system

Abstract

We provide the system. [Solution] A means for generating a problem that is suitable by analyzing past question patterns, Means for transmitting the problem generated by the generation device to a user terminal, A system including an approval device that evaluates answers received from a user terminal and generates answer results and explanations.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

[0003] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern educational environment, the gap due to the economic situation of families has become a major problem. As a result, students who cannot attend cram schools have difficulty obtaining high-quality learning opportunities, and the academic achievement gap is widening. In addition, it is difficult to provide problems suitable for the characteristics of individual schools or entrance examination schools, and there are limitations in self-study. In order to solve these problems, it is necessary to provide an environment in which anyone can receive high-quality learning at a low cost.

Means for Solving the Problems

[0005] This invention provides a system in which a generation device analyzes past question patterns to generate suitable questions and transmits them to a user terminal. This system includes an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. The user terminal has a function to transmit the authentication information entered by the user to a server for authentication. Furthermore, the generation device has a function to generate question formats specialized for each learning field, and the approval device immediately evaluates the received answers and generates feedback. In this way, users can engage in high-quality learning at home while reducing their financial burden.

[0006] A "generator" is a device that has the function of generating suitable questions based on past question patterns.

[0007] A "user terminal" is a device used by a user to submit answers to problems and receive feedback.

[0008] A "server" is a central system that manages user authentication information and works in conjunction with generation and approval devices to generate problems and evaluate answers.

[0009] An "approval device" is a device that evaluates the user's answer, determines whether it is correct or incorrect, and generates an explanation.

[0010] An "answer" is the response that a user enters in response to a given problem.

[0011] "Feedback" refers to the evaluation results and explanatory information that users receive after submitting their answers.

[0012] "Authentication information" refers to verification data such as the ID and password that a user uses to log in to the system. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system for carrying out the present invention has a configuration including a server, a user terminal, a generation device, and an approval device. The specific operation of each component and the program processing are described below in natural language.

[0035] 1. User Login

[0036] Function Description

[0037] This is a mechanism for users to log in to the system using their user terminal.

[0038] Processing flow

[0039] The user enters their ID and password into their user terminal.

[0040] The user terminal sends the entered authentication information to the server.

[0041] The server compares the received authentication information with the database and returns the authentication result to the user's terminal.

[0042] If authentication is successful, the server generates a login token and sends it to the user's device.

[0043] 2. Selection of learning area

[0044] Function Description

[0045] This is a system that allows users to select a specific field they want to learn about.

[0046] Processing flow

[0047] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through an interface on their device.

[0048] The user terminal sends information about the selected field to the server.

[0049] 3. Generating past exam questions

[0050] Function Description

[0051] This system generates questions for users to learn from, based on past question patterns.

[0052] Processing flow

[0053] The server invokes the generator based on information about the learning area selected by the user.

[0054] The generation device retrieves relevant data from a database containing past question patterns and generates questions.

[0055] The generation device creates a question format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[0056] 4. Provision of question and answer format

[0057] Function Description

[0058] This is a mechanism for presenting generated problems to users and receiving their answers.

[0059] Processing flow

[0060] The server sends the generated questions and answer formats to the user's terminal.

[0061] The user terminal displays the question and answer format to the user.

[0062] 5. User's response

[0063] Function Description

[0064] This is a system that allows users to input their answers to questions and send those answers to the server.

[0065] Processing flow

[0066] The user enters their answer to the question displayed on their terminal.

[0067] The user terminal sends the entered answer to the server.

[0068] 6. Evaluation and Feedback of Answers

[0069] Function Description

[0070] This system evaluates user answers and provides evaluation results and explanations.

[0071] Processing flow

[0072] The server sends the received user's answer to the approval device.

[0073] The approval device evaluates the answer and determines whether it is correct or incorrect.

[0074] The approval device generates the correct answer and explanation and sends them to the server.

[0075] The server sends the generated feedback to the user's terminal.

[0076] The user terminal displays feedback to the user.

[0077] Specific example

[0078] Example: When student A, a third-year high school student, is working on an English grammar problem.

[0079] 1. User Login:

[0080] Person A launches the app on their smartphone (user device) and enters their ID and password.

[0081] The server verifies the authentication information and approves the login.

[0082] 2. Selection of study area:

[0083] Person A chooses "English".

[0084] The user terminal sends the selection information to the server.

[0085] 3. Generating past exam questions:

[0086] The server calls the generator and retrieves past exam data for the English section.

[0087] The generator produces multiple-choice grammar questions and sends them to the server.

[0088] 4. Providing the format of questions and answers:

[0089] The server sends the generated problem to the user's terminal.

[0090] The user terminal displays the question and answer format to person A.

[0091] 5. User's answer:

[0092] Person A enters their answer and sends it to the server via their user terminal.

[0093] 6. Evaluation and feedback on the answers:

[0094] The server uses an approval device to evaluate the answers and generate feedback.

[0095] The feedback is sent to the user's device and displayed to person A.

[0096] In this way, this system makes it possible to learn efficiently while reducing the financial burden.

[0097] The following describes the processing flow.

[0098] Program processing flow

[0099] 1. User Login

[0100] Step 1:

[0101] The user enters their ID and password into their user terminal.

[0102] Step 2:

[0103] The user terminal sends the entered authentication information to the server.

[0104] Step 3:

[0105] The server compares the received authentication information with the database.

[0106] Step 4:

[0107] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[0108] 2. Selection of learning area

[0109] Step 1:

[0110] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[0111] Step 2:

[0112] The user terminal sends information about the selected field to the server.

[0113] 3. Generating past exam questions

[0114] Step 1:

[0115] The server invokes the generator based on information about the learning area selected by the user.

[0116] Step 2:

[0117] The generation device retrieves relevant data from a database containing past question patterns.

[0118] Step 3:

[0119] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[0120] 4. Provision of question and answer format

[0121] Step 1:

[0122] The server sends the generated questions and answer formats to the user's terminal.

[0123] Step 2:

[0124] The user terminal displays the question and answer format to the user.

[0125] 5. User's response

[0126] Step 1:

[0127] The user enters their answer to the question displayed on their terminal.

[0128] Step 2:

[0129] The user terminal sends the entered answer to the server.

[0130] 6. Evaluation and Feedback of Answers

[0131] Step 1:

[0132] The server sends the received user's answer to the approval device.

[0133] Step 2:

[0134] The approval device evaluates the answer and determines whether it is correct or incorrect.

[0135] Step 3:

[0136] The approval device generates the correct answer and explanation and sends them to the server.

[0137] Step 4:

[0138] The server sends the generated feedback to the user's terminal.

[0139] Step 5:

[0140] The user terminal displays feedback to the user.

[0141] (Example 1)

[0142] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0143] Conventional learning support systems have struggled to generate problems tailored to each user's learning area and to efficiently evaluate answers, hindering efficient user learning. Furthermore, there were challenges in managing authentication information and ensuring security. As a result, it was difficult for users to learn safely and effectively. To solve these problems, the objective of the present invention is to provide a system that enables users to learn safely and efficiently.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0145] In this invention, the server includes means for a user to input authentication information for logging into the system using a user terminal, and for the user terminal to transmit the authentication information to the server; means for the server to compare the received authentication information with a database and return the authentication result to the user terminal; means for the server to generate a login token and transmit it to the user terminal if authentication is successful; means for the user to select a learning field through the interface of the user terminal and for the user terminal to transmit information of the selected field to the server; means for the server to call a generation device based on the learning field information received, for the generation device to analyze past question patterns and generate suitable questions; means for the generation device to transmit the generated questions to the server, for the server to transmit the generated questions to the user terminal; means for the user to input an answer to a question displayed on the user terminal and for the user terminal to transmit the input answer to the server; means for the server to transmit the received answer to an approval device, for the approval device to evaluate the answer and generate an answer result and explanation; and means for the server to transmit the generated answer result and explanation to the user terminal and display feedback to the user. This enables the user to proceed with learning safely and efficiently.

[0146] A "user terminal" is a device used by a user to select learning areas and input answers to questions through an interface. This includes computers, smartphones, tablets, and other similar devices.

[0147] A "server" is a central device that receives authentication information and learning area selection information sent from the user terminal, performs verification with the database, generates login tokens, distributes questions, and generates answer results and explanations.

[0148] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[0149] A "database" is a collection of information that systematically stores data such as user authentication information, past question patterns, generated questions, and answer results, and which a server references as needed.

[0150] A "login token" is data generated by the server for users who have successfully authenticated, and it contains session information that is valid for a certain period of time.

[0151] A "generator" is a device that analyzes past question patterns based on information about the learning area selected by the user and generates suitable questions.

[0152] An "approval device" is a device that evaluates user answers received from a server and generates answer results and explanations.

[0153] "Feedback" refers to information that includes the evaluation results of the user's answer and the explanations based on those evaluations.

[0154] An "interface" refers to the screens and input methods displayed on a user's device for selecting learning areas or entering answers.

[0155] "Answer Results and Explanations" refers to the approval device's determination of whether the user's answer was correct or incorrect, along with an explanation related to that answer.

[0156] "Past question patterns" refers to information about the format and content of questions that have been asked in the past.

[0157] A "problem" is a question or assignment related to the learning area that is intended for the user to answer.

[0158] A "processing step" refers to a series of operations or procedures performed at each stage when a system is in operation.

[0159] The embodiments for carrying out the present invention will be described in detail below. This system has a configuration including a server, a user terminal, a generation device, and an approval device, thereby enabling the user to efficiently proceed with learning.

[0160] First, the user logs into the system using their user device. This device can be a computer, smartphone, or tablet, and a login screen will appear on the screen. The user enters their ID and password to log in. The user device sends this authentication information to the server. The server uses a database such as MySQL® or PostgreSQL to verify the received authentication information and returns the authentication result to the user device. If authentication is successful, the server generates a JWT (JSON Web Token) and sends this token to the user device to initiate the session.

[0161] Next, the user selects the subject they want to study through an interface on their device. This operation is implemented using front-end frameworks such as React or Vue.js. The user's device sends information about the selected subject to the server. Based on the received information, the server sends a request to a generator written in Python.

[0162] The generation device retrieves relevant data from a database containing past question patterns (e.g., MongoDB) and generates appropriate questions using a generation AI model (e.g., GPT). These questions are formatted into multiple-choice or fill-in-the-blank formats and sent to the server. The server sends the generated questions to the user's terminal, which then displays them to the user.

[0163] The user enters an answer to a question displayed on their terminal and sends the answer to the server. The server sends the received answer to an approval device that uses a machine learning algorithm (e.g., Scikit-learn or TENSORFLOW®). The approval device evaluates the answer and determines whether it is correct or incorrect. The approval device then generates the correct answer and explanation based on this evaluation and sends it to the server. The server sends this to the user terminal and displays it to the user as feedback.

[0164] As an example, consider a high school senior user working on English grammar problems. The user logs in to the app on their smartphone by entering their ID and password. Then, they select the learning field "English" and work on multiple-choice grammar problems generated based on past problem data. After entering their answers, an approval device evaluates the answers via the server, and the user receives feedback including the correct answer and an explanation.

[0165] An example of a prompt sentence to input into a generative AI model would be: "Please describe in natural language the process by which a high school senior user tackles English grammar problems, including the roles of the server, terminal, and user."

[0166] This mechanism helps the system to learn efficiently.

[0167] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0168] Step 1:

[0169] The user enters their ID and password on their device. Specifically, the user uses the keyboard on a device such as a smartphone or tablet to enter their ID and password on the login screen. The ID and password entered by the user are encrypted (e.g., AES encryption) and sent to the server as input data.

[0170] Step 2:

[0171] The user terminal sends the entered authentication information to the server. The user terminal uses an HTTP POST request to send encrypted ID and password to the server. The input data is decrypted on the server side and used for the next process.

[0172] Step 3:

[0173] The server compares the received authentication information with the database. The server compares the received ID and password with hashed data stored in the database (e.g., MySQL) to check for a match. It generates an accurate matching result and outputs it to the next process.

[0174] Step 4:

[0175] The server returns the authentication result to the user's terminal. If authentication is successful, the server generates a JWT (JSON Web Token) and returns this token to the user's terminal. If authentication fails, an error message is generated. The generated token or error message is output to the user's terminal.

[0176] Step 5:

[0177] If authentication is successful, the user's device will display a notification of successful authentication to the user. Specifically, the user's device will display a notification such as "Login successful" on the screen and start the user session. The session information will be used as a login token for the following processes.

[0178] Step 6:

[0179] The user selects a learning area. The user uses the interface on their device to choose a learning area (e.g., English, Mathematics). A selection screen implemented with a frontend framework such as React Native is used, and the selected information is sent to the server as input data.

[0180] Step 7:

[0181] The user terminal sends information about the selected learning area to the server. The user terminal uses an HTTP POST request to send the selected learning area information to the server. This information becomes input data for the next data processing step.

[0182] Step 8:

[0183] The server calls the generator based on the learning area information it receives. The server sends an HTTP request to the generator's API endpoint, which is written in Python, and passes the learning area information as input data. The generator then processes this data.

[0184] Step 9:

[0185] The generator analyzes past question patterns and generates suitable questions. The generator retrieves past question patterns from a database such as MongoDB and uses a generation AI model (e.g., GPT-3®) to generate questions suitable for the user. The generated questions are sent to the server as output.

[0186] Step 10:

[0187] The server sends the generated problem to the user terminal. The server receives the problem from the generator and sends it to the user terminal as an HTTP response. The user terminal receives this and uses it for the next process.

[0188] Step 11:

[0189] The user's device displays the received problem to the user. The problem is displayed on a screen implemented with React or Vue.js, and the user enters their answer. The user's answer becomes the next input data.

[0190] Step 12:

[0191] The user enters the answer, and the user's terminal sends the entered answer to the server. The answer is sent encrypted via an HTTP POST request. The server receives this answer data and sends it to the next process.

[0192] Step 13:

[0193] The server sends the received answer to the approval device. It sends an HTTP request to the approval device to evaluate the answer. The approval device performs the evaluation using an AI model or machine learning algorithm (e.g., Scikit-learn).

[0194] Step 14:

[0195] The approval device evaluates the answer and generates the answer result and explanation. It determines whether the answer is correct or incorrect, generates an explanation based on the correct answer, and sends the result to the server as output.

[0196] Step 15:

[0197] The server sends the generated solution and explanation to the user's terminal. The server receives a feedback message and returns it to the user's terminal as an HTTP response. The user's terminal displays this response.

[0198] Step 16:

[0199] The user's device displays feedback to the user. Specifically, evaluation results and explanations are displayed on the screen, allowing the user to receive learning feedback by reviewing them.

[0200] In this way, the system can perform a series of processes to enable users to learn efficiently.

[0201] (Application Example 1)

[0202] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0203] Traditional learning support systems have problems that make it difficult for users to check their learning progress and learn efficiently. Furthermore, they lack real-time answer evaluation and immediate feedback functions, making it difficult for learning content to be effectively retained. In addition, they do not adequately generate problems tailored to the user's level or provide specialized problem formats for each learning subject. There is a need to solve these problems and realize more efficient and effective learning support.

[0204] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0205] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This enables means for having a timer function to evaluate answers in real time and provide immediate feedback, means for visualizing the user's learning progress and saving past answer history to support review, and means for generating an unlimited number of questions based on past questions using an AI model when a learning field is selected.

[0206] A "generator" is a device that analyzes past question patterns and generates suitable questions.

[0207] A "user terminal" is a device (e.g., a smartphone) that a user uses to log in, select a learning area, and answer generated questions.

[0208] An "approval device" is a device that evaluates the answers received from the user terminal and generates the answer result and explanation.

[0209] The "timer function" is a feature that allows users to set a time limit when answering questions, helping them to concentrate on the problem.

[0210] A "means for evaluating answers in real time" refers to a method of immediately evaluating a user's answer and providing feedback as soon as the user submits it.

[0211] "Feedback" refers to the evaluation results and explanations that users receive after submitting an answer.

[0212] "Methods for visualizing learning progress" refer to methods that visually display the progress of learning, such as graphs, allowing users to grasp their learning status at a glance.

[0213] "A means of saving past answer history to support review" refers to a method of saving the user's previous answers in a database, allowing them to review their level of understanding and areas where they tend to make mistakes as needed.

[0214] An "AI model" is an artificial intelligence algorithm that automatically generates new questions based on past question patterns and the user's learning progress.

[0215] An embodiment of the present invention is configured as a system including a server, a user terminal, a generation device, and an approval device. This system allows users to learn efficiently and effectively.

[0216] 1. Hardware and software to be used

[0217] Hardware:

[0218] Smartphones (iPhone®, ANDROID® devices)

[0219] Servers (AWS®, Google® Cloud, etc.)

[0220] software:

[0221] Backend: Node.js, Express

[0222] Frontend: React Native

[0223] Database: MongoDB

[0224] AI models: TensorFlow, scikit-learn

[0225] 2. Program Description

[0226] User Login

[0227] The server provides functionality for users to log in to the system using their smartphones. Users enter their ID and password on the smartphone app and send the authentication information to the server. The server compares this information with the user information stored in the database, and if authentication is successful, generates a login token and sends it to the user's device. This allows the user to access the system.

[0228] Selection of learning area

[0229] The server provides an interface for users to select a specific subject they wish to study. Users select their learning subject using their smartphone interface, and this information is sent to the server. This generates questions tailored to the user's chosen subject.

[0230] Generating past exam questions

[0231] The server invokes a generator based on information about the selected learning area. The generator retrieves past question patterns and the user's learning progress from a database and generates new questions using an AI model (TensorFlow or scikit-learn). The generated questions are sent to the server and provided to the user's terminal.

[0232] Providing a question and answer format

[0233] The server sends the generated questions and answer formats (multiple choice, fill-in-the-blank, etc.) to the user's terminal. The user's terminal displays the questions and answer formats to the user and uses a timer function to prompt the user to enter their answers within the time limit. This function allows the user to concentrate on the questions.

[0234] User answers and ratings

[0235] When a user submits an answer to a question, the answer is sent to the server. The server uses an approval device to evaluate the answer in real time and provides immediate feedback. This feedback includes an evaluation of the answer and a detailed explanation.

[0236] Managing learning progress

[0237] The server provides a function to visualize the user's learning progress and save past answer history. Users can check their progress as a graph on their smartphone, allowing them to grasp their self-study status at a glance. They can also review based on their past answer history.

[0238] Specific example

[0239] For example, if a high school senior user were to work on English grammar problems, the process would be as follows: The user launches the app on their smartphone, enters their ID and password to log in. Next, they select "English," and the appropriate problems are generated. The user enters their answers and receives immediate feedback. They can check their progress in a graph and review based on their past answer history.

[0240] Example of a prompt:

[0241] "A high school senior user is learning English grammar on their smartphone, following these steps: user login, selection of learning area, generation of past questions, provision of question and answer format, user submission, evaluation of answers, and feedback. Explain how AI-generated questions based on past question patterns can effectively provide feedback."

[0242] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0243] Step 1:

[0244] User Login

[0245] Input: The user enters their ID and password into the smartphone app.

[0246] Processing: Authentication information is sent from the terminal to the server via HTTPS. The server compares it with user data stored in the database (MongoDB) to determine whether authentication was successful.

[0247] Output: If authentication is successful, the server generates a login token and sends it to the terminal. If it fails, an error message is sent.

[0248] Step 2:

[0249] Selection of learning area

[0250] Input: The user selects the subject they want to study (e.g., English, mathematics, science, etc.) using the interface on their smartphone.

[0251] Processing: The terminal sends the selected information to the server. The server saves the received information to its database.

[0252] Output: The results of the selected learning area will be displayed on the device as a confirmation message.

[0253] Step 3:

[0254] Generating past exam questions

[0255] Input: The selected learning area information is sent to the server.

[0256] Processing: The server calls a generator to retrieve past questions and question pattern data for the relevant field from the database. It then generates questions using an AI model (TensorFlow, scikit-learn).

[0257] Output: The generated problem is sent to the terminal via the server.

[0258] Step 4:

[0259] Providing a question and answer format

[0260] Input: The generated question and answer format are sent to the device.

[0261] Processing: The terminal displays the question and answer format to the user and activates a timer function to set a time limit.

[0262] Output: The user begins answering the displayed questions.

[0263] Step 5:

[0264] User's answer

[0265] Input: The user enters their answer to the question into the terminal.

[0266] Processing: The terminal sends the entered answer to the server.

[0267] Output: The answer data reaches the server.

[0268] Step 6:

[0269] Evaluation and feedback on the answers

[0270] Input: User answer data and generated problem data reside on the server.

[0271] Processing: The server uses an approval device to evaluate answers in real time. An AI model is applied to determine whether the answer is correct or not. Evaluation results and detailed explanations are generated, and feedback data is created.

[0272] Output: The generated feedback is sent to the user's terminal via the server.

[0273] Step 7:

[0274] Managing learning progress

[0275] Input: The user's learning history and current performance data are stored in the database.

[0276] Processing: The server processes data to visualize the user's learning progress and generates it as graphs and charts. It also provides efficient review suggestions based on past answer history.

[0277] Output: Learning progress and suggestions are displayed on the user's device.

[0278] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0279] The system of the present invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component and the program processing are described below in natural language.

[0280] 1. User Login

[0281] Function Description

[0282] This is a mechanism for users to log in to the system using their user terminal.

[0283] Processing flow

[0284] The user inputs an ID and a password into the user terminal.

[0285] The user terminal sends the input authentication information to the server.

[0286] The server compares the received authentication information with the database and returns the authentication result to the user terminal.

[0287] If the authentication is successful, the server generates a login token and sends it to the user terminal.

[0288] 2. Selection of Learning Field

[0289] Function Description

[0290] It is a mechanism for the user to select a specific field to learn.

[0291] Processing Flow

[0292] The user selects the field to learn (e.g., English, mathematics, science, etc.) through the interface on the user terminal.

[0293] The user terminal sends the information of the selected field to the server.

[0294] 3. Generation of Past Questions

[0295] Function Description

[0296] It is a mechanism for generating questions for the user to learn based on past question patterns.

[0297] Processing Flow

[0298] The server calls the generation device based on the information of the learning field selected by the user.

