System

Abstract

A system is provided.SOLUTION: A system comprising: means for generating an appropriate question based on a learning progress of a user; means for receiving and analyzing an answer of the user in real time; means for providing feedback to the user; and means for setting a AI as a virtual opponent and providing learning in a form of competing with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's educational environment, teacher shortages are a serious problem, making it difficult to provide education tailored to each student. Sustaining children's motivation to learn is also a challenge. Conventional educational support systems often fail to provide problems tailored to each student's progress or provide effective feedback. Furthermore, the lack of a competitive element makes it difficult to maintain children's motivation over the long term. Therefore, a system that can act as a substitute for a teacher while still allowing children to continue learning in a fun way is needed. [Means for solving the problem]

[0005] This invention provides a system that includes a means for generating appropriate questions based on a user's learning progress, a means for receiving and analyzing the user's answers in real time, a means for providing feedback to the user, and a means for setting up an AI as a virtual opponent and providing a learning format in which the user competes with the AI. This allows the user to constantly tackle questions that correspond to their own level of understanding, and real-time feedback can improve the retention of learning content. Furthermore, by setting up an AI as an opponent, it is possible to incorporate a gamification-like feel into the learning process, maintaining the user's motivation to learn for an extended period of time. This system can provide education tailored to each individual student and contribute to resolving the teacher shortage.

[0006] "User" refers to a learner who uses the educational support system.

[0007] "Study progress" refers to the progress and degree of mastery of the learning content that the user is currently working on.

[0008] "Questions" refer to learning materials such as quizzes and assignments that are generated to assist users in their learning.

[0009] "Means for generating" refers to the function of the server to create appropriate study questions based on the user's learning progress.

[0010] "Answer" refers to the answer that the user enters in response to the question presented.

[0011] "Real-time" refers to the immediate response of the user to the system's operations.

[0012] "Means for analyzing" refers to the function of evaluating the answers entered by the user and determining whether they are correct or incorrect.

[0013] "Feedback" refers to information that returns evaluation and instruction on a user's answer.

[0014] "Virtual competitor" refers to an AI character set up within the educational support system to compete with users.

[0015] "Competitive learning" refers to a learning method in which users tackle the same problems as AI and compare their performance to improve learning outcomes. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0038] This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server mainly generates questions and analyzes answers, while the terminal accepts user operations and smoothly advances the learning process through communication with the server. Users operate the terminal to engage in learning.

[0039] Initial Setup and Login

[0040] 1. The server stores user account information in a database and provides a mechanism for authentication.

[0041] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[0042] 3. The user enters their account information on the login screen of the device and presses the login button.

[0043] Start a study session

[0044] 1. The device displays a "Start learning" button after the user logs in.

[0045] 2. The user clicks the Start Learning button to begin the learning session.

[0046] 3. The device sends a request to the server to start a learning session.

[0047] 4. The server receives this request and retrieves the user's learning history from the database.

[0048] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[0049] Displaying and answering questions

[0050] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[0051] 2. The user enters the answer to the displayed question (e.g., "8") and presses the submit button.

[0052] 3. The device sends the answer to the server.

[0053] Feedback and understanding measurement

[0054] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[0055] 2. The server generates feedback (e.g., "Correct!") based on the result of the judgment.

[0056] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0057] 4. The server sends feedback and a new question (e.g., "7 - 4 = ?") to the device.

[0058] View feedback and new issues

[0059] 1. The device displays feedback and new questions to the user.

[0060] 2. The device displays the answer status of the AI's virtual friend (e.g., "The AI ​​also got the answer right!") to encourage the user.

[0061] 3. The user revisits the problem with a new one.

[0062] Ending a session

[0063] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[0064] 2. The device sends a request to the server to terminate the learning session.

[0065] 3. The server saves the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[0066] 4. The terminal displays a message to the user indicating that the session has ended.

[0067] Specific examples

[0068] Scenario 1: Basic Learning Session

[0069] 1. The user logs in to a terminal and is presented with the problem "5 + 3 = ?"

[0070] 2. The user enters the answer "8" and presses the submit button.

[0071] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[0072] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[0073] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[0074] Scenario 2: Problem suggestions based on level of understanding

[0075] 1. The user answers "3" to the question "7 - 4 = ?"

[0076] 2. The server evaluates the level of understanding with the feedback "Correct!" and generates the next question (e.g., "8 + 6 = ?").

[0077] 3. The device displays the new problem and feedback to the user, prompting them to tackle the next problem.

[0078] In this way, the present invention can provide questions that are optimized for the user's learning progress and maintain high learning motivation through AI virtual competition.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] The user opens the application on the terminal and enters the username and password on the login screen.

[0082] Step 2:

[0083] The terminal transmits the entered login information to the server.

[0084] Step 3:

[0085] The server compares the received login information with the user information in the database and performs authentication.

[0086] Step 4:

[0087] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[0088] Step 5:

[0089] The user clicks the Start Learning button.

[0090] Step 6:

[0091] The terminal sends a request to the server to start a learning session.

[0092] Step 7:

[0093] The server receives this request and retrieves the user's learning history and current learning progress from the database.

[0094] Step 8:

[0095] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[0096] Step 9:

[0097] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[0098] Step 10:

[0099] The user enters the answer to the displayed question and presses the send button.

[0100] Step 11:

[0101] The terminal transmits the user's answer to the server.

[0102] Step 12:

[0103] The server analyzes the user's answer and determines whether it is correct or incorrect.

[0104] Step 13:

[0105] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[0106] Step 14:

[0107] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0108] Step 15:

[0109] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[0110] Step 16:

[0111] The terminal displays feedback and new questions to the user.

[0112] Step 17:

[0113] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[0114] Step 18:

[0115] The user revisits the new problem.

[0116] Step 19:

[0117] After a study session has been running for a certain period of time, the user clicks the end button.

[0118] Step 20:

[0119] The terminal sends a request to end the learning session to the server.

[0120] Step 21:

[0121] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[0122] Step 22:

[0123] The terminal displays a message to the user indicating that the session has ended.

[0124] Example 1

[0125] 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."

[0126] Conventional learning systems have issues with insufficient individual response to the user's learning progress and can only provide fixed questions and feedback. Furthermore, they do not sufficiently motivate users, which tends to reduce their motivation to learn, especially in self-study. Furthermore, they lack a mechanism to stimulate competitive spirit, meaning users lack the experience of competing with other learners or the system.

[0127] 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.

[0128] In this invention, the server includes means for generating appropriate study tasks based on the user's learning progress, means for receiving and analyzing the user's input in real time, means for providing feedback to the user, means for setting an intelligent system as a virtual competitor and providing a form of learning in which the user competes with the system, means for saving the user's account information in a database and authenticating it, means for providing a login screen and prompting the user to enter their account information, means for acquiring the user's answer history and generating new questions, and means for evaluating the user's level of understanding and adjusting the difficulty of the new questions. This makes it possible to provide questions that correspond to the individual user's learning progress and to increase motivation to learn through competition with virtual competitors.

[0129] "User's learning progress" refers to the level of understanding and mastery that the user has achieved in the course of performing learning activities.

[0130] "Study assignments" is a general term for problems, exercises, and assignments presented to the user.

[0131] "Input" refers to the act of a user inputting answers and information for a learning task into a terminal.

[0132] "Real time" refers to a state in which user operations or inputs are processed immediately without delay.

[0133] "Analysis" refers to the process by which the server analyzes the user's input and determines its correctness and appropriateness.

[0134] "Feedback" refers to the evaluation or response provided to a user's actions or answers.

[0135] "Virtual competitors" refer to intelligent systems or agents configured on the system, rather than other real users.

[0136] "Intelligent System" refers to the artificial intelligence programmed on the system to compete with the user.

[0137] "Account information" is information for identifying a user, and generally includes a username, password, email address, and the like.

[0138] A "database" refers to a system for systematically storing and managing large amounts of data.

[0139] "Authentication" refers to the process of verifying access rights when a user uses their account information to access a system.

[0140] "Login screen" refers to an interface for a user to enter account information to access a system.

[0141] "Answer history" refers to a record of the study tasks that the user has answered up to now.

[0142] "Level of understanding" refers to the degree to which the user can accurately understand and answer the learning task.

[0143] "Difficulty of the problem" refers to the level of difficulty of the learning task.

[0144] A specific embodiment of the present invention will now be described. This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server is primarily responsible for generating questions and analyzing answers, while the terminal accepts user operations and smoothly advances the study process through communication with the server. Users engage in study by operating the terminal.

[0145] Hardware and software used

[0146] This system uses the following hardware and software:

[0147] Server: A server machine (e.g., Linux server) equipped with a powerful processor, sufficient memory, and large storage capacity.

[0148] Database software: MySQL

[0149] Programming languages: Python, JavaScript

[0150] Web technologies: HTML, CSS, JavaScript

[0151] Communication protocol: HTTP / HTTPS

[0152] Initial Setup and Login

[0153] 1. The server stores user account information in a MySQL database and provides an authentication mechanism, ensuring the safe management of user personal data.

[0154] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. To do this, a login form is created using HTML and CSS, and the input is validated using JavaScript.

[0155] 3. The user enters their account information on the login screen of the device and presses the login button.

[0156] 4. The device sends the user's input data to the server via an HTTP POST request.

[0157] 5. The server checks the submitted user data against the database, and if authentication is successful, generates session information and returns it to the client.

[0158] Start a study session

[0159] 1. The device displays a "Start learning" button after the user logs in. This button is displayed using HTML and CSS.

[0160] 2. The user clicks the "Start Learning" button.

[0161] 3. Based on the user's operation, the device sends an HTTP POST request to the server to start a learning session.

[0162] 4. The server receives this request and retrieves the user's learning history from the database. For example, it executes the SQL query "SELECT FROM learning history WHERE user ID = '12345'".

[0163] 5. The server generates questions of appropriate difficulty based on the learning history and returns them to the device. This problem generation is done using a question generation algorithm written in Python.

[0164] Displaying and answering questions

[0165] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[0166] 2. The user enters the answer to the displayed question and presses the submit button.

[0167] 3. The device sends the answer data to the server using an HTTP POST request.

[0168] 4. The server receives the answer data and uses Python code to parse it.

[0169] Feedback and understanding measurement

[0170] 1. The server analyzes the user's answers and determines whether they are correct or incorrect. It uses Python to compare the questions and answers.

[0171] 2. The server generates feedback based on the result, such as "Correct!" if the answer is correct, or "Incorrect!" if the answer is incorrect.

[0172] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[0173] 4. The server sends the feedback and new questions to the device.

[0174] View feedback and new issues

[0175] 1. The device displays feedback and new questions to the user, dynamically updating the display using HTML and JavaScript.

[0176] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, to encourage the user. For example, it displays the message "The intelligent system also got the answer right!"

[0177] 3. The user revisits the problem with a new one.

[0178] Ending a session

[0179] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[0180] 2. The device sends an HTTP POST request to the server to end the learning session.

[0181] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('12345', NOW())".

[0182] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[0183] Examples of prompt statements

[0184] By inputting prompt sentences like the following into the generative AI model, we can generate appropriate feedback:

[0185] "User answers 8 to the question 5 + 3 = ?. Please generate a rating and feedback."

[0186] By entering this prompt, the AI ​​can generate accurate feedback (e.g., "Correct!") and provide it to the user.

[0187] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0188] Step 1:

[0189] Initial Setup and Login

[0190] 1. The server stores user account information in a database and provides a mechanism for authentication. Specifically, it stores usernames and hashed passwords in a MySQL database.

[0191] Input: Username, hashed password

[0192] Output: Save results to database

[0193] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. Create a login form using HTML and CSS, and validate the input using JavaScript.

[0194] Input: None

[0195] Output: Login form screen

[0196] 3. The user enters their username and password on the login screen and clicks the login button.

[0197] Input: Username, Password

[0198] Output: Login request

[0199] 4. The device sends the user's input data to the server using an HTTP POST request.

[0200] Input: Username, Password

[0201] Output: HTTP POST request

[0202] 5. The server checks the submitted user data against the database information, and if authentication is successful, generates session information and returns it to the client. The check is performed using an SQL query that retrieves data from the database.

[0203] Input: Username, Password

[0204] Output: Session information, authentication results

[0205] Step 2:

[0206] Start a study session

[0207] 1. The device displays a "Start Learning" button after the user logs in. Generate the button using HTML and CSS.

[0208] Input: Authentication completed session information

[0209] Output: "Start learning" button

[0210] 2. The user clicks the Start Learning button.

[0211] Input: None

[0212] Output: Training start request

[0213] 3. The device sends a learning session start request to the server based on the user's operation using an HTTP POST request.

[0214] Input: Learning start request

[0215] Output: HTTP POST request

[0216] 4. The server receives this request and retrieves the user's learning history from the database. Specifically, it executes the SQL query "SELECT FROM learning history WHERE user ID = 'user ID'".

[0217] Input: User ID

[0218] Output: Learning history data

[0219] 5. The server generates questions of appropriate difficulty based on the learning history and sends them back to the device. Question generation is done using Python.

[0220] Input: Learning history data

[0221] Output: Generated problem

[0222] Step 3:

[0223] Displaying and answering questions

[0224] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[0225] Input: Generated question

[0226] Output: Screen showing the problem

[0227] 2. The user enters the answer to the displayed question and presses the submit button.

[0228] Input: Answer

[0229] Output: Answer submission request

[0230] 3. The device sends the answer data to the server, again using an HTTP POST request.

[0231] Input: Answer

[0232] Output: HTTP POST request

[0233] 4. The server receives the answer data and uses Python code to parse it.

[0234] Input: Answer data

[0235] Output: Analysis results

[0236] Step 4:

[0237] Feedback and understanding measurement

[0238] 1. The server analyzes the user's answer and determines whether it is correct or incorrect. It compares the question and answer using Python.

[0239] Input: Answer data, question data

[0240] Output: Judgment result

[0241] 2. The server generates feedback based on the result, such as a message like "Correct!" if the answer is correct, or "Incorrect" if the answer is incorrect.

[0242] Input: Judgment result

[0243] Output: Feedback message

[0244] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[0245] Input: Answer data, answer history

[0246] Output: New problem difficulty

[0247] 4. The server sends the feedback and new questions to the device.

[0248] Input: Feedback message, new issue

[0249] Output: HTTP POST request

[0250] Step 5:

[0251] View feedback and new issues

[0252] 1. The device displays feedback and new problems to the user, dynamically updating the display using HTML and JavaScript.

[0253] Input: Feedback message, new issue

[0254] Output: A screen showing feedback and new questions

[0255] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, and encourages the user, for example by displaying a message such as "The intelligent system also got the answer right!"

[0256] Input: Answer status of the intelligent system

[0257] Output: Intelligent system solution status message

[0258] 3. The user revisits the problem with a new one.

[0259] Input: None

[0260] Output: Answer submission request

[0261] Step 6:

[0262] Ending a session

[0263] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[0264] Input: None

[0265] Output: Session termination request

[0266] 2. The device sends an HTTP POST request to the server to end the learning session.

[0267] Input: Session termination request

[0268] Output: HTTP POST request

[0269] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('user ID', NOW())".

[0270] Input: User ID, end time

[0271] Output: Database save result, completion confirmation message

[0272] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[0273] Input: Exit confirmation message

[0274] Output: Display session termination message

[0275] (Application example 1)

[0276] 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."

[0277] In today's commercial environment, new staff are required to quickly acquire customer service skills. However, traditional training methods are not based on real-world scenarios, making it difficult to achieve effective learning outcomes, and delays in feedback can lead to a lack of motivation. Furthermore, a lack of training environments that address the specific problems new staff face in brick-and-mortar stores makes efficient learning difficult.

[0278] 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.

[0279] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an AI as a virtual competitor and providing learning in a competitive format with the user, means for generating scenario-based questions for the user to master customer service skills in a physical store in real time, and means for inputting and assisting the user's answers using a smart device. This enables new staff to effectively and continuously improve their customer service skills while receiving immediate feedback through specific customer service scenarios.

[0280] "User" means an individual or organization that uses the system for learning or training.

[0281] "Learning progress" is an indicator of how far a user has progressed in the learning process.

[0282] "Appropriate problems" are tasks and questions that are provided according to the user's learning progress and level, and are designed to maximize learning effectiveness.

[0283] An "answer" is a response or answer that a user provides to a posed question.

[0284] "Real time" is a time concept that indicates that processing is performed immediately without delay.

[0285] "Analysis" is the process of evaluating the user's answer to determine its correctness.

[0286] "Feedback" is information that provides evaluation and advice on the user's answer.

[0287] A "virtual competitor" is a competitive object, such as an artificial intelligence (AI), that is set up to compete with the user.

[0288] "AI" stands for artificial intelligence, a technology that allows computer programs to mimic human intelligence.

[0289] "Scenario-based questions" are learning tasks constructed based on real-world business scenarios.

[0290] A "smart device" is an electronic device that has internet connectivity and computing capabilities.

[0291] "Input" is the act of a user providing information to a system.

[0292] "Assistance" is the process of providing support to help users operate or learn more effectively.

[0293] This invention relates to a training system for brick-and-mortar stores to improve users' customer service skills. The system operates among a server, smart glasses as a terminal, and the user, and is designed to allow users to progress in their learning while receiving effective and immediate feedback.

[0294] Hardware or software used

[0295] Server: Cloud servers and on-premise servers are used. SQLite is used as the database.

[0296] Device: Smart glasses (e.g. Google Glass)

[0297] Software: Python, SQLite for database management, and the operating system of the smart glasses (e.g., Android)

[0298] System Operation

[0299] 1. Initial Setup and Login

[0300] The server stores the user's account information in a database, and the user logs in through the smart glasses. The server authenticates the user, and if authentication is successful, obtains the user's learning progress.

[0301] 2. Start your study session

[0302] After logging in, the user clicks the "Start Learning" button, which sends a request to the server to start a learning session. The server generates appropriate scenario-based questions based on the user's learning history and sends them to the smart glasses.

[0303] 3. Displaying and answering questions

[0304] The smart glasses display the questions received from the server to the user, who then inputs the answers to the questions using voice or tactile input, which are then sent to the server via the smart glasses.

[0305] 4. Feedback and Gauging Understanding

[0306] The server analyzes the user's answers in real time, determines whether they are correct or incorrect, generates feedback, and sends it to the smart glasses. It also evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0307] 5. Displaying new questions and trying again

[0308] The smart glasses display feedback and new questions, showing the progress of AI competitors, motivating users to learn and encourage them to tackle the next problem.

[0309] Specific scenario example

[0310] Example prompt: "How do you respond when a customer asks you about a particular product?"

[0311] Example user answer: "Tell me more about the product"

[0312] In this way, users can improve their skills through specific customer service scenarios in real time. The system provides users with the information they need instantly and provides effective feedback, allowing new staff to learn efficiently and quickly adapt to work in a brick-and-mortar store.

[0313] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0314] Step 1: Initial Setup and Login

[0315] Specific actions

[0316] The server stores user account information in a database and provides a mechanism for authentication.

[0317] input

[0318] The user enters their username and password through the smart glasses.

[0319] Data processing / data calculation

[0320] The server checks the entered username and password against the information in the database and performs authentication.

[0321] output

[0322] If authentication is successful, the user's learning progress data is retrieved and login is completed.

[0323] Step 2: Start your study session

[0324] Specific actions

[0325] The user clicks the "Start Learning" button.

[0326] input

[0327] A request to start a learning session is sent from the terminal to the server.

[0328] Data processing / data calculation

[0329] The server generates appropriate scenario-based questions based on the user's learning history.

[0330] output

[0331] The generated questions are sent to the device.

[0332] Step 3: View and answer questions

[0333] Specific actions

[0334] The terminal displays the problem received from the server to the user.

[0335] input

[0336] The problem data sent by the server.

[0337] Data processing / data calculation

[0338] The terminal displays the content of the problem in a format suitable for the user.

[0339] output

[0340] The user inputs answers through voice input or tactile manipulation.

[0341] Step 4: Submit your answers

[0342] Specific actions

[0343] The user enters the answer and sends it from the terminal to the server.

[0344] input

[0345] User answer data.

[0346] Data processing / data calculation

[0347] The terminal converts the answer data into a format for transmission to the server.

[0348] output

[0349] The answer data is sent to the server.

[0350] Step 5: Feedback and Gauging

[0351] Specific actions

[0352] The server analyzes the user's answers and evaluates the results.

[0353] input

[0354] User answer data.

