areas.

[1230] The server also analyzes learning progress data and generates appropriate feedback. For example, it may generate feedback such as, "Your understanding of functions is improving." The server then sends the generated feedback to the device, which then displays it on the screen. This allows students to check their own learning progress and motivates them to continue learning.

[1231] Specific examples

[1232] Question and answer generation

[1233] If a user asks during an online class, "I don't know how to graph a linear function," the device sends this question to the server. The server uses a generative AI model to generate an answer: "The graph of a linear function has the form y = mx + b, where m is the slope and b is the y-intercept." The device displays this answer, allowing the user to solve the problem on the spot.

[1234] Identifying weak areas and providing content

[1235] If the server analyzes the question log and finds that a particular student frequently asks questions about "functions," it automatically generates learning content such as a "video lecture on the domain of functions." The server sends this content to the device, which then plays the video lecture, allowing the user to deepen their understanding of their weak areas.

[1236] In this way, the system of the present invention improves the quality of education by providing appropriate answers to students' questions and personalized learning support.

[1237] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1238] Step 1:

[1239] During an online class, a user enters a question in text format. For example, they enter a question like, "I don't understand the domain of a function." The device receives this input and sends it to the server using a format and protocol (such as HTTP or WebSocket).

[1240] Input: The question text entered by the user

[1241] Output: The question data sent to the server

[1242] Specific behavior:

[1243] The user uses a web browser or dedicated application to enter a question into a text input field on the interface and press the submit button.

[1244] Step 2:

[1245] The server analyzes the received question. It inputs the question text as a prompt into a generative AI model (e.g., GPT-4) and generates the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[1246] Input: Query data sent to the server

[1247] Output: Answer text generated by the generative AI model

[1248] Specific behavior:

[1249] When the server receives the question text, it accesses the API of the generation AI, sends a prompt, and receives the generated answer.

[1250] Step 3:

[1251] The server sends the generated answer to the terminal, which then displays the answer on its screen, allowing the user to obtain the answer to their question.

[1252] Input: Answer text generated by the generative AI model

[1253] Output: Answer information displayed on the terminal

[1254] Specific behavior:

[1255] The server generates an answer and sends it back to the device as an HTTP response, which the device parses and displays in a browser or application.

[1256] Step 4:

[1257] The server stores the user's questions and generated answers in a database, thus maintaining a history of all questions and answers.

[1258] Input: Question text and generated answer text

[1259] Output: Question and answer history stored in a database

[1260] Specific behavior:

[1261] The server stores the questions and answers in a database such as MySQL or PostgreSQL using an INSERT query.

[1262] Step 5:

[1263] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student repeatedly asks questions about "functions," it will determine that functions are a weak area.

[1264] Input: A history of questions and answers stored in a database

[1265] Output: Information on identified weaknesses

[1266] Specific behavior:

[1267] Scheduled tasks and periodic batch processes are run to aggregate and analyze database logs and identify weak areas based on specific keywords and frequencies.

[1268] Step 6:

[1269] Based on the weak areas identified by the server, appropriate learning content is generated using generative AI. For example, a video course on the domain of functions is automatically generated.

[1270] Input: Information about identified weaknesses

[1271] Output: Generated learning content

[1272] Specific behavior:

[1273] Use generative AI to generate text content and quiz questions, and optionally integrate with video generation tools (e.g., FFmpeg) to generate video content.

[1274] Step 7:

[1275] The server sends the generated learning content to the device, which then displays it, allowing the user to deepen their understanding of weak areas.

[1276] Input: Generated learning content

[1277] Output: Learning content displayed on the device

[1278] Specific behavior:

[1279] The generated content is delivered to the terminal in the form of HTTP streaming or file download, and the terminal displays it.

[1280] Step 8:

[1281] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving," which is sent to the device and displayed.

[1282] Input: Learning progress data

[1283] Output: Generated feedback

[1284] Specific behavior:

[1285] The server analyzes the learning progress data and generates feedback messages using a generation AI. The generated feedback is sent to the device in JSON format, and the device formats it for display on the screen and presents it to the user.

[1286] (Application example 1)

[1287] 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."

[1288] Today's students have a strong desire to make effective use of their time, such as while traveling, for studying. However, conventional online class systems are mainly used in fixed locations, and do not provide sufficient support for learning while traveling. There is also a lack of systems that can quickly answer questions students have during online classes and provide learning content specialized for weak areas. For this reason, there is a need to develop a system that can effectively support learning even while traveling.

[1289] 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.

[1290] In this invention, the server includes means for receiving questions entered by students during online classes, means using artificial intelligence to analyze the received questions and generate optimal answers, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history to identify weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for supporting the students' learning through a terminal installed in the autonomous vehicle, and means for analyzing questions entered by students during learning in real time and providing answers, thereby enabling effective support for students' learning even while on the move.

[1291] "Means for receiving questions entered by students during online classes" refers to a system for receiving questions sent from the devices used by students.

[1292] "Methods using generative artificial intelligence to analyze received questions and generate optimal answers" refers to a method that uses generative AI to understand the content of questions and generate appropriate answers.

[1293] The "means for providing the generated answers to the students" is a mechanism for displaying the generated answers on the students' terminals.

[1294] The "means for recording questions and their answers" is a mechanism for storing received questions and their answers in a database.

[1295] The "means of analyzing the recorded question history to identify areas in which students are weak" is a method of analyzing saved question and answer data to identify areas in which students are weak.

[1296] "Means for automatically generating learning content based on identified areas of weakness" refers to a method for using generative AI to create appropriate learning materials tailored to areas of weakness.

[1297] The "means for providing the generated learning content" is a mechanism for transmitting the generated learning materials to the student's terminal and displaying them.

[1298] "Means to support students' learning through terminals installed inside self-driving vehicles" refers to a system that utilizes terminals installed inside self-driving vehicles to enable students to study while on the move.

[1299] "Means for analyzing questions entered by students in real time while studying and providing answers" refers to a method for instantly analyzing questions entered in real time while studying and generating and providing appropriate answers.

[1300] This invention is a system that allows students to study effectively even while on the move. This system provides the functionality to analyze questions entered during online classes and generate appropriate answers. Furthermore, it can analyze students' learning histories and automatically generate individual learning content. To achieve this, the following specific components and processes are required.

[1301] System configuration

[1302] The overall system consists of the following main components:

[1303] Devices used by students (computers, tablets, devices installed in autonomous vehicles, etc.)

[1304] Server (query processing, data analysis and storage)

[1305] Generative AI (understands the meaning of the question and generates the best answer)

[1306] Hardware and Software

[1307] 1. Terminal

[1308] A terminal is a device used by a student, including a computer, tablet, or learning tablet installed in an autonomous vehicle. The terminal is connected to the internet and has an interface for entering questions and a display for viewing the generated answers.

[1309] 2. Server

[1310] The server is a central management system that receives questions, analyzes them, generates answers, manages the database, etc. This server is operated on a cloud basis and has various software installed for query processing and automatic generation of learning content.

[1311] 3. Generation AI

[1312] The generative AI running on the server analyzes the meaning of the question based on natural language processing and generates appropriate answers. The AI ​​models used include GPT-3 and similar advanced generative AI.

[1313] Program processing

[1314] 1. Receiving Questions

[1315] When a student enters a question in text format during an online class, the device sends the question to the server.

[1316] 2. Question Analysis and Answer Generation

[1317] The server sends the received question to the AI ​​generator, which then generates the best answer. At this time, the AI ​​is given the following prompt as input:

[1318] Q: I don't know the domain of a function.

[1319] A:

[1320] The AI ​​generates the following response to this prompt:

[1321] The domain of a function is the range of values ​​that are input to the function.

[1322] 3. Providing and recording responses

[1323] The server sends the generated answers to the terminal, which displays them to the student, and simultaneously stores the questions and answers in a database.

[1324] Specific examples

[1325] Consider the case where a user (student) asks a question during an online class, "I don't know how to graph a linear function." At this time, the device receives the question and sends it to the server. The server uses generative AI to generate the following answer:

[1326] The graph of a linear function is of the form y = mx + b, where m is the slope and b is the y-intercept.

[1327] As soon as this answer is generated, it is sent to the device and displayed to the student.

[1328] This system can also automatically generate and provide learning content based on identified areas of weakness. For example, if the server identifies a student's weak area as "functions," it can generate a video course on the domain of functions, send it to the student's device, and display it to the student. In this way, it is possible to effectively support students' learning even while they are on the move.

[1329] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1330] Step 1:

[1331] The terminal receives questions from students. When a student enters a question in text format into the terminal, the question is sent to the terminal's receiving interface. For example, the text may be "I don't understand the domain of a function."

[1332] Input: Student question text

[1333] Output: Question data received by the device

[1334] Step 2:

[1335] The terminal sends the received query to the server. The query data is sent to the server via the network. For example, the following data is sent:

[1336] Q: I don't know the domain of a function.

[1337] Input: Received question data

[1338] Output: The query data sent to the server

[1339] Step 3:

[1340] The server sends the received question to the generation AI. The received question data is input into the generation AI model to construct a prompt sentence. For example, the following prompt sentence is generated:

[1341] Q: I don't know the domain of a function.