[0299] The generation device obtains relevant data from the database containing past question patterns and generates questions.

[0300] The generation device creates a problem format (such as a multiple-choice format or a fill-in-the-blank format) suitable for the user and sends it to the server.

[0301] 4. Use of the Emotion Engine

[0302] Function Description

[0303] The emotion engine is used to recognize the user's emotion and adjust the content of the questions and feedback.

[0304] Processing Flow

[0305] The user terminal collects emotion data through the user's expressions and voices.

[0306] The emotion engine analyzes this emotion data and recognizes the user's current emotion.

[0307] The recognized emotion information is sent to the generation device and the approval device through the server.

[0308] The generation device adjusts the question content based on the recognized emotion.

[0309] The approval device adaptively changes the content of the feedback based on the emotion information.

[0310] 5. Provision of Question and Answer Formats

[0311] Function Description

[0312] It is a mechanism for presenting the generated questions to the user and accepting answers.

[0313] Processing Flow

[0314] The server sends the generated questions and answer formats to the user terminal.

[0315] The user terminal displays the questions and answer formats to the user.

[0316] 6. User's response

[0317] Function Description

[0318] This is a system that allows users to input their answers to questions and send those answers to the server.

[0319] Processing flow

[0320] The user enters their answer to the question displayed on their terminal.

[0321] The user terminal sends the entered answer to the server.

[0322] 7. Evaluation and feedback on the answers

[0323] Function Description

[0324] This system evaluates user answers and provides evaluation results and explanations.

[0325] Processing flow

[0326] The server sends the received user's answer to the approval device.

[0327] The approval device evaluates the answer and determines whether it is correct or incorrect.

[0328] The approval device generates the correct answer and explanation and sends them to the server.

[0329] The server sends the generated feedback to the user's terminal.

[0330] The user terminal displays feedback to the user.

[0331] Specific example

[0332] Example: When student A, a third-year high school student, is working on an English grammar problem.

[0333] 1. User Login:

[0334] Person A launches the app on their smartphone (user device) and enters their ID and password.

[0335] The server verifies the authentication information and approves the login.

[0336] 2. Selection of study area:

[0337] Person A chooses "English".

[0338] The user terminal sends the selection information to the server.

[0339] 3. Generating past exam questions:

[0340] The server calls the generator and retrieves past exam data for the English section.

[0341] The generator produces multiple-choice grammar questions and sends them to the server.

[0342] 4. Utilizing the Emotion Engine:

[0343] The user terminal collects emotional data from person A's facial expressions and voice.

[0344] The emotion engine analyzes emotional data and recognizes person A's emotions.

[0345] The recognized emotion information is transmitted to the generation device and the approval device.

[0346] The generator adjusts the question content based on emotional information.

[0347] The approval device adjusts the feedback content.

[0348] 5. Providing question and answer formats:

[0349] The server sends the generated problem to the user's terminal.

[0350] The user terminal displays the question and answer format to person A.

[0351] 6. User's answer:

[0352] Person A enters their answer and sends it to the server via their user terminal.

[0353] 7. Evaluation and feedback on the answers:

[0354] The server uses an approval device to evaluate the answers and generate feedback.

[0355] The feedback is sent to the user's device and displayed to person A.

[0356] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[0357] The following describes the processing flow.

[0358] 1. User Login

[0359] Step 1:

[0360] The user enters their ID and password into their user terminal.

[0361] Step 2:

[0362] The user terminal sends the entered authentication information to the server.

[0363] Step 3:

[0364] The server compares the received authentication information with the database.

[0365] Step 4:

[0366] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[0367] 2. Selection of learning area

[0368] Step 1:

[0369] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[0370] Step 2:

[0371] The user terminal sends information about the selected field to the server.

[0372] 3. Generating past exam questions

[0373] Step 1:

[0374] The server invokes the generator based on information about the learning area selected by the user.

[0375] Step 2:

[0376] The generation device retrieves relevant data from a database containing past question patterns.

[0377] Step 3:

[0378] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[0379] 4. Utilizing the Emotion Engine

[0380] Step 1:

[0381] The user terminal collects emotional data through the user's facial expressions and voice.

[0382] Step 2:

[0383] The emotion engine analyzes collected emotion data to recognize the user's current emotions.

[0384] Step 3:

[0385] The recognized emotion information is transmitted via the server to the generation device and the approval device.

[0386] Step 4:

[0387] The generator adjusts the question content based on the recognized emotions.

[0388] Step 5:

[0389] The approval device adaptively modifies the feedback content based on emotional information.

[0390] 5. Provision of question and answer format

[0391] Step 1:

[0392] The server sends the generated questions and answer formats to the user's terminal.

[0393] Step 2:

[0394] The user terminal displays the question and answer format to the user.

[0395] 6. User's response

[0396] Step 1:

[0397] The user enters their answer to the question displayed on their terminal.

[0398] Step 2:

[0399] The user terminal sends the entered answer to the server.

[0400] 7. Evaluation and feedback on the answers

[0401] Step 1:

[0402] The server sends the received user's answer to the approval device.

[0403] Step 2:

[0404] The approval device evaluates the answer and determines whether it is correct or incorrect.

[0405] Step 3:

[0406] The approval device generates the correct answer and explanation and sends them to the server.

[0407] Step 4:

[0408] The server sends the generated feedback to the user's terminal.

[0409] Step 5:

[0410] The user terminal displays feedback to the user.

[0411] Specific example

[0412] Example: When student A, a third-year high school student, is working on an English grammar problem.

[0413] 1. User Login:

[0414] Step 1:

[0415] Person A launches the app on their smartphone (user device) and enters their ID and password.

[0416] Step 2:

[0417] The user terminal sends the entered authentication information to the server.

[0418] Step 3:

[0419] The server verifies the authentication information and approves the login.

[0420] 2. Selection of study area:

[0421] Step 1:

[0422] Person A chooses "English".

[0423] Step 2:

[0424] The user terminal sends the selection information to the server.

[0425] 3. Generating past exam questions:

[0426] Step 1:

[0427] The server calls the generator and retrieves past exam data for the English section.

[0428] Step 2:

[0429] The generator produces multiple-choice grammar questions and sends them to the server.

[0430] 4. Utilizing the Emotion Engine:

[0431] Step 1:

[0432] The user terminal collects emotional data from person A's facial expressions and voice.

[0433] Step 2:

[0434] The emotion engine analyzes emotional data and recognizes person A's emotions.

[0435] Step 3:

[0436] The recognized emotion information is transmitted to the generator and approval device via the server.

[0437] Step 4:

[0438] The generator adjusts the question content based on emotional information.

[0439] Step 5:

[0440] The approval device adjusts the feedback content.

[0441] 5. Providing question and answer formats:

[0442] Step 1:

[0443] The server sends the generated problem to the user's terminal.

[0444] Step 2:

[0445] The user terminal displays the question and answer format to person A.

[0446] 6. User's answer:

[0447] Step 1:

[0448] Person A enters their answer and sends it to the server via their user terminal.

[0449] 7. Evaluation and feedback on the answers:

[0450] Step 1:

[0451] The server uses an approval device to evaluate the answers and generate feedback.

[0452] Step 2:

[0453] The feedback is sent to the user's device and displayed to person A.

[0454] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[0455] (Example 2)

[0456] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0457] Traditional learning systems often present problems without considering the user's emotional state, which can lead to user stress and reduced learning effectiveness. Furthermore, a lack of specialized problem formats tailored to specific learning areas is a problem, failing to adequately meet users' learning needs. Additionally, the lack of complete security guarantees for user authentication information is also a concern.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0459] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, means including an emotion engine that analyzes emotion data collected from the user terminal and adjusts the difficulty level of the questions and the content of the feedback based on the user's emotional state, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This makes it possible to provide a personalized learning experience based on the user's emotional state and maximize learning effectiveness. Furthermore, by providing question formats specialized for learning fields, it is possible to flexibly respond to the user's learning needs.

[0460] A "generator" is a device that analyzes past question patterns and generates questions that are suitable for the user.

[0461] A "user terminal" refers to a device used by a user to input information or answer questions, and includes smartphones, tablets, and computers.

[0462] An "emotion engine" is a device that analyzes the user's facial expressions and voice, recognizes their emotional state, and adjusts the difficulty level of the problem and the content of the feedback accordingly.

[0463] An "approval device" is a device that evaluates a user's answer and generates the answer result and explanation.

[0464] "Past question patterns" refers to data and information that shows past questions, their trends, and their format.

[0465] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[0466] "Answer evaluation" is the process of determining whether the answers entered by users are correct or incorrect, and then conducting an evaluation based on those results.

[0467] "Feedback content" refers to information such as evaluation results and explanations for the user's answers, and includes learning advice and guidance provided to the user.

[0468] "Question format" refers to the type of question format specific to each learning area, and includes multiple-choice questions and fill-in-the-blank questions.

[0469] "User emotional state" refers to the emotional states a user experiences while learning, and includes, for example, joy, sadness, stress, and excitement.

[0470] The system of this invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component is described below.

[0471] 1. User Login

[0472] Users log in to the system using their user terminal. Specifically, the user enters their ID and password into the user terminal, and the user terminal sends this authentication information to the server. The server compares the received authentication information with the database, and if authentication is successful, generates a login token and sends it to the user terminal. The user terminal stores this token and uses it for subsequent communications.

[0473] 2. Selection of learning area

[0474] The user selects the subject they wish to study through an interface on their device. The user device sends information about the selected subject to the server. The server receives this information and holds it for the next step.

[0475] 3. Generating past exam questions

[0476] The server invokes a generator based on the learning area selected by the user. The generator retrieves relevant data from a database containing past question patterns and generates questions. The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank) and sends it to the server.

[0477] 4. Utilizing the Emotion Engine

[0478] The user terminal collects emotional data through the user's facial expressions and voice. This data is analyzed using a TensorFlow model within the terminal to recognize the user's emotional state. The emotion engine transmits the recognized emotional information to the generator and approval device via the server. The generator adjusts the question content based on the emotional information, and the approval device adaptively changes the feedback content based on the emotional information.

[0479] 5. Provision of question and answer format

[0480] The server sends the generated questions and answer formats to the user's terminal. The user's terminal displays this to the user, assisting them in working on the questions.

[0481] 6. User's response

[0482] The user enters their answer to the question displayed on their terminal. The user terminal sends the entered answer to the server. The server receives this answer and sends it to the approval device.

[0483] 7. Evaluation and feedback on the answers

[0484] The approval device evaluates the user's answer and determines whether it is correct or incorrect. Furthermore, the approval device generates the correct answer and explanation, and sends it to the server. The server sends the generated feedback to the user's terminal, which displays it to the user. The user can then progress in their learning through this feedback.

[0485] Specific example

[0486] For example, consider a high school senior, A, working on English grammar problems. A logs into the system using their smartphone and selects English as their learning subject. The server analyzes past question patterns based on this information and generates appropriate grammar problems. The user terminal collects emotional data from A's facial expressions and voice, and the emotion engine performs analysis. As a result, if A is experiencing stress, adjustments are made, such as lowering the difficulty of the problems. After the problems are displayed, A enters their answers, which are sent to the server. An approval device evaluates the answers, and feedback is provided to A. Through this process, A can enjoy a personalized learning experience.

[0487] Example of a prompt

[0488] Prompt example:

[0489] Assume that student A, a third-year high school student, is using a learning system to study English. The system proceeds through seven steps: user login, selection of learning area, generation of past questions, use of an emotion engine, provision of questions and answers, user response, and evaluation and feedback of the response. Considering that student A is working on grammar questions, please describe the sequence of processing steps and their flow in detail.

[0490] In this way, the present invention takes into account the user's emotional state and provides a more effective and personalized learning experience.

[0491] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0492] Step 1:

[0493] The user enters their ID and password into their user terminal.

[0494] Input: ID, Password

[0495] The user terminal sends the entered authentication information to the server.

[0496] Input: Authentication information sent from the user terminal

[0497] Specific operation: The user terminal sends data to the server using SSL encrypted communication.

[0498] Step 2:

[0499] The server compares the received authentication information with the database.

[0500] Input: Authentication information, user data in the database

[0501] Data processing: Compare and verify user data in the database with received authentication information.

[0502] Output: Authentication result (success / failure)

[0503] Specific operation: The server uses a MySQL database to verify user information.

[0504] Step 3:

[0505] The server returns the authentication result to the user's terminal.

[0506] Input: Authentication result

[0507] Output: Authentication result (success / failure)

[0508] Specific operation: The server generates a JWT (JSON Web Token) and, if successful, returns this token to the user's terminal. The token is used for subsequent communication.

[0509] Step 4:

[0510] Users select the subject they wish to study through an interface on their device.

[0511] Input: Selection information for learning field

[0512] The user terminal sends information about the selected field to the server.

[0513] Input: Selection information for learning field

[0514] Specific operation: The user taps the "Select Learning Area" option from the app's menu and selects an area from the displayed list. The user's device sends the selection information to the server using HTTPS communication.

[0515] Step 5:

[0516] The server invokes the generator based on information about the learning area selected by the user.

[0517] Input: Information on the field of study

[0518] The server retrieves relevant data from a database containing past question patterns.

[0519] Input: Database of past exam patterns, information on learning areas.

[0520] Data processing: Filter past question patterns related to the learning area and select appropriate questions.

[0521] Output: Selected problems

[0522] Specific operation: The server uses a Python API to call a microservice for problem generation and retrieve the relevant data.

[0523] Step 6:

[0524] The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[0525] Input: Past exam data

[0526] Data processing: Generate questions while considering the user's learning history and difficulty settings.

[0527] Output: Formatted problem

[0528] Specific operation: The generation algorithm uses AI to analyze data and generate an optimal set of questions. The generated set of questions is formatted as multiple-choice questions and returned to the server.

[0529] Step 7:

[0530] The user terminal collects emotional data through the user's facial expressions and voice.

[0531] Input: User facial expression data, voice data

[0532] Data processing: Use an emotion engine to analyze emotional data and recognize the user's current emotions.

[0533] Output: Recognized emotion information

[0534] Specific operation: The device's camera and microphone are used to collect data, which is then analyzed using a TensorFlow model.

[0535] Step 8:

[0536] The emotion engine analyzes this emotion data to recognize the user's current emotions.

[0537] Input: Sentiment data

[0538] Data processing: Analyze data using emotion analysis algorithms to determine emotional states.

[0539] Output: Emotional state (e.g., stress, joy, etc.)

[0540] Specific operation: The analysis results are processed as digital signals, and recognized emotional information is generated.

[0541] Step 9:

[0542] The recognized emotion information is transmitted to the generator and approval device via the server.

[0543] Input: Recognized emotion information

[0544] Output: Adjusted question content and feedback

[0545] Specific operation: The analysis results of the emotion engine are transmitted to the generation device and the approval device via the server.

[0546] Step 10:

[0547] The generator adjusts the question content based on the recognized emotions.

[0548] Input: Sentimental information, generated problem

[0549] Data processing: Adjust the difficulty and format of problems based on emotional information.

[0550] Output: Adjusted problem

[0551] Specific action: For example, if the user is feeling stressed, the generator will lower the difficulty level of the problem.

[0552] Step 11:

[0553] The approval device adaptively modifies the content of the feedback based on emotional information.

[0554] Input: Sentimental information, evaluation results

[0555] Data processing: Adaptively modifying feedback content based on emotional information.

[0556] Output: Personalized feedback

[0557] Specific actions: For example, if the user is feeling stressed, generate a message such as "Let's calm down and get to work."

[0558] Step 12:

[0559] The server sends the generated questions and answer formats to the user's terminal.

[0560] Input: Formatted question, sentiment-adjusted content

[0561] Output: Problem data to be displayed on the user terminal

[0562] Specific operation: The server sends formatted problem data to the user's terminal via a REST API.

[0563] Step 13:

[0564] The user terminal displays the question and answer format to the user.

[0565] Input: Problem data

[0566] Output: Displayed problem

[0567] Specific operation: The user terminal uses a UI framework to display the problem on the screen.

[0568] Step 14:

[0569] The user enters their answer to the question displayed on their terminal.

[0570] Input: Answer Information

[0571] The user terminal sends the entered answer to the server.

[0572] Output: Answer data sent to the server

[0573] Specific operation: The user taps an option to enter their answer, and the user's device sends this to the server.

[0574] Step 15:

[0575] The server sends the received user's answer to the approval device.

[0576] Input: Answer data

[0577] Notice: Answer data (to be sent to the approval device)

[0578] Specific operation: The server sends the answer data to the approval device's API.

[0579] Step 16:

[0580] The approval device evaluates the answer and determines whether it is correct or incorrect. Furthermore, it generates the correct answer and an explanation.

[0581] Input: Answer data

[0582] Data processing: Evaluate the answers and generate results and explanations.

[0583] Output: Evaluation results and explanation

[0584] Specific operation: The approval device uses a machine learning model to evaluate the answer and generate accurate feedback.

[0585] Step 17:

[0586] The server sends the generated feedback to the user's terminal.

[0587] Input: Evaluation results and explanation

[0588] Output: Feedback displayed on the user's terminal

[0589] Specific operation: The server sends the generated feedback data to the user's terminal, where it is displayed.

[0590] This allows users to receive detailed feedback and progress in their learning.

[0591] (Application Example 2)

[0592] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0593] Conventional learning and work support systems provide uniform problems and work instructions without considering the user's emotional state, leading to user stress and decreased work efficiency. Furthermore, a lack of individualized feedback made it difficult to provide optimal learning and work environments and to reduce user motivation.

[0594] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0595] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. Furthermore, it includes an emotion engine that collects and analyzes user emotion data, means for the generating device and approval device to adjust the question content and feedback based on the emotion information, and user interface means for providing real-time work support to workers. This enables personalized learning and work support that takes the user's emotional state into account, thereby reducing stress and improving work efficiency.

[0596] A "problem generator" is a device that analyzes past question patterns and generates questions tailored to the user.

[0597] A "user terminal" is a device used by users to access a server and communicate with it to send and receive various types of information.

[0598] An "approval device" is a device that evaluates the answers received from users and generates the results and explanations.

[0599] An "emotion engine" is a device that recognizes a user's emotional state by collecting and analyzing their emotional data.

[0600] A "server" is a central device that manages and processes data for the entire system.

[0601] A "user interface means" is a means of providing an interface for a user to interact with a system.

[0602] "Real-time work support" refers to providing instant instructions and feedback to users while they are performing their tasks.

[0603] "Feedback" refers to evaluations and advice provided based on the user's work and learning results.

[0604] The present invention is a system that includes a generation device that analyzes past question patterns and generates suitable questions, means for transmitting the generated questions to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates the results and explanations. The system also includes an emotion engine that collects and analyzes user emotion data. Furthermore, it is equipped with a user interface means for providing real-time work support to workers. Specific embodiments for carrying out the present invention are shown below.

[0605] Hardware and software configuration

[0606] 1. Hardware Configuration

[0607] User device: Smart glasses (e.g., Google Glass®)

[0608] Server: A computer system that manages and processes data.

[0609] Emotion engine: A device (e.g., camera, microphone) that collects and analyzes user emotional data.

[0610] 2. Software Configuration

[0611] Server applications (e.g., Python(registered trademark) based applications)

[0612] Emotion recognition engine (e.g., Microsoft® Azure® Emotion API)

[0613] Data processing and data calculation workflow

[0614] The server generates a problem suitable for the user based on data from the generation device and sends it to the user terminal. The user terminal displays the problem and sends the user's answer back to the server. Based on this, the approval device evaluates the answer and generates the answer result and explanation.

[0615] Furthermore, the emotion engine collects and analyzes the user's emotional data (facial expressions and voice). Based on this, the generation device generates problems optimized for the user's emotional state, and the approval device adjusts the feedback accordingly. Finally, the user interface provides real-time work support, improving the user's work efficiency and motivation.

[0616] Specific example

[0617] As a concrete example, consider a case where person B is engaged in parts assembly work at a factory. Person B puts on smart glasses and logs into the system. The server verifies Person B's authentication information and approves the login. Next, Person B selects "parts assembly work" and performs the task according to the instructions displayed on the smart glasses.

[0618] In this process, the emotion engine collects and analyzes emotional data from B's facial expressions and voice. If the system detects that B is experiencing stress, it adjusts the work content and assigns simpler tasks to reduce stress.

[0619] Examples of prompts to input into a generative AI model are as follows:

[0620] "For HR professionals, please provide an overview of a factory worker status monitoring system using emotion recognition, and the technologies that support it. Include the following keywords: smart glasses, emotion engine, real-time feedback, and improved worker efficiency."

[0621] As described above, this system allows for real-time monitoring of the user's emotional state and provides optimal work instructions and feedback, thereby improving work efficiency and reducing stress.

[0622] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0623] Step 1:

[0624] The user's terminal sends the user's entered ID and password to the server.

[0625] Input: The user enters their ID and password into the smart glasses.

[0626] Data processing: The server compares the received authentication information with the database.

[0627] Output: Generate a login token as an authentication result and send it back to the user's device.

[0628] Step 2:

[0629] The user selects a learning area or work area.

[0630] Input: The user selects a task, such as "parts assembly work," using smart glasses.

[0631] Data processing: The user terminal sends information from the selected field to the server.

[0632] Output: The server uses that information to call the generator.

[0633] Step 3:

[0634] The server generates tasks using a generator.

[0635] Input: Information about the selected learning / work area.

[0636] Data processing: The generation device analyzes past data and generates suitable tasks.

[0637] Output: Sends the generated task information to the user's terminal.

[0638] Step 4:

[0639] The emotion engine collects and analyzes user emotion data.

[0640] Input: User facial expression data and voice data.

[0641] Data processing: The emotion engine analyzes this data to recognize the user's emotional state.

[0642] Output: Sends recognized emotion information to the server.

[0643] Step 5:

[0644] The server adjusts the question content and work instructions based on the generated tasks and sentiment information.

[0645] Input: Generated task information and recognized emotion information.

[0646] Data processing: Generators and approval devices adjust task content and feedback based on emotional information.

[0647] Output: Sends adjusted tasks and feedback information to the user's terminal.