[0355] Data processing / data calculation

[0356] The server determines whether the answer is correct or incorrect, generates feedback, and evaluates the user's understanding.

[0357] output

[0358] Evaluation data is generated as feedback, and the difficulty of the next question presented is adjusted.

[0359] Step 6: View feedback and new issues

[0360] Specific actions

[0361] The terminal displays the feedback received from the server and any new questions to the user.

[0362] input

[0363] Feedback data and new problem data from the server.

[0364] Data processing / data calculation

[0365] The terminal converts the data into a format for display and presents it to the user visually or audibly.

[0366] output

[0367] The user revisits the new problem.

[0368] Step 7: Ending the study session

[0369] Specific actions

[0370] The user clicks the "End Learning" button.

[0371] input

[0372] A request to end the learning session is sent from the terminal to the server.

[0373] Data processing / data calculation

[0374] The server stores the user's learning log in a database and confirms the end of the session.

[0375] output

[0376] A message indicating the session has ended is displayed on the terminal.

[0377] 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.

[0378] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0379] This invention is an educational support system that operates among a server, a terminal, and a user, and generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and provides a competitive learning experience by setting up an AI as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the learning content and feedback are adjusted according to the user's emotional state.

[0380] Initial Setup and Login

[0381] 1. The server stores user account information in a database and provides a mechanism for authentication.

[0382] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[0383] 3. The user enters their account information on the login screen of the device and presses the login button.

[0384] Start a study session

[0385] 1. The device displays a "Start learning" button after the user logs in.

[0386] 2. The user clicks the Start Learning button to begin the learning session.

[0387] 3. The device sends a request to the server to start a learning session.

[0388] 4. The server receives this request and retrieves the user's learning history and current learning progress from the database.

[0389] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[0390] Displaying and answering questions

[0391] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[0392] 2. The user enters the answer to the displayed question and presses the submit button.

[0393] 3. The device sends the answer to the server.

[0394] Feedback and understanding measurement

[0395] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[0396] 2. The server generates feedback based on the result (e.g., "Correct!").

[0397] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0398] 4. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[0399] View feedback and new issues

[0400] 1. The device displays feedback and new questions to the user.

[0401] 2. The device displays the answer status of the AI's virtual friend and encourages the user (e.g., "The AI ​​got the answer right too!").

[0402] Emotion Engine Operation

[0403] 1. The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[0404] 2. The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, etc.).

[0405] 3. The device sends the emotion engine results to the server.

[0406] 4. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[0407] 5. The device displays tailored feedback and problems to the user.

[0408] Specific examples

[0409] Scenario 1: Basic Learning Session

[0410] 1. The user logs in to a terminal and is presented with a problem (e.g., "5 + 3 = ?").

[0411] 2. The user enters the answer "8" and presses the submit button.

[0412] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[0413] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[0414] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[0415] Scenario 2: Emotion Engine in Action

[0416] 1. When a user solves a problem, if their facial expression is difficult, the emotion engine will judge it as "confused."

[0417] 2. The terminal transmits the user's confusion state to the server.

[0418] 3. The server adjusts the difficulty of the next problem it provides by slightly lowering the difficulty level and providing feedback such as "Try harder, let's try an easier problem!"

[0419] 4. The device displays new problems and tailored feedback to the user to keep them motivated.

[0420] In this way, by combining an emotion engine, the present invention can provide educational support that is adapted to the user's emotional state, maximizing learning outcomes.

[0421] The processing flow will be explained below.

[0422] Step 1:

[0423] The user opens the application on the terminal and enters the username and password on the login screen.

[0424] Step 2:

[0425] The terminal transmits the entered login information to the server.

[0426] Step 3:

[0427] The server compares the received login information with the user information in the database and performs authentication.

[0428] Step 4:

[0429] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[0430] Step 5:

[0431] The user clicks the Start Learning button.

[0432] Step 6:

[0433] The terminal sends a request to the server to start a learning session.

[0434] Step 7:

[0435] Upon receiving this request, the server retrieves the user's learning history and current learning progress from the database.

[0436] Step 8:

[0437] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[0438] Step 9:

[0439] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[0440] Step 10:

[0441] The user enters the answer to the displayed question and presses the send button.

[0442] Step 11:

[0443] The terminal sends the answer to the server.

[0444] Step 12:

[0445] The server analyzes the user's answer and determines whether it is correct or incorrect.

[0446] Step 13:

[0447] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[0448] Step 14:

[0449] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0450] Step 15:

[0451] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[0452] Step 16:

[0453] The terminal displays feedback and new questions to the user.

[0454] Step 17:

[0455] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[0456] Step 18:

[0457] The user revisits the new problem.

[0458] Step 19:

[0459] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[0460] Step 20:

[0461] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, confusion, etc.).

[0462] Step 21:

[0463] The terminal transmits the results of the emotion engine to the server.

[0464] Step 22:

[0465] The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[0466] Step 23:

[0467] The server sends the adjusted feedback and questions to the terminal.

[0468] Step 24:

[0469] The terminal displays the tailored feedback and new questions to the user.

[0470] Step 25:

[0471] The user then re-enters the answer and continues learning.

[0472] Step 26:

[0473] After a study session has been running for a certain period of time, the user clicks the end button.

[0474] Step 27:

[0475] The terminal sends a request to end the learning session to the server.

[0476] Step 28:

[0477] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[0478] Step 29:

[0479] The terminal displays a message to the user indicating that the session has ended.

[0480] Example 2

[0481] 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."

[0482] While conventional educational support systems provide individualized instruction based on the user's learning progress, they do not provide feedback or generate questions that take the user's emotional state into account, making it difficult to maintain motivation and maximize learning outcomes. Furthermore, monotonous learning formats make it difficult to maintain user motivation, and the lack of elements that stimulate competitive spirit is also problematic.

[0483] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an artificial intelligence as a virtual opponent and providing learning in a competitive format with the user, and means for analyzing the user's emotional state and adjusting the difficulty of the questions and the feedback based on that information. This makes it possible to provide individualized instruction according to the user's learning progress and emotional state, and an environment that stimulates competitive spirit.

[0484] A "user" is an individual who uses the system to carry out learning activities.

[0485] "Study progress" is an index showing the learning content and results that the user has achieved up to now.

[0486] "Question generation" refers to the process of creating new study questions based on the user's learning progress and level of understanding.

[0487] "Receiving and analyzing in real time" refers to the process of instantly sending user input (e.g., answers) to the server and instantly judging and evaluating its content.

[0488] "Feedback" refers to messages that provide evaluations and advice based on the user's answers.

[0489] "Artificial intelligence (AI)" refers to technology that enables computers to perform human-like intelligent behavior, in this case acting as learning competitors.

[0490] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, excitement, etc.), and changes depending on the learning situation.

[0491] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice to determine their emotional state.

[0492] A "virtual competitor" is a non-existent entity that mimics an opponent that competes with the user within the system, and is primarily composed of artificial intelligence.

[0493] This is an educational support system that generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and also provides competitive learning by setting up an artificial intelligence (AI) as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the quality of learning is improved by adjusting the learning content and feedback according to the user's emotional state.

[0494] Hardware and software used

[0495] Hardware

[0496] Server: Hosts a database (e.g., MySQL, PostgreSQL) to store and manage users' learning history and progress.

[0497] Terminal: The computer or smartphone used by the user, which provides the interface and sends the user's input and answers to the server.

[0498] Camera and microphone: Used to capture the user's facial expressions and voice and send them to the emotion engine.

[0499] software

[0500] Generative AI models: Used to generate questions based on the user's learning progress (e.g., OpenAI GPT-3).

[0501] Face recognition and speech analysis APIs: Used to implement emotion engines (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text).

[0502] Web Technologies: Uses HTML, CSS, JavaScript for front-end development and communicates with the server using AJAX and fetch APIs.

[0503] Specific operation of the system

[0504] Problem generation

[0505] The server retrieves the user's learning progress from the database and uses a generative AI model to generate questions of appropriate difficulty, for example, "5 + 3 = ?"

[0506] Answer analysis and feedback

[0507] When a user enters an answer to a question on the device and presses the submit button, the device sends the answer to the server. The server analyzes the answer, determines whether it is correct or incorrect, and generates feedback (e.g., "Correct!" or "Think a bit more!"). This feedback is sent back to the device and displayed to the user.

[0508] AI as a virtual competitor

[0509] The AI ​​is set up as a virtual competitor to support the user's learning. For example, the AI ​​can be displayed solving the same problem, allowing the user to compete in a learning competition. The AI's answer is also displayed (e.g., "The AI ​​got the answer right!").

[0510] Emotion Engine

[0511] The user's facial expressions and voice are captured through the camera and microphone and sent to the emotion engine. The emotion engine analyzes this data and determines the user's emotional state (e.g., joy, sadness, excitement, etc.). The server then adjusts the feedback and difficulty of the questions based on this information to provide the user with an appropriate learning experience.

[0512] Specific examples

[0513] Prompt Sentence Examples

[0514] Here are some examples of prompts to input to a generative AI model:

[0515] How can we build an educational support system that generates appropriate questions based on the user's learning progress and emotional state, and provides feedback in real time?

[0516] By using this prompt, the generative AI model is expected to generate answers detailing specific steps and implementation methods.

[0517] As described above, the system of the present invention can provide effective educational support to users, similar to individual instruction, and maximize the results of their learning.

[0518] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0519] Step 1:

[0520] The server stores user account information in a database and provides a mechanism for authentication. Specifically, it hashes the username and password provided by the user when registering and stores them in the database. The input is the username and password, and the output is the hashed account information with the password stored in the database.

[0521] Step 2:

[0522] The terminal provides an interface (username and password entry screen) that allows the user to log in. A UI is constructed to temporarily store the information entered by the user and send it to the server. The input is the username and password, and the input information is sent to the server as the output.

[0523] Step 3:

[0524] The user enters their account information on the login screen of the device and presses the login button. The input is a username and password, and the output is a login request sent from the device to the server.

[0525] Step 4:

[0526] The terminal displays a "Start learning" button after the user logs in. To allow the user to start learning, a dashboard screen is created and a UI including a "Start learning" button is displayed. The input is the user's successful login status, and the output is the display of a "Start learning" button.

[0527] Step 5:

[0528] The user clicks the "Start Learning" button to start a learning session. The input is the clicking of the "Start Learning" button, and the output is a learning session start request sent from the terminal to the server.

[0529] Step 6:

[0530] The device sends a learning session start request to the server. The JavaScript fetch API or AJAX is used to send the learning session start request. The input is a button click event, and the learning session start request is sent to the server as its output.

[0531] Step 7:

[0532] The server receives this request and retrieves the user's learning history and current learning progress from the database. The database is queried for the user's learning history and progress, and the results are stored in memory. The input is a learning session start request, and the output is the user's learning history and progress.

[0533] Step 8:

[0534] The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the device. A generative AI model is used to generate questions based on the user's progress. The input is the user's learning history and progress, and the generated questions are sent to the device as output.

[0535] Step 9:

[0536] The terminal displays the problem received from the server to the user. A UI for displaying the problem is constructed and the problem is displayed. The input is the problem received from the server, and the problem is displayed to the user as the output.

[0537] Step 10:

[0538] The user inputs the answer to the displayed question and presses the send button. The input is the user's answer, and the output is sent to the terminal.

[0539] Step 11:

[0540] The device sends the answer to the server. The fetch API or AJAX is used to send the entered answer to the server. The input is the user's answer, and the answer is sent to the server as the output.

[0541] Step 12:

[0542] The server analyzes the user's answer and determines whether it is correct or incorrect. The analysis is performed using server-side logic or an AI model. The input is the user's answer, and the output is the analysis result.

[0543] Step 13:

[0544] The server generates feedback based on the judgment results. It uses a feedback generation AI model to create a feedback message. The input is the analysis result, and the output is the feedback message.

[0545] Step 14:

[0546] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question they will present. It analyzes the user's answer history and determines the difficulty of the next question. The input is the user's answer history, and the output is the adjusted difficulty of the question.

[0547] Step 15:

[0548] The server sends feedback and new questions to the terminal. The generated feedback and new questions are sent to the terminal. The input is the generated feedback and new questions, and the output is sent to the terminal.

[0549] Step 16:

[0550] The terminal displays feedback and new problems to the user. The UI displays feedback messages and new problems. The input is the feedback and new problems received from the server, and the output is displayed to the user.

[0551] Step 17:

[0552] The device displays the AI's virtual friend's answer status to encourage the user. It also displays the virtual friend's answer results to motivate the user to learn. The input is the AI's answer results, which are displayed as the output.

[0553] Step 18:

[0554] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. It uses a camera and microphone to capture and send the user's facial expression and voice data. The input is the user's facial expression and voice, and the output is sent to the emotion engine.

[0555] Step 19:

[0556] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state. It uses face recognition APIs and voice analysis APIs to determine emotions. The input is facial expression data and voice data, and the output is the user's emotional state.

[0557] Step 20:

[0558] The device sends the results of the emotion engine to the server, which then sends the analysis results to prepare them for the next feedback. The input is the analysis result of the emotional state, which is then sent to the server as the output.

[0559] Step 21:

[0560] The server adjusts the difficulty of questions and the content of feedback based on the user's emotional state. Based on the determined emotional state, it determines the appropriate difficulty of questions and feedback. The input is the analysis result of the emotional state, and the adjusted questions and feedback are obtained as the output.

[0561] Step 22:

[0562] The terminal displays the adjusted feedback and questions to the user. It displays the adjusted feedback and new questions received from the server. The input is the adjusted feedback and questions, and the output is displayed to the user.

[0563] (Application example 2)

[0564] 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."

[0565] Conventional educational systems struggle to provide appropriate feedback and questions based on each learner's individual progress and emotional state, making it difficult to maximize learning outcomes. Furthermore, the ability to improve learning motivation through competition with virtual opponents is limited. Furthermore, the lack of real-time answer analysis and feedback prevents rapid improvement in understanding. Therefore, a comprehensive educational support system that effectively and continuously increases students' motivation to learn is needed.

[0566] 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.

[0567] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for providing learning in a competitive format by setting up an artificial intelligence as a virtual opponent, and means for recognizing the user's emotions and adjusting the learning content. This makes it possible to provide questions and feedback tailored to the progress and emotional state of each learner, thereby effectively and sustainably increasing their motivation to learn.

[0568] "User" refers to an individual or learner who uses the educational support system.

[0569] "Study progress" refers to the level or stage of progress a user has achieved through learning.

[0570] "Problems" are questions or tasks provided to assess a user's learning.

[0571] An "answer" is a response or response provided by a user to a question.

[0572] "Real-time" refers to processing and response occurring almost immediately, with little delay.

[0573] "Feedback" is any rating or comment provided on a user's answer.

[0574] A "virtual competitor" is an artificial intelligence set up to compete with the user within the system.

[0575] "Artificial intelligence" refers to software or systems that learn and make decisions like humans through machine learning and data analysis.

[0576] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[0577] "Difficulty of a question" is an index that indicates how difficult it is to solve a question.

[0578] This invention is an educational support system that operates among three parties: a server, a terminal, and a user, and provides learning support by combining question generation based on the user's learning progress, answer analysis, feedback provision, competition with a virtual opponent (artificial intelligence), and emotion recognition.

[0579] Initial Setup and Login

[0580] The server stores user account information in a database and provides a mechanism for authentication. The terminal provides an interface (a screen for entering a username and password) that allows the user to log in. The user enters their account information on the terminal's login screen and presses the login button.

[0581] Start a study session

[0582] After the user logs in, the terminal displays a "Start learning" button. The user clicks the start learning button to start the learning session. The terminal sends a request to start the learning session to the server. The server receives this request and retrieves the user's learning history and current learning progress from the database. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[0583] Displaying and answering questions

[0584] The terminal displays the questions received from the server to the user. The user enters the answer to the displayed question and presses the send button. The terminal then sends the answer to the server.

[0585] Feedback and understanding measurement

[0586] The server analyzes the user's answers and determines whether they are correct or incorrect. The server generates feedback based on the results of the evaluation (e.g., "Correct!"). The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[0587] View feedback and new issues

[0588] The terminal displays feedback and new problems to the user. The terminal also displays the answer status of a virtual competitor (artificial intelligence) and encourages the user (e.g., "The AI ​​got the answer right too!").

[0589] Emotion Engine Operation

[0590] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and determines their current emotional state (e.g., joy, sadness, excitement, etc.). The device sends the emotion engine's results to the server. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state. The device displays the adjusted feedback and questions to the user.

[0591] Specific examples

[0592] Basic Learning Session

[0593] The user logs in to the terminal and a question (e.g., "5 + 3 = ?") is displayed. The user enters the answer "8" and presses the submit button. The terminal sends the answer to the server and receives feedback that the answer is correct (e.g., "Correct!"). The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal. The terminal displays the new question along with the answer of a virtual competitor (artificial intelligence) (e.g., "AI got it right too!").

[0594] Emotion Engine Operation

[0595] When a user tries to solve a problem, if their facial expression shows difficulty, the emotion engine will judge this as "confusion." The device will then send the user's confused state to the server. The server will then slightly lower the difficulty of the next problem and adjust the feedback to "Try harder and try an easier problem!" The device will then display the new problem and the adjusted feedback to the user, maintaining their motivation.

[0596] Prompt Sentence Examples

[0597] Enter your username and password to sign in:

[0598] text

[0599] Username: example_user, Password: password123

[0600] Submit your answer to the question:

[0601] text

[0602] Username: example_user, Answer: 8

[0603] Sending user facial expression data:

[0604] text

[0605] User name: example_user, Facial image data:<base64_encoded_image>

[0606] In this way, the server of the present invention is capable of managing learning progress, analyzing answers in real time, providing feedback, setting up virtual opponents, and adjusting through emotion recognition, etc. This allows for the realization of advanced educational support that is adapted to the individual needs of each learner.

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

[0608] Step 1:

[0609] The user accesses the login interface using a terminal, enters a username and password, and clicks the login button.

[0610] Input: Username, Password

[0611] Data processing: Convert user authentication information into JSON format to send to the server.

[0612] Output: An authentication request is sent to the server.

[0613] Step 2:

[0614] The server receives the login request, retrieves the user's account information from the database, and performs authentication. If authentication is successful, it returns a login success message.

[0615] Input: Authentication request (username, password)

[0616] Data calculation: Check the username and password.

[0617] Output: Authentication result (success or failure)

[0618] Step 3:

[0619] The terminal displays a "Start Learning" button after the user has successfully logged in.

[0620] Input: Authentication result (success)

[0621] Data processing: Creating a UI for displaying buttons

[0622] Output: A "Start learning" button will be displayed.

[0623] Step 4:

[0624] The user clicks the "Start Learning" button. The device captures this event and sends a request to the server to start a learning session.

[0625] Input: User clicks the button event

[0626] Data processing: generating a request to start a learning session

[0627] Output: A request to start a learning session is sent to the server.

[0628] Step 5:

[0629] The server receives a request to start a learning session, retrieves the user's learning history and current learning progress from a database, and generates questions of appropriate difficulty based on this information and sends them to the terminal.

[0630] Input: Learning session start request

[0631] Data calculation: Obtaining user's learning history and progress, generating questions

[0632] Output: The question is generated and sent to the terminal.

[0633] Step 6:

[0634] The terminal displays the questions received from the server to the user.

[0635] Input: Problem data

[0636] Data processing: Creating a UI for displaying the problem

[0637] Output: The problem is displayed to the user.

[0638] Step 7:

[0639] The user inputs the answer to the displayed question and presses the send button, and the terminal sends the answer to the server.

[0640] Input: User's answer

[0641] Data processing: Generate a request to send answers

[0642] Output: The answer is sent to the server.

[0643] Step 8:

[0644] The server receives the user's answer, analyzes it in real time, and determines whether it is correct or incorrect. Based on the results, it generates feedback and creates new questions to send to the device.

[0645] Input: User's answer

[0646] Data operations: analyzing answers, generating feedback, generating new questions

[0647] Output: Feedback and new questions are sent to the device.

[0648] Step 9:

[0649] The terminal displays the feedback received from the server and new questions to the user, as well as the progress of the virtual competitor's (artificial intelligence) solutions.

[0650] Input: Feedback, new questions, AI solution status

[0651] Data processing: Creating a UI for displaying feedback, questions, and AI solution status

[0652] Output: Feedback and new questions are displayed to the user.

