System

Abstract

A system is provided.SOLUTION: A system comprising: means for providing a user with an interface accessible via a device; means for using a generative AI model to generate customized materials and questions based on the user's level and learning objectives; means for providing the generated materials and questions to the user's device; means for receiving and evaluating the user's answers and generating feedback; and means for recording and analyzing the user's learning-progress information and generating motivational messages.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] As 5G becomes more widespread, advanced technical skills related to mobile phone base stations are required, but telecommunications carriers and infrastructure providers face the challenge of training and recruiting personnel with the necessary skills. Furthermore, existing learning methods make it difficult to provide learning materials and feedback that fully match the level and goals of individual users, making it difficult to achieve efficient learning. This could hinder the training and recruitment of personnel needed to support the growth of the entire telecommunications industry. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a means for providing a user with an interface accessible via a terminal, a means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, a means for providing the generated learning materials and questions to the user's terminal, a means for receiving and evaluating the user's answers and generating feedback, and a means for recording and analyzing the user's learning progress information and generating motivational messages. This allows users to efficiently obtain optimal learning resources according to their own level, thereby contributing to the improvement of technological capabilities and human resource development in the telecommunications industry.

[0006] "User" refers to an end user who uses the system to study.

[0007] A "terminal" is a device through which a user accesses the system, and includes electronic devices such as computers, smartphones, and tablets.

[0008] An "interface" refers to the screen or operation panel through which a user interacts with the system, and is provided via a web browser or dedicated application.

[0009] A "generative AI model" refers to an artificial intelligence algorithm that automatically generates customized teaching materials and questions based on user input.

[0010] "Teaching materials" refers to content provided for users to study, and includes text, video, audio, diagrams, and the like.

[0011] "Questions" refer to tasks that users must answer to check their learning progress, and include multiple-choice questions and essay questions.

[0012] "Evaluation" refers to the process of determining the accuracy of the questions answered by the user and providing feedback on the results.

[0013] "Feedback" refers to messages and hints that include evaluation results for questions answered by the user, and provides information to deepen the user's understanding.

[0014] "Study progress information" refers to data recorded on the user's learning history and progress through the system.

[0015] "Motivational messages" refer to messages of encouragement and advice provided by the system to increase the user's motivation to learn. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The present invention is an online learning system that provides users with learning resources via their terminals, enabling them to effectively acquire knowledge about communication technologies and mobile phone base stations. Below, we will explain the specific program processing of this system in natural language.

[0038] User Registration and Login

[0039] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0040] Setting levels and objectives

[0041] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0042] Providing learning materials and learning

[0043] Users click on the provided learning materials (PDF, video, etc.) to view them. The server records the user's browsing history of the learning materials and manages their learning progress.

[0044] Questions and feedback

[0045] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback is displayed to the user via the device, and the user uses it as a guide to proceed to the next learning step.

[0046] Track your progress and increase motivation

[0047] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages and provides them to the user via their device. The user can check these to increase their motivation for learning.

[0048] Specific examples

[0049] If a user selects "Intermediate-level 5G troubleshooting," the generative AI model generates intermediate-level learning materials and specific troubleshooting problems based on that selection. For example, it includes case studies of typical 5G issues and techniques for improving performance. When the user answers the questions, the server receives the answer and provides feedback such as "That's right! You did a great job!" if the answer is correct, or "The correct answer is ____. Please check this part again." if the answer is incorrect.

[0050] In this way, the system supports efficient learning and skill acquisition by providing learning resources tailored to the user and appropriate feedback.

[0051] The processing flow will be explained below.

[0052] User Registration and Login

[0053] Step 1:

[0054] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[0055] Step 2:

[0056] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[0057] Step 3:

[0058] The user enters the required information and clicks the "Submit" button.

[0059] Step 4:

[0060] The terminal sends the entered information to the server in JSON format.

[0061] Step 5:

[0062] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[0063] Step 6:

[0064] The terminal displays a notification of successful registration to the user.

[0065] Step 7:

[0066] The user clicks the "Login" button on the home screen.

[0067] Step 8:

[0068] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[0069] Step 9:

[0070] The user enters the required information and clicks the "Login" button.

[0071] Step 10:

[0072] The terminal transmits the input information to the server.

[0073] Step 11:

[0074] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[0075] Step 12:

[0076] The server returns a login success message and dashboard data to the terminal.

[0077] Step 13:

[0078] The device displays a dashboard to show the user their current learning status.

[0079] Setting levels and objectives

[0080] Step 1:

[0081] On the dashboard, users click buttons to set their "learning level" and "learning objectives."

[0082] Step 2:

[0083] The terminal displays a drop-down menu, presenting the choices to the user.

[0084] Step 3:

[0085] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[0086] Step 4:

[0087] The terminal transmits the selected information to the server.

[0088] Step 5:

[0089] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[0090] Step 6:

[0091] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[0092] Step 7:

[0093] The terminal displays the generated teaching materials and problem sets to the user.

[0094] Provision of teaching materials

[0095] Step 1:

[0096] The user clicks on the link or file provided.