[1342] A:

[1343] Input: Received question data

[1344] Output: The prompt sent to the generation AI

[1345] Step 4:

[1346] The generation AI analyzes the prompt and generates the most appropriate answer. The generation AI uses natural language processing technology to understand the content of the question and generate an appropriate answer. For example, the following answer may be generated:

[1347] The domain of a function is the range of values ​​that are input to the function.

[1348] Input: prompt statement

[1349] Output: Generated answer text

[1350] Step 5:

[1351] The server sends the generated response to the terminal. The generated response data is returned to the terminal. For example, the following data is sent:

[1352] Answer: The domain of a function is the range of values ​​that are input to the function.

[1353] Input: Generated answer text

[1354] Output: Response data sent to the device

[1355] Step 6:

[1356] The device receives the answer and displays it to the student. The generated answer is displayed on the device's display. For example, the following text might be displayed:

[1357] The domain of a function is the range of values ​​that are input to the function.

[1358] Input: Response data from the server

[1359] Output: Answer text displayed on the screen

[1360] Step 7:

[1361] The server records the question and answer in a database. The question and its answer are stored in a database. For example, the following records are stored:

[1362] Q: I don't know the domain of a function.

[1363] Answer: The domain of a function is the range of values ​​that are input to the function.

[1364] Input: Question and answer data

[1365] Output: Records saved in the database

[1366] Step 8:

[1367] The server periodically analyzes the question history to identify areas where students are weak. The stored data is analyzed to identify frequently asked question categories. For example, if there are many questions about "functions," it will identify that area as a weak area.

[1368] Input: Question history stored in the database

[1369] Output: Identified weak areas

[1370] Step 9:

[1371] The server automatically generates learning content based on weak areas. Generative AI is used to create appropriate learning content (e.g., video courses). For example, the following video courses are generated:

[1372] Video content: Detailed explanation of the domain of a function

[1373] Input: Identified weak areas information

[1374] Output: Generated learning content

[1375] Step 10:

[1376] The server transmits the generated learning content to the terminal, which provides it to the student. The server transmits the generated learning content to the terminal, which plays the content and displays it to the student.

[1377] Input: Generated learning content

[1378] Output: Learning content sent to the device, learning content played on the device

[1379] The above are the specific processing steps for carrying out the present invention.

[1380] 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.

[1381] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[1382] System Overview

[1383] The entire system consists of the following main components:

[1384] Devices used by students (PCs, tablets, etc.)

[1385] Server that processes queries and analyzes and stores data

[1386] Generation AI

[1387] Emotion Engine

[1388] Program processing

[1389] Receiving student questions

[1390] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[1391] The terminal receives the input question and transmits the question data to the server.

[1392] Handling and answering questions

[1393] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[1394] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[1395] Accumulation and analysis of question logs

[1396] The server stores the questions and answers in a database, which records the learning history.

[1397] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," it identifies functions as a weak area.

[1398] Emotion recognition and response using emotion engine

[1399] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize the user's emotional state. For example, it can determine whether the student is confused or happy through the camera and microphone.

[1400] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[1401] Automatic generation and provision of individual learning content based on weak areas and emotional data

[1402] The server combines and analyzes question history and emotional data to identify areas in which students are weak and areas that cause them emotional stress.

[1403] The server automatically generates appropriate learning content based on the identified weaknesses, such as a video course on the domain of functions or emotional support.

[1404] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[1405] Providing progress feedback

[1406] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[1407] The server transmits the feedback to the terminal, and the terminal displays the feedback on the screen to provide it to the user.

[1408] Specific examples

[1409] Student question and answer process

[1410] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[1411] The terminal sends this question to the server.

[1412] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[1413] The device will display this answer, allowing the user to solve the problem on the spot.

[1414] Emotion recognition and response adjustment

[1415] Example: A user asks with a confused look, "I still don't understand the domain of a function."

[1416] The device uses an emotion engine to recognize confusion and transmits this information to the server.

[1417] The server generates an answer with detailed diagrams and additional explanations, specifically answering, "The domain of a function indicates the range of values ​​of x. For example, the function f(x) = √x has x ≥ 0."

[1418] The terminal displays this detailed response and provides it to the user.

[1419] Identifying weak areas and utilizing emotional data

[1420] Example: The server analyzes the question log and sentiment data and discovers that users often ask questions about functions, but that they express confusion and tension each time.

[1421] The server automatically generates learning content including a "video lecture on the domain of functions" and emotional support messages.

[1422] The server sends this content to the terminal, which displays it and provides it to the user.

[1423] The processing flow will be explained below.

[1424] Step 1:

[1425] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[1426] Step 2:

[1427] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[1428] Step 3:

[1429] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[1430] Step 4:

[1431] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[1432] Step 5:

[1433] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[1434] Step 6:

[1435] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[1436] Step 7:

[1437] The server stores the questions and their answers in a database, which records the question history.

[1438] Step 8:

[1439] The server periodically analyzes the question log, which identifies each student's weak areas.

[1440] Step 9:

[1441] The device analyzes the user's facial expressions and voice through an emotion engine to recognize the user's emotional state. For example, it can determine whether a student is confused or happy through a camera or microphone.

[1442] Step 10:

[1443] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[1444] Step 11:

[1445] The server automatically generates appropriate learning content based on the identified weak areas, such as a video course on the domain of functions.

[1446] Step 12:

[1447] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[1448] Step 13:

[1449] The device will display new learning content, allowing users to focus on areas they are weak in.

[1450] Step 14:

[1451] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[1452] Step 15:

[1453] The server generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[1454] Step 16:

[1455] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[1456] Step 17:

[1457] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[1458] Through these steps, the online video lesson system using generative AI and an emotion engine will enable students to answer questions in real time, provide individually customized learning content, and provide emotional support.

[1459] Example 2

[1460] 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."

[1461] With conventional online class systems, it was difficult to provide quick and accurate answers to questions entered by students, and it was also difficult to properly grasp students' learning progress and provide individual support. Furthermore, because it was not possible to respond to students' emotional states, there were problems such as not being able to provide appropriate support when students felt confused or stressed. This limited the effectiveness of online classes and created the issue of not being able to fully motivate students to learn.

[1462] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students, means for transmitting the received questions to the server, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for saving the questions and their answers in a database, means for analyzing the saved question history and identifying the student's weak areas, means for analyzing the student's facial expressions and voice tone on the terminal and recognizing the student's emotional state using an emotion engine, means for adjusting the response content based on the student's emotional state, means for automatically generating learning content based on the identified weak areas, and means for providing the generated learning content. This enables quick and accurate answers to questions entered by students and allows individualized instruction that takes into account the student's learning progress and emotional state, thereby maximizing the effectiveness of online classes.

[1463] A "server" is a computer system that receives requests from clients via a network, processes the requests, and returns the results to the clients.

[1464] A "terminal" is a device that is connected to a network and that a user operates to input and display information, and specifically refers to a personal computer or tablet.

[1465] "Student" refers to a learner who takes an online class and inputs questions through the system.

[1466] "Question data" refers to text data containing questions entered by students during online classes.

[1467] "Generative AI" refers to an algorithm or system that uses natural language processing technology to analyze input questions and generate optimal answers.

[1468] A "database" is a data structure or system that stores data in an organized manner and allows efficient searching and manipulation of that data.

[1469] "Question history" refers to a collection of data that records questions that students have previously asked through the system and the answers to those questions.

[1470] "Weak areas" refer to areas of the learning content that students repeatedly ask about but find difficult to understand.

[1471] An "emotion engine" is an algorithm or system that analyzes and recognizes a user's emotional state based on input information such as the user's facial expressions and voice.

[1472] The "emotional state" refers to the emotion the user is currently feeling, and indicates a psychological state such as confusion or joy.

[1473] "Learning Content" means learning materials in digital form, including teaching materials, lectures, video courses, etc., provided for educational purposes.

[1474] "Feedback" refers to responses such as encouragement, advice, and other messages that are generated based on a student's learning progress.

[1475] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[1476] System Overview

[1477] The entire system consists of the following main components:

[1478] Devices used by students (PCs, tablets, etc.)

[1479] Server that processes queries and analyzes and stores data

[1480] Generation AI

[1481] Emotion Engine

[1482] System Operation

[1483] Receiving student questions

[1484] During online classes, users (students) enter questions in text format. For example, they might enter a question like, "I don't know how to draw the graph of a linear function." This question data is received by the device and sent to the server. The commonly used HTTP protocol is used for communication.

[1485] Handling and answering questions

[1486] The server analyzes the received question data. Specifically, it analyzes the received data in JSON format and extracts the question content. Next, the question content is input into the generation AI, which generates the optimal answer. A high-performance natural language processing model is used as the generation AI model. For example, it generates an answer such as, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The server sends the generated answer to the device, which displays it on the screen and provides it to the user.