[0653] Step 10:

[0654] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[0655] Input: User's facial expression and voice data

[0656] Data processing: Capture and transmit facial and voice data

[0657] Output: Facial expression and voice data is sent to the emotion engine.

[0658] Step 11:

[0659] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state, and returns the results to the device.

[0660] Input: facial expression data, voice data

[0661] Data Computing: Emotional State Analysis

[0662] Output: Emotional state judgment result

[0663] Step 12:

[0664] The device sends the results of the emotion engine to the server, which adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state and sends them to the device, which then displays the adjusted feedback and questions to the user.

[0665] Input: Emotional state judgment result

[0666] Data calculation: Adjusting the difficulty of questions and feedback

[0667] Output: Tailored feedback and problems are displayed to the user.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] [Second embodiment]

[0672] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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).

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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."

[0684] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[0685] This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server mainly generates questions and analyzes answers, while the terminal accepts user operations and smoothly advances the learning process through communication with the server. Users operate the terminal to engage in learning.

[0686] Initial Setup and Login

[0687] 1. The server stores user account information in a database and provides a mechanism for authentication.

[0688] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[0689] 3. The user enters their account information on the login screen of the device and presses the login button.

[0690] Start a study session

[0691] 1. The device displays a "Start learning" button after the user logs in.

[0692] 2. The user clicks the Start Learning button to begin the learning session.

[0693] 3. The device sends a request to the server to start a learning session.

[0694] 4. The server receives this request and retrieves the user's learning history from the database.

[0695] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[0696] Displaying and answering questions

[0697] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[0698] 2. The user enters the answer to the displayed question (e.g., "8") and presses the submit button.

[0699] 3. The device sends the answer to the server.

[0700] Feedback and understanding measurement

[0701] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[0702] 2. The server generates feedback (e.g., "Correct!") based on the result of the judgment.

[0703] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0704] 4. The server sends feedback and a new question (e.g., "7 - 4 = ?") to the device.

[0705] View feedback and new issues

[0706] 1. The device displays feedback and new questions to the user.

[0707] 2. The device displays the answer status of the AI's virtual friend (e.g., "The AI ​​also got the answer right!") to encourage the user.

[0708] 3. The user revisits the problem with a new one.

[0709] Ending a session

[0710] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[0711] 2. The device sends a request to the server to terminate the learning session.

[0712] 3. The server saves the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[0713] 4. The terminal displays a message to the user indicating that the session has ended.

[0714] Specific examples

[0715] Scenario 1: Basic Learning Session

[0716] 1. The user logs in to a terminal and is presented with the problem "5 + 3 = ?"

[0717] 2. The user enters the answer "8" and presses the submit button.

[0718] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[0719] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[0720] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[0721] Scenario 2: Problem suggestions based on level of understanding

[0722] 1. The user answers "3" to the question "7 - 4 = ?"

[0723] 2. The server evaluates the level of understanding with the feedback "Correct!" and generates the next question (e.g., "8 + 6 = ?").

[0724] 3. The device displays the new problem and feedback to the user, prompting them to tackle the next problem.

[0725] In this way, the present invention can provide questions that are optimized for the user's learning progress and maintain high learning motivation through AI virtual competition.

[0726] The processing flow will be explained below.

[0727] Step 1:

[0728] The user opens the application on the terminal and enters the username and password on the login screen.

[0729] Step 2:

[0730] The terminal transmits the entered login information to the server.

[0731] Step 3:

[0732] The server compares the received login information with the user information in the database and performs authentication.

[0733] Step 4:

[0734] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[0735] Step 5:

[0736] The user clicks the Start Learning button.

[0737] Step 6:

[0738] The terminal sends a request to the server to start a learning session.

[0739] Step 7:

[0740] The server receives this request and retrieves the user's learning history and current learning progress from the database.

[0741] Step 8:

[0742] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[0743] Step 9:

[0744] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[0745] Step 10:

[0746] The user enters the answer to the displayed question and presses the send button.

[0747] Step 11:

[0748] The terminal transmits the user's answer to the server.

[0749] Step 12:

[0750] The server analyzes the user's answer and determines whether it is correct or incorrect.

[0751] Step 13:

[0752] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[0753] Step 14:

[0754] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0755] Step 15:

[0756] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[0757] Step 16:

[0758] The terminal displays feedback and new questions to the user.

[0759] Step 17:

[0760] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[0761] Step 18:

[0762] The user revisits the new problem.

[0763] Step 19:

[0764] After a study session has been running for a certain period of time, the user clicks the end button.

[0765] Step 20:

[0766] The terminal sends a request to end the learning session to the server.

[0767] Step 21:

[0768] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[0769] Step 22:

[0770] The terminal displays a message to the user indicating that the session has ended.

[0771] Example 1

[0772] 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."

[0773] Conventional learning systems have issues with insufficient individual response to the user's learning progress and can only provide fixed questions and feedback. Furthermore, they do not sufficiently motivate users, which tends to reduce their motivation to learn, especially in self-study. Furthermore, they lack a mechanism to stimulate competitive spirit, meaning users lack the experience of competing with other learners or the system.

[0774] 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.

[0775] In this invention, the server includes means for generating appropriate study tasks based on the user's learning progress, means for receiving and analyzing the user's input in real time, means for providing feedback to the user, means for setting an intelligent system as a virtual competitor and providing a form of learning in which the user competes with the system, means for saving the user's account information in a database and authenticating it, means for providing a login screen and prompting the user to enter their account information, means for acquiring the user's answer history and generating new questions, and means for evaluating the user's level of understanding and adjusting the difficulty of the new questions. This makes it possible to provide questions that correspond to the individual user's learning progress and to increase motivation to learn through competition with virtual competitors.

[0776] "User's learning progress" refers to the level of understanding and mastery that the user has achieved in the course of performing learning activities.

[0777] "Study assignments" is a general term for problems, exercises, and assignments presented to the user.

[0778] "Input" refers to the act of a user inputting answers and information for a learning task into a terminal.

[0779] "Real time" refers to a state in which user operations or inputs are processed immediately without delay.

[0780] "Analysis" refers to the process by which the server analyzes the user's input and determines its correctness and appropriateness.

[0781] "Feedback" refers to the evaluation or response provided to a user's actions or answers.

[0782] "Virtual competitors" refer to intelligent systems or agents configured on the system, rather than other real users.

[0783] "Intelligent System" refers to the artificial intelligence programmed on the system to compete with the user.

[0784] "Account information" is information for identifying a user, and generally includes a username, password, email address, and the like.

[0785] A "database" refers to a system for systematically storing and managing large amounts of data.

[0786] "Authentication" refers to the process of verifying access rights when a user uses their account information to access a system.

[0787] "Login screen" refers to an interface for a user to enter account information to access a system.

[0788] "Answer history" refers to a record of the study tasks that the user has answered up to now.

[0789] "Level of understanding" refers to the degree to which the user can accurately understand and answer the learning task.

[0790] "Difficulty of the problem" refers to the level of difficulty of the learning task.

[0791] A specific embodiment of the present invention will now be described. This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server is primarily responsible for generating questions and analyzing answers, while the terminal accepts user operations and smoothly advances the study process through communication with the server. Users engage in study by operating the terminal.

[0792] Hardware and software used

[0793] This system uses the following hardware and software:

[0794] Server: A server machine (e.g., Linux server) equipped with a powerful processor, sufficient memory, and large storage capacity.

[0795] Database software: MySQL

[0796] Programming languages: Python, JavaScript

[0797] Web technologies: HTML, CSS, JavaScript

[0798] Communication protocol: HTTP / HTTPS

[0799] Initial Setup and Login

[0800] 1. The server stores user account information in a MySQL database and provides an authentication mechanism, ensuring the safe management of user personal data.

[0801] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. To do this, a login form is created using HTML and CSS, and the input is validated using JavaScript.

[0802] 3. The user enters their account information on the login screen of the device and presses the login button.

[0803] 4. The device sends the user's input data to the server via an HTTP POST request.

[0804] 5. The server checks the submitted user data against the database, and if authentication is successful, generates session information and returns it to the client.

[0805] Start a study session

[0806] 1. The device displays a "Start learning" button after the user logs in. This button is displayed using HTML and CSS.

[0807] 2. The user clicks the "Start Learning" button.

[0808] 3. Based on the user's operation, the device sends an HTTP POST request to the server to start a learning session.

[0809] 4. The server receives this request and retrieves the user's learning history from the database. For example, it executes the SQL query "SELECT FROM learning history WHERE user ID = '12345'".

[0810] 5. The server generates questions of appropriate difficulty based on the learning history and returns them to the device. This problem generation is done using a question generation algorithm written in Python.

[0811] Displaying and answering questions

[0812] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[0813] 2. The user enters the answer to the displayed question and presses the submit button.

[0814] 3. The device sends the answer data to the server using an HTTP POST request.

[0815] 4. The server receives the answer data and uses Python code to parse it.

[0816] Feedback and understanding measurement

[0817] 1. The server analyzes the user's answers and determines whether they are correct or incorrect. It uses Python to compare the questions and answers.

[0818] 2. The server generates feedback based on the result, such as "Correct!" if the answer is correct, or "Incorrect!" if the answer is incorrect.

[0819] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[0820] 4. The server sends the feedback and new questions to the device.

[0821] View feedback and new issues

[0822] 1. The device displays feedback and new questions to the user, dynamically updating the display using HTML and JavaScript.

[0823] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, to encourage the user. For example, it displays the message "The intelligent system also got the answer right!"

[0824] 3. The user revisits the problem with a new one.

[0825] Ending a session

[0826] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[0827] 2. The device sends an HTTP POST request to the server to end the learning session.

[0828] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('12345', NOW())".

[0829] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[0830] Examples of prompt statements

[0831] By inputting prompt sentences like the following into the generative AI model, we can generate appropriate feedback:

[0832] "User answers 8 to the question 5 + 3 = ?. Please generate a rating and feedback."

[0833] By entering this prompt, the AI ​​can generate accurate feedback (e.g., "Correct!") and provide it to the user.

[0834] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0835] Step 1:

[0836] Initial Setup and Login

[0837] 1. The server stores user account information in a database and provides a mechanism for authentication. Specifically, it stores usernames and hashed passwords in a MySQL database.

[0838] Input: Username, hashed password

[0839] Output: Save results to database

[0840] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. Create a login form using HTML and CSS, and validate the input using JavaScript.

[0841] Input: None

[0842] Output: Login form screen

[0843] 3. The user enters their username and password on the login screen and clicks the login button.

[0844] Input: Username, Password

[0845] Output: Login request

[0846] 4. The device sends the user's input data to the server using an HTTP POST request.

[0847] Input: Username, Password

[0848] Output: HTTP POST request

[0849] 5. The server checks the submitted user data against the database information, and if authentication is successful, generates session information and returns it to the client. The check is performed using an SQL query that retrieves data from the database.

[0850] Input: Username, Password

[0851] Output: Session information, authentication results

[0852] Step 2:

[0853] Start a study session

[0854] 1. The device displays a "Start Learning" button after the user logs in. Generate the button using HTML and CSS.

[0855] Input: Authentication completed session information

[0856] Output: "Start learning" button

[0857] 2. The user clicks the Start Learning button.

[0858] Input: None

[0859] Output: Training start request

[0860] 3. The device sends a learning session start request to the server based on the user's operation using an HTTP POST request.

[0861] Input: Learning start request

[0862] Output: HTTP POST request

[0863] 4. The server receives this request and retrieves the user's learning history from the database. Specifically, it executes the SQL query "SELECT FROM learning history WHERE user ID = 'user ID'".

[0864] Input: User ID

[0865] Output: Learning history data

[0866] 5. The server generates questions of appropriate difficulty based on the learning history and sends them back to the device. Question generation is done using Python.

[0867] Input: Learning history data

[0868] Output: Generated problem

[0869] Step 3:

[0870] Displaying and answering questions

[0871] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[0872] Input: Generated question

[0873] Output: Screen showing the problem

[0874] 2. The user enters the answer to the displayed question and presses the submit button.

[0875] Input: Answer

[0876] Output: Answer submission request

[0877] 3. The device sends the answer data to the server, again using an HTTP POST request.

[0878] Input: Answer

[0879] Output: HTTP POST request

[0880] 4. The server receives the answer data and uses Python code to parse it.

[0881] Input: Answer data

[0882] Output: Analysis results

[0883] Step 4:

[0884] Feedback and understanding measurement

[0885] 1. The server analyzes the user's answer and determines whether it is correct or incorrect. It compares the question and answer using Python.

[0886] Input: Answer data, question data

[0887] Output: Judgment result

[0888] 2. The server generates feedback based on the result, such as a message like "Correct!" if the answer is correct, or "Incorrect" if the answer is incorrect.

[0889] Input: Judgment result

[0890] Output: Feedback message

[0891] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[0892] Input: Answer data, answer history

[0893] Output: New problem difficulty

[0894] 4. The server sends the feedback and new questions to the device.

[0895] Input: Feedback message, new issue

[0896] Output: HTTP POST request

[0897] Step 5:

[0898] View feedback and new issues

[0899] 1. The device displays feedback and new problems to the user, dynamically updating the display using HTML and JavaScript.

[0900] Input: Feedback message, new issue

[0901] Output: A screen showing feedback and new questions

[0902] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, and encourages the user, for example by displaying a message such as "The intelligent system also got the answer right!"

[0903] Input: Answer status of the intelligent system

[0904] Output: Intelligent system solution status message

[0905] 3. The user revisits the problem with a new one.

[0906] Input: None

[0907] Output: Answer submission request

[0908] Step 6:

[0909] Ending a session

[0910] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[0911] Input: None

[0912] Output: Session termination request

[0913] 2. The device sends an HTTP POST request to the server to end the learning session.

[0914] Input: Session termination request

[0915] Output: HTTP POST request

[0916] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('user ID', NOW())".

[0917] Input: User ID, end time

[0918] Output: Database save result, completion confirmation message

[0919] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[0920] Input: Exit confirmation message

[0921] Output: Display session termination message

[0922] (Application example 1)

[0923] 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."

[0924] In today's commercial environment, new staff are required to quickly acquire customer service skills. However, traditional training methods are not based on real-world scenarios, making it difficult to achieve effective learning outcomes, and delays in feedback can lead to a lack of motivation. Furthermore, a lack of training environments that address the specific problems new staff face in brick-and-mortar stores makes efficient learning difficult.

[0925] 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.

[0926] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an AI as a virtual competitor and providing learning in a competitive format with the user, means for generating scenario-based questions for the user to master customer service skills in a physical store in real time, and means for inputting and assisting the user's answers using a smart device. This enables new staff to effectively and continuously improve their customer service skills while receiving immediate feedback through specific customer service scenarios.

[0927] "User" means an individual or organization that uses the system for learning or training.

[0928] "Learning progress" is an indicator of how far a user has progressed in the learning process.

[0929] "Appropriate problems" are tasks and questions that are provided according to the user's learning progress and level, and are designed to maximize learning effectiveness.

[0930] An "answer" is a response or answer that a user provides to a posed question.

[0931] "Real time" is a time concept that indicates that processing is performed immediately without delay.

[0932] "Analysis" is the process of evaluating the user's answer to determine its correctness.

[0933] "Feedback" is information that provides evaluation and advice on the user's answer.

[0934] A "virtual competitor" is a competitive object, such as an artificial intelligence (AI), that is set up to compete with the user.

[0935] "AI" stands for artificial intelligence, a technology that allows computer programs to mimic human intelligence.

[0936] "Scenario-based questions" are learning tasks constructed based on real-world business scenarios.

[0937] A "smart device" is an electronic device that has internet connectivity and computing capabilities.

[0938] "Input" is the act of a user providing information to a system.

[0939] "Assistance" is the process of providing support to help users operate or learn more effectively.

[0940] This invention relates to a training system for brick-and-mortar stores to improve users' customer service skills. The system operates among a server, smart glasses as a terminal, and the user, and is designed to allow users to progress in their learning while receiving effective and immediate feedback.

[0941] Hardware or software used

[0942] Server: Cloud servers and on-premise servers are used. SQLite is used as the database.

[0943] Device: Smart glasses (e.g. Google Glass)

[0944] Software: Python, SQLite for database management, and the operating system of the smart glasses (e.g., Android)

[0945] System Operation

[0946] 1. Initial Setup and Login

[0947] The server stores the user's account information in a database, and the user logs in through the smart glasses. The server authenticates the user, and if authentication is successful, obtains the user's learning progress.

[0948] 2. Start your study session

[0949] After logging in, the user clicks the "Start Learning" button, which sends a request to the server to start a learning session. The server generates appropriate scenario-based questions based on the user's learning history and sends them to the smart glasses.

[0950] 3. Displaying and answering questions

[0951] The smart glasses display the questions received from the server to the user, who then inputs the answers to the questions using voice or tactile input, which are then sent to the server via the smart glasses.

[0952] 4. Feedback and Gauging Understanding

[0953] The server analyzes the user's answers in real time, determines whether they are correct or incorrect, generates feedback, and sends it to the smart glasses. It also evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[0954] 5. Displaying new questions and trying again

[0955] The smart glasses display feedback and new questions, showing the progress of AI competitors, motivating users to learn and encourage them to tackle the next problem.

[0956] Specific scenario example

[0957] Example prompt: "How do you respond when a customer asks you about a particular product?"

[0958] Example user answer: "Tell me more about the product"

[0959] In this way, users can improve their skills through specific customer service scenarios in real time. The system provides users with the information they need instantly and provides effective feedback, allowing new staff to learn efficiently and quickly adapt to work in a brick-and-mortar store.

[0960] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0961] Step 1: Initial Setup and Login

[0962] Specific actions

[0963] The server stores user account information in a database and provides a mechanism for authentication.

[0964] input

[0965] The user enters their username and password through the smart glasses.

[0966] Data processing / data calculation

[0967] The server checks the entered username and password against the information in the database and performs authentication.

[0968] output

[0969] If authentication is successful, the user's learning progress data is retrieved and login is completed.

[0970] Step 2: Start your study session

[0971] Specific actions

[0972] The user clicks the "Start Learning" button.

[0973] input

[0974] A request to start a learning session is sent from the terminal to the server.

[0975] Data processing / data calculation

[0976] The server generates appropriate scenario-based questions based on the user's learning history.

[0977] output

[0978] The generated questions are sent to the device.

[0979] Step 3: View and answer questions

[0980] Specific actions

[0981] The terminal displays the problem received from the server to the user.

[0982] input

[0983] The problem data sent by the server.

[0984] Data processing / data calculation

[0985] The terminal displays the content of the problem in a format suitable for the user.

[0986] output

[0987] The user inputs answers through voice input or tactile manipulation.

[0988] Step 4: Submit your answers

[0989] Specific actions

[0990] The user enters the answer and sends it from the terminal to the server.

[0991] input

[0992] User answer data.

[0993] Data processing / data calculation

[0994] The terminal converts the answer data into a format for transmission to the server.

[0995] output

[0996] The answer data is sent to the server.

[0997] Step 5: Feedback and Gauging

[0998] Specific actions

[0999] The server analyzes the user's answers and evaluates the results.

[1000] input

[1001] User answer data.

[1002] Data processing / data calculation

[1003] The server determines whether the answer is correct or incorrect, generates feedback, and evaluates the user's understanding.

[1004] output

[1005] Evaluation data is generated as feedback, and the difficulty of the next question presented is adjusted.

[1006] Step 6: View feedback and new issues

[1007] Specific actions

[1008] The terminal displays the feedback received from the server and any new questions to the user.

[1009] input

[1010] Feedback data and new problem data from the server.

[1011] Data processing / data calculation

[1012] The terminal converts the data into a format for display and presents it to the user visually or audibly.

[1013] output

[1014] The user revisits the new problem.

[1015] Step 7: Ending the study session

[1016] Specific actions

[1017] The user clicks the "End Learning" button.

[1018] input

[1019] A request to end the learning session is sent from the terminal to the server.

[1020] Data processing / data calculation

[1021] The server stores the user's learning log in a database and confirms the end of the session.

[1022] output

[1023] A message indicating the session has ended is displayed on the terminal.

[1024] 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.

[1025] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[1026] This invention is an educational support system that operates among a server, a terminal, and a user, and generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and provides a competitive learning experience by setting up an AI as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the learning content and feedback are adjusted according to the user's emotional state.

[1027] Initial Setup and Login

[1028] 1. The server stores user account information in a database and provides a mechanism for authentication.

[1029] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[1030] 3. The user enters their account information on the login screen of the device and presses the login button.