[0097] Step 2:

[0098] The device will display the linked educational material (PDF, video, etc.) or download the file.

[0099] Step 3:

[0100] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[0101] Questions and feedback

[0102] Step 1:

[0103] The server generates questions at regular intervals according to the user's learning progress.

[0104] Step 2:

[0105] The server sends the generated questions to the terminal.

[0106] Step 3:

[0107] The terminal displays the received problem to the user.

[0108] Step 4:

[0109] The user solves the problem, enters the answer, and clicks the "Submit" button.

[0110] Step 5:

[0111] The terminal transmits the user's answer to the server.

[0112] Step 6:

[0113] The server evaluates the received answers (e.g., determining whether they are correct or incorrect).

[0114] Step 7:

[0115] The server generates evaluation results and feedback messages and sends them to the terminal.

[0116] Step 8:

[0117] The terminal displays the feedback to the user.

[0118] Track your progress and increase motivation

[0119] Step 1:

[0120] The server records progress information for each learning session in a database.

[0121] Step 2:

[0122] The server periodically analyzes the progress and generates reports.

[0123] Step 3:

[0124] The server sends the generated report and motivation message to the terminal.

[0125] Step 4:

[0126] The terminal displays the received reports and messages to the user.

[0127] Step 5:

[0128] Users can check their progress and be motivated to move on to the next learning stage.

[0129] Example 1

[0130] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0131] Conventional online learning systems have limited means for efficiently tracking users' learning progress and providing appropriate feedback, making it difficult for users to effectively acquire knowledge. Furthermore, they often lack the ability to provide customized learning materials tailored to the user's learning level and goals, resulting in a lack of motivation to learn. The present invention aims to solve these problems and improve users' learning experience.

[0132] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0133] In this invention, the server includes: means for providing an interface accessible to a user via a terminal; means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives; means for providing the generated learning materials and questions to the user's terminal; means for receiving and evaluating the user's answers and generating feedback; means for recording and analyzing the user's learning progress information and generating motivational messages; means for recording the user's browsing history and managing the learning progress; and means for transmitting the learning materials and question sets generated based on the user's selected learning level and objectives to the user's terminal and displaying them to the user. This allows the user to receive individually tailored learning materials and feedback, enabling efficient learning and skill acquisition.

[0134] A "terminal" is a device that a user uses to access the system, and includes devices such as PCs, tablets, and smartphones.

[0135] "Interface" refers to the screen and input means that allow a user to interact with a system, and is a user interface that includes on-screen buttons, menus, etc.

[0136] A "generative AI model" refers to artificial intelligence technology that automatically generates customized teaching materials and questions based on user input.

[0137] "Learning Materials" means content provided for learning purposes, including formats such as PDFs, videos, and slide presentations.

[0138] "Questions" refer to questions or tasks that users answer to check their level of understanding, and come in multiple choice, written, and other formats.

[0139] "Feedback" refers to information that includes evaluation results and advice on the user's answers, and is intended to improve the user's learning.

[0140] "Study progress information" refers to data that records the user's learning progress, such as how much learning material the user has viewed and how many questions the user has answered.

[0141] A "motivational message" is a message containing encouragement or advice provided to increase the user's motivation to learn.

[0142] "Generated learning materials and problem sets" refers to learning materials and a set of problems that a generative AI model automatically creates based on the user's level and learning objectives.

[0143] A "study progress report" is a report that is periodically generated summarizing a user's learning progress.

[0144] "Analysis" refers to the process of evaluating the learning progress information collected by the server and identifying the user's learning situation and areas for improvement.

[0145] The present invention is an online learning system configured as follows, which performs various processes using a terminal, a server, and a generative AI model to enable users to effectively acquire knowledge.

[0146] System Configuration

[0147] 1. Hardware Configuration

[0148] Terminal: The device used by the user, including common devices such as PCs, tablets, and smartphones.

[0149] Server: A computer system that processes and manages data for the entire system. Includes database servers, application servers, etc.

[0150] 2. Software Configuration

[0151] Browser or dedicated app: A tool that provides an interface for users to access the system.

[0152] Generative AI model: Built using Python, TensorFlow, etc., it generates customized learning materials and questions based on the user's learning level and goals.

[0153] System action

[0154] User Registration and Login

[0155] Users access the system through a browser or a dedicated app. First, the device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information and verifies the input. Once verification is complete, the server saves the user information in a database and sends a confirmation email to the user. When a user logs in, they enter an email address and password, which the server verifies. If verification is successful, the user is taken to the home screen.

[0156] Setting levels and objectives

[0157] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0158] Providing learning materials and learning

[0159] The user selects and clicks on the material they want to study from the list of materials displayed on the device. For example, this could be a PDF or video. The server records the user's browsing history of the materials and stores it in a database. The user can then browse the materials and proceed with their learning.

[0160] Questions and feedback

[0161] As the learning process progresses, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers these questions and sends the results from the device to the server. The server receives the answers and evaluates them. For example, if the answer is correct, it displays "That's correct. You did a great job." If the answer is incorrect, it generates feedback such as "The correct answer is XX. Please check this part again."