[1487] Accumulation and analysis of question logs

[1488] The server stores questions and their answers in a database. Specifically, it records information such as the question content, answer content, question date and time, and answer date and time. Periodically, the server analyzes the question log to identify each student's weak areas. This analysis is performed using database queries and machine learning algorithms. For example, if a student repeatedly asks questions about "functions," it can determine that the student is weak in functions.

[1489] Emotion recognition and response using emotion engine

[1490] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize their emotional state. For example, it can identify a student's confusion, joy, or nervousness through a camera or microphone. The server then analyzes the student's emotional state and adjusts the response accordingly. For example, if the student is confused, it will generate a response with more detailed explanations and illustrations and send it back to the device.

[1491] Automatic generation and provision of individual learning content based on weak areas and emotional data

[1492] The server combines and analyzes the question history and emotional data to identify areas where the student is weak or stressed. Based on the identified areas of weakness, the server automatically generates appropriate learning content. For example, it might create a "video course on the domain of functions." The generated learning content is then sent back to the device and provided to the user.

[1493] Providing progress feedback

[1494] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions" or "You seem a little confused, but keep trying." This feedback is sent to the device, which displays it on the screen for the user.

[1495] Specific operation example

[1496] During an online class, a user might type a question like, "I don't know how to graph a linear function." The device then sends the question to the server. The server uses generative AI to generate an answer, saying, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The device then displays this answer, allowing the user to solve the problem on the spot. Similarly, if a user asks with a confused expression, "I still don't understand the domain of a function," the device uses its emotion engine to recognize the confusion and sends this information to the server. The server then generates an answer with detailed diagrams and additional explanations, specifically stating, "The domain of a function indicates the extent to which the value of x extends. For example, for the function f(x)=√x, x≥0." The device then displays this detailed answer and provides it to the user.

[1497] This system will enable quick and accurate resolution of problems students encounter during online classes, and will also enable optimal support tailored to individual learning situations.

[1498] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1499] Step 1: Student Question Input

[1500] During an online class, a user (student) inputs a question in text format. For example, a question might be, "I don't know how to draw a graph of a linear function." This input is received by the terminal.

[1501] Input: Student entered text question

[1502] Output: Question data saved on the device

[1503] Step 2: Sending query data via device

[1504] The device sends the received question data to the server using the HTTP protocol, and the data is sent in JSON format.

[1505] Input: Question data saved on the device

[1506] Output: The query data sent to the server

[1507] Step 3: Query data received and parsed by the server

[1508] The server analyzes the received question data. Specifically, it analyzes the received JSON data and extracts the question content.

[1509] Input: JSON-formatted question data sent from the terminal

[1510] Output: Parsed question

[1511] Step 4: Generative AI generates answers

[1512] The server inputs the analyzed question content into the generation AI, which generates the optimal answer. The generation AI model uses natural language processing technology.

[1513] Input: Parsed question content

[1514] Output: Generated answer text

[1515] Step 5: Server sends response

[1516] The server sends the generated response text to the device, again using the HTTP protocol and JSON format.

[1517] Input: Generated answer text

[1518] Output: Answer text sent to the terminal

[1519] Step 6: Displaying the Answer via Terminal

[1520] The terminal displays the received answer text on the screen and provides it to the user. Specifically, the answer is displayed in a text box.

[1521] Input: Answer text sent from the server

[1522] Output: Answer displayed on the device screen

[1523] Step 7: Storing the Questions and Answers in a Database

[1524] The server stores the questions and their answers in a database, which includes the question, the answer, the date and time of the question, and the date and time of the answer.

[1525] Input: Question and answer

[1526] Output: Questions and answers stored in a database

[1527] Step 8: Server Analysis of Question Log

[1528] The server periodically analyzes the database and identifies weak areas based on each student's question history, using machine learning algorithms and database queries.

[1529] Input: Question logs stored in the database

[1530] Output: Identified student weaknesses

[1531] Step 9: Emotion Recognition with the Emotion Engine

[1532] The device analyzes students' facial expressions and voices and uses an emotion engine to recognize their emotional state, for example identifying confusion or joy through the camera and microphone.

[1533] Input: Student facial expressions and voices from the camera and microphone

[1534] Output: Perceived student emotional state

[1535] Step 10: Server Adjustment of Response

[1536] The server analyzes the perceived emotional state and adjusts the response accordingly, for example by adding detailed explanations or using illustrations.

[1537] Input: Perceived emotional state

[1538] Output: Adjusted response content

[1539] Step 11: Automatic generation of personalized learning content

[1540] The server combines and analyzes question history and emotional data, and automatically generates appropriate learning content based on the student's weak areas.

[1541] Input: Question history and sentiment data

[1542] Output: Generated learning content

[1543] Step 12: Sending learning content from the server

[1544] The server sends the generated learning content to the device using the HTTP protocol, with the data sent in JSON format.

[1545] Input: Generated learning content

[1546] Output: Learning content sent to your device

[1547] Step 13: Viewing learning content on your device

[1548] The device displays the received learning content on the screen and provides it to the user, for example, by playing a video lecture or text content.

[1549] Input: Learning content sent from the server

[1550] Output: Learning content displayed on the device screen

[1551] Step 14: Server analyzes progress and generates feedback

[1552] The server analyzes the student's learning progress and generates appropriate feedback, such as a message saying, "You're making progress in understanding functions."

[1553] Input: Student progress data

[1554] Output: The generated feedback message

[1555] Step 15: Displaying feedback via terminal

[1556] The device displays feedback messages on the screen and provides them to the user, allowing the student to understand their own learning progress.

[1557] Input: Feedback message sent by the server

[1558] Output: Feedback displayed on the device screen

[1559] (Application example 2)

[1560] 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."

[1561] Conventional online teaching systems are required to not only provide answers to questions, but also to recognize the emotional state of each student and respond based on that state. Furthermore, they lack the ability to tailor responses based on emotional states and provide individual learning content for weak areas. As a result, students may feel stressed and their learning efficiency may decrease.

[1562] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1563] In this invention, the server includes means for receiving questions entered by students during online classes, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history and identifying weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for recognizing the emotional state of the students, and means for adjusting answers based on the recognized emotional state, thereby enabling flexible learning support tailored to the emotional state of each individual student.

[1564] "Online classes" are a form of education conducted via the Internet, allowing students to participate in classes from remote locations.

[1565] "Means for receiving questions" refers to the functionality of a device or software for obtaining text questions entered by students during online classes.

[1566] "Generative AI" is a technology for analyzing input question data and generating appropriate answers, such as a natural language processing model.

[1567] The "means for providing an answer" refers to the functionality of the device or software for presenting the generated answer to the student.

[1568] A "recording means" is a technique for saving questions and their answers in a format that can be analyzed later.

[1569] "Means for analyzing history" is a function that analyzes recorded question and answer data and extracts specific patterns.

[1570] "Means for identifying weak areas" refers to technology for identifying the content or areas in which a student is weak based on historical data.

[1571] "Means for automatically generating learning content" refers to a function that automatically creates appropriate educational materials and assignments based on students' weak areas.

[1572] "Means of provision" refers to the functions of the device or software used to present the generated learning content and answers to students.

[1573] The "means of recognizing emotional states" is a technology that uses cameras and microphones to analyze and recognize emotions from students' facial expressions and voices.

[1574] "Means for tailoring responses" refers to the ability to change the level of detail and content of generated responses based on the perceived emotional state.

[1575] The present invention is a distance learning video teaching system that uses both a generative AI and an emotion engine, and is implemented with the following configuration.

[1576] System configuration

[1577] The entire system consists of the following main components:

[1578] Device: The device used by students for online classes (computer, tablet, etc.)

[1579] Server: A computing device that processes queries and analyzes and stores data.

[1580] Generative AI: An artificial intelligence engine that analyzes questions and generates answers

[1581] Emotion Engine: An analytical engine for recognizing students' emotional states

[1582] System Program

[1583] Receiving and processing questions

[1584] During online classes, users (students) use their devices to input questions in text format. The input questions are received by the devices and sent to the server.

[1585] Answer generation and provision

[1586] The server analyzes the received question and generates the optimal answer using a generation AI. The generated answer is then sent from the server to the device, which displays the answer on the screen for the user.

[1587] Recording and analyzing questions and answers

[1588] The server stores the questions and answers in a database, which records the learning history.

[1589] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," the server identifies functions as a weak area.

[1590] Emotion recognition and response regulation

[1591] The device uses a camera and microphone to analyze the student's facial expressions and voice, and an emotion engine to recognize the user's emotional state. The server then adjusts the response based on the user's emotional state. For example, if the student is confused, it adds detailed explanations and illustrations. If the student is happy, it provides a concise answer.

[1592] Creation and provision of personalized learning content

[1593] The server combines and analyzes question history and emotional data to identify areas where students are weak or experiencing emotional stress. The server then automatically generates appropriate learning content based on this data. The generated learning content is then sent to the device, which displays it and provides it to the user.

[1594] Hardware and software used

[1595] Hardware: smartphone, tablet, computer, camera, microphone

[1596] software:

[1597] Generative AI: Natural language processing models such as GPT-3

[1598] Emotion engine: Analysis engines such as EmotionEngine

[1599] Database: Data management system such as PostgreSQL

[1600] Server: A web framework such as Flask

[1601] Specific examples

[1602] 1. A user (student) types, "I don't understand the basic concept of differentiation" during an online class.