[1031] Start a study session

[1032] 1. The device displays a "Start learning" button after the user logs in.

[1033] 2. The user clicks the Start Learning button to begin the learning session.

[1034] 3. The device sends a request to the server to start a learning session.

[1035] 4. The server receives this request and retrieves the user's learning history and current learning progress from the database.

[1036] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[1037] Displaying and answering questions

[1038] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[1039] 2. The user enters the answer to the displayed question and presses the submit button.

[1040] 3. The device sends the answer to the server.

[1041] Feedback and understanding measurement

[1042] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[1043] 2. The server generates feedback based on the result (e.g., "Correct!").

[1044] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1045] 4. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[1046] View feedback and new issues

[1047] 1. The device displays feedback and new questions to the user.

[1048] 2. The device displays the answer status of the AI's virtual friend and encourages the user (e.g., "The AI ​​got the answer right too!").

[1049] Emotion Engine Operation

[1050] 1. The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[1051] 2. The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, etc.).

[1052] 3. The device sends the emotion engine results to the server.

[1053] 4. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[1054] 5. The device displays tailored feedback and problems to the user.

[1055] Specific examples

[1056] Scenario 1: Basic Learning Session

[1057] 1. The user logs in to a terminal and is presented with a problem (e.g., "5 + 3 = ?").

[1058] 2. The user enters the answer "8" and presses the submit button.

[1059] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[1060] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[1061] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[1062] Scenario 2: Emotion Engine in Action

[1063] 1. When a user solves a problem, if their facial expression is difficult, the emotion engine will judge it as "confused."

[1064] 2. The terminal transmits the user's confusion state to the server.

[1065] 3. The server adjusts the difficulty of the next problem it provides by slightly lowering the difficulty level and providing feedback such as "Try harder, let's try an easier problem!"

[1066] 4. The device displays new problems and tailored feedback to the user to keep them motivated.

[1067] In this way, by combining an emotion engine, the present invention can provide educational support that is adapted to the user's emotional state, maximizing learning outcomes.

[1068] The processing flow will be explained below.

[1069] Step 1:

[1070] The user opens the application on the terminal and enters the username and password on the login screen.

[1071] Step 2:

[1072] The terminal transmits the entered login information to the server.

[1073] Step 3:

[1074] The server compares the received login information with the user information in the database and performs authentication.

[1075] Step 4:

[1076] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[1077] Step 5:

[1078] The user clicks the Start Learning button.

[1079] Step 6:

[1080] The terminal sends a request to the server to start a learning session.

[1081] Step 7:

[1082] Upon receiving this request, the server retrieves the user's learning history and current learning progress from the database.

[1083] Step 8:

[1084] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[1085] Step 9:

[1086] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[1087] Step 10:

[1088] The user enters the answer to the displayed question and presses the send button.

[1089] Step 11:

[1090] The terminal sends the answer to the server.

[1091] Step 12:

[1092] The server analyzes the user's answer and determines whether it is correct or incorrect.

[1093] Step 13:

[1094] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[1095] Step 14:

[1096] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1097] Step 15:

[1098] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[1099] Step 16:

[1100] The terminal displays feedback and new questions to the user.

[1101] Step 17:

[1102] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[1103] Step 18:

[1104] The user revisits the new problem.

[1105] Step 19:

[1106] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[1107] Step 20:

[1108] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, confusion, etc.).

[1109] Step 21:

[1110] The terminal transmits the results of the emotion engine to the server.

[1111] Step 22:

[1112] The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[1113] Step 23:

[1114] The server sends the adjusted feedback and questions to the terminal.

[1115] Step 24:

[1116] The terminal displays the tailored feedback and new questions to the user.

[1117] Step 25:

[1118] The user then re-enters the answer and continues learning.

[1119] Step 26:

[1120] After a study session has been running for a certain period of time, the user clicks the end button.

[1121] Step 27:

[1122] The terminal sends a request to end the learning session to the server.

[1123] Step 28:

[1124] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[1125] Step 29:

[1126] The terminal displays a message to the user indicating that the session has ended.

[1127] Example 2

[1128] 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."

[1129] While conventional educational support systems provide individualized instruction based on the user's learning progress, they do not provide feedback or generate questions that take the user's emotional state into account, making it difficult to maintain motivation and maximize learning outcomes. Furthermore, monotonous learning formats make it difficult to maintain user motivation, and the lack of elements that stimulate competitive spirit is also problematic.

[1130] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an artificial intelligence as a virtual opponent and providing learning in a competitive format with the user, and means for analyzing the user's emotional state and adjusting the difficulty of the questions and the feedback based on that information. This makes it possible to provide individualized instruction according to the user's learning progress and emotional state, and an environment that stimulates competitive spirit.

[1131] A "user" is an individual who uses the system to carry out learning activities.

[1132] "Study progress" is an index showing the learning content and results that the user has achieved up to now.

[1133] "Question generation" refers to the process of creating new study questions based on the user's learning progress and level of understanding.

[1134] "Receiving and analyzing in real time" refers to the process of instantly sending user input (e.g., answers) to the server and instantly judging and evaluating its content.

[1135] "Feedback" refers to messages that provide evaluations and advice based on the user's answers.

[1136] "Artificial intelligence (AI)" refers to technology that enables computers to perform human-like intelligent behavior, in this case acting as learning competitors.

[1137] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, excitement, etc.), and changes depending on the learning situation.

[1138] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice to determine their emotional state.

[1139] A "virtual competitor" is a non-existent entity that mimics an opponent that competes with the user within the system, and is primarily composed of artificial intelligence.

[1140] This is an educational support system that generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and also provides competitive learning by setting up an artificial intelligence (AI) as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the quality of learning is improved by adjusting the learning content and feedback according to the user's emotional state.

[1141] Hardware and software used

[1142] Hardware

[1143] Server: Hosts a database (e.g., MySQL, PostgreSQL) to store and manage users' learning history and progress.

[1144] Terminal: The computer or smartphone used by the user, which provides the interface and sends the user's input and answers to the server.

[1145] Camera and microphone: Used to capture the user's facial expressions and voice and send them to the emotion engine.

[1146] software

[1147] Generative AI models: Used to generate questions based on the user's learning progress (e.g., OpenAI GPT-3).

[1148] Face recognition and speech analysis APIs: Used to implement emotion engines (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text).

[1149] Web Technologies: Uses HTML, CSS, JavaScript for front-end development and communicates with the server using AJAX and fetch APIs.

[1150] Specific operation of the system

[1151] Problem generation

[1152] The server retrieves the user's learning progress from the database and uses a generative AI model to generate questions of appropriate difficulty, for example, "5 + 3 = ?"

[1153] Answer analysis and feedback

[1154] When a user enters an answer to a question on the device and presses the submit button, the device sends the answer to the server. The server analyzes the answer, determines whether it is correct or incorrect, and generates feedback (e.g., "Correct!" or "Think a bit more!"). This feedback is sent back to the device and displayed to the user.

[1155] AI as a virtual competitor

[1156] The AI ​​is set up as a virtual competitor to support the user's learning. For example, the AI ​​can be displayed solving the same problem, allowing the user to compete in a learning competition. The AI's answer is also displayed (e.g., "The AI ​​got the answer right!").

[1157] Emotion Engine

[1158] The user's facial expressions and voice are captured through the camera and microphone and sent to the emotion engine. The emotion engine analyzes this data and determines the user's emotional state (e.g., joy, sadness, excitement, etc.). The server then adjusts the feedback and difficulty of the questions based on this information to provide the user with an appropriate learning experience.

[1159] Specific examples

[1160] Prompt Sentence Examples

[1161] Here are some examples of prompts to input to a generative AI model:

[1162] How can we build an educational support system that generates appropriate questions based on the user's learning progress and emotional state, and provides feedback in real time?

[1163] By using this prompt, the generative AI model is expected to generate answers detailing specific steps and implementation methods.

[1164] As described above, the system of the present invention can provide effective educational support to users, similar to individual instruction, and maximize the results of their learning.

[1165] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1166] Step 1:

[1167] The server stores user account information in a database and provides a mechanism for authentication. Specifically, it hashes the username and password provided by the user when registering and stores them in the database. The input is the username and password, and the output is the hashed account information with the password stored in the database.

[1168] Step 2:

[1169] The terminal provides an interface (username and password entry screen) that allows the user to log in. A UI is constructed to temporarily store the information entered by the user and send it to the server. The input is the username and password, and the input information is sent to the server as the output.

[1170] Step 3:

[1171] The user enters their account information on the login screen of the device and presses the login button. The input is a username and password, and the output is a login request sent from the device to the server.

[1172] Step 4:

[1173] The terminal displays a "Start learning" button after the user logs in. To allow the user to start learning, a dashboard screen is created and a UI including a "Start learning" button is displayed. The input is the user's successful login status, and the output is the display of a "Start learning" button.

[1174] Step 5:

[1175] The user clicks the "Start Learning" button to start a learning session. The input is the clicking of the "Start Learning" button, and the output is a learning session start request sent from the terminal to the server.

[1176] Step 6:

[1177] The device sends a learning session start request to the server. The JavaScript fetch API or AJAX is used to send the learning session start request. The input is a button click event, and the learning session start request is sent to the server as its output.

[1178] Step 7:

[1179] The server receives this request and retrieves the user's learning history and current learning progress from the database. The database is queried for the user's learning history and progress, and the results are stored in memory. The input is a learning session start request, and the output is the user's learning history and progress.

[1180] Step 8:

[1181] The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the device. A generative AI model is used to generate questions based on the user's progress. The input is the user's learning history and progress, and the generated questions are sent to the device as output.

[1182] Step 9:

[1183] The terminal displays the problem received from the server to the user. A UI for displaying the problem is constructed and the problem is displayed. The input is the problem received from the server, and the problem is displayed to the user as the output.

[1184] Step 10:

[1185] The user inputs the answer to the displayed question and presses the send button. The input is the user's answer, and the output is sent to the terminal.

[1186] Step 11:

[1187] The device sends the answer to the server. The fetch API or AJAX is used to send the entered answer to the server. The input is the user's answer, and the answer is sent to the server as the output.

[1188] Step 12:

[1189] The server analyzes the user's answer and determines whether it is correct or incorrect. The analysis is performed using server-side logic or an AI model. The input is the user's answer, and the output is the analysis result.

[1190] Step 13:

[1191] The server generates feedback based on the judgment results. It uses a feedback generation AI model to create a feedback message. The input is the analysis result, and the output is the feedback message.

[1192] Step 14:

[1193] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question they will present. It analyzes the user's answer history and determines the difficulty of the next question. The input is the user's answer history, and the output is the adjusted difficulty of the question.

[1194] Step 15:

[1195] The server sends feedback and new questions to the terminal. The generated feedback and new questions are sent to the terminal. The input is the generated feedback and new questions, and the output is sent to the terminal.

[1196] Step 16:

[1197] The terminal displays feedback and new problems to the user. The UI displays feedback messages and new problems. The input is the feedback and new problems received from the server, and the output is displayed to the user.

[1198] Step 17:

[1199] The device displays the AI's virtual friend's answer status to encourage the user. It also displays the virtual friend's answer results to motivate the user to learn. The input is the AI's answer results, which are displayed as the output.

[1200] Step 18:

[1201] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. It uses a camera and microphone to capture and send the user's facial expression and voice data. The input is the user's facial expression and voice, and the output is sent to the emotion engine.

[1202] Step 19:

[1203] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state. It uses face recognition APIs and voice analysis APIs to determine emotions. The input is facial expression data and voice data, and the output is the user's emotional state.

[1204] Step 20:

[1205] The device sends the results of the emotion engine to the server, which then sends the analysis results to prepare them for the next feedback. The input is the analysis result of the emotional state, which is then sent to the server as the output.

[1206] Step 21:

[1207] The server adjusts the difficulty of questions and the content of feedback based on the user's emotional state. Based on the determined emotional state, it determines the appropriate difficulty of questions and feedback. The input is the analysis result of the emotional state, and the adjusted questions and feedback are obtained as the output.

[1208] Step 22:

[1209] The terminal displays the adjusted feedback and questions to the user. It displays the adjusted feedback and new questions received from the server. The input is the adjusted feedback and questions, and the output is displayed to the user.

[1210] (Application example 2)

[1211] 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."

[1212] Conventional educational systems struggle to provide appropriate feedback and questions based on each learner's individual progress and emotional state, making it difficult to maximize learning outcomes. Furthermore, the ability to improve learning motivation through competition with virtual opponents is limited. Furthermore, the lack of real-time answer analysis and feedback prevents rapid improvement in understanding. Therefore, a comprehensive educational support system that effectively and continuously increases students' motivation to learn is needed.

[1213] 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.

[1214] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for providing learning in a competitive format by setting up an artificial intelligence as a virtual opponent, and means for recognizing the user's emotions and adjusting the learning content. This makes it possible to provide questions and feedback tailored to the progress and emotional state of each learner, thereby effectively and sustainably increasing their motivation to learn.

[1215] "User" refers to an individual or learner who uses the educational support system.

[1216] "Study progress" refers to the level or stage of progress a user has achieved through learning.

[1217] "Problems" are questions or tasks provided to assess a user's learning.

[1218] An "answer" is a response or response provided by a user to a question.

[1219] "Real-time" refers to processing and response occurring almost immediately, with little delay.

[1220] "Feedback" is any rating or comment provided on a user's answer.

[1221] A "virtual competitor" is an artificial intelligence set up to compete with the user within the system.

[1222] "Artificial intelligence" refers to software or systems that learn and make decisions like humans through machine learning and data analysis.

[1223] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[1224] "Difficulty of a question" is an index that indicates how difficult it is to solve a question.

[1225] This invention is an educational support system that operates among three parties: a server, a terminal, and a user, and provides learning support by combining question generation based on the user's learning progress, answer analysis, feedback provision, competition with a virtual opponent (artificial intelligence), and emotion recognition.

[1226] Initial Setup and Login

[1227] The server stores user account information in a database and provides a mechanism for authentication. The terminal provides an interface (a screen for entering a username and password) that allows the user to log in. The user enters their account information on the terminal's login screen and presses the login button.

[1228] Start a study session

[1229] After the user logs in, the terminal displays a "Start learning" button. The user clicks the start learning button to start the learning session. The terminal sends a request to start the learning session to the server. The server receives this request and retrieves the user's learning history and current learning progress from the database. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[1230] Displaying and answering questions

[1231] The terminal displays the questions received from the server to the user. The user enters the answer to the displayed question and presses the send button. The terminal then sends the answer to the server.

[1232] Feedback and understanding measurement

[1233] The server analyzes the user's answers and determines whether they are correct or incorrect. The server generates feedback based on the results of the evaluation (e.g., "Correct!"). The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[1234] View feedback and new issues

[1235] The terminal displays feedback and new problems to the user. The terminal also displays the answer status of a virtual competitor (artificial intelligence) and encourages the user (e.g., "The AI ​​got the answer right too!").

[1236] Emotion Engine Operation

[1237] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and determines their current emotional state (e.g., joy, sadness, excitement, etc.). The device sends the emotion engine's results to the server. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state. The device displays the adjusted feedback and questions to the user.

[1238] Specific examples

[1239] Basic Learning Session

[1240] The user logs in to the terminal and a question (e.g., "5 + 3 = ?") is displayed. The user enters the answer "8" and presses the submit button. The terminal sends the answer to the server and receives feedback that the answer is correct (e.g., "Correct!"). The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal. The terminal displays the new question along with the answer of a virtual competitor (artificial intelligence) (e.g., "AI got it right too!").

[1241] Emotion Engine Operation

[1242] When a user tries to solve a problem, if their facial expression shows difficulty, the emotion engine will judge this as "confusion." The device will then send the user's confused state to the server. The server will then slightly lower the difficulty of the next problem and adjust the feedback to "Try harder and try an easier problem!" The device will then display the new problem and the adjusted feedback to the user, maintaining their motivation.

[1243] Prompt Sentence Examples

[1244] Enter your username and password to sign in:

[1245] text

[1246] Username: example_user, Password: password123

[1247] Submit your answer to the question:

[1248] text

[1249] Username: example_user, Answer: 8

[1250] Sending user facial expression data:

[1251] text

[1252] User name: example_user, Facial image data:<base64_encoded_image>

[1253] In this way, the server of the present invention is capable of managing learning progress, analyzing answers in real time, providing feedback, setting up virtual opponents, and adjusting through emotion recognition, etc. This allows for the realization of advanced educational support that is adapted to the individual needs of each learner.

[1254] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1255] Step 1:

[1256] The user accesses the login interface using a terminal, enters a username and password, and clicks the login button.

[1257] Input: Username, Password

[1258] Data processing: Convert user authentication information into JSON format to send to the server.

[1259] Output: An authentication request is sent to the server.

[1260] Step 2:

[1261] The server receives the login request, retrieves the user's account information from the database, and performs authentication. If authentication is successful, it returns a login success message.

[1262] Input: Authentication request (username, password)

[1263] Data calculation: Check the username and password.

[1264] Output: Authentication result (success or failure)

[1265] Step 3:

[1266] The terminal displays a "Start Learning" button after the user has successfully logged in.

[1267] Input: Authentication result (success)

[1268] Data processing: Creating a UI for displaying buttons

[1269] Output: A "Start learning" button will be displayed.

[1270] Step 4:

[1271] The user clicks the "Start Learning" button. The device captures this event and sends a request to the server to start a learning session.

[1272] Input: User clicks the button event

[1273] Data processing: generating a request to start a learning session

[1274] Output: A request to start a learning session is sent to the server.

[1275] Step 5:

[1276] The server receives a request to start a learning session, retrieves the user's learning history and current learning progress from a database, and generates questions of appropriate difficulty based on this information and sends them to the terminal.

[1277] Input: Learning session start request

[1278] Data calculation: Obtaining user's learning history and progress, generating questions

[1279] Output: The question is generated and sent to the terminal.

[1280] Step 6:

[1281] The terminal displays the questions received from the server to the user.

[1282] Input: Problem data

[1283] Data processing: Creating a UI for displaying the problem

[1284] Output: The problem is displayed to the user.

[1285] Step 7:

[1286] The user inputs the answer to the displayed question and presses the send button, and the terminal sends the answer to the server.

[1287] Input: User's answer

[1288] Data processing: Generate a request to send answers

[1289] Output: The answer is sent to the server.

[1290] Step 8:

[1291] The server receives the user's answer, analyzes it in real time, and determines whether it is correct or incorrect. Based on the results, it generates feedback and creates new questions to send to the device.

[1292] Input: User's answer

[1293] Data operations: analyzing answers, generating feedback, generating new questions

[1294] Output: Feedback and new questions are sent to the device.

[1295] Step 9:

[1296] The terminal displays the feedback received from the server and new questions to the user, as well as the progress of the virtual competitor's (artificial intelligence) solutions.

[1297] Input: Feedback, new questions, AI solution status

[1298] Data processing: Creating a UI for displaying feedback, questions, and AI solution status

[1299] Output: Feedback and new questions are displayed to the user.

[1300] Step 10:

[1301] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[1302] Input: User's facial expression and voice data

[1303] Data processing: Capture and transmit facial and voice data

[1304] Output: Facial expression and voice data is sent to the emotion engine.

[1305] Step 11:

[1306] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state, and returns the results to the device.

[1307] Input: facial expression data, voice data

[1308] Data Computing: Emotional State Analysis

[1309] Output: Emotional state judgment result

[1310] Step 12:

[1311] The device sends the results of the emotion engine to the server, which adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state and sends them to the device, which then displays the adjusted feedback and questions to the user.

[1312] Input: Emotional state judgment result

[1313] Data calculation: Adjusting the difficulty of questions and feedback

[1314] Output: Tailored feedback and problems are displayed to the user.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] [Third embodiment]

[1319] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1320] 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.

[1321] 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).

[1322] 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.

[1323] 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.

[1324] 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).

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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."

[1331] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[1332] This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server mainly generates questions and analyzes answers, while the terminal accepts user operations and smoothly advances the learning process through communication with the server. Users operate the terminal to engage in learning.

[1333] Initial Setup and Login

[1334] 1. The server stores user account information in a database and provides a mechanism for authentication.

[1335] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[1336] 3. The user enters their account information on the login screen of the device and presses the login button.

[1337] Start a study session

[1338] 1. The device displays a "Start learning" button after the user logs in.

[1339] 2. The user clicks the Start Learning button to begin the learning session.

[1340] 3. The device sends a request to the server to start a learning session.