[0162] Track your progress and increase motivation

[0163] The server records the user's answer history and learning material access history in a database. This data is periodically analyzed and a learning progress report is generated. Along with the progress report, the server also generates a motivational message to motivate the user to study and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[0164] Examples and prompts

[0165] If a user selects "Intermediate Level 5G Troubleshooting," the system will:

[0166] 1. From the home screen, the user navigates to the Learning Settings menu and selects the Intermediate level and 5G Troubleshooting.

[0167] 2. The server uses the generative AI model to generate intermediate-level troubleshooting materials (PDFs, videos, etc.) and problem sets.

[0168] 3. The terminal displays the generated teaching materials and problem sets to the user.

[0169] 4. The user clicks on the learning material and begins viewing it. The server records the user's browsing history.

[0170] 5. When the user has finished studying the learning material, the server sends the generated questions to the terminal, and the user answers the questions.

[0171] 6. The server evaluates the answer and provides feedback such as, "The correct answer is XX. Please check this part again."

[0172] 7. Learning progress information is stored on the server and progress reports are generated periodically.

[0173] This invention enables users to learn and master skills efficiently, and by using a generative AI model, it is possible to provide customized learning materials according to the user's learning needs, thereby enhancing the effectiveness of learning.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1: User Registration

[0176] Users access the system using a browser or a dedicated app. The device displays the home screen, and when the user clicks the "Register" button, a screen for entering required information such as name, email address, and password is displayed. When the user enters the information and clicks the "Submit" button, the device sends the information to the server. The server receives the entered information and verifies the format of the email address and the strength of the password. Once this verification is complete, the server saves the user information in a database and generates and sends a confirmation email. When the user clicks the link in the email, registration is complete.

[0177] Input: User's name, email address, and password

[0178] Output: Sending a confirmation email, saving user information to a database

[0179] Step 2: Log in

[0180] The user enters their email address and password on the login screen. The device receives this and sends it to the server. The server checks the database to verify the information entered, and if it is correct, starts a session. The device displays a login success message and the user is taken to the home screen.

[0181] Input: User's email address, password

[0182] Output: Login success message, home screen displayed

[0183] Step 3: Set your level and objectives

[0184] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0185] Input: User's learning level, learning objectives

[0186] Output: Display of generated teaching materials and problem sets

[0187] Step 4: Provide learning materials and learn

[0188] The user clicks on the learning material (PDF, video, etc.) they want to learn from the list of learning materials displayed on the device and views it. The device displays the learning material and notifies the server of the user's click. The server records the user's browsing history of the learning materials accessed and saves it in a database. The user views the learning material and progresses with their learning.

[0189] Input: User's material selection (click)

[0190] Output: Display of teaching materials, saving of browsing history in database

[0191] Step 5: Questions and Answers

[0192] Once the user has finished viewing the learning material, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers the questions and sends the answers from the device to the server. The server receives the answers and evaluates them. Based on the results, feedback is generated and sent to the device. For example, if the answer is correct, it will display "That's correct. You did a great job." If the answer is incorrect, it will display "The correct answer is XX. Please check this part again."

[0193] Input: Content of the teaching material, user's answers

[0194] Output: Generated questions, feedback

[0195] Step 6: Track your progress and stay motivated

[0196] The server records the user's answer history and learning material access history in a database. It periodically analyzes this data and generates a learning progress report. The server also generates a motivational message along with the progress report and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[0197] Input: Answer history, learning material access history

[0198] Output: progress reports, motivational messages

[0199] (Application example 1)

[0200] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0201] Currently, online learning systems for efficiently learning about communications technology and mobile phone base stations are limited, and they lack the ability to provide learning materials optimized for individual users, track learning progress, and provide learning support for troubleshooting, especially for 5G technology. This reduces users' learning efficiency and makes it difficult to maintain motivation.

[0202] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0203] In this invention, the server includes means for providing a user interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, and means for providing the generated learning materials and questions to the user's terminal, thereby enabling the user to effectively advance their learning related to communication technologies.

[0204] The server also includes a means for displaying and managing customized learning materials for users to advance their learning related to communication technologies, and a means for providing online troubleshooting and practical scenario-based questions and tallying up answers, thereby enabling more practical and interactive learning.

[0205] Furthermore, the generative AI model selects optimal learning materials and questions based on the learning level and learning goals entered by the user, making it possible to provide learning resources optimized for each individual user.The system also includes a means for periodically analyzing the user's learning progress information, generating learning progress reports, and sending these reports to the user's device, allowing the user to always be aware of their own learning progress, making it easier to maintain motivation.

[0206] "User interface" refers to the screen or operation panel that a user uses to access the system via a terminal.

[0207] "Generative AI model" refers to the artificial intelligence algorithm used to generate optimal learning materials and questions based on the user's learning level and goals.

[0208] "Customized learning materials" refers to learning resources created by generative AI models to suit a user's specific learning needs.

[0209] "Troubleshooting" refers to the diagnosis and resolution of system or technology related problems.