[0648] Step 6:

[0649] The user terminal displays the adjusted tasks to the user.

[0650] Input: Coordinated task information sent from the server.

[0651] Data processing: Converts the information into a format necessary for the user's terminal to display it.

[0652] Output: The adjusted task is displayed on the user's glasses screen.

[0653] Step 7:

[0654] The user performs tasks according to the instructions and enters their progress into their terminal.

[0655] Input: The user enters the progress of the task into the user terminal.

[0656] Data processing: The user terminal sends the entered progress data to the server.

[0657] Output: Progress data is sent to the server and recorded.

[0658] Step 8:

[0659] The server evaluates the progress data using an approval device and generates feedback.

[0660] Input: User progress data.

[0661] Data processing: The approval device evaluates the progress data and generates feedback.

[0662] Output: Sends the generated feedback to the user's terminal.

[0663] Step 9:

[0664] The user's device displays feedback to the user.

[0665] Input: Feedback information sent from the server.

[0666] Data processing: Convert the data into a format that the user terminal can use to display feedback information.

[0667] Output: Feedback is displayed on the user's glasses screen.

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0670] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0671] [Second Embodiment]

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

[0673] As shown in Figure 3, the data processing system 210 includes a 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is 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 interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0676] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0678] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0679] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0680] The specific processing program 56 is an example of a "program" relating to the technology of this 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 the data generation model 58 and the 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 processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0683] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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] The system for carrying out the present invention has a configuration including a server, a user terminal, a generation device, and an approval device. The specific operation of each component and the program processing are described below in natural language.

[0685] 1. User Login

[0686] Function Description

[0687] This is a mechanism for users to log in to the system using their user terminal.

[0688] Processing flow

[0689] The user enters their ID and password into their user terminal.

[0690] The user terminal sends the entered authentication information to the server.

[0691] The server compares the received authentication information with the database and returns the authentication result to the user's terminal.

[0692] If authentication is successful, the server generates a login token and sends it to the user's device.

[0693] 2. Selection of learning area

[0694] Function Description

[0695] This is a system that allows users to select a specific field they want to learn about.

[0696] Processing flow

[0697] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through an interface on their device.

[0698] The user terminal sends information about the selected field to the server.

[0699] 3. Generating past exam questions

[0700] Function Description

[0701] This system generates questions for users to learn from, based on past question patterns.

[0702] Processing flow

[0703] The server invokes the generator based on information about the learning area selected by the user.

[0704] The generation device retrieves relevant data from a database containing past question patterns and generates questions.

[0705] The generation device creates a question format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[0706] 4. Provision of question and answer format

[0707] Function Description

[0708] This is a mechanism for presenting generated problems to users and receiving their answers.

[0709] Processing flow

[0710] The server sends the generated questions and answer formats to the user's terminal.

[0711] The user terminal displays the question and answer format to the user.

[0712] 5. User's response

[0713] Function Description

[0714] This is a system that allows users to input their answers to questions and send those answers to the server.

[0715] Processing flow

[0716] The user enters their answer to the question displayed on their terminal.

[0717] The user terminal sends the entered answer to the server.

[0718] 6. Evaluation and Feedback of Answers

[0719] Function Description

[0720] This system evaluates user answers and provides evaluation results and explanations.

[0721] Processing flow

[0722] The server sends the received user's answer to the approval device.

[0723] The approval device evaluates the answer and determines whether it is correct or incorrect.

[0724] The approval device generates the correct answer and explanation and sends them to the server.

[0725] The server sends the generated feedback to the user's terminal.

[0726] The user terminal displays feedback to the user.

[0727] Specific example

[0728] Example: When student A, a third-year high school student, is working on an English grammar problem.

[0729] 1. User Login:

[0730] Person A launches the app on their smartphone (user device) and enters their ID and password.

[0731] The server verifies the authentication information and approves the login.

[0732] 2. Selection of study area:

[0733] Person A chooses "English".

[0734] The user terminal sends the selection information to the server.

[0735] 3. Generating past exam questions:

[0736] The server calls the generator and retrieves past exam data for the English section.

[0737] The generator produces multiple-choice grammar questions and sends them to the server.

[0738] 4. Providing the format of questions and answers:

[0739] The server sends the generated problem to the user's terminal.

[0740] The user terminal displays the question and answer format to person A.

[0741] 5. User's answer:

[0742] Person A enters their answer and sends it to the server via their user terminal.

[0743] 6. Evaluation and feedback on the answers:

[0744] The server uses an approval device to evaluate the answers and generate feedback.

[0745] The feedback is sent to the user's device and displayed to person A.

[0746] In this way, this system makes it possible to learn efficiently while reducing the financial burden.

[0747] The following describes the processing flow.

[0748] Program processing flow

[0749] 1. User Login

[0750] Step 1:

[0751] The user enters their ID and password into their user terminal.

[0752] Step 2:

[0753] The user terminal sends the entered authentication information to the server.

[0754] Step 3:

[0755] The server compares the received authentication information with the database.

[0756] Step 4:

[0757] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[0758] 2. Selection of learning area

[0759] Step 1:

[0760] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[0761] Step 2:

[0762] The user terminal sends information about the selected field to the server.

[0763] 3. Generating past exam questions

[0764] Step 1:

[0765] The server invokes the generator based on information about the learning area selected by the user.

[0766] Step 2:

[0767] The generation device retrieves relevant data from a database containing past question patterns.

[0768] Step 3:

[0769] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[0770] 4. Provision of question and answer format

[0771] Step 1:

[0772] The server sends the generated questions and answer formats to the user's terminal.

[0773] Step 2:

[0774] The user terminal displays the question and answer format to the user.

[0775] 5. User's response

[0776] Step 1:

[0777] The user enters their answer to the question displayed on their terminal.

[0778] Step 2:

[0779] The user terminal sends the entered answer to the server.

[0780] 6. Evaluation and Feedback of Answers

[0781] Step 1:

[0782] The server sends the received user's answer to the approval device.

[0783] Step 2:

[0784] The approval device evaluates the answer and determines whether it is correct or incorrect.

[0785] Step 3:

[0786] The approval device generates the correct answer and explanation and sends them to the server.

[0787] Step 4:

[0788] The server sends the generated feedback to the user's terminal.

[0789] Step 5:

[0790] The user terminal displays feedback to the user.

[0791] (Example 1)

[0792] Next, we will describe Example 1. 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".

[0793] Conventional learning support systems have struggled to generate problems tailored to each user's learning area and to efficiently evaluate answers, hindering efficient user learning. Furthermore, there were challenges in managing authentication information and ensuring security. As a result, it was difficult for users to learn safely and effectively. To solve these problems, the objective of the present invention is to provide a system that enables users to learn safely and efficiently.

[0794] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0795] In this invention, the server includes means for a user to input authentication information for logging into the system using a user terminal, and for the user terminal to transmit the authentication information to the server; means for the server to compare the received authentication information with a database and return the authentication result to the user terminal; means for the server to generate a login token and transmit it to the user terminal if authentication is successful; means for the user to select a learning field through the interface of the user terminal and for the user terminal to transmit information of the selected field to the server; means for the server to call a generation device based on the learning field information received, for the generation device to analyze past question patterns and generate suitable questions; means for the generation device to transmit the generated questions to the server, for the server to transmit the generated questions to the user terminal; means for the user to input an answer to a question displayed on the user terminal and for the user terminal to transmit the input answer to the server; means for the server to transmit the received answer to an approval device, for the approval device to evaluate the answer and generate an answer result and explanation; and means for the server to transmit the generated answer result and explanation to the user terminal and display feedback to the user. This enables the user to proceed with learning safely and efficiently.

[0796] A "user terminal" is a device used by a user to select learning areas and input answers to questions through an interface. This includes computers, smartphones, tablets, and other similar devices.

[0797] A "server" is a central device that receives authentication information and learning area selection information sent from the user terminal, performs verification with the database, generates login tokens, distributes questions, and generates answer results and explanations.

[0798] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[0799] A "database" is a collection of information that systematically stores data such as user authentication information, past question patterns, generated questions, and answer results, and which a server references as needed.

[0800] A "login token" is data generated by the server for users who have successfully authenticated, and it contains session information that is valid for a certain period of time.

[0801] A "generator" is a device that analyzes past question patterns based on information about the learning area selected by the user and generates suitable questions.

[0802] An "approval device" is a device that evaluates user answers received from a server and generates answer results and explanations.

[0803] "Feedback" refers to information that includes the evaluation results of the user's answer and the explanations based on those evaluations.

[0804] An "interface" refers to the screens and input methods displayed on a user's device for selecting learning areas or entering answers.

[0805] "Answer Results and Explanations" refers to the approval device's determination of whether the user's answer was correct or incorrect, along with an explanation related to that answer.

[0806] "Past question patterns" refers to information about the format and content of questions that have been asked in the past.

[0807] A "problem" is a question or assignment related to the learning area that is intended for the user to answer.

[0808] A "processing step" refers to a series of operations or procedures performed at each stage when a system is in operation.

[0809] The embodiments for carrying out the present invention will be described in detail below. This system has a configuration including a server, a user terminal, a generation device, and an approval device, thereby enabling the user to efficiently proceed with learning.

[0810] First, the user logs into the system using their device. This device can be a computer, smartphone, or tablet, and a login screen will appear on the screen. The user enters their ID and password to log in. The user device sends this authentication information to the server. The server uses a database such as MySQL or PostgreSQL to verify the received authentication information and returns the authentication result to the user device. If authentication is successful, the server generates a JWT (JSON Web Token) and sends this token to the user device to initiate the session.

[0811] Next, the user selects the subject they want to study through an interface on their device. This operation is implemented using front-end frameworks such as React or Vue.js. The user's device sends information about the selected subject to the server. Based on the received information, the server sends a request to a generator written in Python.

[0812] The generation device retrieves relevant data from a database containing past question patterns (e.g., MongoDB) and generates appropriate questions using a generation AI model (e.g., GPT). These questions are formatted into multiple-choice or fill-in-the-blank formats and sent to the server. The server sends the generated questions to the user's terminal, which then displays them to the user.

[0813] The user enters an answer to a question displayed on their terminal and sends the answer to the server. The server sends the received answer to an approval device that uses a machine learning algorithm (e.g., Scikit-learn or TensorFlow) to execute it. The approval device evaluates the answer and determines whether it is correct or incorrect. The approval device then generates the correct answer and explanation based on this evaluation and sends it to the server. The server sends this to the user terminal and displays it to the user as feedback.

[0814] As an example, consider a high school senior user working on English grammar problems. The user logs in to the app on their smartphone by entering their ID and password. Then, they select the learning field "English" and work on multiple-choice grammar problems generated based on past problem data. After entering their answers, an approval device evaluates the answers via the server, and the user receives feedback including the correct answer and an explanation.

[0815] An example of a prompt sentence to input into a generative AI model would be: "Please describe in natural language the process by which a high school senior user tackles English grammar problems, including the roles of the server, terminal, and user."

[0816] This mechanism helps the system to learn efficiently.

[0817] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0818] Step 1:

[0819] The user enters their ID and password on their device. Specifically, the user uses the keyboard on a device such as a smartphone or tablet to enter their ID and password on the login screen. The ID and password entered by the user are encrypted (e.g., AES encryption) and sent to the server as input data.

[0820] Step 2:

[0821] The user terminal sends the entered authentication information to the server. The user terminal uses an HTTP POST request to send encrypted ID and password to the server. The input data is decrypted on the server side and used for the next process.

[0822] Step 3:

[0823] The server compares the received authentication information with the database. The server compares the received ID and password with hashed data stored in the database (e.g., MySQL) to check for a match. It generates an accurate matching result and outputs it to the next process.

[0824] Step 4:

[0825] The server returns the authentication result to the user's terminal. If authentication is successful, the server generates a JWT (JSON Web Token) and returns this token to the user's terminal. If authentication fails, an error message is generated. The generated token or error message is output to the user's terminal.

[0826] Step 5:

[0827] If authentication is successful, the user's device will display a notification of successful authentication to the user. Specifically, the user's device will display a notification such as "Login successful" on the screen and start the user session. The session information will be used as a login token for the following processes.

[0828] Step 6:

[0829] The user selects a learning area. The user uses the interface on their device to choose a learning area (e.g., English, Mathematics). A selection screen implemented with a frontend framework such as React Native is used, and the selected information is sent to the server as input data.

[0830] Step 7:

[0831] The user terminal sends information about the selected learning area to the server. The user terminal uses an HTTP POST request to send the selected learning area information to the server. This information becomes input data for the next data processing step.

[0832] Step 8:

[0833] The server calls the generator based on the learning area information it receives. The server sends an HTTP request to the generator's API endpoint, which is written in Python, and passes the learning area information as input data. The generator then processes this data.

[0834] Step 9:

[0835] The generator analyzes past question patterns and generates suitable questions. The generator retrieves past question patterns from a database such as MongoDB and uses a generation AI model (e.g., GPT-3) to generate questions suitable for the user. The generated questions are sent to the server as output.

[0836] Step 10:

[0837] The server sends the generated problem to the user terminal. The server receives the problem from the generator and sends it to the user terminal as an HTTP response. The user terminal receives this and uses it for the next process.

[0838] Step 11:

[0839] The user's device displays the received problem to the user. The problem is displayed on a screen implemented with React or Vue.js, and the user enters their answer. The user's answer becomes the next input data.

[0840] Step 12:

[0841] The user enters the answer, and the user's terminal sends the entered answer to the server. The answer is sent encrypted via an HTTP POST request. The server receives this answer data and sends it to the next process.

[0842] Step 13:

[0843] The server sends the received answer to the approval device. It sends an HTTP request to the approval device to evaluate the answer. The approval device performs the evaluation using an AI model or machine learning algorithm (e.g., Scikit-learn).

[0844] Step 14:

[0845] The approval device evaluates the answer and generates the answer result and explanation. It determines whether the answer is correct or incorrect, generates an explanation based on the correct answer, and sends the result to the server as output.

[0846] Step 15:

[0847] The server sends the generated solution and explanation to the user's terminal. The server receives a feedback message and returns it to the user's terminal as an HTTP response. The user's terminal displays this response.

[0848] Step 16:

[0849] The user's device displays feedback to the user. Specifically, evaluation results and explanations are displayed on the screen, allowing the user to receive learning feedback by reviewing them.

[0850] In this way, the system can perform a series of processes to enable users to learn efficiently.

[0851] (Application Example 1)

[0852] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0853] Traditional learning support systems have problems that make it difficult for users to check their learning progress and learn efficiently. Furthermore, they lack real-time answer evaluation and immediate feedback functions, making it difficult for learning content to be effectively retained. In addition, they do not adequately generate problems tailored to the user's level or provide specialized problem formats for each learning subject. There is a need to solve these problems and realize more efficient and effective learning support.

[0854] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0855] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This enables means for having a timer function to evaluate answers in real time and provide immediate feedback, means for visualizing the user's learning progress and saving past answer history to support review, and means for generating an unlimited number of questions based on past questions using an AI model when a learning field is selected.

[0856] A "generator" is a device that analyzes past question patterns and generates suitable questions.

[0857] A "user terminal" is a device (e.g., a smartphone) that a user uses to log in, select a learning area, and answer generated questions.

[0858] An "approval device" is a device that evaluates the answers received from the user terminal and generates the answer result and explanation.

[0859] The "timer function" is a feature that allows users to set a time limit when answering questions, helping them to concentrate on the problem.

[0860] A "means for evaluating answers in real time" refers to a method of immediately evaluating a user's answer and providing feedback as soon as the user submits it.

[0861] "Feedback" refers to the evaluation results and explanations that users receive after submitting an answer.

[0862] "Methods for visualizing learning progress" refer to methods that visually display the progress of learning, such as graphs, allowing users to grasp their learning status at a glance.

[0863] "A means of saving past answer history to support review" refers to a method of saving the user's previous answers in a database, allowing them to review their level of understanding and areas where they tend to make mistakes as needed.

[0864] An "AI model" is an artificial intelligence algorithm that automatically generates new questions based on past question patterns and the user's learning progress.

[0865] An embodiment of the present invention is configured as a system including a server, a user terminal, a generation device, and an approval device. This system allows users to learn efficiently and effectively.

[0866] 1. Hardware and software to be used

[0867] Hardware:

[0868] Smartphones (iPhone, Android devices)

[0869] Servers (AWS, Google Cloud, etc.)

[0870] software:

[0871] Backend: Node.js, Express

[0872] Frontend: React Native

[0873] Database: MongoDB

[0874] AI models: TensorFlow, scikit-learn

[0875] 2. Program Description

[0876] User Login

[0877] The server provides functionality for users to log in to the system using their smartphones. Users enter their ID and password on the smartphone app and send the authentication information to the server. The server compares this information with the user information stored in the database, and if authentication is successful, generates a login token and sends it to the user's device. This allows the user to access the system.

[0878] Selection of learning area

[0879] The server provides an interface for users to select a specific subject they wish to study. Users select their learning subject using their smartphone interface, and this information is sent to the server. This generates questions tailored to the user's chosen subject.

[0880] Generating past exam questions

[0881] The server invokes a generator based on information about the selected learning area. The generator retrieves past question patterns and the user's learning progress from a database and generates new questions using an AI model (TensorFlow or scikit-learn). The generated questions are sent to the server and provided to the user's terminal.

[0882] Providing a question and answer format

[0883] The server sends the generated questions and answer formats (multiple choice, fill-in-the-blank, etc.) to the user's terminal. The user's terminal displays the questions and answer formats to the user and uses a timer function to prompt the user to enter their answers within the time limit. This function allows the user to concentrate on the questions.

[0884] User answers and ratings

[0885] When a user submits an answer to a question, the answer is sent to the server. The server uses an approval device to evaluate the answer in real time and provides immediate feedback. This feedback includes an evaluation of the answer and a detailed explanation.

[0886] Managing learning progress

[0887] The server provides a function to visualize the user's learning progress and save past answer history. Users can check their progress as a graph on their smartphone, allowing them to grasp their self-study status at a glance. They can also review based on their past answer history.

[0888] Specific example

[0889] For example, if a high school senior user were to work on English grammar problems, the process would be as follows: The user launches the app on their smartphone, enters their ID and password to log in. Next, they select "English," and the appropriate problems are generated. The user enters their answers and receives immediate feedback. They can check their progress in a graph and review based on their past answer history.

[0890] Example of a prompt:

[0891] "A high school senior user is learning English grammar on their smartphone, following these steps: user login, selection of learning area, generation of past questions, provision of question and answer format, user submission, evaluation of answers, and feedback. Explain how AI-generated questions based on past question patterns can effectively provide feedback."

[0892] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0893] Step 1:

[0894] User Login

[0895] Input: The user enters their ID and password into the smartphone app.

[0896] Processing: Authentication information is sent from the terminal to the server via HTTPS. The server compares it with user data stored in the database (MongoDB) to determine whether authentication was successful.

[0897] Output: If authentication is successful, the server generates a login token and sends it to the terminal. If it fails, an error message is sent.

[0898] Step 2:

[0899] Selection of learning area

[0900] Input: The user selects the subject they want to study (e.g., English, mathematics, science, etc.) using the interface on their smartphone.

[0901] Processing: The terminal sends the selected information to the server. The server saves the received information to its database.

[0902] Output: The results of the selected learning area will be displayed on the device as a confirmation message.

[0903] Step 3:

[0904] Generating past exam questions

[0905] Input: The selected learning area information is sent to the server.

[0906] Processing: The server calls a generator to retrieve past questions and question pattern data for the relevant field from the database. It then generates questions using an AI model (TensorFlow, scikit-learn).

[0907] Output: The generated problem is sent to the terminal via the server.

[0908] Step 4:

[0909] Providing a question and answer format

[0910] Input: The generated question and answer format are sent to the device.

[0911] Processing: The terminal displays the question and answer format to the user and activates a timer function to set a time limit.

[0912] Output: The user begins answering the displayed questions.

[0913] Step 5:

[0914] User's answer

[0915] Input: The user enters their answer to the question into the terminal.

[0916] Processing: The terminal sends the entered answer to the server.

[0917] Output: The answer data reaches the server.

[0918] Step 6:

[0919] Evaluation and feedback on the answers

[0920] Input: User answer data and generated problem data reside on the server.

[0921] Processing: The server uses an approval device to evaluate answers in real time. An AI model is applied to determine whether the answer is correct or not. Evaluation results and detailed explanations are generated, and feedback data is created.

[0922] Output: The generated feedback is sent to the user's terminal via the server.

[0923] Step 7:

[0924] Managing learning progress

[0925] Input: The user's learning history and current performance data are stored in the database.

[0926] Processing: The server processes data to visualize the user's learning progress and generates it as graphs and charts. It also provides efficient review suggestions based on past answer history.

[0927] Output: Learning progress and suggestions are displayed on the user's device.

[0928] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0929] The system of the present invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component and the program processing are described below in natural language.

[0930] 1. User Login

[0931] Function Description

[0932] This is a mechanism for users to log in to the system using their user terminal.

[0933] Processing flow

[0934] The user enters their ID and password into their user terminal.

[0935] The user terminal sends the entered authentication information to the server.

[0936] The server compares the received authentication information with the database and returns the authentication result to the user's terminal.

[0937] If authentication is successful, the server generates a login token and sends it to the user's device.

[0938] 2. Selection of learning area

[0939] Function Description

[0940] This is a system that allows users to select a specific field they want to learn about.

[0941] Processing flow

[0942] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through an interface on their device.

[0943] The user terminal sends information about the selected field to the server.

[0944] 3. Generating past exam questions

[0945] Function Description

[0946] This system generates questions for users to learn from, based on past question patterns.

[0947] Processing flow

[0948] The server invokes the generator based on information about the learning area selected by the user.

[0949] The generation device retrieves relevant data from a database containing past question patterns and generates questions.

[0950] The generation device creates a question format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[0951] 4. Utilizing the Emotion Engine

[0952] Function Description

[0953] The emotion engine is used to recognize the user's emotions and adjust the content of questions and feedback accordingly.

[0954] Processing flow

[0955] The user terminal collects emotional data through the user's facial expressions and voice.

[0956] The emotion engine analyzes this emotion data to recognize the user's current emotions.