[1341] 4. The server receives this request and retrieves the user's learning history from the database.

[1342] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[1343] Displaying and answering questions

[1344] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[1345] 2. The user enters the answer to the displayed question (e.g., "8") and presses the submit button.

[1346] 3. The device sends the answer to the server.

[1347] Feedback and understanding measurement

[1348] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[1349] 2. The server generates feedback (e.g., "Correct!") based on the result of the judgment.

[1350] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1351] 4. The server sends feedback and a new question (e.g., "7 - 4 = ?") to the device.

[1352] View feedback and new issues

[1353] 1. The device displays feedback and new questions to the user.

[1354] 2. The device displays the answer status of the AI's virtual friend (e.g., "The AI ​​also got the answer right!") to encourage the user.

[1355] 3. The user revisits the problem with a new one.

[1356] Ending a session

[1357] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[1358] 2. The device sends a request to the server to terminate the learning session.

[1359] 3. The server saves the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[1360] 4. The terminal displays a message to the user indicating that the session has ended.

[1361] Specific examples

[1362] Scenario 1: Basic Learning Session

[1363] 1. The user logs in to a terminal and is presented with the problem "5 + 3 = ?"

[1364] 2. The user enters the answer "8" and presses the submit button.

[1365] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[1366] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[1367] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[1368] Scenario 2: Problem suggestions based on level of understanding

[1369] 1. The user answers "3" to the question "7 - 4 = ?"

[1370] 2. The server evaluates the level of understanding with the feedback "Correct!" and generates the next question (e.g., "8 + 6 = ?").

[1371] 3. The device displays the new problem and feedback to the user, prompting them to tackle the next problem.

[1372] In this way, the present invention can provide questions that are optimized for the user's learning progress and maintain high learning motivation through AI virtual competition.

[1373] The processing flow will be explained below.

[1374] Step 1:

[1375] The user opens the application on the terminal and enters the username and password on the login screen.

[1376] Step 2:

[1377] The terminal transmits the entered login information to the server.

[1378] Step 3:

[1379] The server compares the received login information with the user information in the database and performs authentication.

[1380] Step 4:

[1381] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[1382] Step 5:

[1383] The user clicks the Start Learning button.

[1384] Step 6:

[1385] The terminal sends a request to the server to start a learning session.

[1386] Step 7:

[1387] The server receives this request and retrieves the user's learning history and current learning progress from the database.

[1388] Step 8:

[1389] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[1390] Step 9:

[1391] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[1392] Step 10:

[1393] The user enters the answer to the displayed question and presses the send button.

[1394] Step 11:

[1395] The terminal transmits the user's answer to the server.

[1396] Step 12:

[1397] The server analyzes the user's answer and determines whether it is correct or incorrect.

[1398] Step 13:

[1399] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[1400] Step 14:

[1401] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1402] Step 15:

[1403] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[1404] Step 16:

[1405] The terminal displays feedback and new questions to the user.

[1406] Step 17:

[1407] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[1408] Step 18:

[1409] The user revisits the new problem.

[1410] Step 19:

[1411] After a study session has been running for a certain period of time, the user clicks the end button.

[1412] Step 20:

[1413] The terminal sends a request to end the learning session to the server.

[1414] Step 21:

[1415] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[1416] Step 22:

[1417] The terminal displays a message to the user indicating that the session has ended.

[1418] Example 1

[1419] 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."

[1420] Conventional learning systems have issues with insufficient individual response to the user's learning progress and can only provide fixed questions and feedback. Furthermore, they do not sufficiently motivate users, which tends to reduce their motivation to learn, especially in self-study. Furthermore, they lack a mechanism to stimulate competitive spirit, meaning users lack the experience of competing with other learners or the system.

[1421] 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.

[1422] In this invention, the server includes means for generating appropriate study tasks based on the user's learning progress, means for receiving and analyzing the user's input in real time, means for providing feedback to the user, means for setting an intelligent system as a virtual competitor and providing a form of learning in which the user competes with the system, means for saving the user's account information in a database and authenticating it, means for providing a login screen and prompting the user to enter their account information, means for acquiring the user's answer history and generating new questions, and means for evaluating the user's level of understanding and adjusting the difficulty of the new questions. This makes it possible to provide questions that correspond to the individual user's learning progress and to increase motivation to learn through competition with virtual competitors.

[1423] "User's learning progress" refers to the level of understanding and mastery that the user has achieved in the course of performing learning activities.

[1424] "Study assignments" is a general term for problems, exercises, and assignments presented to the user.

[1425] "Input" refers to the act of a user inputting answers and information for a learning task into a terminal.

[1426] "Real time" refers to a state in which user operations or inputs are processed immediately without delay.

[1427] "Analysis" refers to the process by which the server analyzes the user's input and determines its correctness and appropriateness.

[1428] "Feedback" refers to the evaluation or response provided to a user's actions or answers.

[1429] "Virtual competitors" refer to intelligent systems or agents configured on the system, rather than other real users.

[1430] "Intelligent System" refers to the artificial intelligence programmed on the system to compete with the user.

[1431] "Account information" is information for identifying a user, and generally includes a username, password, email address, and the like.

[1432] A "database" refers to a system for systematically storing and managing large amounts of data.

[1433] "Authentication" refers to the process of verifying access rights when a user uses their account information to access a system.

[1434] "Login screen" refers to an interface for a user to enter account information to access a system.

[1435] "Answer history" refers to a record of the study tasks that the user has answered up to now.

[1436] "Level of understanding" refers to the degree to which the user can accurately understand and answer the learning task.

[1437] "Difficulty of the problem" refers to the level of difficulty of the learning task.

[1438] A specific embodiment of the present invention will now be described. This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server is primarily responsible for generating questions and analyzing answers, while the terminal accepts user operations and smoothly advances the study process through communication with the server. Users engage in study by operating the terminal.

[1439] Hardware and software used

[1440] This system uses the following hardware and software:

[1441] Server: A server machine (e.g., Linux server) equipped with a powerful processor, sufficient memory, and large storage capacity.

[1442] Database software: MySQL

[1443] Programming languages: Python, JavaScript

[1444] Web technologies: HTML, CSS, JavaScript

[1445] Communication protocol: HTTP / HTTPS

[1446] Initial Setup and Login

[1447] 1. The server stores user account information in a MySQL database and provides an authentication mechanism, ensuring the safe management of user personal data.

[1448] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. To do this, a login form is created using HTML and CSS, and the input is validated using JavaScript.

[1449] 3. The user enters their account information on the login screen of the device and presses the login button.

[1450] 4. The device sends the user's input data to the server via an HTTP POST request.

[1451] 5. The server checks the submitted user data against the database, and if authentication is successful, generates session information and returns it to the client.

[1452] Start a study session

[1453] 1. The device displays a "Start learning" button after the user logs in. This button is displayed using HTML and CSS.

[1454] 2. The user clicks the "Start Learning" button.

[1455] 3. Based on the user's operation, the device sends an HTTP POST request to the server to start a learning session.

[1456] 4. The server receives this request and retrieves the user's learning history from the database. For example, it executes the SQL query "SELECT FROM learning history WHERE user ID = '12345'".

[1457] 5. The server generates questions of appropriate difficulty based on the learning history and returns them to the device. This problem generation is done using a question generation algorithm written in Python.

[1458] Displaying and answering questions

[1459] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[1460] 2. The user enters the answer to the displayed question and presses the submit button.

[1461] 3. The device sends the answer data to the server using an HTTP POST request.

[1462] 4. The server receives the answer data and uses Python code to parse it.

[1463] Feedback and understanding measurement

[1464] 1. The server analyzes the user's answers and determines whether they are correct or incorrect. It uses Python to compare the questions and answers.

[1465] 2. The server generates feedback based on the result, such as "Correct!" if the answer is correct, or "Incorrect!" if the answer is incorrect.

[1466] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[1467] 4. The server sends the feedback and new questions to the device.

[1468] View feedback and new issues

[1469] 1. The device displays feedback and new questions to the user, dynamically updating the display using HTML and JavaScript.

[1470] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, to encourage the user. For example, it displays the message "The intelligent system also got the answer right!"

[1471] 3. The user revisits the problem with a new one.

[1472] Ending a session

[1473] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[1474] 2. The device sends an HTTP POST request to the server to end the learning session.

[1475] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('12345', NOW())".

[1476] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[1477] Examples of prompt statements

[1478] By inputting prompt sentences like the following into the generative AI model, we can generate appropriate feedback:

[1479] "User answers 8 to the question 5 + 3 = ?. Please generate a rating and feedback."

[1480] By entering this prompt, the AI ​​can generate accurate feedback (e.g., "Correct!") and provide it to the user.

[1481] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1482] Step 1:

[1483] Initial Setup and Login

[1484] 1. The server stores user account information in a database and provides a mechanism for authentication. Specifically, it stores usernames and hashed passwords in a MySQL database.

[1485] Input: Username, hashed password

[1486] Output: Save results to database

[1487] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. Create a login form using HTML and CSS, and validate the input using JavaScript.

[1488] Input: None

[1489] Output: Login form screen

[1490] 3. The user enters their username and password on the login screen and clicks the login button.

[1491] Input: Username, Password

[1492] Output: Login request

[1493] 4. The device sends the user's input data to the server using an HTTP POST request.

[1494] Input: Username, Password

[1495] Output: HTTP POST request

[1496] 5. The server checks the submitted user data against the database information, and if authentication is successful, generates session information and returns it to the client. The check is performed using an SQL query that retrieves data from the database.

[1497] Input: Username, Password

[1498] Output: Session information, authentication results

[1499] Step 2:

[1500] Start a study session

[1501] 1. The device displays a "Start Learning" button after the user logs in. Generate the button using HTML and CSS.

[1502] Input: Authentication completed session information

[1503] Output: "Start learning" button

[1504] 2. The user clicks the Start Learning button.

[1505] Input: None

[1506] Output: Training start request

[1507] 3. The device sends a learning session start request to the server based on the user's operation using an HTTP POST request.

[1508] Input: Learning start request

[1509] Output: HTTP POST request

[1510] 4. The server receives this request and retrieves the user's learning history from the database. Specifically, it executes the SQL query "SELECT FROM learning history WHERE user ID = 'user ID'".

[1511] Input: User ID

[1512] Output: Learning history data

[1513] 5. The server generates questions of appropriate difficulty based on the learning history and sends them back to the device. Question generation is done using Python.

[1514] Input: Learning history data

[1515] Output: Generated problem

[1516] Step 3:

[1517] Displaying and answering questions

[1518] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[1519] Input: Generated question

[1520] Output: Screen showing the problem

[1521] 2. The user enters the answer to the displayed question and presses the submit button.

[1522] Input: Answer

[1523] Output: Answer submission request

[1524] 3. The device sends the answer data to the server, again using an HTTP POST request.

[1525] Input: Answer

[1526] Output: HTTP POST request

[1527] 4. The server receives the answer data and uses Python code to parse it.

[1528] Input: Answer data

[1529] Output: Analysis results

[1530] Step 4:

[1531] Feedback and understanding measurement

[1532] 1. The server analyzes the user's answer and determines whether it is correct or incorrect. It compares the question and answer using Python.

[1533] Input: Answer data, question data

[1534] Output: Judgment result

[1535] 2. The server generates feedback based on the result, such as a message like "Correct!" if the answer is correct, or "Incorrect" if the answer is incorrect.

[1536] Input: Judgment result

[1537] Output: Feedback message

[1538] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[1539] Input: Answer data, answer history

[1540] Output: New problem difficulty

[1541] 4. The server sends the feedback and new questions to the device.

[1542] Input: Feedback message, new issue

[1543] Output: HTTP POST request

[1544] Step 5:

[1545] View feedback and new issues

[1546] 1. The device displays feedback and new problems to the user, dynamically updating the display using HTML and JavaScript.

[1547] Input: Feedback message, new issue

[1548] Output: A screen showing feedback and new questions

[1549] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, and encourages the user, for example by displaying a message such as "The intelligent system also got the answer right!"

[1550] Input: Answer status of the intelligent system

[1551] Output: Intelligent system solution status message

[1552] 3. The user revisits the problem with a new one.

[1553] Input: None

[1554] Output: Answer submission request

[1555] Step 6:

[1556] Ending a session

[1557] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[1558] Input: None

[1559] Output: Session termination request

[1560] 2. The device sends an HTTP POST request to the server to end the learning session.

[1561] Input: Session termination request

[1562] Output: HTTP POST request

[1563] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('user ID', NOW())".

[1564] Input: User ID, end time

[1565] Output: Database save result, completion confirmation message

[1566] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[1567] Input: Exit confirmation message

[1568] Output: Display session termination message

[1569] (Application example 1)

[1570] 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."

[1571] In today's commercial environment, new staff are required to quickly acquire customer service skills. However, traditional training methods are not based on real-world scenarios, making it difficult to achieve effective learning outcomes, and delays in feedback can lead to a lack of motivation. Furthermore, a lack of training environments that address the specific problems new staff face in brick-and-mortar stores makes efficient learning difficult.

[1572] 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.

[1573] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an AI as a virtual competitor and providing learning in a competitive format with the user, means for generating scenario-based questions for the user to master customer service skills in a physical store in real time, and means for inputting and assisting the user's answers using a smart device. This enables new staff to effectively and continuously improve their customer service skills while receiving immediate feedback through specific customer service scenarios.

[1574] "User" means an individual or organization that uses the system for learning or training.

[1575] "Learning progress" is an indicator of how far a user has progressed in the learning process.

[1576] "Appropriate problems" are tasks and questions that are provided according to the user's learning progress and level, and are designed to maximize learning effectiveness.

[1577] An "answer" is a response or answer that a user provides to a posed question.

[1578] "Real time" is a time concept that indicates that processing is performed immediately without delay.

[1579] "Analysis" is the process of evaluating the user's answer to determine its correctness.

[1580] "Feedback" is information that provides evaluation and advice on the user's answer.

[1581] A "virtual competitor" is a competitive object, such as an artificial intelligence (AI), that is set up to compete with the user.

[1582] "AI" stands for artificial intelligence, a technology that allows computer programs to mimic human intelligence.

[1583] "Scenario-based questions" are learning tasks constructed based on real-world business scenarios.

[1584] A "smart device" is an electronic device that has internet connectivity and computing capabilities.

[1585] "Input" is the act of a user providing information to a system.

[1586] "Assistance" is the process of providing support to help users operate or learn more effectively.

[1587] This invention relates to a training system for brick-and-mortar stores to improve users' customer service skills. The system operates among a server, smart glasses as a terminal, and the user, and is designed to allow users to progress in their learning while receiving effective and immediate feedback.

[1588] Hardware or software used

[1589] Server: Cloud servers and on-premise servers are used. SQLite is used as the database.

[1590] Device: Smart glasses (e.g. Google Glass)

[1591] Software: Python, SQLite for database management, and the operating system of the smart glasses (e.g., Android)

[1592] System Operation

[1593] 1. Initial Setup and Login

[1594] The server stores the user's account information in a database, and the user logs in through the smart glasses. The server authenticates the user, and if authentication is successful, obtains the user's learning progress.

[1595] 2. Start your study session

[1596] After logging in, the user clicks the "Start Learning" button, which sends a request to the server to start a learning session. The server generates appropriate scenario-based questions based on the user's learning history and sends them to the smart glasses.

[1597] 3. Displaying and answering questions

[1598] The smart glasses display the questions received from the server to the user, who then inputs the answers to the questions using voice or tactile input, which are then sent to the server via the smart glasses.

[1599] 4. Feedback and Gauging Understanding

[1600] The server analyzes the user's answers in real time, determines whether they are correct or incorrect, generates feedback, and sends it to the smart glasses. It also evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1601] 5. Displaying new questions and trying again

[1602] The smart glasses display feedback and new questions, showing the progress of AI competitors, motivating users to learn and encourage them to tackle the next problem.

[1603] Specific scenario example

[1604] Example prompt: "How do you respond when a customer asks you about a particular product?"

[1605] Example user answer: "Tell me more about the product"

[1606] In this way, users can improve their skills through specific customer service scenarios in real time. The system provides users with the information they need instantly and provides effective feedback, allowing new staff to learn efficiently and quickly adapt to work in a brick-and-mortar store.

[1607] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1608] Step 1: Initial Setup and Login

[1609] Specific actions

[1610] The server stores user account information in a database and provides a mechanism for authentication.

[1611] input

[1612] The user enters their username and password through the smart glasses.

[1613] Data processing / data calculation

[1614] The server checks the entered username and password against the information in the database and performs authentication.

[1615] output

[1616] If authentication is successful, the user's learning progress data is retrieved and login is completed.

[1617] Step 2: Start your study session

[1618] Specific actions

[1619] The user clicks the "Start Learning" button.

[1620] input

[1621] A request to start a learning session is sent from the terminal to the server.

[1622] Data processing / data calculation

[1623] The server generates appropriate scenario-based questions based on the user's learning history.

[1624] output

[1625] The generated questions are sent to the device.

[1626] Step 3: View and answer questions

[1627] Specific actions

[1628] The terminal displays the problem received from the server to the user.

[1629] input

[1630] The problem data sent by the server.

[1631] Data processing / data calculation

[1632] The terminal displays the content of the problem in a format suitable for the user.

[1633] output

[1634] The user inputs answers through voice input or tactile manipulation.

[1635] Step 4: Submit your answers

[1636] Specific actions

[1637] The user enters the answer and sends it from the terminal to the server.

[1638] input

[1639] User answer data.

[1640] Data processing / data calculation

[1641] The terminal converts the answer data into a format for transmission to the server.

[1642] output

[1643] The answer data is sent to the server.

[1644] Step 5: Feedback and Gauging

[1645] Specific actions

[1646] The server analyzes the user's answers and evaluates the results.

[1647] input

[1648] User answer data.

[1649] Data processing / data calculation

[1650] The server determines whether the answer is correct or incorrect, generates feedback, and evaluates the user's understanding.

[1651] output

[1652] Evaluation data is generated as feedback, and the difficulty of the next question presented is adjusted.

[1653] Step 6: View feedback and new issues

[1654] Specific actions

[1655] The terminal displays the feedback received from the server and any new questions to the user.

[1656] input

[1657] Feedback data and new problem data from the server.

[1658] Data processing / data calculation

[1659] The terminal converts the data into a format for display and presents it to the user visually or audibly.

[1660] output

[1661] The user revisits the new problem.

[1662] Step 7: Ending the study session

[1663] Specific actions

[1664] The user clicks the "End Learning" button.

[1665] input

[1666] A request to end the learning session is sent from the terminal to the server.

[1667] Data processing / data calculation

[1668] The server stores the user's learning log in a database and confirms the end of the session.

[1669] output

[1670] A message indicating the session has ended is displayed on the terminal.

[1671] 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.

[1672] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[1673] This invention is an educational support system that operates among a server, a terminal, and a user, and generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and provides a competitive learning experience by setting up an AI as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the learning content and feedback are adjusted according to the user's emotional state.

[1674] Initial Setup and Login

[1675] 1. The server stores user account information in a database and provides a mechanism for authentication.

[1676] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[1677] 3. The user enters their account information on the login screen of the device and presses the login button.

[1678] Start a study session

[1679] 1. The device displays a "Start learning" button after the user logs in.

[1680] 2. The user clicks the Start Learning button to begin the learning session.

[1681] 3. The device sends a request to the server to start a learning session.

[1682] 4. The server receives this request and retrieves the user's learning history and current learning progress from the database.

[1683] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[1684] Displaying and answering questions

[1685] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[1686] 2. The user enters the answer to the displayed question and presses the submit button.

[1687] 3. The device sends the answer to the server.

[1688] Feedback and understanding measurement

[1689] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[1690] 2. The server generates feedback based on the result (e.g., "Correct!").

[1691] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1692] 4. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[1693] View feedback and new issues

[1694] 1. The device displays feedback and new questions to the user.

[1695] 2. The device displays the answer status of the AI's virtual friend and encourages the user (e.g., "The AI ​​got the answer right too!").

[1696] Emotion Engine Operation

[1697] 1. The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[1698] 2. The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, etc.).

[1699] 3. The device sends the emotion engine results to the server.

[1700] 4. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[1701] 5. The device displays tailored feedback and problems to the user.

[1702] Specific examples

[1703] Scenario 1: Basic Learning Session

[1704] 1. The user logs in to a terminal and is presented with a problem (e.g., "5 + 3 = ?").

[1705] 2. The user enters the answer "8" and presses the submit button.