[0210] "Study progress information" refers to data that records the learning progress achieved by a user within a specific period of time.

[0211] "Motivational messages" refer to messages of encouragement and evaluation that are generated to increase the user's motivation to learn.

[0212] "Study Progress Report" refers to a document or digital file that compiles and analyzes a user's learning progress information.

[0213] "Terminal" refers to the electronic device (smartphone, tablet, PC, etc.) that a user uses to access the system.

[0214] The present invention is an online learning system for enabling users to efficiently learn about communication technologies and mobile phone base stations. A specific embodiment of this system will be described below.

[0215] System Overview

[0216] The server provides a user interface accessible to users via their terminals, through which users can access the system and perform operations such as registration, login, and learning progress.

[0217] The server generates customized learning materials and questions using a generative AI model based on the user's learning level and goals. This generative AI model selects the optimal learning materials and questions based on user input and provides them to the user's device.

[0218] Hardware and software used

[0219] Hardware: Devices (smartphones, tablets, PCs, etc.), servers

[0220] Software: User interface (browser or dedicated app), generative AI model, database, communication protocol (HTTP / HTTPS)

[0221] Data processing and calculation

[0222] 1. User Registration and Login:

[0223] A user accesses the user interface from a terminal and completes the registration process by entering their name, email address, and password. The server receives this information, stores it in a database, and sends a confirmation email. When logging in, the user enters their username and password, which the server verifies and starts a session.

[0224] 2. Setting learning levels and goals:

[0225] After logging in, users select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The selected information is sent to the server, and the generative AI model generates customized learning materials and questions.

[0226] 3. Providing learning materials and learning:

[0227] Users study using the provided learning materials (text, images, videos, etc.). The server records the user's learning progress in a database and manages the progress.

[0228] 4. Questions and feedback:

[0229] During the learning process, the server periodically generates questions and sends them to the user's device. The user answers the questions and sends them to the server. The server evaluates the answers, generates feedback, and provides it to the user.

[0230] 5. Track your progress and increase your motivation:

[0231] The server periodically analyzes the user's learning progress and generates progress reports and motivational messages, which are sent to the user's device, allowing the user to check their progress and stay motivated.

[0232] Example: User's intermediate level 5G troubleshooting training

[0233] If a user specifies that they want to learn "intermediate level 5G troubleshooting," the generative AI model will generate customized learning materials based on prompts like the following:

[0234] Example prompt sentence:

[0235] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[0236] 1. Basic troubleshooting techniques

[0237] 2. Common Problem Examples and Solutions

[0238] 3. Practical scenarios in the field

[0239] The generated learning materials include basic troubleshooting techniques, typical 5G trouble cases, and detailed explanations of their solutions. Users can use these learning materials to study and acquire practical knowledge by answering questions provided by the server.

[0240] In this way, users can learn about communication technologies at their own pace and the system can provide appropriate feedback and support.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] User Registration and Login

[0244] A user accesses the user interface using a terminal.

[0245] Input: Name, Email Address, Password

[0246] Processing: The server receives the entered information and stores it in a database. Once registration is complete, the server sends a confirmation email. When a user logs in, they enter their username and password, which the server checks against the database, and if authentication is successful, a session begins.

[0247] Output: Login confirmation message, user session started

[0248] Step 2:

[0249] Setting learning levels and goals

[0250] After logging in, the user proceeds to a screen where they can set their learning level and goals.

[0251] Input: Learning level (beginner, intermediate, advanced), learning objectives (e.g., 5G basic knowledge, troubleshooting)

[0252] Processing: The device sends the selected information to the server, which uses a generative AI model to generate customized learning materials and questions based on the input learning level and objectives.

[0253] Output: A customized set of study materials and questions

[0254] Step 3:

[0255] Providing learning materials and learning

[0256] Users can view the provided learning materials (text, images, videos, etc.) on their devices and progress through their studies.

[0257] Input: Generated teaching materials

[0258] Processing: The server records the user's access history and learning progress in a database.

[0259] Output: Teaching material viewing screen, learning progress information update

[0260] Step 4:

[0261] Questions and feedback

[0262] As the user progresses with their studies, the server periodically generates questions and sends them to the device. The user answers the questions and sends them to the server.

[0263] Input: User's answer

[0264] Processing: The server evaluates the answer and generates feedback such as "That's right. You did a great job." if the answer is correct, or "The correct answer is XX. Please check this part again." if the answer is incorrect, and sends it to the device.

[0265] Output: Feedback message

[0266] Step 5:

[0267] Tracking learning progress and generating motivational messages

[0268] The server periodically analyzes the user's learning progress information and generates progress reports and motivational messages, which are provided to the user via the terminal.

[0269] Input: Learning progress information

[0270] Processing: The server analyzes the learning progress information recorded in the database, generates a progress report and a motivation message, and sends the generated report and message to the terminal.