[0957] The recognized emotion information is transmitted to the generator and approval device via the server.

[0958] The generator adjusts the question content based on the recognized emotions.

[0959] The approval device adaptively modifies the content of the feedback based on emotional information.

[0960] 5. Provision of question and answer format

[0961] Function Description

[0962] This is a mechanism for presenting generated problems to users and receiving their answers.

[0963] Processing flow

[0964] The server sends the generated questions and answer formats to the user's terminal.

[0965] The user terminal displays the question and answer format to the user.

[0966] 6. User's response

[0967] Function Description

[0968] This is a system that allows users to input their answers to questions and send those answers to the server.

[0969] Processing flow

[0970] The user enters their answer to the question displayed on their terminal.

[0971] The user terminal sends the entered answer to the server.

[0972] 7. Evaluation and feedback on the answers

[0973] Function Description

[0974] This system evaluates user answers and provides evaluation results and explanations.

[0975] Processing flow

[0976] The server sends the received user's answer to the approval device.

[0977] The approval device evaluates the answer and determines whether it is correct or incorrect.

[0978] The approval device generates the correct answer and explanation and sends them to the server.

[0979] The server sends the generated feedback to the user's terminal.

[0980] The user terminal displays feedback to the user.

[0981] Specific example

[0982] Example: When student A, a third-year high school student, is working on an English grammar problem.

[0983] 1. User Login:

[0984] Person A launches the app on their smartphone (user device) and enters their ID and password.

[0985] The server verifies the authentication information and approves the login.

[0986] 2. Selection of study area:

[0987] Person A chooses "English".

[0988] The user terminal sends the selection information to the server.

[0989] 3. Generating past exam questions:

[0990] The server calls the generator and retrieves past exam data for the English section.

[0991] The generator produces multiple-choice grammar questions and sends them to the server.

[0992] 4. Utilizing the Emotion Engine:

[0993] The user terminal collects emotional data from person A's facial expressions and voice.

[0994] The emotion engine analyzes emotional data and recognizes person A's emotions.

[0995] The recognized emotion information is transmitted to the generation device and the approval device.

[0996] The generator adjusts the question content based on emotional information.

[0997] The approval device adjusts the feedback content.

[0998] 5. Providing question and answer formats:

[0999] The server sends the generated problem to the user's terminal.

[1000] The user terminal displays the question and answer format to person A.

[1001] 6. User's answer:

[1002] Person A enters their answer and sends it to the server via their user terminal.

[1003] 7. Evaluation and feedback on the answers:

[1004] The server uses an approval device to evaluate the answers and generate feedback.

[1005] The feedback is sent to the user's device and displayed to person A.

[1006] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[1007] The following describes the processing flow.

[1008] 1. User Login

[1009] Step 1:

[1010] The user enters their ID and password into their user terminal.

[1011] Step 2:

[1012] The user terminal sends the entered authentication information to the server.

[1013] Step 3:

[1014] The server compares the received authentication information with the database.

[1015] Step 4:

[1016] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[1017] 2. Selection of learning area

[1018] Step 1:

[1019] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[1020] Step 2:

[1021] The user terminal sends information about the selected field to the server.

[1022] 3. Generating past exam questions

[1023] Step 1:

[1024] The server invokes the generator based on information about the learning area selected by the user.

[1025] Step 2:

[1026] The generation device retrieves relevant data from a database containing past question patterns.

[1027] Step 3:

[1028] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[1029] 4. Utilizing the Emotion Engine

[1030] Step 1:

[1031] The user terminal collects emotional data through the user's facial expressions and voice.

[1032] Step 2:

[1033] The emotion engine analyzes collected emotion data to recognize the user's current emotions.

[1034] Step 3:

[1035] The recognized emotion information is transmitted via the server to the generation device and the approval device.

[1036] Step 4:

[1037] The generator adjusts the question content based on the recognized emotions.

[1038] Step 5:

[1039] The approval device adaptively modifies the feedback content based on emotional information.

[1040] 5. Provision of question and answer format

[1041] Step 1:

[1042] The server sends the generated questions and answer formats to the user's terminal.

[1043] Step 2:

[1044] The user terminal displays the question and answer format to the user.

[1045] 6. User's response

[1046] Step 1:

[1047] The user enters their answer to the question displayed on their terminal.

[1048] Step 2:

[1049] The user terminal sends the entered answer to the server.

[1050] 7. Evaluation and feedback on the answers

[1051] Step 1:

[1052] The server sends the received user's answer to the approval device.

[1053] Step 2:

[1054] The approval device evaluates the answer and determines whether it is correct or incorrect.

[1055] Step 3:

[1056] The approval device generates the correct answer and explanation and sends them to the server.

[1057] Step 4:

[1058] The server sends the generated feedback to the user's terminal.

[1059] Step 5:

[1060] The user terminal displays feedback to the user.

[1061] Specific example

[1062] Example: When student A, a third-year high school student, is working on an English grammar problem.

[1063] 1. User Login:

[1064] Step 1:

[1065] Person A launches the app on their smartphone (user device) and enters their ID and password.

[1066] Step 2:

[1067] The user terminal sends the entered authentication information to the server.

[1068] Step 3:

[1069] The server verifies the authentication information and approves the login.

[1070] 2. Selection of study area:

[1071] Step 1:

[1072] Person A chooses "English".

[1073] Step 2:

[1074] The user terminal sends the selection information to the server.

[1075] 3. Generating past exam questions:

[1076] Step 1:

[1077] The server calls the generator and retrieves past exam data for the English section.

[1078] Step 2:

[1079] The generator produces multiple-choice grammar questions and sends them to the server.

[1080] 4. Utilizing the Emotion Engine:

[1081] Step 1:

[1082] The user terminal collects emotional data from person A's facial expressions and voice.

[1083] Step 2:

[1084] The emotion engine analyzes emotional data and recognizes person A's emotions.

[1085] Step 3:

[1086] The recognized emotion information is transmitted to the generator and approval device via the server.

[1087] Step 4:

[1088] The generator adjusts the question content based on emotional information.

[1089] Step 5:

[1090] The approval device adjusts the feedback content.

[1091] 5. Providing question and answer formats:

[1092] Step 1:

[1093] The server sends the generated problem to the user's terminal.

[1094] Step 2:

[1095] The user terminal displays the question and answer format to person A.

[1096] 6. User's answer:

[1097] Step 1:

[1098] Person A enters their answer and sends it to the server via their user terminal.

[1099] 7. Evaluation and feedback on the answers:

[1100] Step 1:

[1101] The server uses an approval device to evaluate the answers and generate feedback.

[1102] Step 2:

[1103] The feedback is sent to the user's device and displayed to person A.

[1104] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[1105] (Example 2)

[1106] Next, we will describe Example 2. 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".

[1107] Traditional learning systems often present problems without considering the user's emotional state, which can lead to user stress and reduced learning effectiveness. Furthermore, a lack of specialized problem formats tailored to specific learning areas is a problem, failing to adequately meet users' learning needs. Additionally, the lack of complete security guarantees for user authentication information is also a concern.

[1108] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1109] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, means including an emotion engine that analyzes emotion data collected from the user terminal and adjusts the difficulty level of the questions and the content of the feedback based on the user's emotional state, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This makes it possible to provide a personalized learning experience based on the user's emotional state and maximize learning effectiveness. Furthermore, by providing question formats specialized for learning fields, it is possible to flexibly respond to the user's learning needs.

[1110] A "generator" is a device that analyzes past question patterns and generates questions that are suitable for the user.

[1111] A "user terminal" refers to a device used by a user to input information or answer questions, and includes smartphones, tablets, and computers.

[1112] An "emotion engine" is a device that analyzes the user's facial expressions and voice, recognizes their emotional state, and adjusts the difficulty level of the problem and the content of the feedback accordingly.

[1113] An "approval device" is a device that evaluates a user's answer and generates the answer result and explanation.

[1114] "Past question patterns" refers to data and information that shows past questions, their trends, and their format.

[1115] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[1116] "Answer evaluation" is the process of determining whether the answers entered by users are correct or incorrect, and then conducting an evaluation based on those results.

[1117] "Feedback content" refers to information such as evaluation results and explanations for the user's answers, and includes learning advice and guidance provided to the user.

[1118] "Question format" refers to the type of question format specific to each learning area, and includes multiple-choice questions and fill-in-the-blank questions.

[1119] "User emotional state" refers to the emotional states a user experiences while learning, and includes, for example, joy, sadness, stress, and excitement.

[1120] The system of this invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component is described below.

[1121] 1. User Login

[1122] Users log in to the system using their user terminal. Specifically, the user enters their ID and password into the user terminal, and the user terminal sends this authentication information to the server. The server compares the received authentication information with the database, and if authentication is successful, generates a login token and sends it to the user terminal. The user terminal stores this token and uses it for subsequent communications.

[1123] 2. Selection of learning area

[1124] The user selects the subject they wish to study through an interface on their device. The user device sends information about the selected subject to the server. The server receives this information and holds it for the next step.

[1125] 3. Generating past exam questions

[1126] The server invokes a generator based on the learning area selected by the user. The generator retrieves relevant data from a database containing past question patterns and generates questions. The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank) and sends it to the server.

[1127] 4. Utilizing the Emotion Engine

[1128] The user terminal collects emotional data through the user's facial expressions and voice. This data is analyzed using a TensorFlow model within the terminal to recognize the user's emotional state. The emotion engine transmits the recognized emotional information to the generator and approval device via the server. The generator adjusts the question content based on the emotional information, and the approval device adaptively changes the feedback content based on the emotional information.

[1129] 5. Provision of question and answer format

[1130] The server sends the generated questions and answer formats to the user's terminal. The user's terminal displays this to the user, assisting them in working on the questions.

[1131] 6. User's response

[1132] The user enters their answer to the question displayed on their terminal. The user terminal sends the entered answer to the server. The server receives this answer and sends it to the approval device.

[1133] 7. Evaluation and feedback on the answers

[1134] The approval device evaluates the user's answer and determines whether it is correct or incorrect. Furthermore, the approval device generates the correct answer and explanation, and sends it to the server. The server sends the generated feedback to the user's terminal, which displays it to the user. The user can then progress in their learning through this feedback.

[1135] Specific example

[1136] For example, consider a high school senior, A, working on English grammar problems. A logs into the system using their smartphone and selects English as their learning subject. The server analyzes past question patterns based on this information and generates appropriate grammar problems. The user terminal collects emotional data from A's facial expressions and voice, and the emotion engine performs analysis. As a result, if A is experiencing stress, adjustments are made, such as lowering the difficulty of the problems. After the problems are displayed, A enters their answers, which are sent to the server. An approval device evaluates the answers, and feedback is provided to A. Through this process, A can enjoy a personalized learning experience.

[1137] Example of a prompt

[1138] Prompt example:

[1139] Assume that student A, a third-year high school student, is using a learning system to study English. The system proceeds through seven steps: user login, selection of learning area, generation of past questions, use of an emotion engine, provision of questions and answers, user response, and evaluation and feedback of the response. Considering that student A is working on grammar questions, please describe the sequence of processing steps and their flow in detail.

[1140] In this way, the present invention takes into account the user's emotional state and provides a more effective and personalized learning experience.

[1141] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1142] Step 1:

[1143] The user enters their ID and password into their user terminal.

[1144] Input: ID, Password

[1145] The user terminal sends the entered authentication information to the server.

[1146] Input: Authentication information sent from the user terminal

[1147] Specific operation: The user terminal sends data to the server using SSL encrypted communication.

[1148] Step 2:

[1149] The server compares the received authentication information with the database.

[1150] Input: Authentication information, user data in the database

[1151] Data processing: Compare and verify user data in the database with received authentication information.

[1152] Output: Authentication result (success / failure)

[1153] Specific operation: The server uses a MySQL database to verify user information.

[1154] Step 3:

[1155] The server returns the authentication result to the user's terminal.

[1156] Input: Authentication result

[1157] Output: Authentication result (success / failure)

[1158] Specific operation: The server generates a JWT (JSON Web Token) and, if successful, returns this token to the user's terminal. The token is used for subsequent communication.

[1159] Step 4:

[1160] Users select the subject they wish to study through an interface on their device.

[1161] Input: Selection information for learning field

[1162] The user terminal sends information about the selected field to the server.

[1163] Input: Selection information for learning field

[1164] Specific operation: The user taps the "Select Learning Area" option from the app's menu and selects an area from the displayed list. The user's device sends the selection information to the server using HTTPS communication.

[1165] Step 5:

[1166] The server invokes the generator based on information about the learning area selected by the user.

[1167] Input: Information on the field of study

[1168] The server retrieves relevant data from a database containing past question patterns.

[1169] Input: Database of past exam patterns, information on learning areas.

[1170] Data processing: Filter past question patterns related to the learning area and select appropriate questions.

[1171] Output: Selected problems

[1172] Specific operation: The server uses a Python API to call a microservice for problem generation and retrieve the relevant data.

[1173] Step 6:

[1174] The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[1175] Input: Past exam data

[1176] Data processing: Generate questions while considering the user's learning history and difficulty settings.

[1177] Output: Formatted problem

[1178] Specific operation: The generation algorithm uses AI to analyze data and generate an optimal set of questions. The generated set of questions is formatted as multiple-choice questions and returned to the server.

[1179] Step 7:

[1180] The user terminal collects emotional data through the user's facial expressions and voice.

[1181] Input: User facial expression data, voice data

[1182] Data processing: Use an emotion engine to analyze emotional data and recognize the user's current emotions.

[1183] Output: Recognized emotion information

[1184] Specific operation: The device's camera and microphone are used to collect data, which is then analyzed using a TensorFlow model.

[1185] Step 8:

[1186] The emotion engine analyzes this emotion data to recognize the user's current emotions.

[1187] Input: Sentiment data

[1188] Data processing: Analyze data using emotion analysis algorithms to determine emotional states.

[1189] Output: Emotional state (e.g., stress, joy, etc.)

[1190] Specific operation: The analysis results are processed as digital signals, and recognized emotional information is generated.

[1191] Step 9:

[1192] The recognized emotion information is transmitted to the generator and approval device via the server.

[1193] Input: Recognized emotion information

[1194] Output: Adjusted question content and feedback

[1195] Specific operation: The analysis results of the emotion engine are transmitted to the generation device and the approval device via the server.

[1196] Step 10:

[1197] The generator adjusts the question content based on the recognized emotions.

[1198] Input: Sentimental information, generated problem

[1199] Data processing: Adjust the difficulty and format of problems based on emotional information.

[1200] Output: Adjusted problem

[1201] Specific action: For example, if the user is feeling stressed, the generator will lower the difficulty level of the problem.

[1202] Step 11:

[1203] The approval device adaptively modifies the content of the feedback based on emotional information.

[1204] Input: Sentimental information, evaluation results

[1205] Data processing: Adaptively modifying feedback content based on emotional information.

[1206] Output: Personalized feedback

[1207] Specific actions: For example, if the user is feeling stressed, generate a message such as "Let's calm down and get to work."

[1208] Step 12:

[1209] The server sends the generated questions and answer formats to the user's terminal.

[1210] Input: Formatted question, sentiment-adjusted content

[1211] Output: Problem data to be displayed on the user terminal

[1212] Specific operation: The server sends formatted problem data to the user's terminal via a REST API.

[1213] Step 13:

[1214] The user terminal displays the question and answer format to the user.

[1215] Input: Problem data

[1216] Output: Displayed problem

[1217] Specific operation: The user terminal uses a UI framework to display the problem on the screen.

[1218] Step 14:

[1219] The user enters their answer to the question displayed on their terminal.

[1220] Input: Answer Information

[1221] The user terminal sends the entered answer to the server.

[1222] Output: Answer data sent to the server

[1223] Specific operation: The user taps an option to enter their answer, and the user's device sends this to the server.

[1224] Step 15:

[1225] The server sends the received user's answer to the approval device.

[1226] Input: Answer data

[1227] Notice: Answer data (to be sent to the approval device)

[1228] Specific operation: The server sends the answer data to the approval device's API.

[1229] Step 16:

[1230] The approval device evaluates the answer and determines whether it is correct or incorrect. Furthermore, it generates the correct answer and an explanation.

[1231] Input: Answer data

[1232] Data processing: Evaluate the answers and generate results and explanations.

[1233] Output: Evaluation results and explanation

[1234] Specific operation: The approval device uses a machine learning model to evaluate the answer and generate accurate feedback.

[1235] Step 17:

[1236] The server sends the generated feedback to the user's terminal.

[1237] Input: Evaluation results and explanation

[1238] Output: Feedback displayed on the user's terminal

[1239] Specific operation: The server sends the generated feedback data to the user's terminal, where it is displayed.

[1240] This allows users to receive detailed feedback and progress in their learning.

[1241] (Application Example 2)

[1242] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1243] Conventional learning and work support systems provide uniform problems and work instructions without considering the user's emotional state, leading to user stress and decreased work efficiency. Furthermore, a lack of individualized feedback made it difficult to provide optimal learning and work environments and to reduce user motivation.

[1244] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1245] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. Furthermore, it includes an emotion engine that collects and analyzes user emotion data, means for the generating device and approval device to adjust the question content and feedback based on the emotion information, and user interface means for providing real-time work support to workers. This enables personalized learning and work support that takes the user's emotional state into account, thereby reducing stress and improving work efficiency.

[1246] A "problem generator" is a device that analyzes past question patterns and generates questions tailored to the user.

[1247] A "user terminal" is a device used by users to access a server and communicate with it to send and receive various types of information.

[1248] An "approval device" is a device that evaluates the answers received from users and generates the results and explanations.

[1249] An "emotion engine" is a device that recognizes a user's emotional state by collecting and analyzing their emotional data.

[1250] A "server" is a central device that manages and processes data for the entire system.

[1251] A "user interface means" is a means of providing an interface for a user to interact with a system.

[1252] "Real-time work support" refers to providing instant instructions and feedback to users while they are performing their tasks.

[1253] "Feedback" refers to evaluations and advice provided based on the user's work and learning results.

[1254] The present invention is a system that includes a generation device that analyzes past question patterns and generates suitable questions, means for transmitting the generated questions to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates the results and explanations. The system also includes an emotion engine that collects and analyzes user emotion data. Furthermore, it is equipped with a user interface means for providing real-time work support to workers. Specific embodiments for carrying out the present invention are shown below.

[1255] Hardware and software configuration

[1256] 1. Hardware Configuration

[1257] User device: Smart glasses (e.g., Google Glass)

[1258] Server: A computer system that manages and processes data.

[1259] Emotion engine: A device (e.g., camera, microphone) that collects and analyzes user emotional data.

[1260] 2. Software Configuration

[1261] Server applications (e.g., Python-based applications)

[1262] Emotion recognition engine (e.g., Microsoft Azure's Emotion API)

[1263] Data processing and data calculation workflow

[1264] The server generates a problem suitable for the user based on data from the generation device and sends it to the user terminal. The user terminal displays the problem and sends the user's answer back to the server. Based on this, the approval device evaluates the answer and generates the answer result and explanation.

[1265] Furthermore, the emotion engine collects and analyzes the user's emotional data (facial expressions and voice). Based on this, the generation device generates problems optimized for the user's emotional state, and the approval device adjusts the feedback accordingly. Finally, the user interface provides real-time work support, improving the user's work efficiency and motivation.

[1266] Specific example

[1267] As a concrete example, consider a case where person B is engaged in parts assembly work at a factory. Person B puts on smart glasses and logs into the system. The server verifies Person B's authentication information and approves the login. Next, Person B selects "parts assembly work" and performs the task according to the instructions displayed on the smart glasses.

[1268] In this process, the emotion engine collects and analyzes emotional data from B's facial expressions and voice. If the system detects that B is experiencing stress, it adjusts the work content and assigns simpler tasks to reduce stress.

[1269] Examples of prompts to input into a generative AI model are as follows:

[1270] "For HR professionals, please provide an overview of a factory worker status monitoring system using emotion recognition, and the technologies that support it. Include the following keywords: smart glasses, emotion engine, real-time feedback, and improved worker efficiency."

[1271] As described above, this system allows for real-time monitoring of the user's emotional state and provides optimal work instructions and feedback, thereby improving work efficiency and reducing stress.

[1272] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1273] Step 1:

[1274] The user's terminal sends the user's entered ID and password to the server.

[1275] Input: The user enters their ID and password into the smart glasses.

[1276] Data processing: The server compares the received authentication information with the database.

[1277] Output: Generate a login token as an authentication result and send it back to the user's device.

[1278] Step 2:

[1279] The user selects a learning area or work area.

[1280] Input: The user selects a task, such as "parts assembly work," using smart glasses.

[1281] Data processing: The user terminal sends information from the selected field to the server.

[1282] Output: The server uses that information to call the generator.

[1283] Step 3:

[1284] The server generates tasks using a generator.

[1285] Input: Information about the selected learning / work area.

[1286] Data processing: The generation device analyzes past data and generates suitable tasks.

[1287] Output: Sends the generated task information to the user's terminal.

[1288] Step 4:

[1289] The emotion engine collects and analyzes user emotion data.

[1290] Input: User facial expression data and voice data.

[1291] Data processing: The emotion engine analyzes this data to recognize the user's emotional state.

[1292] Output: Sends recognized emotion information to the server.

[1293] Step 5:

[1294] The server adjusts the question content and work instructions based on the generated tasks and sentiment information.

[1295] Input: Generated task information and recognized emotion information.

[1296] Data processing: Generators and approval devices adjust task content and feedback based on emotional information.

[1297] Output: Sends adjusted tasks and feedback information to the user's terminal.

[1298] Step 6:

[1299] The user terminal displays the adjusted tasks to the user.

[1300] Input: Coordinated task information sent from the server.

[1301] Data processing: Converts the information into a format necessary for the user's terminal to display it.

[1302] Output: The adjusted task is displayed on the user's glasses screen.

[1303] Step 7:

[1304] The user performs tasks according to the instructions and enters their progress into their terminal.

[1305] Input: The user enters the progress of the task into the user terminal.

[1306] Data processing: The user terminal sends the entered progress data to the server.

[1307] Output: Progress data is sent to the server and recorded.