[1603] 2. The device sends this question to the server.

[1604] 3. The server uses a generative AI to generate the answer: "The basic concept of differentiation is that it shows the instantaneous rate of change of a function."

[1605] 4. The emotion engine recognizes the user's confused facial expression through the camera and microphone.

[1606] 5. The server adds a more detailed response: "The derivative shows the rate of change of a function at each point."

[1607] 6. The device displays this detailed response and provides it to the user.

[1608] Example prompt sentence:

[1609] text

[1610] "Student typed question: I don't understand the basic concept of differentiation.

[1611] Emotion recognition result: Confused.

[1612] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[1613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1614] Processing Steps

[1615] Step 1:

[1616] A user enters a question in text format during an online class.

[1617] Input: Students type in their questions in text format.

[1618] Action: The user enters a question using the device's input device (keyboard or touchscreen).

[1619] Output: The question text is saved on the device and sent to the server.

[1620] Step 2:

[1621] The terminal receives the entered question and transmits it to the server.

[1622] Input: The question text entered in step 1.

[1623] How it works: The device sends the question text to the server via an HTTP request.

[1624] Output: The question text arrives at the server and is queued for analysis.

[1625] Step 3:

[1626] The server analyzes the received question and uses a generative AI to generate the optimal answer.

[1627] Input: The question text stored on the server.

[1628] How it works: The server provides the generative AI model with question text and prompts to generate an answer. The generative AI model analyzes the meaning of the question and generates an appropriate answer.

[1629] Output: The generated answer text.

[1630] Step 4:

[1631] The generated answer is sent to the terminal, which displays the answer on the screen.

[1632] Input: The answer text generated by the server.

[1633] Operation: The server sends the answer text to the device as an HTTP response. The device displays the received answer on the screen.

[1634] Output: The answer text displayed on the terminal.

[1635] Step 5:

[1636] Store the questions and their answers in a database.

[1637] Input: Question text and answer text.

[1638] How it works: The server stores question and answer data pairs in a database.

[1639] Output: A new question and answer entry is saved in the database.

[1640] Step 6:

[1641] The server analyzes the question log and identifies areas in which students are weak.

[1642] Input: A history of questions and answers stored in a database.

[1643] How it works: The server uses data mining techniques to analyze the question log and find out whether questions in specific areas are common. An algorithm is then applied to identify areas of weakness.

[1644] Output: Identified weak areas.

[1645] Step 7:

[1646] Automatically generate learning content based on identified areas of weakness.

[1647] Input: Information about identified weaknesses.

[1648] How it works: The server inputs information about weak areas as prompts into the generative AI model, which then generates learning content based on that information. The generative AI model then generates appropriate video courses and text materials.

[1649] Output: The generated learning content.

[1650] Step 8:

[1651] The generated learning content is sent to the terminal, which displays it.

[1652] Input: Generated learning content.

[1653] Operation: The server sends the learning content to the device via HTTP response, and the device displays the received content on the screen.

[1654] Output: The learning content displayed on your device.

[1655] Step 9:

[1656] The device analyzes the student's facial expressions and voice to recognize their emotional state.

[1657] Input: Facial expression and vocal data captured by a camera or microphone.

[1658] How it works: The device uses an emotion engine to analyze facial expressions and voice to recognize the student's emotional state.

[1659] Output: Recognized emotional state data.

[1660] Step 10:

[1661] The server tailors the response based on the perceived emotional state.

[1662] Input: Recognized emotional state data and generated answer text.

[1663] How it works: The server regenerates or adjusts the answer text based on the emotional state, providing a tailored response, such as adding detailed explanations or illustrations.

[1664] Output: The adjusted answer text.

[1665] Example prompt sentence:

[1666] text

[1667] "Student typed question: I don't understand the basic concept of differentiation.

[1668] Emotion recognition result: Confused.

[1669] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] [Fourth embodiment]

[1674] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1675] 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.

[1676] 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).

[1677] 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.

[1678] 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.

[1679] 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).

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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."

[1687] The present invention is a distance learning video lesson system that utilizes generative AI, and is implemented as follows.

[1688] System Overview

[1689] The overall system consists of the following main components:

[1690] Devices used by students (PCs, tablets, etc.)

[1691] Server that processes queries and analyzes and stores data

[1692] Generation AI

[1693] Program processing

[1694] Receiving student questions

[1695] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[1696] The terminal receives the input question and transmits the question data to the server.

[1697] Handling and answering questions

[1698] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[1699] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[1700] Accumulation and analysis of question logs

[1701] The server stores the questions and answers in a database, which records the learning history.

[1702] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student frequently asks questions about "functions," it will identify functions as an area in which the student is weak.

[1703] Automatic generation and provision of individual learning content

[1704] The server automatically generates appropriate learning content based on weak areas. For example, it generates a video course on the domain of functions.

[1705] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[1706] Providing progress feedback

[1707] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving."

[1708] The server transmits the feedback to the terminal, and the terminal displays the feedback to provide it to the user.

[1709] Specific examples

[1710] Student question and answer process

[1711] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[1712] The terminal sends this question to the server.

[1713] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[1714] The device will display this answer, allowing the user to solve the problem on the spot.

[1715] Identifying weak areas and providing content

[1716] Example: The server analyzes the question log and discovers that a particular student frequently asks questions about "functions."

[1717] The server automatically generates learning content such as a "video course on the domain of functions."

[1718] The server transmits this content to the terminal, which then plays the video lecture, allowing the user to deepen their learning in areas they are weak at.

[1719] In this way, the distance learning video lesson system of the present invention can effectively support students' learning and provide equal educational opportunities.

[1720] The processing flow will be explained below.

[1721] Step 1:

[1722] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[1723] Step 2:

[1724] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[1725] Step 3:

[1726] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[1727] Step 4:

[1728] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[1729] Step 5:

[1730] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[1731] Step 6:

[1732] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[1733] Step 7:

[1734] The server stores the questions and their answers in a database, which records the question history.

[1735] Step 8:

[1736] The server periodically analyzes the question log, which identifies each student's weak areas.

[1737] Step 9:

[1738] The server automatically generates appropriate learning content based on the identified weak areas, for example, a video course on the domain of functions.

[1739] Step 10:

[1740] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[1741] Step 11:

[1742] The device will display new learning content, allowing users to focus on areas they are weak in.

[1743] Step 12:

[1744] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[1745] Step 13:

[1746] The server generates appropriate feedback, for example, "You're making progress in understanding functions."

[1747] Step 14:

[1748] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[1749] Step 15:

[1750] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[1751] Through these steps, the online video lesson system using generative AI will enable students to ask and answer questions in real time and provide individually customized learning content.

[1752] Example 1

[1753] 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."

[1754] Conventional online teaching systems lack the ability to not only quickly and accurately answer students' questions, but also to identify each student's weak areas and provide personalized learning content. This makes it difficult to continuously monitor each student's level of understanding and progress and provide appropriate feedback. In addition, there is a lack of efficient means to accumulate question history data and provide automatically generated learning content. This makes it difficult to improve students' learning efficiency and enhance the quality of education.

[1755] 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.

[1756] In this invention, the server includes: means for receiving questions entered by students during online classes; means for analyzing the received questions and using a generative AI model to generate optimal answers; means for providing the generated answers to the students; means for recording questions and their answers; means for analyzing the recorded question history to identify the student's weak areas; means for automatically generating learning content based on the identified weak areas; means for providing the generated learning content; means for generating video courses to address the identified weak areas; and means for using prompt sentences in the generative AI model when analyzing the questions entered by the students. This not only enables prompt and accurate answers to students' questions, but also identifies individual weak areas and automatically generates and provides appropriate learning content. Furthermore, by accumulating and analyzing the question history, it becomes possible to grasp the student's learning progress and provide appropriate feedback, thereby improving the student's learning efficiency and enhancing the quality of education.

[1757] "Online classes" are classes that students take via the internet.

[1758] "Student" refers to a user who takes an online class and enters questions.

[1759] "Question" refers to a text inquiry entered by a student during an online class.

[1760] "Devices" refer to computers and tablets used by students, and are devices used to take online classes and input and send questions.

[1761] A "server" is a central processing unit that receives student questions, performs analysis, and generates and provides answers.

[1762] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate optimal answers to input questions.

[1763] "Prompt sentence" refers to the input-style sentence that a generative AI model uses to generate an answer to a question.

[1764] "Answer" refers to the answer information generated by the generative AI model in response to a question.

[1765] "Question history" refers to a record of questions previously entered by students and the answers generated.

[1766] "Weak areas" refer to areas of learning that a student has difficulty understanding, identified based on an analysis of their question history.

[1767] "Learning content" refers to teaching materials and information created to strengthen students' knowledge in areas of weakness.

[1768] "Video Course" refers to educational material in video format that is produced to explain specific learning content.

[1769] "Feedback" refers to the assessments and comments provided to students based on their learning progress.