[0271] Output: progress reports, motivational messages

[0272] Example: User's intermediate level 5G troubleshooting training

[0273] If a user wants to learn "Intermediate Level 5G Troubleshooting," the server will provide the generative AI model with prompts like this:

[0274] Example prompt sentence:

[0275] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[0276] 1. Basic troubleshooting techniques

[0277] 2. Common Problem Examples and Solutions

[0278] 3. Practical scenarios in the field

[0279] Based on this prompt, the generative AI model generates customized learning materials and provides them to the user's device. The user can then view the materials and answer questions to acquire practical knowledge.

[0280] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0281] The present invention provides an online learning system that allows users to effectively acquire knowledge about communication technologies and mobile phone base stations by providing an interface accessible via a terminal. It also combines an emotion engine that recognizes the user's emotions and adjusts the learning content and feedback accordingly, providing a more personalized educational experience.

[0282] User Registration and Login

[0283] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0284] Setting levels and objectives

[0285] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0286] Emotion engine integration

[0287] The system integrates an emotion engine to recognize the user's emotional state in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotional data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[0288] Providing learning materials and learning

[0289] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed.

[0290] Questions and feedback

[0291] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[0292] Track your progress and increase motivation

[0293] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[0294] Specific examples

[0295] For example, if a user selects "Intermediate-level 5G troubleshooting," the generative AI model will generate intermediate-level learning materials and specific troubleshooting problems based on that selection. While the user is answering the questions, the emotion engine analyzes the user's facial expressions and voice. If confusion or stress is detected, the server will provide additional explanations or simplified questions. If the answer is correct, the server will provide positive feedback, such as "Great! Keep it up!"

[0296] In this way, the system recognizes the user's emotional state and provides appropriate feedback and learning resources accordingly, greatly improving the user's learning efficiency and motivation.

[0297] The processing flow will be explained below.

[0298] User Registration and Login

[0299] Step 1:

[0300] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[0301] Step 2:

[0302] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[0303] Step 3:

[0304] The user enters the required information and clicks the "Submit" button.

[0305] Step 4:

[0306] The terminal sends the entered information to the server in JSON format.

[0307] Step 5:

[0308] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[0309] Step 6:

[0310] The terminal displays a notification of successful registration to the user.

[0311] Step 7:

[0312] The user clicks the "Login" button on the home screen.

[0313] Step 8:

[0314] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[0315] Step 9:

[0316] The user enters the required information and clicks the "Login" button.

[0317] Step 10:

[0318] The terminal transmits the input information to the server.

[0319] Step 11:

[0320] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[0321] Step 12:

[0322] The server returns a login success message and dashboard data to the terminal.

[0323] Step 13:

[0324] The device displays a dashboard to show the user their current learning status.

[0325] Setting levels and objectives

[0326] Step 1:

[0327] Users click buttons on the dashboard to set their "learning level" and "learning objectives."

[0328] Step 2:

[0329] The terminal displays a drop-down menu, presenting the choices to the user.

[0330] Step 3:

[0331] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[0332] Step 4:

[0333] The terminal transmits the selected information to the server.

[0334] Step 5:

[0335] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[0336] Step 6:

[0337] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[0338] Step 7:

[0339] The terminal displays the generated teaching materials and problem sets to the user.

[0340] Emotion engine integration

[0341] Step 1:

[0342] The server collects the user's facial expression, voice and input data in real time through the emotion engine.

[0343] Step 2:

[0344] The server analyzes the data obtained from the emotion engine to determine the user's emotional state (e.g., confusion, stress, concentration).

[0345] Step 3:

[0346] The server takes into account the user's emotional state and adjusts the learning content and feedback provided to the user.

[0347] Providing learning materials and learning

[0348] Step 1:

[0349] Users click on the provided link or file to view the materials.

[0350] Step 2:

[0351] The device will display the linked educational material (PDF, video, etc.) or download the file.

[0352] Step 3:

[0353] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[0354] Step 4:

[0355] The emotion engine assesses the user's level of attention and comprehension and adjusts the learning content as needed.

[0356] Step 5:

[0357] If the user is feeling stressed, the server provides supplementary materials to help them relax.

[0358] Questions and feedback

[0359] Step 1:

[0360] The server periodically generates questions according to the user's learning progress.

[0361] Step 2:

[0362] The server sends the generated questions to the terminal.

[0363] Step 3:

[0364] The terminal displays the received problem to the user.

[0365] Step 4:

[0366] The user solves the problem, enters the answer, and clicks the "Submit" button.

[0367] Step 5:

[0368] The terminal transmits the user's answer to the server.

[0369] Step 6:

[0370] The server evaluates the received answers (e.g., determining whether they are correct or incorrect) and generates feedback.

[0371] Step 7:

[0372] Feedback includes advice and motivational messages based on data obtained from the emotion engine.

[0373] Step 8:

[0374] The terminal displays the feedback to the user.

[0375] Track your progress and increase motivation

[0376] Step 1:

[0377] The server records progress information for each learning session in a database.

[0378] Step 2:

[0379] The server periodically analyzes the progress and generates a progress report.

[0380] Step 3:

[0381] The server sends the generated report and motivation message to the terminal.

[0382] Step 4:

[0383] The terminal displays the received reports and messages to the user.