[1308] Step 8:

[1309] The server evaluates the progress data using an approval device and generates feedback.

[1310] Input: User progress data.

[1311] Data processing: The approval device evaluates the progress data and generates feedback.

[1312] Output: Sends the generated feedback to the user's terminal.

[1313] Step 9:

[1314] The user's device displays feedback to the user.

[1315] Input: Feedback information sent from the server.

[1316] Data processing: Convert the data into a format that the user terminal can use to display feedback information.

[1317] Output: Feedback is displayed on the user's glasses screen.

[1318] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1319] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1320] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1321] [Third Embodiment]

[1322] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1323] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1324] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1325] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1326] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1327] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1328] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1329] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1330] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1331] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1332] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1333] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1334] The system for carrying out the present invention has a configuration including a server, a user terminal, a generation device, and an approval device. The specific operation of each component and the program processing are described below in natural language.

[1335] 1. User Login

[1336] Function Description

[1337] This is a mechanism for users to log in to the system using their user terminal.

[1338] Processing flow

[1339] The user enters their ID and password into their user terminal.

[1340] The user terminal sends the entered authentication information to the server.

[1341] The server compares the received authentication information with the database and returns the authentication result to the user's terminal.

[1342] If authentication is successful, the server generates a login token and sends it to the user's device.

[1343] 2. Selection of learning area

[1344] Function Description

[1345] This is a system that allows users to select a specific field they want to learn about.

[1346] Processing flow

[1347] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through an interface on their device.

[1348] The user terminal sends information about the selected field to the server.

[1349] 3. Generating past exam questions

[1350] Function Description

[1351] This system generates questions for users to learn from, based on past question patterns.

[1352] Processing flow

[1353] The server invokes the generator based on information about the learning area selected by the user.

[1354] The generation device retrieves relevant data from a database containing past question patterns and generates questions.

[1355] The generation device creates a question format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[1356] 4. Provision of question and answer format

[1357] Function Description

[1358] This is a mechanism for presenting generated problems to users and receiving their answers.

[1359] Processing flow

[1360] The server sends the generated questions and answer formats to the user's terminal.

[1361] The user terminal displays the question and answer format to the user.

[1362] 5. User's response

[1363] Function Description

[1364] This is a system that allows users to input their answers to questions and send those answers to the server.

[1365] Processing flow

[1366] The user enters their answer to the question displayed on their terminal.

[1367] The user terminal sends the entered answer to the server.

[1368] 6. Evaluation and Feedback of Answers

[1369] Function Description

[1370] This system evaluates user answers and provides evaluation results and explanations.

[1371] Processing flow

[1372] The server sends the received user's answer to the approval device.

[1373] The approval device evaluates the answer and determines whether it is correct or incorrect.

[1374] The approval device generates the correct answer and explanation and sends them to the server.

[1375] The server sends the generated feedback to the user's terminal.

[1376] The user terminal displays feedback to the user.

[1377] Specific example

[1378] Example: When student A, a third-year high school student, is working on an English grammar problem.

[1379] 1. User Login:

[1380] Person A launches the app on their smartphone (user device) and enters their ID and password.

[1381] The server verifies the authentication information and approves the login.

[1382] 2. Selection of study area:

[1383] Person A chooses "English".

[1384] The user terminal sends the selection information to the server.

[1385] 3. Generating past exam questions:

[1386] The server calls the generator and retrieves past exam data for the English section.

[1387] The generator produces multiple-choice grammar questions and sends them to the server.

[1388] 4. Providing the format of questions and answers:

[1389] The server sends the generated problem to the user's terminal.

[1390] The user terminal displays the question and answer format to person A.

[1391] 5. User's answer:

[1392] Person A enters their answer and sends it to the server via their user terminal.

[1393] 6. Evaluation and feedback on the answers:

[1394] The server uses an approval device to evaluate the answers and generate feedback.

[1395] The feedback is sent to the user's device and displayed to person A.

[1396] In this way, this system makes it possible to learn efficiently while reducing the financial burden.

[1397] The following describes the processing flow.

[1398] Program processing flow

[1399] 1. User Login

[1400] Step 1:

[1401] The user enters their ID and password into their user terminal.

[1402] Step 2:

[1403] The user terminal sends the entered authentication information to the server.

[1404] Step 3:

[1405] The server compares the received authentication information with the database.

[1406] Step 4:

[1407] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[1408] 2. Selection of learning area

[1409] Step 1:

[1410] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[1411] Step 2:

[1412] The user terminal sends information about the selected field to the server.

[1413] 3. Generating past exam questions

[1414] Step 1:

[1415] The server invokes the generator based on information about the learning area selected by the user.

[1416] Step 2:

[1417] The generation device retrieves relevant data from a database containing past question patterns.

[1418] Step 3:

[1419] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[1420] 4. Provision of question and answer format

[1421] Step 1:

[1422] The server sends the generated questions and answer formats to the user's terminal.

[1423] Step 2:

[1424] The user terminal displays the question and answer format to the user.

[1425] 5. User's response

[1426] Step 1:

[1427] The user enters their answer to the question displayed on their terminal.

[1428] Step 2:

[1429] The user terminal sends the entered answer to the server.

[1430] 6. Evaluation and Feedback of Answers

[1431] Step 1:

[1432] The server sends the received user's answer to the approval device.

[1433] Step 2:

[1434] The approval device evaluates the answer and determines whether it is correct or incorrect.

[1435] Step 3:

[1436] The approval device generates the correct answer and explanation and sends them to the server.

[1437] Step 4:

[1438] The server sends the generated feedback to the user's terminal.

[1439] Step 5:

[1440] The user terminal displays feedback to the user.

[1441] (Example 1)

[1442] Next, we will describe Example 1. 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."

[1443] Conventional learning support systems have struggled to generate problems tailored to each user's learning area and to efficiently evaluate answers, hindering efficient user learning. Furthermore, there were challenges in managing authentication information and ensuring security. As a result, it was difficult for users to learn safely and effectively. To solve these problems, the objective of the present invention is to provide a system that enables users to learn safely and efficiently.

[1444] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1445] In this invention, the server includes means for a user to input authentication information for logging into the system using a user terminal, and for the user terminal to transmit the authentication information to the server; means for the server to compare the received authentication information with a database and return the authentication result to the user terminal; means for the server to generate a login token and transmit it to the user terminal if authentication is successful; means for the user to select a learning field through the interface of the user terminal and for the user terminal to transmit information of the selected field to the server; means for the server to call a generation device based on the learning field information received, for the generation device to analyze past question patterns and generate suitable questions; means for the generation device to transmit the generated questions to the server, for the server to transmit the generated questions to the user terminal; means for the user to input an answer to a question displayed on the user terminal and for the user terminal to transmit the input answer to the server; means for the server to transmit the received answer to an approval device, for the approval device to evaluate the answer and generate an answer result and explanation; and means for the server to transmit the generated answer result and explanation to the user terminal and display feedback to the user. This enables the user to proceed with learning safely and efficiently.

[1446] A "user terminal" is a device used by a user to select learning areas and input answers to questions through an interface. This includes computers, smartphones, tablets, and other similar devices.

[1447] A "server" is a central device that receives authentication information and learning area selection information sent from the user terminal, performs verification with the database, generates login tokens, distributes questions, and generates answer results and explanations.

[1448] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[1449] A "database" is a collection of information that systematically stores data such as user authentication information, past question patterns, generated questions, and answer results, and which a server references as needed.

[1450] A "login token" is data generated by the server for users who have successfully authenticated, and it contains session information that is valid for a certain period of time.

[1451] A "generator" is a device that analyzes past question patterns based on information about the learning area selected by the user and generates suitable questions.

[1452] An "approval device" is a device that evaluates user answers received from a server and generates answer results and explanations.

[1453] "Feedback" refers to information that includes the evaluation results of the user's answer and the explanations based on those evaluations.

[1454] An "interface" refers to the screens and input methods displayed on a user's device for selecting learning areas or entering answers.

[1455] "Answer Results and Explanations" refers to the approval device's determination of whether the user's answer was correct or incorrect, along with an explanation related to that answer.

[1456] "Past question patterns" refers to information about the format and content of questions that have been asked in the past.

[1457] A "problem" is a question or assignment related to the learning area that is intended for the user to answer.

[1458] A "processing step" refers to a series of operations or procedures performed at each stage when a system is in operation.

[1459] The embodiments for carrying out the present invention will be described in detail below. This system has a configuration including a server, a user terminal, a generation device, and an approval device, thereby enabling the user to efficiently proceed with learning.

[1460] First, the user logs into the system using their device. This device can be a computer, smartphone, or tablet, and a login screen will appear on the screen. The user enters their ID and password to log in. The user device sends this authentication information to the server. The server uses a database such as MySQL or PostgreSQL to verify the received authentication information and returns the authentication result to the user device. If authentication is successful, the server generates a JWT (JSON Web Token) and sends this token to the user device to initiate the session.

[1461] Next, the user selects the subject they want to study through an interface on their device. This operation is implemented using front-end frameworks such as React or Vue.js. The user's device sends information about the selected subject to the server. Based on the received information, the server sends a request to a generator written in Python.

[1462] The generation device retrieves relevant data from a database containing past question patterns (e.g., MongoDB) and generates appropriate questions using a generation AI model (e.g., GPT). These questions are formatted into multiple-choice or fill-in-the-blank formats and sent to the server. The server sends the generated questions to the user's terminal, which then displays them to the user.

[1463] The user enters an answer to a question displayed on their terminal and sends the answer to the server. The server sends the received answer to an approval device that uses a machine learning algorithm (e.g., Scikit-learn or TensorFlow) to execute it. The approval device evaluates the answer and determines whether it is correct or incorrect. The approval device then generates the correct answer and explanation based on this evaluation and sends it to the server. The server sends this to the user terminal and displays it to the user as feedback.

[1464] As an example, consider a high school senior user working on English grammar problems. The user logs in to the app on their smartphone by entering their ID and password. Then, they select the learning field "English" and work on multiple-choice grammar problems generated based on past problem data. After entering their answers, an approval device evaluates the answers via the server, and the user receives feedback including the correct answer and an explanation.

[1465] An example of a prompt sentence to input into a generative AI model would be: "Please describe in natural language the process by which a high school senior user tackles English grammar problems, including the roles of the server, terminal, and user."

[1466] This mechanism helps the system to learn efficiently.

[1467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1468] Step 1:

[1469] The user enters their ID and password on their device. Specifically, the user uses the keyboard on a device such as a smartphone or tablet to enter their ID and password on the login screen. The ID and password entered by the user are encrypted (e.g., AES encryption) and sent to the server as input data.

[1470] Step 2:

[1471] The user terminal sends the entered authentication information to the server. The user terminal uses an HTTP POST request to send encrypted ID and password to the server. The input data is decrypted on the server side and used for the next process.

[1472] Step 3:

[1473] The server compares the received authentication information with the database. The server compares the received ID and password with hashed data stored in the database (e.g., MySQL) to check for a match. It generates an accurate matching result and outputs it to the next process.

[1474] Step 4:

[1475] The server returns the authentication result to the user's terminal. If authentication is successful, the server generates a JWT (JSON Web Token) and returns this token to the user's terminal. If authentication fails, an error message is generated. The generated token or error message is output to the user's terminal.

[1476] Step 5:

[1477] If authentication is successful, the user's device will display a notification of successful authentication to the user. Specifically, the user's device will display a notification such as "Login successful" on the screen and start the user session. The session information will be used as a login token for the following processes.

[1478] Step 6:

[1479] The user selects a learning area. The user uses the interface on their device to choose a learning area (e.g., English, Mathematics). A selection screen implemented with a frontend framework such as React Native is used, and the selected information is sent to the server as input data.

[1480] Step 7:

[1481] The user terminal sends information about the selected learning area to the server. The user terminal uses an HTTP POST request to send the selected learning area information to the server. This information becomes input data for the next data processing step.

[1482] Step 8:

[1483] The server calls the generator based on the learning area information it receives. The server sends an HTTP request to the generator's API endpoint, which is written in Python, and passes the learning area information as input data. The generator then processes this data.

[1484] Step 9:

[1485] The generator analyzes past question patterns and generates suitable questions. The generator retrieves past question patterns from a database such as MongoDB and uses a generation AI model (e.g., GPT-3) to generate questions suitable for the user. The generated questions are sent to the server as output.

[1486] Step 10:

[1487] The server sends the generated problem to the user terminal. The server receives the problem from the generator and sends it to the user terminal as an HTTP response. The user terminal receives this and uses it for the next process.

[1488] Step 11:

[1489] The user's device displays the received problem to the user. The problem is displayed on a screen implemented with React or Vue.js, and the user enters their answer. The user's answer becomes the next input data.

[1490] Step 12:

[1491] The user enters the answer, and the user's terminal sends the entered answer to the server. The answer is sent encrypted via an HTTP POST request. The server receives this answer data and sends it to the next process.

[1492] Step 13:

[1493] The server sends the received answer to the approval device. It sends an HTTP request to the approval device to evaluate the answer. The approval device performs the evaluation using an AI model or machine learning algorithm (e.g., Scikit-learn).

[1494] Step 14:

[1495] The approval device evaluates the answer and generates the answer result and explanation. It determines whether the answer is correct or incorrect, generates an explanation based on the correct answer, and sends the result to the server as output.

[1496] Step 15:

[1497] The server sends the generated solution and explanation to the user's terminal. The server receives a feedback message and returns it to the user's terminal as an HTTP response. The user's terminal displays this response.

[1498] Step 16:

[1499] The user's device displays feedback to the user. Specifically, evaluation results and explanations are displayed on the screen, allowing the user to receive learning feedback by reviewing them.

[1500] In this way, the system can perform a series of processes to enable users to learn efficiently.

[1501] (Application Example 1)

[1502] Next, we will explain Application Example 1. In the following explanation, 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."

[1503] Traditional learning support systems have problems that make it difficult for users to check their learning progress and learn efficiently. Furthermore, they lack real-time answer evaluation and immediate feedback functions, making it difficult for learning content to be effectively retained. In addition, they do not adequately generate problems tailored to the user's level or provide specialized problem formats for each learning subject. There is a need to solve these problems and realize more efficient and effective learning support.

[1504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1505] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This enables means for having a timer function to evaluate answers in real time and provide immediate feedback, means for visualizing the user's learning progress and saving past answer history to support review, and means for generating an unlimited number of questions based on past questions using an AI model when a learning field is selected.

[1506] A "generator" is a device that analyzes past question patterns and generates suitable questions.

[1507] A "user terminal" is a device (e.g., a smartphone) that a user uses to log in, select a learning area, and answer generated questions.

[1508] An "approval device" is a device that evaluates the answers received from the user terminal and generates the answer result and explanation.

[1509] The "timer function" is a feature that allows users to set a time limit when answering questions, helping them to concentrate on the problem.

[1510] A "means for evaluating answers in real time" refers to a method of immediately evaluating a user's answer and providing feedback as soon as the user submits it.

[1511] "Feedback" refers to the evaluation results and explanations that users receive after submitting an answer.

[1512] "Methods for visualizing learning progress" refer to methods that visually display the progress of learning, such as graphs, allowing users to grasp their learning status at a glance.

[1513] "A means of saving past answer history to support review" refers to a method of saving the user's previous answers in a database, allowing them to review their level of understanding and areas where they tend to make mistakes as needed.

[1514] An "AI model" is an artificial intelligence algorithm that automatically generates new questions based on past question patterns and the user's learning progress.

[1515] An embodiment of the present invention is configured as a system including a server, a user terminal, a generation device, and an approval device. This system allows users to learn efficiently and effectively.

[1516] 1. Hardware and software to be used

[1517] Hardware:

[1518] Smartphones (iPhone, Android devices)

[1519] Servers (AWS, Google Cloud, etc.)

[1520] software:

[1521] Backend: Node.js, Express

[1522] Frontend: React Native

[1523] Database: MongoDB

[1524] AI models: TensorFlow, scikit-learn

[1525] 2. Program Description

[1526] User Login

[1527] The server provides functionality for users to log in to the system using their smartphones. Users enter their ID and password on the smartphone app and send the authentication information to the server. The server compares this information with the user information stored in the database, and if authentication is successful, generates a login token and sends it to the user's device. This allows the user to access the system.

[1528] Selection of learning area

[1529] The server provides an interface for users to select a specific subject they wish to study. Users select their learning subject using their smartphone interface, and this information is sent to the server. This generates questions tailored to the user's chosen subject.

[1530] Generating past exam questions

[1531] The server invokes a generator based on information about the selected learning area. The generator retrieves past question patterns and the user's learning progress from a database and generates new questions using an AI model (TensorFlow or scikit-learn). The generated questions are sent to the server and provided to the user's terminal.

[1532] Providing a question and answer format

[1533] The server sends the generated questions and answer formats (multiple choice, fill-in-the-blank, etc.) to the user's terminal. The user's terminal displays the questions and answer formats to the user and uses a timer function to prompt the user to enter their answers within the time limit. This function allows the user to concentrate on the questions.

[1534] User answers and ratings

[1535] When a user submits an answer to a question, the answer is sent to the server. The server uses an approval device to evaluate the answer in real time and provides immediate feedback. This feedback includes an evaluation of the answer and a detailed explanation.

[1536] Managing learning progress

[1537] The server provides a function to visualize the user's learning progress and save past answer history. Users can check their progress as a graph on their smartphone, allowing them to grasp their self-study status at a glance. They can also review based on their past answer history.

[1538] Specific example

[1539] For example, if a high school senior user were to work on English grammar problems, the process would be as follows: The user launches the app on their smartphone, enters their ID and password to log in. Next, they select "English," and the appropriate problems are generated. The user enters their answers and receives immediate feedback. They can check their progress in a graph and review based on their past answer history.

[1540] Example of a prompt:

[1541] "A high school senior user is learning English grammar on their smartphone, following these steps: user login, selection of learning area, generation of past questions, provision of question and answer format, user submission, evaluation of answers, and feedback. Explain how AI-generated questions based on past question patterns can effectively provide feedback."

[1542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1543] Step 1:

[1544] User Login

[1545] Input: The user enters their ID and password into the smartphone app.

[1546] Processing: Authentication information is sent from the terminal to the server via HTTPS. The server compares it with user data stored in the database (MongoDB) to determine whether authentication was successful.

[1547] Output: If authentication is successful, the server generates a login token and sends it to the terminal. If it fails, an error message is sent.

[1548] Step 2:

[1549] Selection of learning area

[1550] Input: The user selects the subject they want to study (e.g., English, mathematics, science, etc.) using the interface on their smartphone.

[1551] Processing: The terminal sends the selected information to the server. The server saves the received information to its database.

[1552] Output: The results of the selected learning area will be displayed on the device as a confirmation message.

[1553] Step 3:

[1554] Generating past exam questions

[1555] Input: The selected learning area information is sent to the server.

[1556] Processing: The server calls a generator to retrieve past questions and question pattern data for the relevant field from the database. It then generates questions using an AI model (TensorFlow, scikit-learn).

[1557] Output: The generated problem is sent to the terminal via the server.

[1558] Step 4:

[1559] Providing a question and answer format

[1560] Input: The generated question and answer format are sent to the device.

[1561] Processing: The terminal displays the question and answer format to the user and activates a timer function to set a time limit.

[1562] Output: The user begins answering the displayed questions.

[1563] Step 5:

[1564] User's answer

[1565] Input: The user enters their answer to the question into the terminal.

[1566] Processing: The terminal sends the entered answer to the server.

[1567] Output: The answer data reaches the server.

[1568] Step 6:

[1569] Evaluation and feedback on the answers

[1570] Input: User answer data and generated problem data reside on the server.

[1571] Processing: The server uses an approval device to evaluate answers in real time. An AI model is applied to determine whether the answer is correct or not. Evaluation results and detailed explanations are generated, and feedback data is created.

[1572] Output: The generated feedback is sent to the user's terminal via the server.

[1573] Step 7:

[1574] Managing learning progress

[1575] Input: The user's learning history and current performance data are stored in the database.

[1576] Processing: The server processes data to visualize the user's learning progress and generates it as graphs and charts. It also provides efficient review suggestions based on past answer history.

[1577] Output: Learning progress and suggestions are displayed on the user's device.

[1578] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1579] The system of the present invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component and the program processing are described below in natural language.

[1580] 1. User Login

[1581] Function Description

[1582] This is a mechanism for users to log in to the system using their user terminal.

[1583] Processing flow

[1584] The user enters their ID and password into their user terminal.

[1585] The user terminal sends the entered authentication information to the server.

[1586] The server compares the received authentication information with the database and returns the authentication result to the user's terminal.

[1587] If authentication is successful, the server generates a login token and sends it to the user's device.

[1588] 2. Selection of learning area

[1589] Function Description

[1590] This is a system that allows users to select a specific field they want to learn about.

[1591] Processing flow

[1592] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through an interface on their device.

[1593] The user terminal sends information about the selected field to the server.

[1594] 3. Generating past exam questions

[1595] Function Description

[1596] This system generates questions for users to learn from, based on past question patterns.

[1597] Processing flow

[1598] The server invokes the generator based on information about the learning area selected by the user.

[1599] The generation device retrieves relevant data from a database containing past question patterns and generates questions.

[1600] The generation device creates a question format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[1601] 4. Utilizing the Emotion Engine

[1602] Function Description

[1603] The emotion engine is used to recognize the user's emotions and adjust the content of questions and feedback accordingly.

[1604] Processing flow

[1605] The user terminal collects emotional data through the user's facial expressions and voice.

[1606] The emotion engine analyzes this emotion data to recognize the user's current emotions.

[1607] The recognized emotion information is transmitted to the generator and approval device via the server.

[1608] The generator adjusts the question content based on the recognized emotions.

[1609] The approval device adaptively modifies the content of the feedback based on emotional information.

[1610] 5. Provision of question and answer format

[1611] Function Description

[1612] This is a mechanism for presenting generated problems to users and receiving their answers.

[1613] Processing flow

[1614] The server sends the generated questions and answer formats to the user's terminal.

[1615] The user terminal displays the question and answer format to the user.

[1616] 6. User's response

[1617] Function Description

[1618] This is a system that allows users to input their answers to questions and send those answers to the server.