[1770] "Database" refers to a recording device for storing the history of questions and answers and its management system.

[1771] This invention is a distance learning video lesson system that utilizes a generative AI model to provide quick and accurate answers to questions entered by students during online lessons, identify areas in which each student is weak, and support their understanding.

[1772] The system uses the following major hardware and software components:

[1773] Device: Student's computer, tablet, or other device

[1774] Server: A central processing unit that processes queries and analyzes and stores data.

[1775] Generative AI models: Algorithms and programs used to analyze questions and generate answers (e.g., GPT-4)

[1776] During online classes, users (students) enter questions in text format about points they do not understand. For example, they enter a question such as, "I don't understand the domain of a function." The device receives this question and sends it to the server using a format and protocol (e.g., HTTP, WebSocket).

[1777] The server receives the user's question and analyzes its contents. At this time, the question text is input as a prompt to the generative AI model, which generates the optimal answer. For example, it generates an answer such as "The domain of a function is the range of values ​​that are input to the function." The server sends the generated answer to the device, which displays it on the screen.

[1778] The server stores the user's questions and the generated answers in a database. This keeps a history of all questions and answers. At regular intervals, the server analyzes the log data and identifies the student's weak areas. For example, if a student asks many questions about "functions" multiple times, it will determine that functions are a weak area.

[1779] Based on the identified weak areas, the server uses a generative AI model to generate appropriate learning content. For example, it automatically generates a video lecture on the domain of functions. The server then sends the generated content to the device, which then displays it. This allows students to deepen their understanding of their weak areas.

[1780] The server also analyzes learning progress data and generates appropriate feedback. For example, it may generate feedback such as, "Your understanding of functions is improving." The server then sends the generated feedback to the device, which then displays it on the screen. This allows students to check their own learning progress and motivates them to continue learning.

[1781] Specific examples

[1782] Question and answer generation

[1783] If a user asks during an online class, "I don't know how to graph a linear function," the device sends this question to the server. The server uses a generative AI model to generate an answer: "The graph of a linear function has the form y = mx + b, where m is the slope and b is the y-intercept." The device displays this answer, allowing the user to solve the problem on the spot.

[1784] Identifying weak areas and providing content

[1785] If the server analyzes the question log and finds that a particular student frequently asks questions about "functions," it automatically generates learning content such as a "video lecture on the domain of functions." The server sends this content to the device, which then plays the video lecture, allowing the user to deepen their understanding of their weak areas.

[1786] In this way, the system of the present invention improves the quality of education by providing appropriate answers to students' questions and personalized learning support.

[1787] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1788] Step 1:

[1789] During an online class, a user enters a question in text format. For example, they enter a question like, "I don't understand the domain of a function." The device receives this input and sends it to the server using a format and protocol (such as HTTP or WebSocket).

[1790] Input: The question text entered by the user

[1791] Output: The question data sent to the server

[1792] Specific behavior:

[1793] The user uses a web browser or dedicated application to enter a question into a text input field on the interface and press the submit button.

[1794] Step 2:

[1795] The server analyzes the received question. It inputs the question text as a prompt into a generative AI model (e.g., GPT-4) and generates the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[1796] Input: Query data sent to the server

[1797] Output: Answer text generated by the generative AI model

[1798] Specific behavior:

[1799] When the server receives the question text, it accesses the API of the generation AI, sends a prompt, and receives the generated answer.

[1800] Step 3:

[1801] The server sends the generated answer to the terminal, which then displays the answer on its screen, allowing the user to obtain the answer to their question.

[1802] Input: Answer text generated by the generative AI model

[1803] Output: Answer information displayed on the terminal

[1804] Specific behavior:

[1805] The server generates an answer and sends it back to the device as an HTTP response, which the device parses and displays in a browser or application.

[1806] Step 4:

[1807] The server stores the user's questions and generated answers in a database, thus maintaining a history of all questions and answers.

[1808] Input: Question text and generated answer text

[1809] Output: Question and answer history stored in a database

[1810] Specific behavior:

[1811] The server stores the questions and answers in a database such as MySQL or PostgreSQL using an INSERT query.

[1812] Step 5:

[1813] The server periodically analyzes the question log and identifies areas in which students are weak. For example, if a student repeatedly asks questions about "functions," it will determine that functions are a weak area.

[1814] Input: A history of questions and answers stored in a database

[1815] Output: Information on identified weaknesses

[1816] Specific behavior:

[1817] Scheduled tasks and periodic batch processes are run to aggregate and analyze database logs and identify weak areas based on specific keywords and frequencies.

[1818] Step 6:

[1819] Based on the weak areas identified by the server, appropriate learning content is generated using generative AI. For example, a video course on the domain of functions is automatically generated.

[1820] Input: Information about identified weaknesses

[1821] Output: Generated learning content

[1822] Specific behavior:

[1823] Use generative AI to generate text content and quiz questions, and optionally integrate with video generation tools (e.g., FFmpeg) to generate video content.

[1824] Step 7:

[1825] The server sends the generated learning content to the device, which then displays it, allowing the user to deepen their understanding of weak areas.

[1826] Input: Generated learning content

[1827] Output: Learning content displayed on the device

[1828] Specific behavior:

[1829] The generated content is delivered to the terminal in the form of HTTP streaming or file download, and the terminal displays it.

[1830] Step 8:

[1831] The server analyzes the student's learning progress and generates appropriate feedback, such as "Your understanding of functions is improving," which is sent to the device and displayed.

[1832] Input: Learning progress data

[1833] Output: Generated feedback

[1834] Specific behavior:

[1835] The server analyzes the learning progress data and generates feedback messages using a generation AI. The generated feedback is sent to the device in JSON format, and the device formats it for display on the screen and presents it to the user.

[1836] (Application example 1)

[1837] 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."

[1838] Today's students have a strong desire to make effective use of their time, such as while traveling, for studying. However, conventional online class systems are mainly used in fixed locations, and do not provide sufficient support for learning while traveling. There is also a lack of systems that can quickly answer questions students have during online classes and provide learning content specialized for weak areas. For this reason, there is a need to develop a system that can effectively support learning even while traveling.

[1839] 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.

[1840] In this invention, the server includes means for receiving questions entered by students during online classes, means using artificial intelligence to analyze the received questions and generate optimal answers, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history to identify weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for supporting the students' learning through a terminal installed in the autonomous vehicle, and means for analyzing questions entered by students during learning in real time and providing answers, thereby enabling effective support for students' learning even while on the move.

[1841] "Means for receiving questions entered by students during online classes" refers to a system for receiving questions sent from the devices used by students.

[1842] "Methods using generative artificial intelligence to analyze received questions and generate optimal answers" refers to a method that uses generative AI to understand the content of questions and generate appropriate answers.

[1843] The "means for providing the generated answers to the students" is a mechanism for displaying the generated answers on the students' terminals.

[1844] The "means for recording questions and their answers" is a mechanism for storing received questions and their answers in a database.

[1845] The "means of analyzing the recorded question history to identify areas in which students are weak" is a method of analyzing saved question and answer data to identify areas in which students are weak.

[1846] "Means for automatically generating learning content based on identified areas of weakness" refers to a method for using generative AI to create appropriate learning materials tailored to areas of weakness.

[1847] The "means for providing the generated learning content" is a mechanism for transmitting the generated learning materials to the student's terminal and displaying them.

[1848] "Means to support students' learning through terminals installed inside self-driving vehicles" refers to a system that utilizes terminals installed inside self-driving vehicles to enable students to study while on the move.

[1849] "Means for analyzing questions entered by students in real time while studying and providing answers" refers to a method for instantly analyzing questions entered in real time while studying and generating and providing appropriate answers.

[1850] This invention is a system that allows students to study effectively even while on the move. This system provides the functionality to analyze questions entered during online classes and generate appropriate answers. Furthermore, it can analyze students' learning histories and automatically generate individual learning content. To achieve this, the following specific components and processes are required.

[1851] System configuration

[1852] The overall system consists of the following main components:

[1853] Devices used by students (computers, tablets, devices installed in autonomous vehicles, etc.)

[1854] Server (query processing, data analysis and storage)

[1855] Generative AI (understands the meaning of the question and generates the best answer)

[1856] Hardware and Software

[1857] 1. Terminal

[1858] A terminal is a device used by a student, including a computer, tablet, or learning tablet installed in an autonomous vehicle. The terminal is connected to the internet and has an interface for entering questions and a display for viewing the generated answers.

[1859] 2. Server

[1860] The server is a central management system that receives questions, analyzes them, generates answers, manages the database, etc. This server is operated on a cloud basis and has various software installed for query processing and automatic generation of learning content.

[1861] 3. Generation AI

[1862] The generative AI running on the server analyzes the meaning of the question based on natural language processing and generates appropriate answers. The AI ​​models used include GPT-3 and similar advanced generative AI.

[1863] Program processing

[1864] 1. Receiving Questions

[1865] When a student enters a question in text format during an online class, the device sends the question to the server.

[1866] 2. Question Analysis and Answer Generation

[1867] The server sends the received question to the AI ​​generator, which then generates the best answer. At this time, the AI ​​is given the following prompt as input:

[1868] Q: I don't know the domain of a function.