[0384] Step 5:

[0385] Users can check their progress and be motivated to move on to the next learning stage.

[0386] Specific examples

[0387] Step 1:

[0388] A user selects "Intermediate Level 5G Troubleshooting."

[0389] Step 2:

[0390] The generative AI model generates intermediate-level learning materials and specific troubleshooting problems.

[0391] Step 3:

[0392] As the user browses the learning materials and answers the questions, the server analyzes the user's facial expressions and voice using an emotion engine.

[0393] Step 4:

[0394] If confusion or stress is detected, the server will provide additional explanations or simplified questions.

[0395] Step 5:

[0396] If you get it right, the server generates positive feedback like "Great! Keep it up next time!"

[0397] Step 6:

[0398] To provide motivational messages as appropriate so that users can maintain their motivation while studying.

[0399] This is the specific processing flow of a system that integrates an emotion engine. By recognizing the user's emotional state in real time and providing appropriate feedback and learning resources accordingly, it is possible to significantly improve the user's learning efficiency and motivation.

[0400] Example 2

[0401] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0402] Conventional online learning systems have the problem of being difficult to respond to the learning level and emotional state of individual users. As a result, it is difficult to expect improvements in user learning efficiency and motivation. In addition, learning progress and feedback are often simplified, making it difficult to provide a fully personalized learning experience.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0404] In this invention, the server includes means for providing a user with an interface accessible via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for recognizing the user's emotional state in real time, and means for adjusting learning content and feedback based on the recognized emotions, thereby enabling a personalized learning experience according to each user's emotional state and learning progress.

[0405] "User" refers to an individual or corporation that accesses the system and engages in learning activities.

[0406] "Terminal" refers to a device, such as a computer, smartphone, or tablet, that a user uses to access the system.

[0407] "Interface" refers to the software and hardware components that provide the screens and controls for a user to interact with a system.

[0408] A "generative AI model" refers to an artificial intelligence model that automatically generates optimal teaching materials and questions based on user input information (e.g., learning level, learning objectives).

[0409] "Teaching materials" refers to educational content such as PDFs, videos, and texts that users can use to study.

[0410] "Questions" refer to questions or exercises that are used to assess whether the user has understood the learning content.

[0411] "Feedback" refers to the evaluation and advice provided to the user based on the answers they provide.

[0412] "Study progress information" refers to data about how far a user has progressed in their studies (e.g., the learning materials accessed, the questions answered, the level of understanding, etc.).

[0413] "Motivational messages" refer to messages of encouragement or advice provided to increase a user's motivation to learn.

[0414] "Emotional state" refers to the user's current emotion (e.g., joy, confusion, stress, etc.).

[0415] An "emotion engine" refers to a system that recognizes a user's emotional state in real time from facial expressions, voice, input data, etc.

[0416] A "personalized learning experience" refers to a learning experience that is customized according to the learning level, learning goals, and emotional state of each individual user.

[0417] The system of the present invention is an online learning system that provides users with an interface accessible via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. The system is implemented using the following hardware and software.

[0418] Hardware and Software

[0419] Hardware: The devices used by users (e.g., computers, smartphones, tablets) and servers.

[0420] Software: A browser or dedicated app to provide the interface, a generative AI model (e.g., GPT-3), and an emotion engine (e.g., Emotion API).

[0421] User Registration and Login

[0422] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information, verifies it, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0423] Setting levels and objectives

[0424] Once a user logs in, they are directed to a menu where they can set their learning level and goal. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0425] An example prompt is:

[0426] "Create intermediate level teaching materials and questions on the basics of 5G."

[0427] Examples include:

[0428] Emotion engine integration

[0429] The system integrates an emotion engine to recognize the user's emotions in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotion data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[0430] Providing learning materials and learning

[0431] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension, and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed. This creates a personalized learning experience.

[0432] Questions and feedback

[0433] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[0434] Track your progress and increase motivation

[0435] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[0436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0437] Step 1: User Registration

[0438] Input: The user launches a browser or a dedicated app and accesses the home screen. The user clicks the "Register" button and enters information such as their name, email address, and password.

[0439] Specific operation: The terminal displays an input form into which the user enters information. Once the information is entered, the terminal sends it to the server.

[0440] Data processing and calculation: The server verifies the received information and checks whether the information is correct. If it is correct, it stores it in the database.

[0441] Output: The server sends a confirmation email to the user informing them that their registration is complete.

[0442] Step 2: Log in

[0443] Input: The user opens the login screen and enters their username and password.

[0444] Specific operation: The terminal displays a login form, the user enters information, and the terminal sends the input information to the server.

[0445] Data processing and calculation: The server checks the received information against the database and determines whether the authentication was successful.

[0446] Output: The server notifies the terminal that the login was successful and redirects the user to the dashboard screen.

[0447] Step 3: Set your learning level and goals

[0448] Input: After logging in, the user selects the learning level (beginner, intermediate, advanced) and purpose (e.g., 5G basic knowledge, troubleshooting).

[0449] Specific operation: The device displays a drop-down menu, the user selects an option, and the device sends the selected information to the server.