[1706] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[1707] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[1708] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[1709] Scenario 2: Emotion Engine in Action

[1710] 1. When a user solves a problem, if their facial expression is difficult, the emotion engine will judge it as "confused."

[1711] 2. The terminal transmits the user's confusion state to the server.

[1712] 3. The server adjusts the difficulty of the next problem it provides by slightly lowering the difficulty level and providing feedback such as "Try harder, let's try an easier problem!"

[1713] 4. The device displays new problems and tailored feedback to the user to keep them motivated.

[1714] In this way, by combining an emotion engine, the present invention can provide educational support that is adapted to the user's emotional state, maximizing learning outcomes.

[1715] The processing flow will be explained below.

[1716] Step 1:

[1717] The user opens the application on the terminal and enters the username and password on the login screen.

[1718] Step 2:

[1719] The terminal transmits the entered login information to the server.

[1720] Step 3:

[1721] The server compares the received login information with the user information in the database and performs authentication.

[1722] Step 4:

[1723] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[1724] Step 5:

[1725] The user clicks the Start Learning button.

[1726] Step 6:

[1727] The terminal sends a request to the server to start a learning session.

[1728] Step 7:

[1729] Upon receiving this request, the server retrieves the user's learning history and current learning progress from the database.

[1730] Step 8:

[1731] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[1732] Step 9:

[1733] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[1734] Step 10:

[1735] The user enters the answer to the displayed question and presses the send button.

[1736] Step 11:

[1737] The terminal sends the answer to the server.

[1738] Step 12:

[1739] The server analyzes the user's answer and determines whether it is correct or incorrect.

[1740] Step 13:

[1741] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[1742] Step 14:

[1743] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1744] Step 15:

[1745] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[1746] Step 16:

[1747] The terminal displays feedback and new questions to the user.

[1748] Step 17:

[1749] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[1750] Step 18:

[1751] The user revisits the new problem.

[1752] Step 19:

[1753] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[1754] Step 20:

[1755] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, confusion, etc.).

[1756] Step 21:

[1757] The terminal transmits the results of the emotion engine to the server.

[1758] Step 22:

[1759] The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[1760] Step 23:

[1761] The server sends the adjusted feedback and questions to the terminal.

[1762] Step 24:

[1763] The terminal displays the tailored feedback and new questions to the user.

[1764] Step 25:

[1765] The user then re-enters the answer and continues learning.

[1766] Step 26:

[1767] After a study session has been running for a certain period of time, the user clicks the end button.

[1768] Step 27:

[1769] The terminal sends a request to end the learning session to the server.

[1770] Step 28:

[1771] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[1772] Step 29:

[1773] The terminal displays a message to the user indicating that the session has ended.

[1774] Example 2

[1775] 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."

[1776] While conventional educational support systems provide individualized instruction based on the user's learning progress, they do not provide feedback or generate questions that take the user's emotional state into account, making it difficult to maintain motivation and maximize learning outcomes. Furthermore, monotonous learning formats make it difficult to maintain user motivation, and the lack of elements that stimulate competitive spirit is also problematic.

[1777] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an artificial intelligence as a virtual opponent and providing learning in a competitive format with the user, and means for analyzing the user's emotional state and adjusting the difficulty of the questions and the feedback based on that information. This makes it possible to provide individualized instruction according to the user's learning progress and emotional state, and an environment that stimulates competitive spirit.

[1778] A "user" is an individual who uses the system to carry out learning activities.

[1779] "Study progress" is an index showing the learning content and results that the user has achieved up to now.

[1780] "Question generation" refers to the process of creating new study questions based on the user's learning progress and level of understanding.

[1781] "Receiving and analyzing in real time" refers to the process of instantly sending user input (e.g., answers) to the server and instantly judging and evaluating its content.

[1782] "Feedback" refers to messages that provide evaluations and advice based on the user's answers.

[1783] "Artificial intelligence (AI)" refers to technology that enables computers to perform human-like intelligent behavior, in this case acting as learning competitors.

[1784] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, excitement, etc.), and changes depending on the learning situation.

[1785] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice to determine their emotional state.

[1786] A "virtual competitor" is a non-existent entity that mimics an opponent that competes with the user within the system, and is primarily composed of artificial intelligence.

[1787] This is an educational support system that generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and also provides competitive learning by setting up an artificial intelligence (AI) as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the quality of learning is improved by adjusting the learning content and feedback according to the user's emotional state.

[1788] Hardware and software used

[1789] Hardware

[1790] Server: Hosts a database (e.g., MySQL, PostgreSQL) to store and manage users' learning history and progress.

[1791] Terminal: The computer or smartphone used by the user, which provides the interface and sends the user's input and answers to the server.

[1792] Camera and microphone: Used to capture the user's facial expressions and voice and send them to the emotion engine.

[1793] software

[1794] Generative AI models: Used to generate questions based on the user's learning progress (e.g., OpenAI GPT-3).

[1795] Face recognition and speech analysis APIs: Used to implement emotion engines (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text).

[1796] Web Technologies: Uses HTML, CSS, JavaScript for front-end development and communicates with the server using AJAX and fetch APIs.

[1797] Specific operation of the system

[1798] Problem generation

[1799] The server retrieves the user's learning progress from the database and uses a generative AI model to generate questions of appropriate difficulty, for example, "5 + 3 = ?"

[1800] Answer analysis and feedback

[1801] When a user enters an answer to a question on the device and presses the submit button, the device sends the answer to the server. The server analyzes the answer, determines whether it is correct or incorrect, and generates feedback (e.g., "Correct!" or "Think a bit more!"). This feedback is sent back to the device and displayed to the user.

[1802] AI as a virtual competitor

[1803] The AI ​​is set up as a virtual competitor to support the user's learning. For example, the AI ​​can be displayed solving the same problem, allowing the user to compete in a learning competition. The AI's answer is also displayed (e.g., "The AI ​​got the answer right!").

[1804] Emotion Engine

[1805] The user's facial expressions and voice are captured through the camera and microphone and sent to the emotion engine. The emotion engine analyzes this data and determines the user's emotional state (e.g., joy, sadness, excitement, etc.). The server then adjusts the feedback and difficulty of the questions based on this information to provide the user with an appropriate learning experience.

[1806] Specific examples

[1807] Prompt Sentence Examples

[1808] Here are some examples of prompts to input to a generative AI model:

[1809] How can we build an educational support system that generates appropriate questions based on the user's learning progress and emotional state, and provides feedback in real time?

[1810] By using this prompt, the generative AI model is expected to generate answers detailing specific steps and implementation methods.

[1811] As described above, the system of the present invention can provide effective educational support to users, similar to individual instruction, and maximize the results of their learning.

[1812] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1813] Step 1:

[1814] The server stores user account information in a database and provides a mechanism for authentication. Specifically, it hashes the username and password provided by the user when registering and stores them in the database. The input is the username and password, and the output is the hashed account information with the password stored in the database.

[1815] Step 2:

[1816] The terminal provides an interface (username and password entry screen) that allows the user to log in. A UI is constructed to temporarily store the information entered by the user and send it to the server. The input is the username and password, and the input information is sent to the server as the output.

[1817] Step 3:

[1818] The user enters their account information on the login screen of the device and presses the login button. The input is a username and password, and the output is a login request sent from the device to the server.

[1819] Step 4:

[1820] The terminal displays a "Start learning" button after the user logs in. To allow the user to start learning, a dashboard screen is created and a UI including a "Start learning" button is displayed. The input is the user's successful login status, and the output is the display of a "Start learning" button.

[1821] Step 5:

[1822] The user clicks the "Start Learning" button to start a learning session. The input is the clicking of the "Start Learning" button, and the output is a learning session start request sent from the terminal to the server.

[1823] Step 6:

[1824] The device sends a learning session start request to the server. The JavaScript fetch API or AJAX is used to send the learning session start request. The input is a button click event, and the learning session start request is sent to the server as its output.

[1825] Step 7:

[1826] The server receives this request and retrieves the user's learning history and current learning progress from the database. The database is queried for the user's learning history and progress, and the results are stored in memory. The input is a learning session start request, and the output is the user's learning history and progress.

[1827] Step 8:

[1828] The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the device. A generative AI model is used to generate questions based on the user's progress. The input is the user's learning history and progress, and the generated questions are sent to the device as output.

[1829] Step 9:

[1830] The terminal displays the problem received from the server to the user. A UI for displaying the problem is constructed and the problem is displayed. The input is the problem received from the server, and the problem is displayed to the user as the output.

[1831] Step 10:

[1832] The user inputs the answer to the displayed question and presses the send button. The input is the user's answer, and the output is sent to the terminal.

[1833] Step 11:

[1834] The device sends the answer to the server. The fetch API or AJAX is used to send the entered answer to the server. The input is the user's answer, and the answer is sent to the server as the output.

[1835] Step 12:

[1836] The server analyzes the user's answer and determines whether it is correct or incorrect. The analysis is performed using server-side logic or an AI model. The input is the user's answer, and the output is the analysis result.

[1837] Step 13:

[1838] The server generates feedback based on the judgment results. It uses a feedback generation AI model to create a feedback message. The input is the analysis result, and the output is the feedback message.

[1839] Step 14:

[1840] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question they will present. It analyzes the user's answer history and determines the difficulty of the next question. The input is the user's answer history, and the output is the adjusted difficulty of the question.

[1841] Step 15:

[1842] The server sends feedback and new questions to the terminal. The generated feedback and new questions are sent to the terminal. The input is the generated feedback and new questions, and the output is sent to the terminal.

[1843] Step 16:

[1844] The terminal displays feedback and new problems to the user. The UI displays feedback messages and new problems. The input is the feedback and new problems received from the server, and the output is displayed to the user.

[1845] Step 17:

[1846] The device displays the AI's virtual friend's answer status to encourage the user. It also displays the virtual friend's answer results to motivate the user to learn. The input is the AI's answer results, which are displayed as the output.

[1847] Step 18:

[1848] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. It uses a camera and microphone to capture and send the user's facial expression and voice data. The input is the user's facial expression and voice, and the output is sent to the emotion engine.

[1849] Step 19:

[1850] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state. It uses face recognition APIs and voice analysis APIs to determine emotions. The input is facial expression data and voice data, and the output is the user's emotional state.

[1851] Step 20:

[1852] The device sends the results of the emotion engine to the server, which then sends the analysis results to prepare them for the next feedback. The input is the analysis result of the emotional state, which is then sent to the server as the output.

[1853] Step 21:

[1854] The server adjusts the difficulty of questions and the content of feedback based on the user's emotional state. Based on the determined emotional state, it determines the appropriate difficulty of questions and feedback. The input is the analysis result of the emotional state, and the adjusted questions and feedback are obtained as the output.

[1855] Step 22:

[1856] The terminal displays the adjusted feedback and questions to the user. It displays the adjusted feedback and new questions received from the server. The input is the adjusted feedback and questions, and the output is displayed to the user.

[1857] (Application example 2)

[1858] 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."

[1859] Conventional educational systems struggle to provide appropriate feedback and questions based on each learner's individual progress and emotional state, making it difficult to maximize learning outcomes. Furthermore, the ability to improve learning motivation through competition with virtual opponents is limited. Furthermore, the lack of real-time answer analysis and feedback prevents rapid improvement in understanding. Therefore, a comprehensive educational support system that effectively and continuously increases students' motivation to learn is needed.

[1860] 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.

[1861] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for providing learning in a competitive format by setting up an artificial intelligence as a virtual opponent, and means for recognizing the user's emotions and adjusting the learning content. This makes it possible to provide questions and feedback tailored to the progress and emotional state of each learner, thereby effectively and sustainably increasing their motivation to learn.

[1862] "User" refers to an individual or learner who uses the educational support system.

[1863] "Study progress" refers to the level or stage of progress a user has achieved through learning.

[1864] "Problems" are questions or tasks provided to assess a user's learning.

[1865] An "answer" is a response or response provided by a user to a question.

[1866] "Real-time" refers to processing and response occurring almost immediately, with little delay.

[1867] "Feedback" is any rating or comment provided on a user's answer.

[1868] A "virtual competitor" is an artificial intelligence set up to compete with the user within the system.

[1869] "Artificial intelligence" refers to software or systems that learn and make decisions like humans through machine learning and data analysis.

[1870] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[1871] "Difficulty of a question" is an index that indicates how difficult it is to solve a question.

[1872] This invention is an educational support system that operates among three parties: a server, a terminal, and a user, and provides learning support by combining question generation based on the user's learning progress, answer analysis, feedback provision, competition with a virtual opponent (artificial intelligence), and emotion recognition.

[1873] Initial Setup and Login

[1874] The server stores user account information in a database and provides a mechanism for authentication. The terminal provides an interface (a screen for entering a username and password) that allows the user to log in. The user enters their account information on the terminal's login screen and presses the login button.

[1875] Start a study session

[1876] After the user logs in, the terminal displays a "Start learning" button. The user clicks the start learning button to start the learning session. The terminal sends a request to start the learning session to the server. The server receives this request and retrieves the user's learning history and current learning progress from the database. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[1877] Displaying and answering questions

[1878] The terminal displays the questions received from the server to the user. The user enters the answer to the displayed question and presses the send button. The terminal then sends the answer to the server.

[1879] Feedback and understanding measurement

[1880] The server analyzes the user's answers and determines whether they are correct or incorrect. The server generates feedback based on the results of the evaluation (e.g., "Correct!"). The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[1881] View feedback and new issues

[1882] The terminal displays feedback and new problems to the user. The terminal also displays the answer status of a virtual competitor (artificial intelligence) and encourages the user (e.g., "The AI ​​got the answer right too!").

[1883] Emotion Engine Operation

[1884] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and determines their current emotional state (e.g., joy, sadness, excitement, etc.). The device sends the emotion engine's results to the server. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state. The device displays the adjusted feedback and questions to the user.

[1885] Specific examples

[1886] Basic Learning Session

[1887] The user logs in to the terminal and a question (e.g., "5 + 3 = ?") is displayed. The user enters the answer "8" and presses the submit button. The terminal sends the answer to the server and receives feedback that the answer is correct (e.g., "Correct!"). The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal. The terminal displays the new question along with the answer of a virtual competitor (artificial intelligence) (e.g., "AI got it right too!").

[1888] Emotion Engine Operation

[1889] When a user tries to solve a problem, if their facial expression shows difficulty, the emotion engine will judge this as "confusion." The device will then send the user's confused state to the server. The server will then slightly lower the difficulty of the next problem and adjust the feedback to "Try harder and try an easier problem!" The device will then display the new problem and the adjusted feedback to the user, maintaining their motivation.

[1890] Prompt Sentence Examples

[1891] Enter your username and password to sign in:

[1892] text

[1893] Username: example_user, Password: password123

[1894] Submit your answer to the question:

[1895] text

[1896] Username: example_user, Answer: 8

[1897] Sending user facial expression data:

[1898] text

[1899] User name: example_user, Facial image data:<base64_encoded_image>

[1900] In this way, the server of the present invention is capable of managing learning progress, analyzing answers in real time, providing feedback, setting up virtual opponents, and adjusting through emotion recognition, etc. This allows for the realization of advanced educational support that is adapted to the individual needs of each learner.

[1901] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1902] Step 1:

[1903] The user accesses the login interface using a terminal, enters a username and password, and clicks the login button.

[1904] Input: Username, Password

[1905] Data processing: Convert user authentication information into JSON format to send to the server.

[1906] Output: An authentication request is sent to the server.

[1907] Step 2:

[1908] The server receives the login request, retrieves the user's account information from the database, and performs authentication. If authentication is successful, it returns a login success message.

[1909] Input: Authentication request (username, password)

[1910] Data calculation: Check the username and password.

[1911] Output: Authentication result (success or failure)

[1912] Step 3:

[1913] The terminal displays a "Start Learning" button after the user has successfully logged in.

[1914] Input: Authentication result (success)

[1915] Data processing: Creating a UI for displaying buttons

[1916] Output: A "Start learning" button will be displayed.

[1917] Step 4:

[1918] The user clicks the "Start Learning" button. The device captures this event and sends a request to the server to start a learning session.

[1919] Input: User clicks the button event

[1920] Data processing: generating a request to start a learning session

[1921] Output: A request to start a learning session is sent to the server.

[1922] Step 5:

[1923] The server receives a request to start a learning session, retrieves the user's learning history and current learning progress from a database, and generates questions of appropriate difficulty based on this information and sends them to the terminal.

[1924] Input: Learning session start request

[1925] Data calculation: Obtaining user's learning history and progress, generating questions

[1926] Output: The question is generated and sent to the terminal.

[1927] Step 6:

[1928] The terminal displays the questions received from the server to the user.

[1929] Input: Problem data

[1930] Data processing: Creating a UI for displaying the problem

[1931] Output: The problem is displayed to the user.

[1932] Step 7:

[1933] The user inputs the answer to the displayed question and presses the send button, and the terminal sends the answer to the server.

[1934] Input: User's answer

[1935] Data processing: Generate a request to send answers

[1936] Output: The answer is sent to the server.

[1937] Step 8:

[1938] The server receives the user's answer, analyzes it in real time, and determines whether it is correct or incorrect. Based on the results, it generates feedback and creates new questions to send to the device.

[1939] Input: User's answer

[1940] Data operations: analyzing answers, generating feedback, generating new questions

[1941] Output: Feedback and new questions are sent to the device.

[1942] Step 9:

[1943] The terminal displays the feedback received from the server and new questions to the user, as well as the progress of the virtual competitor's (artificial intelligence) solutions.

[1944] Input: Feedback, new questions, AI solution status

[1945] Data processing: Creating a UI for displaying feedback, questions, and AI solution status

[1946] Output: Feedback and new questions are displayed to the user.

[1947] Step 10:

[1948] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[1949] Input: User's facial expression and voice data

[1950] Data processing: Capture and transmit facial and voice data

[1951] Output: Facial expression and voice data is sent to the emotion engine.

[1952] Step 11:

[1953] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state, and returns the results to the device.

[1954] Input: facial expression data, voice data

[1955] Data Computing: Emotional State Analysis

[1956] Output: Emotional state judgment result

[1957] Step 12:

[1958] The device sends the results of the emotion engine to the server, which adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state and sends them to the device, which then displays the adjusted feedback and questions to the user.

[1959] Input: Emotional state judgment result

[1960] Data calculation: Adjusting the difficulty of questions and feedback

[1961] Output: Tailored feedback and problems are displayed to the user.

[1962] 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.

[1963] 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.

[1964] 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.

[1965] [Fourth embodiment]

[1966] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1967] 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.

[1968] 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).

[1969] 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.

[1970] 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.

[1971] 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).

[1972] 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.

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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."

[1979] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[1980] This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server mainly generates questions and analyzes answers, while the terminal accepts user operations and smoothly advances the learning process through communication with the server. Users operate the terminal to engage in learning.

[1981] Initial Setup and Login

[1982] 1. The server stores user account information in a database and provides a mechanism for authentication.

[1983] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[1984] 3. The user enters their account information on the login screen of the device and presses the login button.

[1985] Start a study session

[1986] 1. The device displays a "Start learning" button after the user logs in.

[1987] 2. The user clicks the Start Learning button to begin the learning session.

[1988] 3. The device sends a request to the server to start a learning session.

[1989] 4. The server receives this request and retrieves the user's learning history from the database.

[1990] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[1991] Displaying and answering questions

[1992] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[1993] 2. The user enters the answer to the displayed question (e.g., "8") and presses the submit button.

[1994] 3. The device sends the answer to the server.

[1995] Feedback and understanding measurement

[1996] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[1997] 2. The server generates feedback (e.g., "Correct!") based on the result of the judgment.

[1998] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[1999] 4. The server sends feedback and a new question (e.g., "7 - 4 = ?") to the device.

[2000] View feedback and new issues

[2001] 1. The device displays feedback and new questions to the user.

[2002] 2. The device displays the answer status of the AI's virtual friend (e.g., "The AI ​​also got the answer right!") to encourage the user.

[2003] 3. The user revisits the problem with a new one.

[2004] Ending a session

[2005] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[2006] 2. The device sends a request to the server to terminate the learning session.

[2007] 3. The server saves the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[2008] 4. The terminal displays a message to the user indicating that the session has ended.

[2009] Specific examples

[2010] Scenario 1: Basic Learning Session

[2011] 1. The user logs in to a terminal and is presented with the problem "5 + 3 = ?"

[2012] 2. The user enters the answer "8" and presses the submit button.