[1869] A:

[1870] The AI ​​generates the following response to this prompt:

[1871] The domain of a function is the range of values ​​that are input to the function.

[1872] 3. Providing and recording responses

[1873] The server sends the generated answers to the terminal, which displays them to the student, and simultaneously stores the questions and answers in a database.

[1874] Specific examples

[1875] Consider the case where a user (student) asks a question during an online class, "I don't know how to graph a linear function." At this time, the device receives the question and sends it to the server. The server uses generative AI to generate the following answer:

[1876] The graph of a linear function is of the form y = mx + b, where m is the slope and b is the y-intercept.

[1877] As soon as this answer is generated, it is sent to the device and displayed to the student.

[1878] This system can also automatically generate and provide learning content based on identified areas of weakness. For example, if the server identifies a student's weak area as "functions," it can generate a video course on the domain of functions, send it to the student's device, and display it to the student. In this way, it is possible to effectively support students' learning even while they are on the move.

[1879] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1880] Step 1:

[1881] The terminal receives questions from students. When a student enters a question in text format into the terminal, the question is sent to the terminal's receiving interface. For example, the text may be "I don't understand the domain of a function."

[1882] Input: Student question text

[1883] Output: Question data received by the device

[1884] Step 2:

[1885] The terminal sends the received query to the server. The query data is sent to the server via the network. For example, the following data is sent:

[1886] Q: I don't know the domain of a function.

[1887] Input: Received question data

[1888] Output: The query data sent to the server

[1889] Step 3:

[1890] The server sends the received question to the generation AI. The received question data is input into the generation AI model to construct a prompt sentence. For example, the following prompt sentence is generated:

[1891] Q: I don't know the domain of a function.

[1892] A:

[1893] Input: Received question data

[1894] Output: The prompt sent to the generation AI

[1895] Step 4:

[1896] The generation AI analyzes the prompt and generates the most appropriate answer. The generation AI uses natural language processing technology to understand the content of the question and generate an appropriate answer. For example, the following answer may be generated:

[1897] The domain of a function is the range of values ​​that are input to the function.

[1898] Input: prompt statement

[1899] Output: Generated answer text

[1900] Step 5:

[1901] The server sends the generated response to the terminal. The generated response data is returned to the terminal. For example, the following data is sent:

[1902] Answer: The domain of a function is the range of values ​​that are input to the function.

[1903] Input: Generated answer text

[1904] Output: Response data sent to the device

[1905] Step 6:

[1906] The device receives the answer and displays it to the student. The generated answer is displayed on the device's display. For example, the following text might be displayed:

[1907] The domain of a function is the range of values ​​that are input to the function.

[1908] Input: Response data from the server

[1909] Output: Answer text displayed on the screen

[1910] Step 7:

[1911] The server records the question and answer in a database. The question and its answer are stored in a database. For example, the following records are stored:

[1912] Q: I don't know the domain of a function.

[1913] Answer: The domain of a function is the range of values ​​that are input to the function.

[1914] Input: Question and answer data

[1915] Output: Records saved in the database

[1916] Step 8:

[1917] The server periodically analyzes the question history to identify areas where students are weak. The stored data is analyzed to identify frequently asked question categories. For example, if there are many questions about "functions," it will identify that area as a weak area.

[1918] Input: Question history stored in the database

[1919] Output: Identified weak areas

[1920] Step 9:

[1921] The server automatically generates learning content based on weak areas. Generative AI is used to create appropriate learning content (e.g., video courses). For example, the following video courses are generated:

[1922] Video content: Detailed explanation of the domain of a function

[1923] Input: Identified weak areas information

[1924] Output: Generated learning content

[1925] Step 10:

[1926] The server transmits the generated learning content to the terminal, which provides it to the student. The server transmits the generated learning content to the terminal, which plays the content and displays it to the student.

[1927] Input: Generated learning content

[1928] Output: Learning content sent to the device, learning content played on the device

[1929] The above are the specific processing steps for carrying out the present invention.

[1930] 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.

[1931] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[1932] System Overview

[1933] The entire system consists of the following main components:

[1934] Devices used by students (PCs, tablets, etc.)

[1935] Server that processes queries and analyzes and stores data

[1936] Generation AI

[1937] Emotion Engine

[1938] Program processing

[1939] Receiving student questions

[1940] A user (student) enters a question in text format during an online class. For example, the question is, "I don't understand the domain of a function."

[1941] The terminal receives the input question and transmits the question data to the server.

[1942] Handling and answering questions

[1943] The server analyzes the question it receives. It uses generative AI to understand the meaning of the question and generate the optimal answer. For example, it generates an answer such as, "The domain of a function is the range of values ​​that are input to the function."

[1944] The server transmits the generated answer to the terminal, which displays the answer on the screen and provides it to the user.

[1945] Accumulation and analysis of question logs

[1946] The server stores the questions and answers in a database, which records the learning history.

[1947] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," it identifies functions as a weak area.

[1948] Emotion recognition and response using emotion engine

[1949] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize the user's emotional state. For example, it can determine whether the student is confused or happy through the camera and microphone.

[1950] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[1951] Automatic generation and provision of individual learning content based on weak areas and emotional data

[1952] The server combines and analyzes question history and emotional data to identify areas in which students are weak and areas that cause them emotional stress.

[1953] The server automatically generates appropriate learning content based on the identified weaknesses, such as a video course on the domain of functions or emotional support.

[1954] The server transmits the generated learning content to the terminal, which displays it and provides it to the user.

[1955] Providing progress feedback

[1956] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[1957] The server transmits the feedback to the terminal, and the terminal displays the feedback on the screen to provide it to the user.

[1958] Specific examples

[1959] Student question and answer process

[1960] Example: During an online class, a user asks, "I don't know how to graph a linear function."

[1961] The terminal sends this question to the server.

[1962] The server uses a generative AI to generate an answer and sends it back, saying, "The graph of a linear function is in the form y = mx + b, where m is the slope and b is the y-intercept."

[1963] The device will display this answer, allowing the user to solve the problem on the spot.

[1964] Emotion recognition and response adjustment

[1965] Example: A user asks with a confused look, "I still don't understand the domain of a function."

[1966] The device uses an emotion engine to recognize confusion and transmits this information to the server.

[1967] The server generates an answer with detailed diagrams and additional explanations, specifically answering, "The domain of a function indicates the range of values ​​of x. For example, the function f(x) = √x has x ≥ 0."

[1968] The terminal displays this detailed response and provides it to the user.

[1969] Identifying weak areas and utilizing emotional data

[1970] Example: The server analyzes the question log and sentiment data and discovers that users often ask questions about functions, but that they express confusion and tension each time.

[1971] The server automatically generates learning content including a "video lecture on the domain of functions" and emotional support messages.

[1972] The server sends this content to the terminal, which displays it and provides it to the user.

[1973] The processing flow will be explained below.

[1974] Step 1:

[1975] A user enters a question in text format during an online class. For example, they may enter, "I don't understand the domain of a function."

[1976] Step 2:

[1977] The device receives the entered question and sends the question data to the server using an HTTP POST request or WebSocket.

[1978] Step 3:

[1979] The server analyzes the received question, using natural language processing (NLP) techniques to understand the meaning of the question.

[1980] Step 4:

[1981] The server uses generative AI to generate the best answer to the question, for example, "The domain of a function is the range of values ​​that are input to the function."

[1982] Step 5:

[1983] The server then sends the generated response to the device, also using an HTTP POST request or WebSocket.

[1984] Step 6:

[1985] The device will display the answer it receives on the screen, allowing users to resolve their questions on the spot.

[1986] Step 7:

[1987] The server stores the questions and their answers in a database, which records the question history.

[1988] Step 8:

[1989] The server periodically analyzes the question log, which identifies each student's weak areas.

[1990] Step 9:

[1991] The device analyzes the user's facial expressions and voice through an emotion engine to recognize the user's emotional state. For example, it can determine whether a student is confused or happy through a camera or microphone.

[1992] Step 10:

[1993] The server analyzes the user's emotional state and adjusts its responses accordingly: for example, if the student is confused, it adds detailed explanations and illustrations; if they are happy, it provides a concise answer.

[1994] Step 11:

[1995] The server automatically generates appropriate learning content based on the identified weak areas, such as a video course on the domain of functions.

[1996] Step 12:

[1997] The server sends the generated learning content to the device, using a content delivery network (CDN) or streaming method.

[1998] Step 13:

[1999] The device will display new learning content, allowing users to focus on areas they are weak in.

[2000] Step 14:

[2001] The server analyzes students' learning progress, allowing the evaluation of each student's grades and learning progress.

[2002] Step 15:

[2003] The server generates appropriate feedback, such as "You're making progress in understanding functions," or "You seem a little confused, but keep trying," which includes emotional support.

[2004] Step 16:

[2005] The server sends the feedback to the device, also using an HTTP POST request or WebSocket.

[2006] Step 17:

[2007] The device displays feedback on the screen, giving users encouragement and advice to help them stay motivated to learn.