[0450] Data processing and calculation: Based on the received information, the server sends prompts to the generative AI model, requesting it to generate appropriate teaching materials and questions.

[0451] Output: The server receives the teaching materials and problem sets generated by the generative AI model and sends them to the user's device for display.

[0452] Step 4: Emotion Recognition

[0453] Input: Facial expressions and voice data while the user is using the system.

[0454] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice, and transmits the data to the server in real time.

[0455] Data processing and calculation: The server uses an emotion engine to analyze the data and recognize the user's emotional state.

[0456] Output: The server generates emotion-based learning feedback and motivational messages and sends them to the device.

[0457] Step 5: Provide learning materials and learn

[0458] Input: The user selects and views the provided learning materials (PDF, video, etc.).

[0459] Specific operation: The terminal displays a link to the teaching material, and when the user clicks on it, the teaching material is displayed.

[0460] Data processing and calculation: The server records which learning materials the user has viewed and tracks the user's browsing history. The emotion engine evaluates the user's level of concentration and comprehension.

[0461] Output: The server adjusts the learning content or provides supplementary material as needed.

[0462] Step 6: Questions and feedback

[0463] Input: Questions presented to the user as they progress through the learning process.

[0464] Specific operation: The server sends prompts to the generative AI model to generate appropriate questions. The device displays the questions and the user inputs the answers.

[0465] Data processing and calculation: The server evaluates the received answers and determines whether they are correct or not. It also refers to the emotion engine to consider the user's emotions.

[0466] Output: The server generates evaluation results and feedback and sends them to the device.

[0467] Step 7: Track your progress and stay motivated

[0468] Input: User's learning progress data.

[0469] Specific operation: The server records the user's learning progress information in a database.

[0470] Data Processing and Computation: The server periodically analyzes the progress data and uses the generative AI model to generate progress reports and motivational messages.

[0471] Output: The generated reports and motivation messages are sent to the terminal and presented to the user.

[0472] (Application example 2)

[0473] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0474] Conventional online learning systems can provide learning materials based on a user's learning progress and level of understanding, but lack the functionality to adjust learning content and feedback taking into account the user's emotional state. As a result, users often become discouraged or lose motivation to learn, making efficient learning difficult. Furthermore, because there is no mechanism for recognizing emotions in real time and adjusting learning content and feedback based on that, it is not possible to provide an optimal educational experience for each individual user. This problem needed to be solved.

[0475] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for using an emotion engine that recognizes the user's emotional state in real time using the terminal's camera and microphone, and means for adjusting learning feedback and motivational messages based on the emotional state. This allows the server to provide optimal learning feedback and motivational messages according to the user's emotional state, enabling efficient and effective learning.

[0476] A "terminal" is a digital device that allows a user to access and operate the system.

[0477] An "interface" is a screen or operating means that allows a user to access and operate the system via a terminal.

[0478] A "generative AI model" is an artificial intelligence algorithm that generates customized learning materials and questions based on the user's level and learning goals.

[0479] "Teaching materials" refers to information and materials for users to study, and includes formats such as e-books, videos, and PDFs.

[0480] "Questions" are questions or tasks that allow users to check their learning content and measure their level of understanding.

[0481] "Feedback" refers to evaluations and advice provided based on the user's answers and learning progress.

[0482] "Motivational messages" are messages of encouragement or encouragement provided to increase the user's motivation to learn.

[0483] The "emotion engine" is a system that uses the device's camera and microphone to recognize and analyze the user's emotional state in real time.

[0484] "Emotional state" refers to the state or change of an emotion a user is feeling, including, for example, confusion, stress, excitement, joy, and the like.

[0485] The present invention provides an online learning system that provides users with an interface that can be accessed via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. Specific methods for implementing the present invention are described below.

[0486] User Registration and Login

[0487] Users access the application through a device such as a smartphone or tablet. A "Register" button appears on the device screen, and the user enters the required information. The server then verifies this information and stores it in a database. Once registration is complete, a confirmation email is sent. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0488] Setting levels and objectives

[0489] After logging in, users are directed to a screen where they can select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The device displays these options, and the user selects the appropriate option. This information is sent to the server, and the generative AI model uses this information to generate optimal learning materials and problem sets. The generated learning materials and problem sets are then sent to the device and displayed to the user.

[0490] Providing learning materials and learning

[0491] Users click on the provided learning materials (e.g., PDFs, videos) to view them. The server records the user's browsing history and manages their learning progress. The content and display of the learning materials are handled by HTML5, PDF viewers, video streaming players, etc. The emotion engine uses the device's camera and microphone to recognize the user's emotional state in real time and adjust the learning content accordingly.

[0492] Emotion engine integration

[0493] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice to recognize the user's emotional state. This allows it to provide additional hints or simplified explanations if the user is confused, and positive feedback if the user is successful. This is done using emotion recognition technologies such as Google Cloud Vision API and Microsoft Azure Emotion API.