[1619] Processing flow

[1620] The user enters their answer to the question displayed on their terminal.

[1621] The user terminal sends the entered answer to the server.

[1622] 7. Evaluation and feedback on the answers

[1623] Function Description

[1624] This system evaluates user answers and provides evaluation results and explanations.

[1625] Processing flow

[1626] The server sends the received user's answer to the approval device.

[1627] The approval device evaluates the answer and determines whether it is correct or incorrect.

[1628] The approval device generates the correct answer and explanation and sends them to the server.

[1629] The server sends the generated feedback to the user's terminal.

[1630] The user terminal displays feedback to the user.

[1631] Specific example

[1632] Example: When student A, a third-year high school student, is working on an English grammar problem.

[1633] 1. User Login:

[1634] Person A launches the app on their smartphone (user device) and enters their ID and password.

[1635] The server verifies the authentication information and approves the login.

[1636] 2. Selection of study area:

[1637] Person A chooses "English".

[1638] The user terminal sends the selection information to the server.

[1639] 3. Generating past exam questions:

[1640] The server calls the generator and retrieves past exam data for the English section.

[1641] The generator produces multiple-choice grammar questions and sends them to the server.

[1642] 4. Utilizing the Emotion Engine:

[1643] The user terminal collects emotional data from person A's facial expressions and voice.

[1644] The emotion engine analyzes emotional data and recognizes person A's emotions.

[1645] The recognized emotion information is transmitted to the generation device and the approval device.

[1646] The generator adjusts the question content based on emotional information.

[1647] The approval device adjusts the feedback content.

[1648] 5. Providing question and answer formats:

[1649] The server sends the generated problem to the user's terminal.

[1650] The user terminal displays the question and answer format to person A.

[1651] 6. User's answer:

[1652] Person A enters their answer and sends it to the server via their user terminal.

[1653] 7. Evaluation and feedback on the answers:

[1654] The server uses an approval device to evaluate the answers and generate feedback.

[1655] The feedback is sent to the user's device and displayed to person A.

[1656] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[1657] The following describes the processing flow.

[1658] 1. User Login

[1659] Step 1:

[1660] The user enters their ID and password into their user terminal.

[1661] Step 2:

[1662] The user terminal sends the entered authentication information to the server.

[1663] Step 3:

[1664] The server compares the received authentication information with the database.

[1665] Step 4:

[1666] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[1667] 2. Selection of learning area

[1668] Step 1:

[1669] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[1670] Step 2:

[1671] The user terminal sends information about the selected field to the server.

[1672] 3. Generating past exam questions

[1673] Step 1:

[1674] The server invokes the generator based on information about the learning area selected by the user.

[1675] Step 2:

[1676] The generation device retrieves relevant data from a database containing past question patterns.

[1677] Step 3:

[1678] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[1679] 4. Utilizing the Emotion Engine

[1680] Step 1:

[1681] The user terminal collects emotional data through the user's facial expressions and voice.

[1682] Step 2:

[1683] The emotion engine analyzes collected emotion data to recognize the user's current emotions.

[1684] Step 3:

[1685] The recognized emotion information is transmitted via the server to the generation device and the approval device.

[1686] Step 4:

[1687] The generator adjusts the question content based on the recognized emotions.

[1688] Step 5:

[1689] The approval device adaptively modifies the feedback content based on emotional information.

[1690] 5. Provision of question and answer format

[1691] Step 1:

[1692] The server sends the generated questions and answer formats to the user's terminal.

[1693] Step 2:

[1694] The user terminal displays the question and answer format to the user.

[1695] 6. User's response

[1696] Step 1:

[1697] The user enters their answer to the question displayed on their terminal.

[1698] Step 2:

[1699] The user terminal sends the entered answer to the server.

[1700] 7. Evaluation and feedback on the answers

[1701] Step 1:

[1702] The server sends the received user's answer to the approval device.

[1703] Step 2:

[1704] The approval device evaluates the answer and determines whether it is correct or incorrect.

[1705] Step 3:

[1706] The approval device generates the correct answer and explanation and sends them to the server.

[1707] Step 4:

[1708] The server sends the generated feedback to the user's terminal.

[1709] Step 5:

[1710] The user terminal displays feedback to the user.

[1711] Specific example

[1712] Example: When student A, a third-year high school student, is working on an English grammar problem.

[1713] 1. User Login:

[1714] Step 1:

[1715] Person A launches the app on their smartphone (user device) and enters their ID and password.

[1716] Step 2:

[1717] The user terminal sends the entered authentication information to the server.

[1718] Step 3:

[1719] The server verifies the authentication information and approves the login.

[1720] 2. Selection of study area:

[1721] Step 1:

[1722] Person A chooses "English".

[1723] Step 2:

[1724] The user terminal sends the selection information to the server.

[1725] 3. Generating past exam questions:

[1726] Step 1:

[1727] The server calls the generator and retrieves past exam data for the English section.

[1728] Step 2:

[1729] The generator produces multiple-choice grammar questions and sends them to the server.

[1730] 4. Utilizing the Emotion Engine:

[1731] Step 1:

[1732] The user terminal collects emotional data from person A's facial expressions and voice.

[1733] Step 2:

[1734] The emotion engine analyzes emotional data and recognizes person A's emotions.

[1735] Step 3:

[1736] The recognized emotion information is transmitted to the generator and approval device via the server.

[1737] Step 4:

[1738] The generator adjusts the question content based on emotional information.

[1739] Step 5:

[1740] The approval device adjusts the feedback content.

[1741] 5. Providing question and answer formats:

[1742] Step 1:

[1743] The server sends the generated problem to the user's terminal.

[1744] Step 2:

[1745] The user terminal displays the question and answer format to person A.

[1746] 6. User's answer:

[1747] Step 1:

[1748] Person A enters their answer and sends it to the server via their user terminal.

[1749] 7. Evaluation and feedback on the answers:

[1750] Step 1:

[1751] The server uses an approval device to evaluate the answers and generate feedback.

[1752] Step 2:

[1753] The feedback is sent to the user's device and displayed to person A.

[1754] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[1755] (Example 2)

[1756] Next, we will describe Example 2. 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."

[1757] Traditional learning systems often present problems without considering the user's emotional state, which can lead to user stress and reduced learning effectiveness. Furthermore, a lack of specialized problem formats tailored to specific learning areas is a problem, failing to adequately meet users' learning needs. Additionally, the lack of complete security guarantees for user authentication information is also a concern.

[1758] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1759] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, means including an emotion engine that analyzes emotion data collected from the user terminal and adjusts the difficulty level of the questions and the content of the feedback based on the user's emotional state, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This makes it possible to provide a personalized learning experience based on the user's emotional state and maximize learning effectiveness. Furthermore, by providing question formats specialized for learning fields, it is possible to flexibly respond to the user's learning needs.

[1760] A "generator" is a device that analyzes past question patterns and generates questions that are suitable for the user.

[1761] A "user terminal" refers to a device used by a user to input information or answer questions, and includes smartphones, tablets, and computers.

[1762] An "emotion engine" is a device that analyzes the user's facial expressions and voice, recognizes their emotional state, and adjusts the difficulty level of the problem and the content of the feedback accordingly.

[1763] An "approval device" is a device that evaluates a user's answer and generates the answer result and explanation.

[1764] "Past question patterns" refers to data and information that shows past questions, their trends, and their format.

[1765] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[1766] "Answer evaluation" is the process of determining whether the answers entered by users are correct or incorrect, and then conducting an evaluation based on those results.

[1767] "Feedback content" refers to information such as evaluation results and explanations for the user's answers, and includes learning advice and guidance provided to the user.

[1768] "Question format" refers to the type of question format specific to each learning area, and includes multiple-choice questions and fill-in-the-blank questions.

[1769] "User emotional state" refers to the emotional states a user experiences while learning, and includes, for example, joy, sadness, stress, and excitement.

[1770] The system of this invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component is described below.

[1771] 1. User Login

[1772] Users log in to the system using their user terminal. Specifically, the user enters their ID and password into the user terminal, and the user terminal sends this authentication information to the server. The server compares the received authentication information with the database, and if authentication is successful, generates a login token and sends it to the user terminal. The user terminal stores this token and uses it for subsequent communications.

[1773] 2. Selection of learning area

[1774] The user selects the subject they wish to study through an interface on their device. The user device sends information about the selected subject to the server. The server receives this information and holds it for the next step.

[1775] 3. Generating past exam questions

[1776] The server invokes a generator based on the learning area selected by the user. The generator retrieves relevant data from a database containing past question patterns and generates questions. The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank) and sends it to the server.

[1777] 4. Utilizing the Emotion Engine

[1778] The user terminal collects emotional data through the user's facial expressions and voice. This data is analyzed using a TensorFlow model within the terminal to recognize the user's emotional state. The emotion engine transmits the recognized emotional information to the generator and approval device via the server. The generator adjusts the question content based on the emotional information, and the approval device adaptively changes the feedback content based on the emotional information.

[1779] 5. Provision of question and answer format

[1780] The server sends the generated questions and answer formats to the user's terminal. The user's terminal displays this to the user, assisting them in working on the questions.

[1781] 6. User's response

[1782] The user enters their answer to the question displayed on their terminal. The user terminal sends the entered answer to the server. The server receives this answer and sends it to the approval device.

[1783] 7. Evaluation and feedback on the answers

[1784] The approval device evaluates the user's answer and determines whether it is correct or incorrect. Furthermore, the approval device generates the correct answer and explanation, and sends it to the server. The server sends the generated feedback to the user's terminal, which displays it to the user. The user can then progress in their learning through this feedback.

[1785] Specific example

[1786] For example, consider a high school senior, A, working on English grammar problems. A logs into the system using their smartphone and selects English as their learning subject. The server analyzes past question patterns based on this information and generates appropriate grammar problems. The user terminal collects emotional data from A's facial expressions and voice, and the emotion engine performs analysis. As a result, if A is experiencing stress, adjustments are made, such as lowering the difficulty of the problems. After the problems are displayed, A enters their answers, which are sent to the server. An approval device evaluates the answers, and feedback is provided to A. Through this process, A can enjoy a personalized learning experience.

[1787] Example of a prompt

[1788] Prompt example:

[1789] Assume that student A, a third-year high school student, is using a learning system to study English. The system proceeds through seven steps: user login, selection of learning area, generation of past questions, use of an emotion engine, provision of questions and answers, user response, and evaluation and feedback of the response. Considering that student A is working on grammar questions, please describe the sequence of processing steps and their flow in detail.

[1790] In this way, the present invention takes into account the user's emotional state and provides a more effective and personalized learning experience.

[1791] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1792] Step 1:

[1793] The user enters their ID and password into their user terminal.

[1794] Input: ID, Password

[1795] The user terminal sends the entered authentication information to the server.

[1796] Input: Authentication information sent from the user terminal

[1797] Specific operation: The user terminal sends data to the server using SSL encrypted communication.

[1798] Step 2:

[1799] The server compares the received authentication information with the database.

[1800] Input: Authentication information, user data in the database

[1801] Data processing: Compare and verify user data in the database with received authentication information.

[1802] Output: Authentication result (success / failure)

[1803] Specific operation: The server uses a MySQL database to verify user information.

[1804] Step 3:

[1805] The server returns the authentication result to the user's terminal.

[1806] Input: Authentication result

[1807] Output: Authentication result (success / failure)

[1808] Specific operation: The server generates a JWT (JSON Web Token) and, if successful, returns this token to the user's terminal. The token is used for subsequent communication.

[1809] Step 4:

[1810] Users select the subject they wish to study through an interface on their device.

[1811] Input: Selection information for learning field

[1812] The user terminal sends information about the selected field to the server.

[1813] Input: Selection information for learning field

[1814] Specific operation: The user taps the "Select Learning Area" option from the app's menu and selects an area from the displayed list. The user's device sends the selection information to the server using HTTPS communication.

[1815] Step 5:

[1816] The server invokes the generator based on information about the learning area selected by the user.

[1817] Input: Information on the field of study

[1818] The server retrieves relevant data from a database containing past question patterns.

[1819] Input: Database of past exam patterns, information on learning areas.

[1820] Data processing: Filter past question patterns related to the learning area and select appropriate questions.

[1821] Output: Selected problems

[1822] Specific operation: The server uses a Python API to call a microservice for problem generation and retrieve the relevant data.

[1823] Step 6:

[1824] The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[1825] Input: Past exam data

[1826] Data processing: Generate questions while considering the user's learning history and difficulty settings.

[1827] Output: Formatted problem

[1828] Specific operation: The generation algorithm uses AI to analyze data and generate an optimal set of questions. The generated set of questions is formatted as multiple-choice questions and returned to the server.

[1829] Step 7:

[1830] The user terminal collects emotional data through the user's facial expressions and voice.

[1831] Input: User facial expression data, voice data

[1832] Data processing: Use an emotion engine to analyze emotional data and recognize the user's current emotions.

[1833] Output: Recognized emotion information

[1834] Specific operation: The device's camera and microphone are used to collect data, which is then analyzed using a TensorFlow model.

[1835] Step 8:

[1836] The emotion engine analyzes this emotion data to recognize the user's current emotions.

[1837] Input: Sentiment data

[1838] Data processing: Analyze data using emotion analysis algorithms to determine emotional states.

[1839] Output: Emotional state (e.g., stress, joy, etc.)

[1840] Specific operation: The analysis results are processed as digital signals, and recognized emotional information is generated.

[1841] Step 9:

[1842] The recognized emotion information is transmitted to the generator and approval device via the server.

[1843] Input: Recognized emotion information

[1844] Output: Adjusted question content and feedback

[1845] Specific operation: The analysis results of the emotion engine are transmitted to the generation device and the approval device via the server.

[1846] Step 10:

[1847] The generator adjusts the question content based on the recognized emotions.

[1848] Input: Sentimental information, generated problem

[1849] Data processing: Adjust the difficulty and format of problems based on emotional information.

[1850] Output: Adjusted problem

[1851] Specific action: For example, if the user is feeling stressed, the generator will lower the difficulty level of the problem.

[1852] Step 11:

[1853] The approval device adaptively modifies the content of the feedback based on emotional information.

[1854] Input: Sentimental information, evaluation results

[1855] Data processing: Adaptively modifying feedback content based on emotional information.

[1856] Output: Personalized feedback

[1857] Specific actions: For example, if the user is feeling stressed, generate a message such as "Let's calm down and get to work."

[1858] Step 12:

[1859] The server sends the generated questions and answer formats to the user's terminal.

[1860] Input: Formatted question, sentiment-adjusted content

[1861] Output: Problem data to be displayed on the user terminal

[1862] Specific operation: The server sends formatted problem data to the user's terminal via a REST API.

[1863] Step 13:

[1864] The user terminal displays the question and answer format to the user.

[1865] Input: Problem data

[1866] Output: Displayed problem

[1867] Specific operation: The user terminal uses a UI framework to display the problem on the screen.

[1868] Step 14:

[1869] The user enters their answer to the question displayed on their terminal.

[1870] Input: Answer Information

[1871] The user terminal sends the entered answer to the server.

[1872] Output: Answer data sent to the server

[1873] Specific operation: The user taps an option to enter their answer, and the user's device sends this to the server.

[1874] Step 15:

[1875] The server sends the received user's answer to the approval device.

[1876] Input: Answer data

[1877] Notice: Answer data (to be sent to the approval device)

[1878] Specific operation: The server sends the answer data to the approval device's API.

[1879] Step 16:

[1880] The approval device evaluates the answer and determines whether it is correct or incorrect. Furthermore, it generates the correct answer and an explanation.

[1881] Input: Answer data

[1882] Data processing: Evaluate the answers and generate results and explanations.

[1883] Output: Evaluation results and explanation

[1884] Specific operation: The approval device uses a machine learning model to evaluate the answer and generate accurate feedback.

[1885] Step 17:

[1886] The server sends the generated feedback to the user's terminal.

[1887] Input: Evaluation results and explanation

[1888] Output: Feedback displayed on the user's terminal

[1889] Specific operation: The server sends the generated feedback data to the user's terminal, where it is displayed.

[1890] This allows users to receive detailed feedback and progress in their learning.

[1891] (Application Example 2)

[1892] Next, we will explain application example 2. In the following explanation, 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."

[1893] Conventional learning and work support systems provide uniform problems and work instructions without considering the user's emotional state, leading to user stress and decreased work efficiency. Furthermore, a lack of individualized feedback made it difficult to provide optimal learning and work environments and to reduce user motivation.

[1894] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1895] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. Furthermore, it includes an emotion engine that collects and analyzes user emotion data, means for the generating device and approval device to adjust the question content and feedback based on the emotion information, and user interface means for providing real-time work support to workers. This enables personalized learning and work support that takes the user's emotional state into account, thereby reducing stress and improving work efficiency.

[1896] A "problem generator" is a device that analyzes past question patterns and generates questions tailored to the user.

[1897] A "user terminal" is a device used by users to access a server and communicate with it to send and receive various types of information.

[1898] An "approval device" is a device that evaluates the answers received from users and generates the results and explanations.

[1899] An "emotion engine" is a device that recognizes a user's emotional state by collecting and analyzing their emotional data.

[1900] A "server" is a central device that manages and processes data for the entire system.

[1901] A "user interface means" is a means of providing an interface for a user to interact with a system.

[1902] "Real-time work support" refers to providing instant instructions and feedback to users while they are performing their tasks.

[1903] "Feedback" refers to evaluations and advice provided based on the user's work and learning results.

[1904] The present invention is a system that includes a generation device that analyzes past question patterns and generates suitable questions, means for transmitting the generated questions to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates the results and explanations. The system also includes an emotion engine that collects and analyzes user emotion data. Furthermore, it is equipped with a user interface means for providing real-time work support to workers. Specific embodiments for carrying out the present invention are shown below.

[1905] Hardware and software configuration

[1906] 1. Hardware Configuration

[1907] User device: Smart glasses (e.g., Google Glass)

[1908] Server: A computer system that manages and processes data.

[1909] Emotion engine: A device (e.g., camera, microphone) that collects and analyzes user emotional data.

[1910] 2. Software Configuration

[1911] Server applications (e.g., Python-based applications)

[1912] Emotion recognition engine (e.g., Microsoft Azure's Emotion API)

[1913] Data processing and data calculation workflow

[1914] The server generates a problem suitable for the user based on data from the generation device and sends it to the user terminal. The user terminal displays the problem and sends the user's answer back to the server. Based on this, the approval device evaluates the answer and generates the answer result and explanation.

[1915] Furthermore, the emotion engine collects and analyzes the user's emotional data (facial expressions and voice). Based on this, the generation device generates problems optimized for the user's emotional state, and the approval device adjusts the feedback accordingly. Finally, the user interface provides real-time work support, improving the user's work efficiency and motivation.

[1916] Specific example

[1917] As a concrete example, consider a case where person B is engaged in parts assembly work at a factory. Person B puts on smart glasses and logs into the system. The server verifies Person B's authentication information and approves the login. Next, Person B selects "parts assembly work" and performs the task according to the instructions displayed on the smart glasses.

[1918] In this process, the emotion engine collects and analyzes emotional data from B's facial expressions and voice. If the system detects that B is experiencing stress, it adjusts the work content and assigns simpler tasks to reduce stress.

[1919] Examples of prompts to input into a generative AI model are as follows:

[1920] "For HR professionals, please provide an overview of a factory worker status monitoring system using emotion recognition, and the technologies that support it. Include the following keywords: smart glasses, emotion engine, real-time feedback, and improved worker efficiency."

[1921] As described above, this system allows for real-time monitoring of the user's emotional state and provides optimal work instructions and feedback, thereby improving work efficiency and reducing stress.

[1922] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1923] Step 1:

[1924] The user's terminal sends the user's entered ID and password to the server.

[1925] Input: The user enters their ID and password into the smart glasses.

[1926] Data processing: The server compares the received authentication information with the database.

[1927] Output: Generate a login token as an authentication result and send it back to the user's device.

[1928] Step 2:

[1929] The user selects a learning area or work area.

[1930] Input: The user selects a task, such as "parts assembly work," using smart glasses.

[1931] Data processing: The user terminal sends information from the selected field to the server.

[1932] Output: The server uses that information to call the generator.

[1933] Step 3:

[1934] The server generates tasks using a generator.

[1935] Input: Information about the selected learning / work area.

[1936] Data processing: The generation device analyzes past data and generates suitable tasks.

[1937] Output: Sends the generated task information to the user's terminal.

[1938] Step 4:

[1939] The emotion engine collects and analyzes user emotion data.

[1940] Input: User facial expression data and voice data.

[1941] Data processing: The emotion engine analyzes this data to recognize the user's emotional state.

[1942] Output: Sends recognized emotion information to the server.

[1943] Step 5:

[1944] The server adjusts the question content and work instructions based on the generated tasks and sentiment information.

[1945] Input: Generated task information and recognized emotion information.

[1946] Data processing: Generators and approval devices adjust task content and feedback based on emotional information.

[1947] Output: Sends adjusted tasks and feedback information to the user's terminal.

[1948] Step 6:

[1949] The user terminal displays the adjusted tasks to the user.

[1950] Input: Coordinated task information sent from the server.

[1951] Data processing: Converts the information into a format necessary for the user's terminal to display it.

[1952] Output: The adjusted task is displayed on the user's glasses screen.

[1953] Step 7:

[1954] The user performs tasks according to the instructions and enters their progress into their terminal.

[1955] Input: The user enters the progress of the task into the user terminal.

[1956] Data processing: The user terminal sends the entered progress data to the server.

[1957] Output: Progress data is sent to the server and recorded.

[1958] Step 8:

[1959] The server evaluates the progress data using an approval device and generates feedback.

[1960] Input: User progress data.

[1961] Data processing: The approval device evaluates the progress data and generates feedback.

[1962] Output: Sends the generated feedback to the user's terminal.

[1963] Step 9:

[1964] The user's device displays feedback to the user.

[1965] Input: Feedback information sent from the server.

[1966] Data processing: Convert the data into a format that the user terminal can use to display feedback information.

[1967] Output: Feedback is displayed on the user's glasses screen.