[2008] Through these steps, the online video lesson system using generative AI and an emotion engine will enable students to answer questions in real time, provide individually customized learning content, and provide emotional support.

[2009] Example 2

[2010] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2011] With conventional online class systems, it was difficult to provide quick and accurate answers to questions entered by students, and it was also difficult to properly grasp students' learning progress and provide individual support. Furthermore, because it was not possible to respond to students' emotional states, there were problems such as not being able to provide appropriate support when students felt confused or stressed. This limited the effectiveness of online classes and created the issue of not being able to fully motivate students to learn.

[2012] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by students, means for transmitting the received questions to the server, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for saving the questions and their answers in a database, means for analyzing the saved question history and identifying the student's weak areas, means for analyzing the student's facial expressions and voice tone on the terminal and recognizing the student's emotional state using an emotion engine, means for adjusting the response content based on the student's emotional state, means for automatically generating learning content based on the identified weak areas, and means for providing the generated learning content. This enables quick and accurate answers to questions entered by students and allows individualized instruction that takes into account the student's learning progress and emotional state, thereby maximizing the effectiveness of online classes.

[2013] A "server" is a computer system that receives requests from clients via a network, processes the requests, and returns the results to the clients.

[2014] A "terminal" is a device that is connected to a network and that a user operates to input and display information, and specifically refers to a personal computer or tablet.

[2015] "Student" refers to a learner who takes an online class and inputs questions through the system.

[2016] "Question data" refers to text data containing questions entered by students during online classes.

[2017] "Generative AI" refers to an algorithm or system that uses natural language processing technology to analyze input questions and generate optimal answers.

[2018] A "database" is a data structure or system that stores data in an organized manner and allows efficient searching and manipulation of that data.

[2019] "Question history" refers to a collection of data that records questions that students have previously asked through the system and the answers to those questions.

[2020] "Weak areas" refer to areas of the learning content that students repeatedly ask about but find difficult to understand.

[2021] An "emotion engine" is an algorithm or system that analyzes and recognizes a user's emotional state based on input information such as the user's facial expressions and voice.

[2022] The "emotional state" refers to the emotion the user is currently feeling, and indicates a psychological state such as confusion or joy.

[2023] "Learning Content" means learning materials in digital form, including teaching materials, lectures, video courses, etc., provided for educational purposes.

[2024] "Feedback" refers to responses such as encouragement, advice, and other messages that are generated based on a student's learning progress.

[2025] The present invention is a distance learning video lesson system that uses both a generative AI and an emotion engine, and is implemented as follows.

[2026] System Overview

[2027] The entire system consists of the following main components:

[2028] Devices used by students (PCs, tablets, etc.)

[2029] Server that processes queries and analyzes and stores data

[2030] Generation AI

[2031] Emotion Engine

[2032] System Operation

[2033] Receiving student questions

[2034] During online classes, users (students) enter questions in text format. For example, they might enter a question like, "I don't know how to draw the graph of a linear function." This question data is received by the device and sent to the server. The commonly used HTTP protocol is used for communication.

[2035] Handling and answering questions

[2036] The server analyzes the received question data. Specifically, it analyzes the received data in JSON format and extracts the question content. Next, the question content is input into the generation AI, which generates the optimal answer. A high-performance natural language processing model is used as the generation AI model. For example, it generates an answer such as, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The server sends the generated answer to the device, which displays it on the screen and provides it to the user.

[2037] Accumulation and analysis of question logs

[2038] The server stores questions and their answers in a database. Specifically, it records information such as the question content, answer content, question date and time, and answer date and time. Periodically, the server analyzes the question log to identify each student's weak areas. This analysis is performed using database queries and machine learning algorithms. For example, if a student repeatedly asks questions about "functions," it can determine that the student is weak in functions.

[2039] Emotion recognition and response using emotion engine

[2040] The device analyzes the student's facial expressions and voice and uses an emotion engine to recognize their emotional state. For example, it can identify a student's confusion, joy, or nervousness through a camera or microphone. The server then analyzes the student's emotional state and adjusts the response accordingly. For example, if the student is confused, it will generate a response with more detailed explanations and illustrations and send it back to the device.

[2041] Automatic generation and provision of individual learning content based on weak areas and emotional data

[2042] The server combines and analyzes the question history and emotional data to identify areas where the student is weak or stressed. Based on the identified areas of weakness, the server automatically generates appropriate learning content. For example, it might create a "video course on the domain of functions." The generated learning content is then sent back to the device and provided to the user.

[2043] Providing progress feedback

[2044] The server analyzes the student's learning progress and generates appropriate feedback, such as "You're making progress in understanding functions" or "You seem a little confused, but keep trying." This feedback is sent to the device, which displays it on the screen for the user.

[2045] Specific operation example

[2046] During an online class, a user might type a question like, "I don't know how to graph a linear function." The device then sends the question to the server. The server uses generative AI to generate an answer, saying, "The graph of a linear function has the form y=mx+b, where m is the slope and b is the y-intercept." The device then displays this answer, allowing the user to solve the problem on the spot. Similarly, if a user asks with a confused expression, "I still don't understand the domain of a function," the device uses its emotion engine to recognize the confusion and sends this information to the server. The server then generates an answer with detailed diagrams and additional explanations, specifically stating, "The domain of a function indicates the extent to which the value of x extends. For example, for the function f(x)=√x, x≥0." The device then displays this detailed answer and provides it to the user.

[2047] This system will enable quick and accurate resolution of problems students encounter during online classes, and will also enable optimal support tailored to individual learning situations.

[2048] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2049] Step 1: Student Question Input

[2050] During an online class, a user (student) inputs a question in text format. For example, a question might be, "I don't know how to draw a graph of a linear function." This input is received by the terminal.

[2051] Input: Student entered text question

[2052] Output: Question data saved on the device

[2053] Step 2: Sending query data via device

[2054] The device sends the received question data to the server using the HTTP protocol, and the data is sent in JSON format.

[2055] Input: Question data saved on the device

[2056] Output: The query data sent to the server

[2057] Step 3: Query data received and parsed by the server

[2058] The server analyzes the received question data. Specifically, it analyzes the received JSON data and extracts the question content.

[2059] Input: JSON-formatted question data sent from the terminal

[2060] Output: Parsed question

[2061] Step 4: Generative AI generates answers

[2062] The server inputs the analyzed question content into the generation AI, which generates the optimal answer. The generation AI model uses natural language processing technology.

[2063] Input: Parsed question content

[2064] Output: Generated answer text

[2065] Step 5: Server sends response

[2066] The server sends the generated response text to the device, again using the HTTP protocol and JSON format.

[2067] Input: Generated answer text

[2068] Output: Answer text sent to the terminal

[2069] Step 6: Displaying the Answer via Terminal

[2070] The terminal displays the received answer text on the screen and provides it to the user. Specifically, the answer is displayed in a text box.

[2071] Input: Answer text sent from the server

[2072] Output: Answer displayed on the device screen

[2073] Step 7: Storing the Questions and Answers in a Database

[2074] The server stores the questions and their answers in a database, which includes the question, the answer, the date and time of the question, and the date and time of the answer.

[2075] Input: Question and answer

[2076] Output: Questions and answers stored in a database

[2077] Step 8: Server Analysis of Question Log

[2078] The server periodically analyzes the database and identifies weak areas based on each student's question history, using machine learning algorithms and database queries.

[2079] Input: Question logs stored in the database

[2080] Output: Identified student weaknesses

[2081] Step 9: Emotion Recognition with the Emotion Engine

[2082] The device analyzes students' facial expressions and voices and uses an emotion engine to recognize their emotional state, for example identifying confusion or joy through the camera and microphone.

[2083] Input: Student facial expressions and voices from the camera and microphone

[2084] Output: Perceived student emotional state

[2085] Step 10: Server Adjustment of Response

[2086] The server analyzes the perceived emotional state and adjusts the response accordingly, for example by adding detailed explanations or using illustrations.

[2087] Input: Perceived emotional state

[2088] Output: Adjusted response content

[2089] Step 11: Automatic generation of personalized learning content

[2090] The server combines and analyzes question history and emotional data, and automatically generates appropriate learning content based on the student's weak areas.

[2091] Input: Question history and sentiment data

[2092] Output: Generated learning content

[2093] Step 12: Sending learning content from the server

[2094] The server sends the generated learning content to the device using the HTTP protocol, with the data sent in JSON format.

[2095] Input: Generated learning content

[2096] Output: Learning content sent to your device

[2097] Step 13: Viewing learning content on your device

[2098] The device displays the received learning content on the screen and provides it to the user, for example, by playing a video lecture or text content.

[2099] Input: Learning content sent from the server

[2100] Output: Learning content displayed on the device screen

[2101] Step 14: Server analyzes progress and generates feedback

[2102] The server analyzes the student's learning progress and generates appropriate feedback, such as a message saying, "You're making progress in understanding functions."

[2103] Input: Student progress data

[2104] Output: The generated feedback message

[2105] Step 15: Displaying feedback via terminal

[2106] The device displays feedback messages on the screen and provides them to the user, allowing the student to understand their own learning progress.