[2013] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[2014] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[2015] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[2016] Scenario 2: Problem suggestions based on level of understanding

[2017] 1. The user answers "3" to the question "7 - 4 = ?"

[2018] 2. The server evaluates the level of understanding with the feedback "Correct!" and generates the next question (e.g., "8 + 6 = ?").

[2019] 3. The device displays the new problem and feedback to the user, prompting them to tackle the next problem.

[2020] In this way, the present invention can provide questions that are optimized for the user's learning progress and maintain high learning motivation through AI virtual competition.

[2021] The processing flow will be explained below.

[2022] Step 1:

[2023] The user opens the application on the terminal and enters the username and password on the login screen.

[2024] Step 2:

[2025] The terminal transmits the entered login information to the server.

[2026] Step 3:

[2027] The server compares the received login information with the user information in the database and performs authentication.

[2028] Step 4:

[2029] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[2030] Step 5:

[2031] The user clicks the Start Learning button.

[2032] Step 6:

[2033] The terminal sends a request to the server to start a learning session.

[2034] Step 7:

[2035] The server receives this request and retrieves the user's learning history and current learning progress from the database.

[2036] Step 8:

[2037] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[2038] Step 9:

[2039] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[2040] Step 10:

[2041] The user enters the answer to the displayed question and presses the send button.

[2042] Step 11:

[2043] The terminal transmits the user's answer to the server.

[2044] Step 12:

[2045] The server analyzes the user's answer and determines whether it is correct or incorrect.

[2046] Step 13:

[2047] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[2048] Step 14:

[2049] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[2050] Step 15:

[2051] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[2052] Step 16:

[2053] The terminal displays feedback and new questions to the user.

[2054] Step 17:

[2055] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[2056] Step 18:

[2057] The user revisits the new problem.

[2058] Step 19:

[2059] After a study session has been running for a certain period of time, the user clicks the end button.

[2060] Step 20:

[2061] The terminal sends a request to end the learning session to the server.

[2062] Step 21:

[2063] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[2064] Step 22:

[2065] The terminal displays a message to the user indicating that the session has ended.

[2066] Example 1

[2067] 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."

[2068] Conventional learning systems have issues with insufficient individual response to the user's learning progress and can only provide fixed questions and feedback. Furthermore, they do not sufficiently motivate users, which tends to reduce their motivation to learn, especially in self-study. Furthermore, they lack a mechanism to stimulate competitive spirit, meaning users lack the experience of competing with other learners or the system.

[2069] 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.

[2070] In this invention, the server includes means for generating appropriate study tasks based on the user's learning progress, means for receiving and analyzing the user's input in real time, means for providing feedback to the user, means for setting an intelligent system as a virtual competitor and providing a form of learning in which the user competes with the system, means for saving the user's account information in a database and authenticating it, means for providing a login screen and prompting the user to enter their account information, means for acquiring the user's answer history and generating new questions, and means for evaluating the user's level of understanding and adjusting the difficulty of the new questions. This makes it possible to provide questions that correspond to the individual user's learning progress and to increase motivation to learn through competition with virtual competitors.

[2071] "User's learning progress" refers to the level of understanding and mastery that the user has achieved in the course of performing learning activities.

[2072] "Study assignments" is a general term for problems, exercises, and assignments presented to the user.

[2073] "Input" refers to the act of a user inputting answers and information for a learning task into a terminal.

[2074] "Real time" refers to a state in which user operations or inputs are processed immediately without delay.

[2075] "Analysis" refers to the process by which the server analyzes the user's input and determines its correctness and appropriateness.

[2076] "Feedback" refers to the evaluation or response provided to a user's actions or answers.

[2077] "Virtual competitors" refer to intelligent systems or agents configured on the system, rather than other real users.

[2078] "Intelligent System" refers to the artificial intelligence programmed on the system to compete with the user.

[2079] "Account information" is information for identifying a user, and generally includes a username, password, email address, and the like.

[2080] A "database" refers to a system for systematically storing and managing large amounts of data.

[2081] "Authentication" refers to the process of verifying access rights when a user uses their account information to access a system.

[2082] "Login screen" refers to an interface for a user to enter account information to access a system.

[2083] "Answer history" refers to a record of the study tasks that the user has answered up to now.

[2084] "Level of understanding" refers to the degree to which the user can accurately understand and answer the learning task.

[2085] "Difficulty of the problem" refers to the level of difficulty of the learning task.

[2086] A specific embodiment of the present invention will now be described. This educational support system operates among three parties: a server, a terminal, and a user, and is designed to enable users to study effectively. The server is primarily responsible for generating questions and analyzing answers, while the terminal accepts user operations and smoothly advances the study process through communication with the server. Users engage in study by operating the terminal.

[2087] Hardware and software used

[2088] This system uses the following hardware and software:

[2089] Server: A server machine (e.g., Linux server) equipped with a powerful processor, sufficient memory, and large storage capacity.

[2090] Database software: MySQL

[2091] Programming languages: Python, JavaScript

[2092] Web technologies: HTML, CSS, JavaScript

[2093] Communication protocol: HTTP / HTTPS

[2094] Initial Setup and Login

[2095] 1. The server stores user account information in a MySQL database and provides an authentication mechanism, ensuring the safe management of user personal data.

[2096] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. To do this, a login form is created using HTML and CSS, and the input is validated using JavaScript.

[2097] 3. The user enters their account information on the login screen of the device and presses the login button.

[2098] 4. The device sends the user's input data to the server via an HTTP POST request.

[2099] 5. The server checks the submitted user data against the database, and if authentication is successful, generates session information and returns it to the client.

[2100] Start a study session

[2101] 1. The device displays a "Start learning" button after the user logs in. This button is displayed using HTML and CSS.

[2102] 2. The user clicks the "Start Learning" button.

[2103] 3. Based on the user's operation, the device sends an HTTP POST request to the server to start a learning session.

[2104] 4. The server receives this request and retrieves the user's learning history from the database. For example, it executes the SQL query "SELECT FROM learning history WHERE user ID = '12345'".

[2105] 5. The server generates questions of appropriate difficulty based on the learning history and returns them to the device. This problem generation is done using a question generation algorithm written in Python.

[2106] Displaying and answering questions

[2107] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[2108] 2. The user enters the answer to the displayed question and presses the submit button.

[2109] 3. The device sends the answer data to the server using an HTTP POST request.

[2110] 4. The server receives the answer data and uses Python code to parse it.

[2111] Feedback and understanding measurement

[2112] 1. The server analyzes the user's answers and determines whether they are correct or incorrect. It uses Python to compare the questions and answers.

[2113] 2. The server generates feedback based on the result, such as "Correct!" if the answer is correct, or "Incorrect!" if the answer is incorrect.

[2114] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[2115] 4. The server sends the feedback and new questions to the device.

[2116] View feedback and new issues

[2117] 1. The device displays feedback and new questions to the user, dynamically updating the display using HTML and JavaScript.

[2118] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, to encourage the user. For example, it displays the message "The intelligent system also got the answer right!"

[2119] 3. The user revisits the problem with a new one.

[2120] Ending a session

[2121] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[2122] 2. The device sends an HTTP POST request to the server to end the learning session.

[2123] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('12345', NOW())".

[2124] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[2125] Examples of prompt statements

[2126] By inputting prompt sentences like the following into the generative AI model, we can generate appropriate feedback:

[2127] "User answers 8 to the question 5 + 3 = ?. Please generate a rating and feedback."

[2128] By entering this prompt, the AI ​​can generate accurate feedback (e.g., "Correct!") and provide it to the user.

[2129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2130] Step 1:

[2131] Initial Setup and Login

[2132] 1. The server stores user account information in a database and provides a mechanism for authentication. Specifically, it stores usernames and hashed passwords in a MySQL database.

[2133] Input: Username, hashed password

[2134] Output: Save results to database

[2135] 2. The terminal provides an interface (username and password entry screen) that allows the user to log in. Create a login form using HTML and CSS, and validate the input using JavaScript.

[2136] Input: None

[2137] Output: Login form screen

[2138] 3. The user enters their username and password on the login screen and clicks the login button.

[2139] Input: Username, Password

[2140] Output: Login request

[2141] 4. The device sends the user's input data to the server using an HTTP POST request.

[2142] Input: Username, Password

[2143] Output: HTTP POST request

[2144] 5. The server checks the submitted user data against the database information, and if authentication is successful, generates session information and returns it to the client. The check is performed using an SQL query that retrieves data from the database.

[2145] Input: Username, Password

[2146] Output: Session information, authentication results

[2147] Step 2:

[2148] Start a study session

[2149] 1. The device displays a "Start Learning" button after the user logs in. Generate the button using HTML and CSS.

[2150] Input: Authentication completed session information

[2151] Output: "Start learning" button

[2152] 2. The user clicks the Start Learning button.

[2153] Input: None

[2154] Output: Training start request

[2155] 3. The device sends a learning session start request to the server based on the user's operation using an HTTP POST request.

[2156] Input: Learning start request

[2157] Output: HTTP POST request

[2158] 4. The server receives this request and retrieves the user's learning history from the database. Specifically, it executes the SQL query "SELECT FROM learning history WHERE user ID = 'user ID'".

[2159] Input: User ID

[2160] Output: Learning history data

[2161] 5. The server generates questions of appropriate difficulty based on the learning history and sends them back to the device. Question generation is done using Python.

[2162] Input: Learning history data

[2163] Output: Generated problem

[2164] Step 3:

[2165] Displaying and answering questions

[2166] 1. The terminal displays the questions received from the server to the user. The questions are dynamically updated using HTML and JavaScript.

[2167] Input: Generated question

[2168] Output: Screen showing the problem

[2169] 2. The user enters the answer to the displayed question and presses the submit button.

[2170] Input: Answer

[2171] Output: Answer submission request

[2172] 3. The device sends the answer data to the server, again using an HTTP POST request.

[2173] Input: Answer

[2174] Output: HTTP POST request

[2175] 4. The server receives the answer data and uses Python code to parse it.

[2176] Input: Answer data

[2177] Output: Analysis results

[2178] Step 4:

[2179] Feedback and understanding measurement

[2180] 1. The server analyzes the user's answer and determines whether it is correct or incorrect. It compares the question and answer using Python.

[2181] Input: Answer data, question data

[2182] Output: Judgment result

[2183] 2. The server generates feedback based on the result, such as a message like "Correct!" if the answer is correct, or "Incorrect" if the answer is incorrect.

[2184] Input: Judgment result

[2185] Output: Feedback message

[2186] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of new questions. For example, if the user has answered the last five questions correctly, the difficulty level will be increased.

[2187] Input: Answer data, answer history

[2188] Output: New problem difficulty

[2189] 4. The server sends the feedback and new questions to the device.

[2190] Input: Feedback message, new issue

[2191] Output: HTTP POST request

[2192] Step 5:

[2193] View feedback and new issues

[2194] 1. The device displays feedback and new problems to the user, dynamically updating the display using HTML and JavaScript.

[2195] Input: Feedback message, new issue

[2196] Output: A screen showing feedback and new questions

[2197] 2. The terminal displays the answer status of the virtual competitor, the intelligent system, and encourages the user, for example by displaying a message such as "The intelligent system also got the answer right!"

[2198] Input: Answer status of the intelligent system

[2199] Output: Intelligent system solution status message

[2200] 3. The user revisits the problem with a new one.

[2201] Input: None

[2202] Output: Answer submission request

[2203] Step 6:

[2204] Ending a session

[2205] 1. The user engages in a learning session for a set period of time and then clicks the end button.

[2206] Input: None

[2207] Output: Session termination request

[2208] 2. The device sends an HTTP POST request to the server to end the learning session.

[2209] Input: Session termination request

[2210] Output: HTTP POST request

[2211] 3. The server saves the user's learning log in the database and sends a message to the terminal confirming the end of the session. As a concrete example, it executes the SQL statement "INSERT INTO learning log (user ID, end time) VALUES ('user ID', NOW())".

[2212] Input: User ID, end time

[2213] Output: Database save result, completion confirmation message

[2214] 4. The device displays a message to the user indicating the session is over. This can be done using HTML, such as "Session over, see you next time!"

[2215] Input: Exit confirmation message

[2216] Output: Display session termination message

[2217] (Application example 1)

[2218] 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."

[2219] In today's commercial environment, new staff are required to quickly acquire customer service skills. However, traditional training methods are not based on real-world scenarios, making it difficult to achieve effective learning outcomes, and delays in feedback can lead to a lack of motivation. Furthermore, a lack of training environments that address the specific problems new staff face in brick-and-mortar stores makes efficient learning difficult.

[2220] 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.

[2221] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an AI as a virtual competitor and providing learning in a competitive format with the user, means for generating scenario-based questions for the user to master customer service skills in a physical store in real time, and means for inputting and assisting the user's answers using a smart device. This enables new staff to effectively and continuously improve their customer service skills while receiving immediate feedback through specific customer service scenarios.

[2222] "User" means an individual or organization that uses the system for learning or training.

[2223] "Learning progress" is an indicator of how far a user has progressed in the learning process.

[2224] "Appropriate problems" are tasks and questions that are provided according to the user's learning progress and level, and are designed to maximize learning effectiveness.

[2225] An "answer" is a response or answer that a user provides to a posed question.

[2226] "Real time" is a time concept that indicates that processing is performed immediately without delay.

[2227] "Analysis" is the process of evaluating the user's answer to determine its correctness.

[2228] "Feedback" is information that provides evaluation and advice on the user's answer.

[2229] A "virtual competitor" is a competitive object, such as an artificial intelligence (AI), that is set up to compete with the user.

[2230] "AI" stands for artificial intelligence, a technology that allows computer programs to mimic human intelligence.

[2231] "Scenario-based questions" are learning tasks constructed based on real-world business scenarios.

[2232] A "smart device" is an electronic device that has internet connectivity and computing capabilities.

[2233] "Input" is the act of a user providing information to a system.

[2234] "Assistance" is the process of providing support to help users operate or learn more effectively.

[2235] This invention relates to a training system for brick-and-mortar stores to improve users' customer service skills. The system operates among a server, smart glasses as a terminal, and the user, and is designed to allow users to progress in their learning while receiving effective and immediate feedback.

[2236] Hardware or software used

[2237] Server: Cloud servers and on-premise servers are used. SQLite is used as the database.

[2238] Device: Smart glasses (e.g. Google Glass)

[2239] Software: Python, SQLite for database management, and the operating system of the smart glasses (e.g., Android)

[2240] System Operation

[2241] 1. Initial Setup and Login

[2242] The server stores the user's account information in a database, and the user logs in through the smart glasses. The server authenticates the user, and if authentication is successful, obtains the user's learning progress.

[2243] 2. Start your study session

[2244] After logging in, the user clicks the "Start Learning" button, which sends a request to the server to start a learning session. The server generates appropriate scenario-based questions based on the user's learning history and sends them to the smart glasses.

[2245] 3. Displaying and answering questions

[2246] The smart glasses display the questions received from the server to the user, who then inputs the answers to the questions using voice or tactile input, which are then sent to the server via the smart glasses.

[2247] 4. Feedback and Gauging Understanding

[2248] The server analyzes the user's answers in real time, determines whether they are correct or incorrect, generates feedback, and sends it to the smart glasses. It also evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[2249] 5. Displaying new questions and trying again

[2250] The smart glasses display feedback and new questions, showing the progress of AI competitors, motivating users to learn and encourage them to tackle the next problem.

[2251] Specific scenario example

[2252] Example prompt: "How do you respond when a customer asks you about a particular product?"

[2253] Example user answer: "Tell me more about the product"

[2254] In this way, users can improve their skills through specific customer service scenarios in real time. The system provides users with the information they need instantly and provides effective feedback, allowing new staff to learn efficiently and quickly adapt to work in a brick-and-mortar store.

[2255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2256] Step 1: Initial Setup and Login

[2257] Specific actions

[2258] The server stores user account information in a database and provides a mechanism for authentication.

[2259] input

[2260] The user enters their username and password through the smart glasses.

[2261] Data processing / data calculation

[2262] The server checks the entered username and password against the information in the database and performs authentication.

[2263] output

[2264] If authentication is successful, the user's learning progress data is retrieved and login is completed.

[2265] Step 2: Start your study session

[2266] Specific actions

[2267] The user clicks the "Start Learning" button.

[2268] input

[2269] A request to start a learning session is sent from the terminal to the server.

[2270] Data processing / data calculation

[2271] The server generates appropriate scenario-based questions based on the user's learning history.

[2272] output

[2273] The generated questions are sent to the device.

[2274] Step 3: View and answer questions

[2275] Specific actions

[2276] The terminal displays the problem received from the server to the user.

[2277] input

[2278] The problem data sent by the server.

[2279] Data processing / data calculation

[2280] The terminal displays the content of the problem in a format suitable for the user.

[2281] output

[2282] The user inputs answers through voice input or tactile manipulation.

[2283] Step 4: Submit your answers

[2284] Specific actions

[2285] The user enters the answer and sends it from the terminal to the server.

[2286] input

[2287] User answer data.

[2288] Data processing / data calculation

[2289] The terminal converts the answer data into a format for transmission to the server.

[2290] output

[2291] The answer data is sent to the server.

[2292] Step 5: Feedback and Gauging

[2293] Specific actions

[2294] The server analyzes the user's answers and evaluates the results.

[2295] input

[2296] User answer data.

[2297] Data processing / data calculation

[2298] The server determines whether the answer is correct or incorrect, generates feedback, and evaluates the user's understanding.

[2299] output

[2300] Evaluation data is generated as feedback, and the difficulty of the next question presented is adjusted.

[2301] Step 6: View feedback and new issues

[2302] Specific actions

[2303] The terminal displays the feedback received from the server and any new questions to the user.

[2304] input

[2305] Feedback data and new problem data from the server.

[2306] Data processing / data calculation

[2307] The terminal converts the data into a format for display and presents it to the user visually or audibly.

[2308] output

[2309] The user revisits the new problem.

[2310] Step 7: Ending the study session

[2311] Specific actions

[2312] The user clicks the "End Learning" button.

[2313] input

[2314] A request to end the learning session is sent from the terminal to the server.

[2315] Data processing / data calculation

[2316] The server stores the user's learning log in a database and confirms the end of the session.

[2317] output

[2318] A message indicating the session has ended is displayed on the terminal.

[2319] 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.

[2320] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes specific embodiments of the present invention.

[2321] This invention is an educational support system that operates among a server, a terminal, and a user, and generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and provides a competitive learning experience by setting up an AI as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the learning content and feedback are adjusted according to the user's emotional state.

[2322] Initial Setup and Login

[2323] 1. The server stores user account information in a database and provides a mechanism for authentication.

[2324] 2. The terminal provides an interface (username and password entry screen) that the user can use to log in.

[2325] 3. The user enters their account information on the login screen of the device and presses the login button.

[2326] Start a study session

[2327] 1. The device displays a "Start learning" button after the user logs in.

[2328] 2. The user clicks the Start Learning button to begin the learning session.

[2329] 3. The device sends a request to the server to start a learning session.

[2330] 4. The server receives this request and retrieves the user's learning history and current learning progress from the database.

[2331] 5. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[2332] Displaying and answering questions

[2333] 1. The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[2334] 2. The user enters the answer to the displayed question and presses the submit button.

[2335] 3. The device sends the answer to the server.

[2336] Feedback and understanding measurement

[2337] 1. The server analyzes the user's answer and determines whether it is correct or incorrect.

[2338] 2. The server generates feedback based on the result (e.g., "Correct!").

[2339] 3. The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[2340] 4. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[2341] View feedback and new issues

[2342] 1. The device displays feedback and new questions to the user.

[2343] 2. The device displays the answer status of the AI's virtual friend and encourages the user (e.g., "The AI ​​got the answer right too!").

[2344] Emotion Engine Operation

[2345] 1. The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[2346] 2. The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, etc.).

[2347] 3. The device sends the emotion engine results to the server.

[2348] 4. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[2349] 5. The device displays tailored feedback and problems to the user.

[2350] Specific examples

[2351] Scenario 1: Basic Learning Session

[2352] 1. The user logs in to a terminal and is presented with a problem (e.g., "5 + 3 = ?").

[2353] 2. The user enters the answer "8" and presses the submit button.

[2354] 3. The device sends the answer to the server and receives feedback on the correct answer (e.g., "Correct!").

[2355] 4. The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal.

[2356] 5. The device will display the new question along with the answer from the AI's virtual friend (e.g., "AI got it right too!").