[1968] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1969] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1970] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1971] [Fourth Embodiment]

[1972] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1973] As shown in Figure 7, the 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.

[1974] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1975] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1976] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1977] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1978] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1979] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1980] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1981] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1982] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1983] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1984] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1985] The system for carrying out the present invention has a configuration including a server, a user terminal, a generation device, and an approval device. The specific operation of each component and the program processing are described below in natural language.

[1986] 1. User Login

[1987] Function Description

[1988] This is a mechanism for users to log in to the system using their user terminal.

[1989] Processing flow

[1990] The user enters their ID and password into their user terminal.

[1991] The user terminal sends the entered authentication information to the server.

[1992] The server compares the received authentication information with the database and returns the authentication result to the user's terminal.

[1993] If authentication is successful, the server generates a login token and sends it to the user's device.

[1994] 2. Selection of learning area

[1995] Function Description

[1996] This is a system that allows users to select a specific field they want to learn about.

[1997] Processing flow

[1998] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through an interface on their device.

[1999] The user terminal sends information about the selected field to the server.

[2000] 3. Generating past exam questions

[2001] Function Description

[2002] This system generates questions for users to learn from, based on past question patterns.

[2003] Processing flow

[2004] The server invokes the generator based on information about the learning area selected by the user.

[2005] The generation device retrieves relevant data from a database containing past question patterns and generates questions.

[2006] The generation device creates a question format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[2007] 4. Provision of question and answer format

[2008] Function Description

[2009] This is a mechanism for presenting generated problems to users and receiving their answers.

[2010] Processing flow

[2011] The server sends the generated questions and answer formats to the user's terminal.

[2012] The user terminal displays the question and answer format to the user.

[2013] 5. User's response

[2014] Function Description

[2015] This is a system that allows users to input their answers to questions and send those answers to the server.

[2016] Processing flow

[2017] The user enters their answer to the question displayed on their terminal.

[2018] The user terminal sends the entered answer to the server.

[2019] 6. Evaluation and Feedback of Answers

[2020] Function Description

[2021] This system evaluates user answers and provides evaluation results and explanations.

[2022] Processing flow

[2023] The server sends the received user's answer to the approval device.

[2024] The approval device evaluates the answer and determines whether it is correct or incorrect.

[2025] The approval device generates the correct answer and explanation and sends them to the server.

[2026] The server sends the generated feedback to the user's terminal.

[2027] The user terminal displays feedback to the user.

[2028] Specific example

[2029] Example: When student A, a third-year high school student, is working on an English grammar problem.

[2030] 1. User Login:

[2031] Person A launches the app on their smartphone (user device) and enters their ID and password.

[2032] The server verifies the authentication information and approves the login.

[2033] 2. Selection of study area:

[2034] Person A chooses "English".

[2035] The user terminal sends the selection information to the server.

[2036] 3. Generating past exam questions:

[2037] The server calls the generator and retrieves past exam data for the English section.

[2038] The generator produces multiple-choice grammar questions and sends them to the server.

[2039] 4. Providing the format of questions and answers:

[2040] The server sends the generated problem to the user's terminal.

[2041] The user terminal displays the question and answer format to person A.

[2042] 5. User's answer:

[2043] Person A enters their answer and sends it to the server via their user terminal.

[2044] 6. Evaluation and feedback on the answers:

[2045] The server uses an approval device to evaluate the answers and generate feedback.

[2046] The feedback is sent to the user's device and displayed to person A.

[2047] In this way, this system makes it possible to learn efficiently while reducing the financial burden.

[2048] The following describes the processing flow.

[2049] Program processing flow

[2050] 1. User Login

[2051] Step 1:

[2052] The user enters their ID and password into their user terminal.

[2053] Step 2:

[2054] The user terminal sends the entered authentication information to the server.

[2055] Step 3:

[2056] The server compares the received authentication information with the database.

[2057] Step 4:

[2058] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[2059] 2. Selection of learning area

[2060] Step 1:

[2061] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[2062] Step 2:

[2063] The user terminal sends information about the selected field to the server.

[2064] 3. Generating past exam questions

[2065] Step 1:

[2066] The server invokes the generator based on information about the learning area selected by the user.

[2067] Step 2:

[2068] The generation device retrieves relevant data from a database containing past question patterns.

[2069] Step 3:

[2070] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[2071] 4. Provision of question and answer format

[2072] Step 1:

[2073] The server sends the generated questions and answer formats to the user's terminal.

[2074] Step 2:

[2075] The user terminal displays the question and answer format to the user.

[2076] 5. User's response

[2077] Step 1:

[2078] The user enters their answer to the question displayed on their terminal.

[2079] Step 2:

[2080] The user terminal sends the entered answer to the server.

[2081] 6. Evaluation and Feedback of Answers

[2082] Step 1:

[2083] The server sends the received user's answer to the approval device.

[2084] Step 2:

[2085] The approval device evaluates the answer and determines whether it is correct or incorrect.

[2086] Step 3:

[2087] The approval device generates the correct answer and explanation and sends them to the server.

[2088] Step 4:

[2089] The server sends the generated feedback to the user's terminal.

[2090] Step 5:

[2091] The user terminal displays feedback to the user.

[2092] (Example 1)

[2093] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2094] Conventional learning support systems have struggled to generate problems tailored to each user's learning area and to efficiently evaluate answers, hindering efficient user learning. Furthermore, there were challenges in managing authentication information and ensuring security. As a result, it was difficult for users to learn safely and effectively. To solve these problems, the objective of the present invention is to provide a system that enables users to learn safely and efficiently.

[2095] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[2096] In this invention, the server includes means for a user to input authentication information for logging into the system using a user terminal, and for the user terminal to transmit the authentication information to the server; means for the server to compare the received authentication information with a database and return the authentication result to the user terminal; means for the server to generate a login token and transmit it to the user terminal if authentication is successful; means for the user to select a learning field through the interface of the user terminal and for the user terminal to transmit information of the selected field to the server; means for the server to call a generation device based on the learning field information received, for the generation device to analyze past question patterns and generate suitable questions; means for the generation device to transmit the generated questions to the server, for the server to transmit the generated questions to the user terminal; means for the user to input an answer to a question displayed on the user terminal and for the user terminal to transmit the input answer to the server; means for the server to transmit the received answer to an approval device, for the approval device to evaluate the answer and generate an answer result and explanation; and means for the server to transmit the generated answer result and explanation to the user terminal and display feedback to the user. This enables the user to proceed with learning safely and efficiently.

[2097] A "user terminal" is a device used by a user to select learning areas and input answers to questions through an interface. This includes computers, smartphones, tablets, and other similar devices.

[2098] A "server" is a central device that receives authentication information and learning area selection information sent from the user terminal, performs verification with the database, generates login tokens, distributes questions, and generates answer results and explanations.

[2099] "Authentication information" refers to information such as the ID and password that a user uses to log in to a system.

[2100] A "database" is a collection of information that systematically stores data such as user authentication information, past question patterns, generated questions, and answer results, and which a server references as needed.

[2101] A "login token" is data generated by the server for users who have successfully authenticated, and it contains session information that is valid for a certain period of time.

[2102] A "generator" is a device that analyzes past question patterns based on information about the learning area selected by the user and generates suitable questions.

[2103] An "approval device" is a device that evaluates user answers received from a server and generates answer results and explanations.

[2104] "Feedback" refers to information that includes the evaluation results of the user's answer and the explanations based on those evaluations.

[2105] An "interface" refers to the screens and input methods displayed on a user's device for selecting learning areas or entering answers.

[2106] "Answer Results and Explanations" refers to the approval device's determination of whether the user's answer was correct or incorrect, along with an explanation related to that answer.

[2107] "Past question patterns" refers to information about the format and content of questions that have been asked in the past.

[2108] A "problem" is a question or assignment related to the learning area that is intended for the user to answer.

[2109] A "processing step" refers to a series of operations or procedures performed at each stage when a system is in operation.

[2110] The embodiments for carrying out the present invention will be described in detail below. This system has a configuration including a server, a user terminal, a generation device, and an approval device, thereby enabling the user to efficiently proceed with learning.

[2111] First, the user logs into the system using their device. This device can be a computer, smartphone, or tablet, and a login screen will appear on the screen. The user enters their ID and password to log in. The user device sends this authentication information to the server. The server uses a database such as MySQL or PostgreSQL to verify the received authentication information and returns the authentication result to the user device. If authentication is successful, the server generates a JWT (JSON Web Token) and sends this token to the user device to initiate the session.

[2112] Next, the user selects the subject they want to study through an interface on their device. This operation is implemented using front-end frameworks such as React or Vue.js. The user's device sends information about the selected subject to the server. Based on the received information, the server sends a request to a generator written in Python.

[2113] The generation device retrieves relevant data from a database containing past question patterns (e.g., MongoDB) and generates appropriate questions using a generation AI model (e.g., GPT). These questions are formatted into multiple-choice or fill-in-the-blank formats and sent to the server. The server sends the generated questions to the user's terminal, which then displays them to the user.

[2114] The user enters an answer to a question displayed on their terminal and sends the answer to the server. The server sends the received answer to an approval device that uses a machine learning algorithm (e.g., Scikit-learn or TensorFlow) to execute it. The approval device evaluates the answer and determines whether it is correct or incorrect. The approval device then generates the correct answer and explanation based on this evaluation and sends it to the server. The server sends this to the user terminal and displays it to the user as feedback.

[2115] As an example, consider a high school senior user working on English grammar problems. The user logs in to the app on their smartphone by entering their ID and password. Then, they select the learning field "English" and work on multiple-choice grammar problems generated based on past problem data. After entering their answers, an approval device evaluates the answers via the server, and the user receives feedback including the correct answer and an explanation.

[2116] An example of a prompt sentence to input into a generative AI model would be: "Please describe in natural language the process by which a high school senior user tackles English grammar problems, including the roles of the server, terminal, and user."

[2117] This mechanism helps the system to learn efficiently.

[2118] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2119] Step 1:

[2120] The user enters their ID and password on their device. Specifically, the user uses the keyboard on a device such as a smartphone or tablet to enter their ID and password on the login screen. The ID and password entered by the user are encrypted (e.g., AES encryption) and sent to the server as input data.

[2121] Step 2:

[2122] The user terminal sends the entered authentication information to the server. The user terminal uses an HTTP POST request to send encrypted ID and password to the server. The input data is decrypted on the server side and used for the next process.

[2123] Step 3:

[2124] The server compares the received authentication information with the database. The server compares the received ID and password with hashed data stored in the database (e.g., MySQL) to check for a match. It generates an accurate matching result and outputs it to the next process.

[2125] Step 4:

[2126] The server returns the authentication result to the user's terminal. If authentication is successful, the server generates a JWT (JSON Web Token) and returns this token to the user's terminal. If authentication fails, an error message is generated. The generated token or error message is output to the user's terminal.

[2127] Step 5:

[2128] If authentication is successful, the user's device will display a notification of successful authentication to the user. Specifically, the user's device will display a notification such as "Login successful" on the screen and start the user session. The session information will be used as a login token for the following processes.

[2129] Step 6:

[2130] The user selects a learning area. The user uses the interface on their device to choose a learning area (e.g., English, Mathematics). A selection screen implemented with a frontend framework such as React Native is used, and the selected information is sent to the server as input data.

[2131] Step 7:

[2132] The user terminal sends information about the selected learning area to the server. The user terminal uses an HTTP POST request to send the selected learning area information to the server. This information becomes input data for the next data processing step.

[2133] Step 8:

[2134] The server calls the generator based on the learning area information it receives. The server sends an HTTP request to the generator's API endpoint, which is written in Python, and passes the learning area information as input data. The generator then processes this data.

[2135] Step 9:

[2136] The generator analyzes past question patterns and generates suitable questions. The generator retrieves past question patterns from a database such as MongoDB and uses a generation AI model (e.g., GPT-3) to generate questions suitable for the user. The generated questions are sent to the server as output.

[2137] Step 10:

[2138] The server sends the generated problem to the user terminal. The server receives the problem from the generator and sends it to the user terminal as an HTTP response. The user terminal receives this and uses it for the next process.

[2139] Step 11:

[2140] The user's device displays the received problem to the user. The problem is displayed on a screen implemented with React or Vue.js, and the user enters their answer. The user's answer becomes the next input data.

[2141] Step 12:

[2142] The user enters the answer, and the user's terminal sends the entered answer to the server. The answer is sent encrypted via an HTTP POST request. The server receives this answer data and sends it to the next process.

[2143] Step 13:

[2144] The server sends the received answer to the approval device. It sends an HTTP request to the approval device to evaluate the answer. The approval device performs the evaluation using an AI model or machine learning algorithm (e.g., Scikit-learn).

[2145] Step 14:

[2146] The approval device evaluates the answer and generates the answer result and explanation. It determines whether the answer is correct or incorrect, generates an explanation based on the correct answer, and sends the result to the server as output.

[2147] Step 15:

[2148] The server sends the generated solution and explanation to the user's terminal. The server receives a feedback message and returns it to the user's terminal as an HTTP response. The user's terminal displays this response.

[2149] Step 16:

[2150] The user's device displays feedback to the user. Specifically, evaluation results and explanations are displayed on the screen, allowing the user to receive learning feedback by reviewing them.

[2151] In this way, the system can perform a series of processes to enable users to learn efficiently.

[2152] (Application Example 1)

[2153] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2154] Traditional learning support systems have problems that make it difficult for users to check their learning progress and learn efficiently. Furthermore, they lack real-time answer evaluation and immediate feedback functions, making it difficult for learning content to be effectively retained. In addition, they do not adequately generate problems tailored to the user's level or provide specialized problem formats for each learning subject. There is a need to solve these problems and realize more efficient and effective learning support.

[2155] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[2156] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This enables means for having a timer function to evaluate answers in real time and provide immediate feedback, means for visualizing the user's learning progress and saving past answer history to support review, and means for generating an unlimited number of questions based on past questions using an AI model when a learning field is selected.

[2157] A "generator" is a device that analyzes past question patterns and generates suitable questions.

[2158] A "user terminal" is a device (e.g., a smartphone) that a user uses to log in, select a learning area, and answer generated questions.

[2159] An "approval device" is a device that evaluates the answers received from the user terminal and generates the answer result and explanation.

[2160] The "timer function" is a feature that allows users to set a time limit when answering questions, helping them to concentrate on the problem.

[2161] A "means for evaluating answers in real time" refers to a method of immediately evaluating a user's answer and providing feedback as soon as the user submits it.

[2162] "Feedback" refers to the evaluation results and explanations that users receive after submitting an answer.

[2163] "Methods for visualizing learning progress" refer to methods that visually display the progress of learning, such as graphs, allowing users to grasp their learning status at a glance.

[2164] "A means of saving past answer history to support review" refers to a method of saving the user's previous answers in a database, allowing them to review their level of understanding and areas where they tend to make mistakes as needed.

[2165] An "AI model" is an artificial intelligence algorithm that automatically generates new questions based on past question patterns and the user's learning progress.

[2166] An embodiment of the present invention is configured as a system including a server, a user terminal, a generation device, and an approval device. This system allows users to learn efficiently and effectively.

[2167] 1. Hardware and software to be used

[2168] Hardware:

[2169] Smartphones (iPhone, Android devices)

[2170] Servers (AWS, Google Cloud, etc.)

[2171] software:

[2172] Backend: Node.js, Express

[2173] Frontend: React Native

[2174] Database: MongoDB

[2175] AI models: TensorFlow, scikit-learn

[2176] 2. Program Description

[2177] User Login

[2178] The server provides functionality for users to log in to the system using their smartphones. Users enter their ID and password on the smartphone app and send the authentication information to the server. The server compares this information with the user information stored in the database, and if authentication is successful, generates a login token and sends it to the user's device. This allows the user to access the system.

[2179] Selection of learning area

[2180] The server provides an interface for users to select a specific subject they wish to study. Users select their learning subject using their smartphone interface, and this information is sent to the server. This generates questions tailored to the user's chosen subject.

[2181] Generating past exam questions

[2182] The server invokes a generator based on information about the selected learning area. The generator retrieves past question patterns and the user's learning progress from a database and generates new questions using an AI model (TensorFlow or scikit-learn). The generated questions are sent to the server and provided to the user's terminal.

[2183] Providing a question and answer format

[2184] The server sends the generated questions and answer formats (multiple choice, fill-in-the-blank, etc.) to the user's terminal. The user's terminal displays the questions and answer formats to the user and uses a timer function to prompt the user to enter their answers within the time limit. This function allows the user to concentrate on the questions.

[2185] User answers and ratings

[2186] When a user submits an answer to a question, the answer is sent to the server. The server uses an approval device to evaluate the answer in real time and provides immediate feedback. This feedback includes an evaluation of the answer and a detailed explanation.

[2187] Managing learning progress

[2188] The server provides a function to visualize the user's learning progress and save past answer history. Users can check their progress as a graph on their smartphone, allowing them to grasp their self-study status at a glance. They can also review based on their past answer history.

[2189] Specific example

[2190] For example, if a high school senior user were to work on English grammar problems, the process would be as follows: The user launches the app on their smartphone, enters their ID and password to log in. Next, they select "English," and the appropriate problems are generated. The user enters their answers and receives immediate feedback. They can check their progress in a graph and review based on their past answer history.

[2191] Example of a prompt:

[2192] "A high school senior user is learning English grammar on their smartphone, following these steps: user login, selection of learning area, generation of past questions, provision of question and answer format, user submission, evaluation of answers, and feedback. Explain how AI-generated questions based on past question patterns can effectively provide feedback."

[2193] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2194] Step 1:

[2195] User Login

[2196] Input: The user enters their ID and password into the smartphone app.

[2197] Processing: Authentication information is sent from the terminal to the server via HTTPS. The server compares it with user data stored in the database (MongoDB) to determine whether authentication was successful.

[2198] Output: If authentication is successful, the server generates a login token and sends it to the terminal. If it fails, an error message is sent.

[2199] Step 2:

[2200] Selection of learning area

[2201] Input: The user selects the subject they want to study (e.g., English, mathematics, science, etc.) using the interface on their smartphone.

[2202] Processing: The terminal sends the selected information to the server. The server saves the received information to its database.

[2203] Output: The results of the selected learning area will be displayed on the device as a confirmation message.

[2204] Step 3:

[2205] Generating past exam questions

[2206] Input: The selected learning area information is sent to the server.

[2207] Processing: The server calls a generator to retrieve past questions and question pattern data for the relevant field from the database. It then generates questions using an AI model (TensorFlow, scikit-learn).

[2208] Output: The generated problem is sent to the terminal via the server.

[2209] Step 4:

[2210] Providing a question and answer format

[2211] Input: The generated question and answer format are sent to the device.

[2212] Processing: The terminal displays the question and answer format to the user and activates a timer function to set a time limit.

[2213] Output: The user begins answering the displayed questions.

[2214] Step 5:

[2215] User's answer

[2216] Input: The user enters their answer to the question into the terminal.

[2217] Processing: The terminal sends the entered answer to the server.

[2218] Output: The answer data reaches the server.

[2219] Step 6:

[2220] Evaluation and feedback on the answers

[2221] Input: User answer data and generated problem data reside on the server.

[2222] Processing: The server uses an approval device to evaluate answers in real time. An AI model is applied to determine whether the answer is correct or not. Evaluation results and detailed explanations are generated, and feedback data is created.

[2223] Output: The generated feedback is sent to the user's terminal via the server.

[2224] Step 7:

[2225] Managing learning progress

[2226] Input: The user's learning history and current performance data are stored in the database.

[2227] Processing: The server processes data to visualize the user's learning progress and generates it as graphs and charts. It also provides efficient review suggestions based on past answer history.

[2228] Output: Learning progress and suggestions are displayed on the user's device.

[2229] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[2230] The system of the present invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component and the program processing are described below in natural language.

[2231] 1. User Login

[2232] Function Description

[2233] This is a mechanism for users to log in to the system using their user terminal.

[2234] Processing flow

[2235] The user enters their ID and password into their user terminal.

[2236] The user terminal sends the entered authentication information to the server.

[2237] The server compares the received authentication information with the database and returns the authentication result to the user's terminal.

[2238] If authentication is successful, the server generates a login token and sends it to the user's device.

[2239] 2. Selection of learning area

[2240] Function Description

[2241] This is a system that allows users to select a specific field they want to learn about.

[2242] Processing flow

[2243] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through an interface on their device.

[2244] The user terminal sends information about the selected field to the server.

[2245] 3. Generating past exam questions

[2246] Function Description

[2247] This system generates questions for users to learn from, based on past question patterns.

[2248] Processing flow

[2249] The server invokes the generator based on information about the learning area selected by the user.

[2250] The generation device retrieves relevant data from a database containing past question patterns and generates questions.

[2251] The generation device creates a question format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[2252] 4. Utilizing the Emotion Engine

[2253] Function Description

[2254] The emotion engine is used to recognize the user's emotions and adjust the content of questions and feedback accordingly.

[2255] Processing flow

[2256] The user terminal collects emotional data through the user's facial expressions and voice.

[2257] The emotion engine analyzes this emotion data to recognize the user's current emotions.

[2258] The recognized emotion information is transmitted to the generator and approval device via the server.

[2259] The generator adjusts the question content based on the recognized emotions.

[2260] The approval device adaptively modifies the content of the feedback based on emotional information.

[2261] 5. Provision of question and answer format

[2262] Function Description

[2263] This is a mechanism for presenting generated problems to users and receiving their answers.

[2264] Processing flow

[2265] The server sends the generated questions and answer formats to the user's terminal.

[2266] The user terminal displays the question and answer format to the user.

[2267] 6. User's response

[2268] Function Description

[2269] This is a system that allows users to input their answers to questions and send those answers to the server.

[2270] Processing flow

[2271] The user enters their answer to the question displayed on their terminal.

[2272] The user terminal sends the entered answer to the server.

[2273] 7. Evaluation and feedback on the answers

[2274] Function Description

[2275] This system evaluates user answers and provides evaluation results and explanations.

[2276] Processing flow

[2277] The server sends the received user's answer to the approval device.

[2278] The approval device evaluates the answer and determines whether it is correct or incorrect.