[2107] Input: Feedback message sent by the server

[2108] Output: Feedback displayed on the device screen

[2109] (Application example 2)

[2110] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2111] Conventional online teaching systems are required to not only provide answers to questions, but also to recognize the emotional state of each student and respond based on that state. Furthermore, they lack the ability to tailor responses based on emotional states and provide individual learning content for weak areas. As a result, students may feel stressed and their learning efficiency may decrease.

[2112] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2113] In this invention, the server includes means for receiving questions entered by students during online classes, means for analyzing the received questions and generating optimal answers using artificial intelligence, means for providing the generated answers to the students, means for recording questions and their answers, means for analyzing the recorded question history and identifying weak areas of the students, means for automatically generating learning content based on the identified weak areas, means for providing the generated learning content, means for recognizing the emotional state of the students, and means for adjusting answers based on the recognized emotional state, thereby enabling flexible learning support tailored to the emotional state of each individual student.

[2114] "Online classes" are a form of education conducted via the Internet, allowing students to participate in classes from remote locations.

[2115] "Means for receiving questions" refers to the functionality of a device or software for obtaining text questions entered by students during online classes.

[2116] "Generative AI" is a technology for analyzing input question data and generating appropriate answers, such as a natural language processing model.

[2117] The "means for providing an answer" refers to the functionality of the device or software for presenting the generated answer to the student.

[2118] A "recording means" is a technique for saving questions and their answers in a format that can be analyzed later.

[2119] "Means for analyzing history" is a function that analyzes recorded question and answer data and extracts specific patterns.

[2120] "Means for identifying weak areas" refers to technology for identifying the content or areas in which a student is weak based on historical data.

[2121] "Means for automatically generating learning content" refers to a function that automatically creates appropriate educational materials and assignments based on students' weak areas.

[2122] "Means of provision" refers to the functions of the device or software used to present the generated learning content and answers to students.

[2123] The "means of recognizing emotional states" is a technology that uses cameras and microphones to analyze and recognize emotions from students' facial expressions and voices.

[2124] "Means for tailoring responses" refers to the ability to change the level of detail and content of generated responses based on the perceived emotional state.

[2125] The present invention is a distance learning video teaching system that uses both a generative AI and an emotion engine, and is implemented with the following configuration.

[2126] System configuration

[2127] The entire system consists of the following main components:

[2128] Device: The device used by students for online classes (computer, tablet, etc.)

[2129] Server: A computing device that processes queries and analyzes and stores data.

[2130] Generative AI: An artificial intelligence engine that analyzes questions and generates answers

[2131] Emotion Engine: An analytical engine for recognizing students' emotional states

[2132] System Program

[2133] Receiving and processing questions

[2134] During online classes, users (students) use their devices to input questions in text format. The input questions are received by the devices and sent to the server.

[2135] Answer generation and provision

[2136] The server analyzes the received question and generates the optimal answer using a generation AI. The generated answer is then sent from the server to the device, which displays the answer on the screen for the user.

[2137] Recording and analyzing questions and answers

[2138] The server stores the questions and answers in a database, which records the learning history.

[2139] The server periodically analyzes the question log and identifies each student's weak areas. For example, if a student frequently asks questions about "functions," the server identifies functions as a weak area.

[2140] Emotion recognition and response regulation

[2141] The device uses a camera and microphone to analyze the student's facial expressions and voice, and an emotion engine to recognize the user's emotional state. The server then adjusts the response based on the user's emotional state. For example, if the student is confused, it adds detailed explanations and illustrations. If the student is happy, it provides a concise answer.

[2142] Creation and provision of personalized learning content

[2143] The server combines and analyzes question history and emotional data to identify areas where students are weak or experiencing emotional stress. The server then automatically generates appropriate learning content based on this data. The generated learning content is then sent to the device, which displays it and provides it to the user.

[2144] Hardware and software used

[2145] Hardware: smartphone, tablet, computer, camera, microphone

[2146] software:

[2147] Generative AI: Natural language processing models such as GPT-3

[2148] Emotion engine: Analysis engines such as EmotionEngine

[2149] Database: Data management system such as PostgreSQL

[2150] Server: A web framework such as Flask

[2151] Specific examples

[2152] 1. A user (student) types, "I don't understand the basic concept of differentiation" during an online class.

[2153] 2. The device sends this question to the server.

[2154] 3. The server uses a generative AI to generate the answer: "The basic concept of differentiation is that it shows the instantaneous rate of change of a function."

[2155] 4. The emotion engine recognizes the user's confused facial expression through the camera and microphone.

[2156] 5. The server adds a more detailed response: "The derivative shows the rate of change of a function at each point."

[2157] 6. The device displays this detailed response and provides it to the user.

[2158] Example prompt sentence:

[2159] text

[2160] "Student typed question: I don't understand the basic concept of differentiation.

[2161] Emotion recognition result: Confused.

[2162] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[2163] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2164] Processing Steps

[2165] Step 1:

[2166] A user enters a question in text format during an online class.

[2167] Input: Students type in their questions in text format.

[2168] Action: The user enters a question using the device's input device (keyboard or touchscreen).

[2169] Output: The question text is saved on the device and sent to the server.

[2170] Step 2:

[2171] The terminal receives the entered question and transmits it to the server.

[2172] Input: The question text entered in step 1.

[2173] How it works: The device sends the question text to the server via an HTTP request.

[2174] Output: The question text arrives at the server and is queued for analysis.

[2175] Step 3:

[2176] The server analyzes the received question and uses a generative AI to generate the optimal answer.

[2177] Input: The question text stored on the server.

[2178] How it works: The server provides the generative AI model with question text and prompts to generate an answer. The generative AI model analyzes the meaning of the question and generates an appropriate answer.

[2179] Output: The generated answer text.

[2180] Step 4:

[2181] The generated answer is sent to the terminal, which displays the answer on the screen.

[2182] Input: The answer text generated by the server.

[2183] Operation: The server sends the answer text to the device as an HTTP response. The device displays the received answer on the screen.

[2184] Output: The answer text displayed on the terminal.

[2185] Step 5:

[2186] Store the questions and their answers in a database.

[2187] Input: Question text and answer text.

[2188] How it works: The server stores question and answer data pairs in a database.

[2189] Output: A new question and answer entry is saved in the database.

[2190] Step 6:

[2191] The server analyzes the question log and identifies areas in which students are weak.

[2192] Input: A history of questions and answers stored in a database.

[2193] How it works: The server uses data mining techniques to analyze the question log and find out whether questions in specific areas are common. An algorithm is then applied to identify areas of weakness.

[2194] Output: Identified weak areas.

[2195] Step 7:

[2196] Automatically generate learning content based on identified areas of weakness.

[2197] Input: Information about identified weaknesses.

[2198] How it works: The server inputs information about weak areas as prompts into the generative AI model, which then generates learning content based on that information. The generative AI model then generates appropriate video courses and text materials.

[2199] Output: The generated learning content.

[2200] Step 8:

[2201] The generated learning content is sent to the terminal, which displays it.

[2202] Input: Generated learning content.

[2203] Operation: The server sends the learning content to the device via HTTP response, and the device displays the received content on the screen.

[2204] Output: The learning content displayed on your device.

[2205] Step 9:

[2206] The device analyzes the student's facial expressions and voice to recognize their emotional state.

[2207] Input: Facial expression and vocal data captured by a camera or microphone.

[2208] How it works: The device uses an emotion engine to analyze facial expressions and voice to recognize the student's emotional state.

[2209] Output: Recognized emotional state data.

[2210] Step 10:

[2211] The server tailors the response based on the perceived emotional state.

[2212] Input: Recognized emotional state data and generated answer text.

[2213] How it works: The server regenerates or adjusts the answer text based on the emotional state, providing a tailored response, such as adding detailed explanations or illustrations.

[2214] Output: The adjusted answer text.

[2215] Example prompt sentence:

[2216] text

[2217] "Student typed question: I don't understand the basic concept of differentiation.

[2218] Emotion recognition result: Confused.

[2219] Good answer (with detailed explanation): The basic concept of differentiation is that it shows the instantaneous rate of change of a function. More specifically, differentiation is a way to find the slope of a function at each point, which involves the process of taking limits. For example, the derivative of a function f(x) is denoted as f'(x), and is calculated as the limit value of Δy / Δx.

[2220] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2221] 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.

[2222] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2223] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2224] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2225] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2226] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2227] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2228] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2229] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2230] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2231] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2232] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2233] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2234] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2235] The hardware resource for executing a specific process can be any of the foll...

Claims

1. A means for receiving questions entered by students during online classes; A means using generative artificial intelligence to analyze the received question and generate an optimal answer; a means of providing the generated answers to the student; a means of recording questions and their answers; A means of analyzing the recorded question history to identify weak areas of students; means for automatically generating learning content based on the identified areas of weakness; a means for providing the generated learning content; A system including:

2. A means for analyzing learning progress in weak areas and generating appropriate feedback; The system of claim 1 further comprising means for providing the generated feedback to the student.

3. 10. The system of claim 1, further comprising means for storing student questions and generated answers in a database.

Images