[2357] Scenario 2: Emotion Engine in Action

[2358] 1. When a user solves a problem, if their facial expression is difficult, the emotion engine will judge it as "confused."

[2359] 2. The terminal transmits the user's confusion state to the server.

[2360] 3. The server adjusts the difficulty of the next problem it provides by slightly lowering the difficulty level and providing feedback such as "Try harder, let's try an easier problem!"

[2361] 4. The device displays new problems and tailored feedback to the user to keep them motivated.

[2362] In this way, by combining an emotion engine, the present invention can provide educational support that is adapted to the user's emotional state, maximizing learning outcomes.

[2363] The processing flow will be explained below.

[2364] Step 1:

[2365] The user opens the application on the terminal and enters the username and password on the login screen.

[2366] Step 2:

[2367] The terminal transmits the entered login information to the server.

[2368] Step 3:

[2369] The server compares the received login information with the user information in the database and performs authentication.

[2370] Step 4:

[2371] If the authentication is successful, the server causes the terminal to display a message indicating successful authentication along with a learning start button.

[2372] Step 5:

[2373] The user clicks the Start Learning button.

[2374] Step 6:

[2375] The terminal sends a request to the server to start a learning session.

[2376] Step 7:

[2377] Upon receiving this request, the server retrieves the user's learning history and current learning progress from the database.

[2378] Step 8:

[2379] The server generates questions of appropriate difficulty based on the user's learning progress and transmits them to the terminal.

[2380] Step 9:

[2381] The terminal displays the problem received from the server to the user (e.g., "5 + 3 = ?").

[2382] Step 10:

[2383] The user enters the answer to the displayed question and presses the send button.

[2384] Step 11:

[2385] The terminal sends the answer to the server.

[2386] Step 12:

[2387] The server analyzes the user's answer and determines whether it is correct or incorrect.

[2388] Step 13:

[2389] The server generates feedback based on the result of the judgment (e.g., "Correct!").

[2390] Step 14:

[2391] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented.

[2392] Step 15:

[2393] The server sends feedback and a new question to the device (e.g., "7 - 4 = ?").

[2394] Step 16:

[2395] The terminal displays feedback and new questions to the user.

[2396] Step 17:

[2397] The device displays the AI's virtual friend's answer status and encourages the user (e.g., "The AI ​​got the answer right too!").

[2398] Step 18:

[2399] The user revisits the new problem.

[2400] Step 19:

[2401] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[2402] Step 20:

[2403] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state (e.g., joy, sadness, excitement, confusion, etc.).

[2404] Step 21:

[2405] The terminal transmits the results of the emotion engine to the server.

[2406] Step 22:

[2407] The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state.

[2408] Step 23:

[2409] The server sends the adjusted feedback and questions to the terminal.

[2410] Step 24:

[2411] The terminal displays the tailored feedback and new questions to the user.

[2412] Step 25:

[2413] The user then re-enters the answer and continues learning.

[2414] Step 26:

[2415] After a study session has been running for a certain period of time, the user clicks the end button.

[2416] Step 27:

[2417] The terminal sends a request to end the learning session to the server.

[2418] Step 28:

[2419] The server stores the user's learning log in a database and sends a message to the terminal confirming the end of the session.

[2420] Step 29:

[2421] The terminal displays a message to the user indicating that the session has ended.

[2422] Example 2

[2423] 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."

[2424] While conventional educational support systems provide individualized instruction based on the user's learning progress, they do not provide feedback or generate questions that take the user's emotional state into account, making it difficult to maintain motivation and maximize learning outcomes. Furthermore, monotonous learning formats make it difficult to maintain user motivation, and the lack of elements that stimulate competitive spirit is also problematic.

[2425] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for setting an artificial intelligence as a virtual opponent and providing learning in a competitive format with the user, and means for analyzing the user's emotional state and adjusting the difficulty of the questions and the feedback based on that information. This makes it possible to provide individualized instruction according to the user's learning progress and emotional state, and an environment that stimulates competitive spirit.

[2426] A "user" is an individual who uses the system to carry out learning activities.

[2427] "Study progress" is an index showing the learning content and results that the user has achieved up to now.

[2428] "Question generation" refers to the process of creating new study questions based on the user's learning progress and level of understanding.

[2429] "Receiving and analyzing in real time" refers to the process of instantly sending user input (e.g., answers) to the server and instantly judging and evaluating its content.

[2430] "Feedback" refers to messages that provide evaluations and advice based on the user's answers.

[2431] "Artificial intelligence (AI)" refers to technology that enables computers to perform human-like intelligent behavior, in this case acting as learning competitors.

[2432] "Emotional state" refers to the user's current emotion (e.g., joy, sadness, excitement, etc.), and changes depending on the learning situation.

[2433] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice to determine their emotional state.

[2434] A "virtual competitor" is a non-existent entity that mimics an opponent that competes with the user within the system, and is primarily composed of artificial intelligence.

[2435] This is an educational support system that generates questions based on the user's learning progress, analyzes answers in real time, provides feedback, and also provides competitive learning by setting up an artificial intelligence (AI) as a virtual opponent.Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the quality of learning is improved by adjusting the learning content and feedback according to the user's emotional state.

[2436] Hardware and software used

[2437] Hardware

[2438] Server: Hosts a database (e.g., MySQL, PostgreSQL) to store and manage users' learning history and progress.

[2439] Terminal: The computer or smartphone used by the user, which provides the interface and sends the user's input and answers to the server.

[2440] Camera and microphone: Used to capture the user's facial expressions and voice and send them to the emotion engine.

[2441] software

[2442] Generative AI models: Used to generate questions based on the user's learning progress (e.g., OpenAI GPT-3).

[2443] Face recognition and speech analysis APIs: Used to implement emotion engines (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text).

[2444] Web Technologies: Uses HTML, CSS, JavaScript for front-end development and communicates with the server using AJAX and fetch APIs.

[2445] Specific operation of the system

[2446] Problem generation

[2447] The server retrieves the user's learning progress from the database and uses a generative AI model to generate questions of appropriate difficulty, for example, "5 + 3 = ?"

[2448] Answer analysis and feedback

[2449] When a user enters an answer to a question on the device and presses the submit button, the device sends the answer to the server. The server analyzes the answer, determines whether it is correct or incorrect, and generates feedback (e.g., "Correct!" or "Think a bit more!"). This feedback is sent back to the device and displayed to the user.

[2450] AI as a virtual competitor

[2451] The AI ​​is set up as a virtual competitor to support the user's learning. For example, the AI ​​can be displayed solving the same problem, allowing the user to compete in a learning competition. The AI's answer is also displayed (e.g., "The AI ​​got the answer right!").

[2452] Emotion Engine

[2453] The user's facial expressions and voice are captured through the camera and microphone and sent to the emotion engine. The emotion engine analyzes this data and determines the user's emotional state (e.g., joy, sadness, excitement, etc.). The server then adjusts the feedback and difficulty of the questions based on this information to provide the user with an appropriate learning experience.

[2454] Specific examples

[2455] Prompt Sentence Examples

[2456] Here are some examples of prompts to input to a generative AI model:

[2457] How can we build an educational support system that generates appropriate questions based on the user's learning progress and emotional state, and provides feedback in real time?

[2458] By using this prompt, the generative AI model is expected to generate answers detailing specific steps and implementation methods.

[2459] As described above, the system of the present invention can provide effective educational support to users, similar to individual instruction, and maximize the results of their learning.

[2460] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2461] Step 1:

[2462] The server stores user account information in a database and provides a mechanism for authentication. Specifically, it hashes the username and password provided by the user when registering and stores them in the database. The input is the username and password, and the output is the hashed account information with the password stored in the database.

[2463] Step 2:

[2464] The terminal provides an interface (username and password entry screen) that allows the user to log in. A UI is constructed to temporarily store the information entered by the user and send it to the server. The input is the username and password, and the input information is sent to the server as the output.

[2465] Step 3:

[2466] The user enters their account information on the login screen of the device and presses the login button. The input is a username and password, and the output is a login request sent from the device to the server.

[2467] Step 4:

[2468] The terminal displays a "Start learning" button after the user logs in. To allow the user to start learning, a dashboard screen is created and a UI including a "Start learning" button is displayed. The input is the user's successful login status, and the output is the display of a "Start learning" button.

[2469] Step 5:

[2470] The user clicks the "Start Learning" button to start a learning session. The input is the clicking of the "Start Learning" button, and the output is a learning session start request sent from the terminal to the server.

[2471] Step 6:

[2472] The device sends a learning session start request to the server. The JavaScript fetch API or AJAX is used to send the learning session start request. The input is a button click event, and the learning session start request is sent to the server as its output.

[2473] Step 7:

[2474] The server receives this request and retrieves the user's learning history and current learning progress from the database. The database is queried for the user's learning history and progress, and the results are stored in memory. The input is a learning session start request, and the output is the user's learning history and progress.

[2475] Step 8:

[2476] The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the device. A generative AI model is used to generate questions based on the user's progress. The input is the user's learning history and progress, and the generated questions are sent to the device as output.

[2477] Step 9:

[2478] The terminal displays the problem received from the server to the user. A UI for displaying the problem is constructed and the problem is displayed. The input is the problem received from the server, and the problem is displayed to the user as the output.

[2479] Step 10:

[2480] The user inputs the answer to the displayed question and presses the send button. The input is the user's answer, and the output is sent to the terminal.

[2481] Step 11:

[2482] The device sends the answer to the server. The fetch API or AJAX is used to send the entered answer to the server. The input is the user's answer, and the answer is sent to the server as the output.

[2483] Step 12:

[2484] The server analyzes the user's answer and determines whether it is correct or incorrect. The analysis is performed using server-side logic or an AI model. The input is the user's answer, and the output is the analysis result.

[2485] Step 13:

[2486] The server generates feedback based on the judgment results. It uses a feedback generation AI model to create a feedback message. The input is the analysis result, and the output is the feedback message.

[2487] Step 14:

[2488] The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question they will present. It analyzes the user's answer history and determines the difficulty of the next question. The input is the user's answer history, and the output is the adjusted difficulty of the question.

[2489] Step 15:

[2490] The server sends feedback and new questions to the terminal. The generated feedback and new questions are sent to the terminal. The input is the generated feedback and new questions, and the output is sent to the terminal.

[2491] Step 16:

[2492] The terminal displays feedback and new problems to the user. The UI displays feedback messages and new problems. The input is the feedback and new problems received from the server, and the output is displayed to the user.

[2493] Step 17:

[2494] The device displays the AI's virtual friend's answer status to encourage the user. It also displays the virtual friend's answer results to motivate the user to learn. The input is the AI's answer results, which are displayed as the output.

[2495] Step 18:

[2496] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. It uses a camera and microphone to capture and send the user's facial expression and voice data. The input is the user's facial expression and voice, and the output is sent to the emotion engine.

[2497] Step 19:

[2498] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state. It uses face recognition APIs and voice analysis APIs to determine emotions. The input is facial expression data and voice data, and the output is the user's emotional state.

[2499] Step 20:

[2500] The device sends the results of the emotion engine to the server, which then sends the analysis results to prepare them for the next feedback. The input is the analysis result of the emotional state, which is then sent to the server as the output.

[2501] Step 21:

[2502] The server adjusts the difficulty of questions and the content of feedback based on the user's emotional state. Based on the determined emotional state, it determines the appropriate difficulty of questions and feedback. The input is the analysis result of the emotional state, and the adjusted questions and feedback are obtained as the output.

[2503] Step 22:

[2504] The terminal displays the adjusted feedback and questions to the user. It displays the adjusted feedback and new questions received from the server. The input is the adjusted feedback and questions, and the output is displayed to the user.

[2505] (Application example 2)

[2506] 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."

[2507] Conventional educational systems struggle to provide appropriate feedback and questions based on each learner's individual progress and emotional state, making it difficult to maximize learning outcomes. Furthermore, the ability to improve learning motivation through competition with virtual opponents is limited. Furthermore, the lack of real-time answer analysis and feedback prevents rapid improvement in understanding. Therefore, a comprehensive educational support system that effectively and continuously increases students' motivation to learn is needed.

[2508] 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.

[2509] In this invention, the server includes means for generating appropriate questions based on the user's learning progress, means for receiving and analyzing the user's answers in real time, means for providing feedback to the user, means for providing learning in a competitive format by setting up an artificial intelligence as a virtual opponent, and means for recognizing the user's emotions and adjusting the learning content. This makes it possible to provide questions and feedback tailored to the progress and emotional state of each learner, thereby effectively and sustainably increasing their motivation to learn.

[2510] "User" refers to an individual or learner who uses the educational support system.

[2511] "Study progress" refers to the level or stage of progress a user has achieved through learning.

[2512] "Problems" are questions or tasks provided to assess a user's learning.

[2513] An "answer" is a response or response provided by a user to a question.

[2514] "Real-time" refers to processing and response occurring almost immediately, with little delay.

[2515] "Feedback" is any rating or comment provided on a user's answer.

[2516] A "virtual competitor" is an artificial intelligence set up to compete with the user within the system.

[2517] "Artificial intelligence" refers to software or systems that learn and make decisions like humans through machine learning and data analysis.

[2518] "Emotion recognition" is a technology that determines a user's emotional state from their facial expressions and voice.

[2519] "Difficulty of a question" is an index that indicates how difficult it is to solve a question.

[2520] This invention is an educational support system that operates among three parties: a server, a terminal, and a user, and provides learning support by combining question generation based on the user's learning progress, answer analysis, feedback provision, competition with a virtual opponent (artificial intelligence), and emotion recognition.

[2521] Initial Setup and Login

[2522] The server stores user account information in a database and provides a mechanism for authentication. The terminal provides an interface (a screen for entering a username and password) that allows the user to log in. The user enters their account information on the terminal's login screen and presses the login button.

[2523] Start a study session

[2524] After the user logs in, the terminal displays a "Start learning" button. The user clicks the start learning button to start the learning session. The terminal sends a request to start the learning session to the server. The server receives this request and retrieves the user's learning history and current learning progress from the database. The server generates questions of appropriate difficulty based on the user's learning progress and sends them to the terminal.

[2525] Displaying and answering questions

[2526] The terminal displays the questions received from the server to the user. The user enters the answer to the displayed question and presses the send button. The terminal then sends the answer to the server.

[2527] Feedback and understanding measurement

[2528] The server analyzes the user's answers and determines whether they are correct or incorrect. The server generates feedback based on the results of the evaluation (e.g., "Correct!"). The server evaluates the user's level of understanding based on their answer history and adjusts the difficulty of the next question presented. The server sends the feedback and a new question to the device (e.g., "7 - 4 = ?").

[2529] View feedback and new issues

[2530] The terminal displays feedback and new problems to the user. The terminal also displays the answer status of a virtual competitor (artificial intelligence) and encourages the user (e.g., "The AI ​​got the answer right too!").

[2531] Emotion Engine Operation

[2532] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and determines their current emotional state (e.g., joy, sadness, excitement, etc.). The device sends the emotion engine's results to the server. The server adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state. The device displays the adjusted feedback and questions to the user.

[2533] Specific examples

[2534] Basic Learning Session

[2535] The user logs in to the terminal and a question (e.g., "5 + 3 = ?") is displayed. The user enters the answer "8" and presses the submit button. The terminal sends the answer to the server and receives feedback that the answer is correct (e.g., "Correct!"). The server generates a new question (e.g., "7 - 4 = ?") and sends it to the terminal. The terminal displays the new question along with the answer of a virtual competitor (artificial intelligence) (e.g., "AI got it right too!").

[2536] Emotion Engine Operation

[2537] When a user tries to solve a problem, if their facial expression shows difficulty, the emotion engine will judge this as "confusion." The device will then send the user's confused state to the server. The server will then slightly lower the difficulty of the next problem and adjust the feedback to "Try harder and try an easier problem!" The device will then display the new problem and the adjusted feedback to the user, maintaining their motivation.

[2538] Prompt Sentence Examples

[2539] Enter your username and password to sign in:

[2540] text

[2541] Username: example_user, Password: password123

[2542] Submit your answer to the question:

[2543] text

[2544] Username: example_user, Answer: 8

[2545] Sending user facial expression data:

[2546] text

[2547] User name: example_user, Facial image data:<base64_encoded_image>

[2548] In this way, the server of the present invention is capable of managing learning progress, analyzing answers in real time, providing feedback, setting up virtual opponents, and adjusting through emotion recognition, etc. This allows for the realization of advanced educational support that is adapted to the individual needs of each learner.

[2549] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2550] Step 1:

[2551] The user accesses the login interface using a terminal, enters a username and password, and clicks the login button.

[2552] Input: Username, Password

[2553] Data processing: Convert user authentication information into JSON format to send to the server.

[2554] Output: An authentication request is sent to the server.

[2555] Step 2:

[2556] The server receives the login request, retrieves the user's account information from the database, and performs authentication. If authentication is successful, it returns a login success message.

[2557] Input: Authentication request (username, password)

[2558] Data calculation: Check the username and password.

[2559] Output: Authentication result (success or failure)

[2560] Step 3:

[2561] The terminal displays a "Start Learning" button after the user has successfully logged in.

[2562] Input: Authentication result (success)

[2563] Data processing: Creating a UI for displaying buttons

[2564] Output: A "Start learning" button will be displayed.

[2565] Step 4:

[2566] The user clicks the "Start Learning" button. The device captures this event and sends a request to the server to start a learning session.

[2567] Input: User clicks the button event

[2568] Data processing: generating a request to start a learning session

[2569] Output: A request to start a learning session is sent to the server.

[2570] Step 5:

[2571] The server receives a request to start a learning session, retrieves the user's learning history and current learning progress from a database, and generates questions of appropriate difficulty based on this information and sends them to the terminal.

[2572] Input: Learning session start request

[2573] Data calculation: Obtaining user's learning history and progress, generating questions

[2574] Output: The question is generated and sent to the terminal.

[2575] Step 6:

[2576] The terminal displays the questions received from the server to the user.

[2577] Input: Problem data

[2578] Data processing: Creating a UI for displaying the problem

[2579] Output: The problem is displayed to the user.

[2580] Step 7:

[2581] The user inputs the answer to the displayed question and presses the send button, and the terminal sends the answer to the server.

[2582] Input: User's answer

[2583] Data processing: Generate a request to send answers

[2584] Output: The answer is sent to the server.

[2585] Step 8:

[2586] The server receives the user's answer, analyzes it in real time, and determines whether it is correct or incorrect. Based on the results, it generates feedback and creates new questions to send to the device.

[2587] Input: User's answer

[2588] Data operations: analyzing answers, generating feedback, generating new questions

[2589] Output: Feedback and new questions are sent to the device.

[2590] Step 9:

[2591] The terminal displays the feedback received from the server and new questions to the user, as well as the progress of the virtual competitor's (artificial intelligence) solutions.

[2592] Input: Feedback, new questions, AI solution status

[2593] Data processing: Creating a UI for displaying feedback, questions, and AI solution status

[2594] Output: Feedback and new questions are displayed to the user.

[2595] Step 10:

[2596] The device captures the user's facial expressions and voice in real time and sends them to the emotion engine.

[2597] Input: User's facial expression and voice data

[2598] Data processing: Capture and transmit facial and voice data

[2599] Output: Facial expression and voice data is sent to the emotion engine.

[2600] Step 11:

[2601] The emotion engine analyzes the user's facial expressions and voice to determine their current emotional state, and returns the results to the device.

[2602] Input: facial expression data, voice data

[2603] Data Computing: Emotional State Analysis

[2604] Output: Emotional state judgment result

[2605] Step 12:

[2606] The device sends the results of the emotion engine to the server, which adjusts the difficulty of the questions and the content of the feedback based on the user's emotional state and sends them to the device, which then displays the adjusted feedback and questions to the user.

[2607] Input: Emotional state judgment result

[2608] Data calculation: Adjusting the difficulty of questions and feedback

[2609] Output: Tailored feedback and problems are displayed to the user.

[2610] 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.

[2611] 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.

[2612] 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.

[2613] The...

Claims

1. means for generating appropriate questions based on the user's learning progress; means for receiving and analyzing user answers in real time; a means for providing feedback to the user; A means of providing learning in a competitive format by setting AI as a virtual competitor with users; A system including:

2. The system according to claim 1 , further comprising means for displaying a state in which the AI ​​set as a virtual competitor solves the same problem as the user.

3. The system according to claim 1, further comprising means for displaying the answer results of the AI ​​as a competitor to increase the user's motivation to learn.

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