[2279] The approval device generates the correct answer and explanation and sends them to the server.

[2280] The server sends the generated feedback to the user's terminal.

[2281] The user terminal displays feedback to the user.

[2282] Specific example

[2283] Example: When student A, a third-year high school student, is working on an English grammar problem.

[2284] 1. User Login:

[2285] Person A launches the app on their smartphone (user device) and enters their ID and password.

[2286] The server verifies the authentication information and approves the login.

[2287] 2. Selection of study area:

[2288] Person A chooses "English".

[2289] The user terminal sends the selection information to the server.

[2290] 3. Generating past exam questions:

[2291] The server calls the generator and retrieves past exam data for the English section.

[2292] The generator produces multiple-choice grammar questions and sends them to the server.

[2293] 4. Utilizing the Emotion Engine:

[2294] The user terminal collects emotional data from person A's facial expressions and voice.

[2295] The emotion engine analyzes emotional data and recognizes person A's emotions.

[2296] The recognized emotion information is transmitted to the generation device and the approval device.

[2297] The generator adjusts the question content based on emotional information.

[2298] The approval device adjusts the feedback content.

[2299] 5. Providing question and answer formats:

[2300] The server sends the generated problem to the user's terminal.

[2301] The user terminal displays the question and answer format to person A.

[2302] 6. User's answer:

[2303] Person A enters their answer and sends it to the server via their user terminal.

[2304] 7. Evaluation and feedback on the answers:

[2305] The server uses an approval device to evaluate the answers and generate feedback.

[2306] The feedback is sent to the user's device and displayed to person A.

[2307] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[2308] The following describes the processing flow.

[2309] 1. User Login

[2310] Step 1:

[2311] The user enters their ID and password into their user terminal.

[2312] Step 2:

[2313] The user terminal sends the entered authentication information to the server.

[2314] Step 3:

[2315] The server compares the received authentication information with the database.

[2316] Step 4:

[2317] The server returns the authentication result to the user's terminal. If authentication is successful, a login token is generated and sent to the user's terminal.

[2318] 2. Selection of learning area

[2319] Step 1:

[2320] The user selects the subject they want to study (e.g., English, mathematics, science, etc.) through the interface on their device.

[2321] Step 2:

[2322] The user terminal sends information about the selected field to the server.

[2323] 3. Generating past exam questions

[2324] Step 1:

[2325] The server invokes the generator based on information about the learning area selected by the user.

[2326] Step 2:

[2327] The generation device retrieves relevant data from a database containing past question patterns.

[2328] Step 3:

[2329] The generation device generates questions in a format suitable for the user (multiple choice, fill-in-the-blank, etc.) and sends them to the server.

[2330] 4. Utilizing the Emotion Engine

[2331] Step 1:

[2332] The user terminal collects emotional data through the user's facial expressions and voice.

[2333] Step 2:

[2334] The emotion engine analyzes collected emotion data to recognize the user's current emotions.

[2335] Step 3:

[2336] The recognized emotion information is transmitted via the server to the generation device and the approval device.

[2337] Step 4:

[2338] The generator adjusts the question content based on the recognized emotions.

[2339] Step 5:

[2340] The approval device adaptively modifies the feedback content based on emotional information.

[2341] 5. Provision of question and answer format

[2342] Step 1:

[2343] The server sends the generated questions and answer formats to the user's terminal.

[2344] Step 2:

[2345] The user terminal displays the question and answer format to the user.

[2346] 6. User's response

[2347] Step 1:

[2348] The user enters their answer to the question displayed on their terminal.

[2349] Step 2:

[2350] The user terminal sends the entered answer to the server.

[2351] 7. Evaluation and feedback on the answers

[2352] Step 1:

[2353] The server sends the received user's answer to the approval device.

[2354] Step 2:

[2355] The approval device evaluates the answer and determines whether it is correct or incorrect.

[2356] Step 3:

[2357] The approval device generates the correct answer and explanation and sends them to the server.

[2358] Step 4:

[2359] The server sends the generated feedback to the user's terminal.

[2360] Step 5:

[2361] The user terminal displays feedback to the user.

[2362] Specific example

[2363] Example: When student A, a third-year high school student, is working on an English grammar problem.

[2364] 1. User Login:

[2365] Step 1:

[2366] Person A launches the app on their smartphone (user device) and enters their ID and password.

[2367] Step 2:

[2368] The user terminal sends the entered authentication information to the server.

[2369] Step 3:

[2370] The server verifies the authentication information and approves the login.

[2371] 2. Selection of study area:

[2372] Step 1:

[2373] Person A chooses "English".

[2374] Step 2:

[2375] The user terminal sends the selection information to the server.

[2376] 3. Generating past exam questions:

[2377] Step 1:

[2378] The server calls the generator and retrieves past exam data for the English section.

[2379] Step 2:

[2380] The generator produces multiple-choice grammar questions and sends them to the server.

[2381] 4. Utilizing the Emotion Engine:

[2382] Step 1:

[2383] The user terminal collects emotional data from person A's facial expressions and voice.

[2384] Step 2:

[2385] The emotion engine analyzes emotional data and recognizes person A's emotions.

[2386] Step 3:

[2387] The recognized emotion information is transmitted to the generator and approval device via the server.

[2388] Step 4:

[2389] The generator adjusts the question content based on emotional information.

[2390] Step 5:

[2391] The approval device adjusts the feedback content.

[2392] 5. Providing question and answer formats:

[2393] Step 1:

[2394] The server sends the generated problem to the user's terminal.

[2395] Step 2:

[2396] The user terminal displays the question and answer format to person A.

[2397] 6. User's answer:

[2398] Step 1:

[2399] Person A enters their answer and sends it to the server via their user terminal.

[2400] 7. Evaluation and feedback on the answers:

[2401] Step 1:

[2402] The server uses an approval device to evaluate the answers and generate feedback.

[2403] Step 2:

[2404] The feedback is sent to the user's device and displayed to person A.

[2405] In this way, the system can take user emotions into account and provide a more effective and personalized learning experience.

[2406] (Example 2)

[2407] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2408] Traditional learning systems often present problems without considering the user's emotional state, which can lead to user stress and reduced learning effectiveness. Furthermore, a lack of specialized problem formats tailored to specific learning areas is a problem, failing to adequately meet users' learning needs. Additionally, the lack of complete security guarantees for user authentication information is also a concern.

[2409] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[2410] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, means including an emotion engine that analyzes emotion data collected from the user terminal and adjusts the difficulty level of the questions and the content of the feedback based on the user's emotional state, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. This makes it possible to provide a personalized learning experience based on the user's emotional state and maximize learning effectiveness. Furthermore, by providing question formats specialized for learning fields, it is possible to flexibly respond to the user's learning needs.

[2411] A "generator" is a device that analyzes past question patterns and generates questions that are suitable for the user.

[2412] A "user terminal" refers to a device used by a user to input information or answer questions, and includes smartphones, tablets, and computers.

[2413] An "emotion engine" is a device that analyzes the user's facial expressions and voice, recognizes their emotional state, and adjusts the difficulty level of the problem and the content of the feedback accordingly.

[2414] An "approval device" is a device that evaluates a user's answer and generates the answer result and explanation.

[2415] "Past question patterns" refers to data and information that shows past questions, their trends, and their format.

[2416] "Authentication information" refers to information used to identify and authenticate a user, such as a user ID and password.

[2417] "Answer evaluation" is the process of determining whether the answers entered by users are correct or incorrect, and then conducting an evaluation based on those results.

[2418] "Feedback content" refers to information such as evaluation results and explanations for the user's answers, and includes learning advice and guidance provided to the user.

[2419] "Question format" refers to the type of question format specific to each learning area, and includes multiple-choice questions and fill-in-the-blank questions.

[2420] "User emotional state" refers to the emotional states a user experiences while learning, and includes, for example, joy, sadness, stress, and excitement.

[2421] The system of this invention comprises a server, a user terminal, a generation device, an approval device, and an emotion engine. The specific operation of each component is described below.

[2422] 1. User Login

[2423] Users log in to the system using their user terminal. Specifically, the user enters their ID and password into the user terminal, and the user terminal sends this authentication information to the server. The server compares the received authentication information with the database, and if authentication is successful, generates a login token and sends it to the user terminal. The user terminal stores this token and uses it for subsequent communications.

[2424] 2. Selection of learning area

[2425] The user selects the subject they wish to study through an interface on their device. The user device sends information about the selected subject to the server. The server receives this information and holds it for the next step.

[2426] 3. Generating past exam questions

[2427] The server invokes a generator based on the learning area selected by the user. The generator retrieves relevant data from a database containing past question patterns and generates questions. The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank) and sends it to the server.

[2428] 4. Utilizing the Emotion Engine

[2429] The user terminal collects emotional data through the user's facial expressions and voice. This data is analyzed using a TensorFlow model within the terminal to recognize the user's emotional state. The emotion engine transmits the recognized emotional information to the generator and approval device via the server. The generator adjusts the question content based on the emotional information, and the approval device adaptively changes the feedback content based on the emotional information.

[2430] 5. Provision of question and answer format

[2431] The server sends the generated questions and answer formats to the user's terminal. The user's terminal displays this to the user, assisting them in working on the questions.

[2432] 6. User's response

[2433] The user enters their answer to the question displayed on their terminal. The user terminal sends the entered answer to the server. The server receives this answer and sends it to the approval device.

[2434] 7. Evaluation and feedback on the answers

[2435] The approval device evaluates the user's answer and determines whether it is correct or incorrect. Furthermore, the approval device generates the correct answer and explanation, and sends it to the server. The server sends the generated feedback to the user's terminal, which displays it to the user. The user can then progress in their learning through this feedback.

[2436] Specific example

[2437] For example, consider a high school senior, A, working on English grammar problems. A logs into the system using their smartphone and selects English as their learning subject. The server analyzes past question patterns based on this information and generates appropriate grammar problems. The user terminal collects emotional data from A's facial expressions and voice, and the emotion engine performs analysis. As a result, if A is experiencing stress, adjustments are made, such as lowering the difficulty of the problems. After the problems are displayed, A enters their answers, which are sent to the server. An approval device evaluates the answers, and feedback is provided to A. Through this process, A can enjoy a personalized learning experience.

[2438] Example of a prompt

[2439] Prompt example:

[2440] Assume that student A, a third-year high school student, is using a learning system to study English. The system proceeds through seven steps: user login, selection of learning area, generation of past questions, use of an emotion engine, provision of questions and answers, user response, and evaluation and feedback of the response. Considering that student A is working on grammar questions, please describe the sequence of processing steps and their flow in detail.

[2441] In this way, the present invention takes into account the user's emotional state and provides a more effective and personalized learning experience.

[2442] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2443] Step 1:

[2444] The user enters their ID and password into their user terminal.

[2445] Input: ID, Password

[2446] The user terminal sends the entered authentication information to the server.

[2447] Input: Authentication information sent from the user terminal

[2448] Specific operation: The user terminal sends data to the server using SSL encrypted communication.

[2449] Step 2:

[2450] The server compares the received authentication information with the database.

[2451] Input: Authentication information, user data in the database

[2452] Data processing: Compare and verify user data in the database with received authentication information.

[2453] Output: Authentication result (success / failure)

[2454] Specific operation: The server uses a MySQL database to verify user information.

[2455] Step 3:

[2456] The server returns the authentication result to the user's terminal.

[2457] Input: Authentication result

[2458] Output: Authentication result (success / failure)

[2459] Specific operation: The server generates a JWT (JSON Web Token) and, if successful, returns this token to the user's terminal. The token is used for subsequent communication.

[2460] Step 4:

[2461] Users select the subject they wish to study through an interface on their device.

[2462] Input: Selection information for learning field

[2463] The user terminal sends information about the selected field to the server.

[2464] Input: Selection information for learning field

[2465] Specific operation: The user taps the "Select Learning Area" option from the app's menu and selects an area from the displayed list. The user's device sends the selection information to the server using HTTPS communication.

[2466] Step 5:

[2467] The server invokes the generator based on information about the learning area selected by the user.

[2468] Input: Information on the field of study

[2469] The server retrieves relevant data from a database containing past question patterns.

[2470] Input: Database of past exam patterns, information on learning areas.

[2471] Data processing: Filter past question patterns related to the learning area and select appropriate questions.

[2472] Output: Selected problems

[2473] Specific operation: The server uses a Python API to call a microservice for problem generation and retrieve the relevant data.

[2474] Step 6:

[2475] The generator creates a question format suitable for the user (e.g., multiple choice, fill-in-the-blank, etc.) and sends it to the server.

[2476] Input: Past exam data

[2477] Data processing: Generate questions while considering the user's learning history and difficulty settings.

[2478] Output: Formatted problem

[2479] Specific operation: The generation algorithm uses AI to analyze data and generate an optimal set of questions. The generated set of questions is formatted as multiple-choice questions and returned to the server.

[2480] Step 7:

[2481] The user terminal collects emotional data through the user's facial expressions and voice.

[2482] Input: User facial expression data, voice data

[2483] Data processing: Use an emotion engine to analyze emotional data and recognize the user's current emotions.

[2484] Output: Recognized emotion information

[2485] Specific operation: The device's camera and microphone are used to collect data, which is then analyzed using a TensorFlow model.

[2486] Step 8:

[2487] The emotion engine analyzes this emotion data to recognize the user's current emotions.

[2488] Input: Sentiment data

[2489] Data processing: Analyze data using emotion analysis algorithms to determine emotional states.

[2490] Output: Emotional state (e.g., stress, joy, etc.)

[2491] Specific operation: The analysis results are processed as digital signals, and recognized emotional information is generated.

[2492] Step 9:

[2493] The recognized emotion information is transmitted to the generator and approval device via the server.

[2494] Input: Recognized emotion information

[2495] Output: Adjusted question content and feedback

[2496] Specific operation: The analysis results of the emotion engine are transmitted to the generation device and the approval device via the server.

[2497] Step 10:

[2498] The generator adjusts the question content based on the recognized emotions.

[2499] Input: Sentimental information, generated problem

[2500] Data processing: Adjust the difficulty and format of problems based on emotional information.

[2501] Output: Adjusted problem

[2502] Specific action: For example, if the user is feeling stressed, the generator will lower the difficulty level of the problem.

[2503] Step 11:

[2504] The approval device adaptively modifies the content of the feedback based on emotional information.

[2505] Input: Sentimental information, evaluation results

[2506] Data processing: Adaptively modifying feedback content based on emotional information.

[2507] Output: Personalized feedback

[2508] Specific actions: For example, if the user is feeling stressed, generate a message such as "Let's calm down and get to work."

[2509] Step 12:

[2510] The server sends the generated questions and answer formats to the user's terminal.

[2511] Input: Formatted question, sentiment-adjusted content

[2512] Output: Problem data to be displayed on the user terminal

[2513] Specific operation: The server sends formatted problem data to the user's terminal via a REST API.

[2514] Step 13:

[2515] The user terminal displays the question and answer format to the user.

[2516] Input: Problem data

[2517] Output: Displayed problem

[2518] Specific operation: The user terminal uses a UI framework to display the problem on the screen.

[2519] Step 14:

[2520] The user enters their answer to the question displayed on their terminal.

[2521] Input: Answer Information

[2522] The user terminal sends the entered answer to the server.

[2523] Output: Answer data sent to the server

[2524] Specific operation: The user taps an option to enter their answer, and the user's device sends this to the server.

[2525] Step 15:

[2526] The server sends the received user's answer to the approval device.

[2527] Input: Answer data

[2528] Notice: Answer data (to be sent to the approval device)

[2529] Specific operation: The server sends the answer data to the approval device's API.

[2530] Step 16:

[2531] The approval device evaluates the answer and determines whether it is correct or incorrect. Furthermore, it generates the correct answer and an explanation.

[2532] Input: Answer data

[2533] Data processing: Evaluate the answers and generate results and explanations.

[2534] Output: Evaluation results and explanation

[2535] Specific operation: The approval device uses a machine learning model to evaluate the answer and generate accurate feedback.

[2536] Step 17:

[2537] The server sends the generated feedback to the user's terminal.

[2538] Input: Evaluation results and explanation

[2539] Output: Feedback displayed on the user's terminal

[2540] Specific operation: The server sends the generated feedback data to the user's terminal, where it is displayed.

[2541] This allows users to receive detailed feedback and progress in their learning.

[2542] (Application Example 2)

[2543] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2544] Conventional learning and work support systems provide uniform problems and work instructions without considering the user's emotional state, leading to user stress and decreased work efficiency. Furthermore, a lack of individualized feedback made it difficult to provide optimal learning and work environments and to reduce user motivation.

[2545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2546] In this invention, the server includes means for a generating device to analyze past question patterns and generate suitable questions, means for transmitting the questions generated by the generating device to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates answer results and explanations. Furthermore, it includes an emotion engine that collects and analyzes user emotion data, means for the generating device and approval device to adjust the question content and feedback based on the emotion information, and user interface means for providing real-time work support to workers. This enables personalized learning and work support that takes the user's emotional state into account, thereby reducing stress and improving work efficiency.

[2547] A "problem generator" is a device that analyzes past question patterns and generates questions tailored to the user.

[2548] A "user terminal" is a device used by users to access a server and communicate with it to send and receive various types of information.

[2549] An "approval device" is a device that evaluates the answers received from users and generates the results and explanations.

[2550] An "emotion engine" is a device that recognizes a user's emotional state by collecting and analyzing their emotional data.

[2551] A "server" is a central device that manages and processes data for the entire system.

[2552] A "user interface means" is a means of providing an interface for a user to interact with a system.

[2553] "Real-time work support" refers to providing instant instructions and feedback to users while they are performing their tasks.

[2554] "Feedback" refers to evaluations and advice provided based on the user's work and learning results.

[2555] The present invention is a system that includes a generation device that analyzes past question patterns and generates suitable questions, means for transmitting the generated questions to a user terminal, and an approval device that evaluates the answers received from the user terminal and generates the results and explanations. The system also includes an emotion engine that collects and analyzes user emotion data. Furthermore, it is equipped with a user interface means for providing real-time work support to workers. Specific embodiments for carrying out the present invention are shown below.

[2556] Hardware and software configuration

[2557] 1. Hardware Configuration

[2558] User device: Smart glasses (e.g., Google Glass)

[2559] Server: A computer system that manages and processes data.

[2560] Emotion engine: A device (e.g., camera, microphone) that collects and analyzes user emotional data.

[2561] 2. Software Configuration

[2562] Server applications (e.g., Python-based applications)

[2563] Emotion recognition engine (e.g., Microsoft Azure's Emotion API)

[2564] Data processing and data calculation workflow

[2565] The server generates a problem suitable for the user based on data from the generation device and sends it to the user terminal. The user terminal displays the problem and sends the user's answer back to the server. Based on this, the approval device evaluates the answer and generates the answer result and explanation.

[2566] Furthermore, the emotion engine collects and analyzes the user's emotional data (facial expressions and voice). Based on this, the generation device generates problems optimized for the user's emotional state, and the approval device adjusts the feedback accordingly. Finally, the user interface provides real-time work support, improving the user's work efficiency and motivation.

[2567] Specific example

[2568] As a concrete example, consider a case where person B is engaged in parts assembly work at a factory. Person B puts on smart glasses and logs into the system. The server verifies Person B's authentication information and approves the login. Next, Person B selects "parts assembly work" and performs the task according to the instructions displayed on the smart glasses.

[2569] In this process, the emotion engine collects and analyzes emotional data from B's facial expressions and voice. If the system detects that B is experiencing stress, it adjusts the work content and assigns simpler tasks to reduce stress.

[2570] Examples of prompts to input into a generative AI model are as follows:

[2571] "For HR professionals, please provide an overview of a factory worker status monitoring system using emotion recognition, and the technologies that support it. Include the following keywords: smart glasses, emotion engine, real-time feedback, and improved worker efficiency."

[2572] As described above, this system allows for real-time monitoring of the user's emotional state and provides optimal work instructions and feedback, thereby improving work efficiency and reducing stress.

[2573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2574] Step 1:

[2575] The user's terminal sends the user's entered ID and password to the server.

[2576] Input: The user enters their ID and password into the smart glasses.

[2577] Data processing: The server compares the received authentication information with the database.

[2578] Output: Generate a login token as an authentication result and send it back to the user's device.

[2579] Step 2:

[2580] The user selects a learning area or work area.

[2581] Input: The user selects a task, such as "parts assembly work," using smart glasses.

[2582] Data processing: The user terminal sends information from the selected field to the server.

[2583] Output: The server uses that information to call the generator.

[2584] Step 3:

[2585] The server generates tasks using a generator.

[2586] Input: Information about the selected learning / work area.

[2587] Data processing: The generation device analyzes past data and generates suitable tasks.

[2588] Output: Sends the generated task information to the user's terminal.

[2589] Step 4:

[2590] The emotion engine collects and analyzes user emotion data.

[2591] Input: User facial expression data and voice data.

[2592] Data processing: The emotion engine analyzes this data to recognize the user's emotional state.

[2593] Output: Sends recognized emotion information to the server.

[2594] Step 5:

[2595] The server adjusts the question content and work instructions based on the generated tasks and sentiment information.

[2596] Input: Generated task information and recognized emotion information.

[2597] Data processing: Generators and approval devices adjust task content and feedback based on emotional information.

[2598] Output: Sends adjusted tasks and feedback information to the user's terminal.

[2599] Step 6:

[2600] The user terminal displays the adjusted tasks to the user.

[2601] Input: Coordinated task information sent from the server.

[2602] Data processing: Converts the information into a format necessary for the user's terminal to display it.

[2603] Output: The adjusted task is displayed on the user's glasses screen. ...

Claims

1. A means for generating a suitable question by analyzing past question patterns, Means for transmitting the problem generated by the generation device to a user terminal, A system including an approval device that evaluates answers received from a user terminal and generates answer results and explanations.

2. The system according to claim 1, wherein the user terminal is equipped with means for transmitting authentication information entered by the user to a server and performing authentication.

3. The system according to claim 1, wherein the generating device includes means for generating problem formats specialized for each learning field.

4. The system according to claim 1, wherein the approval device includes means for immediately evaluating the received answer and generating feedback.

Images