[0494] Questions and feedback

[0495] The server periodically generates questions based on the user's learning progress using a generative AI model and sends them to the device. When the user answers the questions, the results are sent to the server, which evaluates the answers, determines whether they are correct, and generates feedback. For example, it may display an encouraging message such as "You'll succeed next time," motivating the user to continue learning.

[0496] Track your progress and increase motivation

[0497] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates progress reports and motivational messages, which are then provided to the user via their device. The server generates the most effective motivational messages by taking into account the data from the emotion engine.

[0498] Specific examples

[0499] For example, if the user selects "Intermediate level 5G troubleshooting," the following prompt sentence is input to the generative AI model:

[0500] Example prompt sentence:

[0501] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[0502] Based on this prompt, the generative AI model generates intermediate-level learning materials and specific questions for the user, providing appropriate feedback based on the user's emotional state, significantly improving learning efficiency and motivation.

[0503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0504] Step 1:

[0505] A user accesses the application using a terminal, clicks the "Register" button, and enters the required information (email address, password, etc.). The server receives the entered information and validates it before saving it to the database. If the validation passes, a confirmation email is sent to the user, notifying them that registration is complete. The input is the user's registration information, and the output is the registration information that will be saved in the database.

[0506] Step 2:

[0507] The user enters a username and password on the login screen. The device sends this information to the server, which checks it against information in a database. If the check is successful, the server starts a session and displays the user's home screen on the device. The input is the user's login information, and the output is the start of a session and the display of the home screen.

[0508] Step 3:

[0509] The user sets their learning level and learning goals on the home screen. The device displays these options, and the user selects the appropriate option. The selection information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. The generated learning materials and problem sets are sent to the device and displayed to the user. The input is the user's learning level and goals, and the output is a display of the customized learning materials and problem sets.

[0510] Step 4:

[0511] Users click on the provided learning materials to view them and progress through their studies. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time and evaluates the user's level of concentration and understanding. The input is the browsing history and emotion data, and the output is learning progress management and evaluation data.

[0512] Step 5:

[0513] As the user progresses with their learning, the server periodically generates questions using the generative AI model and sends them to the device. The user answers the questions and sends the answers to the server. The server evaluates the answers, determines whether they are correct or not, and generates the necessary feedback, which is sent to the device and displayed to the user. The input is the user's answer, and the output is the evaluation result and feedback.

[0514] Step 6:

[0515] The server periodically analyzes the user's learning progress information. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the terminal. Data from the emotion engine is also taken into consideration, and if the user is confused, the server provides additional explanations or simplified problems, and if the user is successful, it provides positive feedback. The input is learning progress information and emotion data, and the output is a progress report and motivational messages.

[0516] Specific examples

[0517] For example, if a user selects "Intermediate Level 5G Troubleshooting," the following prompt sentences will be input into the generative AI model to generate appropriate learning materials and questions:

[0518] Example prompt sentence:

[0519] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[0520] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0521] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0522] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0523] [Second embodiment]

[0524] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0525] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0526] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0527] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0528] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0529] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0530] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0531] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0532] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0533] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0534] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0535] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0536] The present invention is an online learning system that provides users with learning resources via their terminals, enabling them to effectively acquire knowledge about communication technologies and mobile phone base stations. Below, we will explain the specific program processing of this system in natural language.

[0537] User Registration and Login

[0538] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0539] Setting levels and objectives

[0540] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0541] Providing learning materials and learning

[0542] Users click on the provided learning materials (PDF, video, etc.) to view them. The server records the user's browsing history of the learning materials and manages their learning progress.

[0543] Questions and feedback

[0544] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback is displayed to the user via the device, and the user uses it as a guide to proceed to the next learning step.

[0545] Track your progress and increase motivation

[0546] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages and provides them to the user via their device. The user can check these to increase their motivation for learning.

[0547] Specific examples

[0548] If a user selects "Intermediate-level 5G troubleshooting," the generative AI model generates intermediate-level learning materials and specific troubleshooting problems based on that selection. For example, it includes case studies of typical 5G issues and techniques for improving performance. When the user answers the questions, the server receives the answer and provides feedback such as "That's right! You did a great job!" if the answer is correct, or "The correct answer is ____. Please check this part again." if the answer is incorrect.

[0549] In this way, the system supports efficient learning and skill acquisition by providing learning resources tailored to the user and appropriate feedback.

[0550] The processing flow will be explained below.

[0551] User Registration and Login

[0552] Step 1:

[0553] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[0554] Step 2:

[0555] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[0556] Step 3:

[0557] The user enters the required information and clicks the "Submit" button.

[0558] Step 4:

[0559] The terminal sends the entered information to the server in JSON format.

[0560] Step 5:

[0561] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[0562] Step 6:

[0563] The terminal displays a notification of successful registration to the user.

[0564] Step 7:

[0565] The user clicks the "Login" button on the home screen.

[0566] Step 8:

[0567] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[0568] Step 9:

[0569] The user enters the required information and clicks the "Login" button.

[0570] Step 10:

[0571] The terminal transmits the input information to the server.

[0572] Step 11:

[0573] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[0574] Step 12:

[0575] The server returns a login success message and dashboard data to the terminal.

[0576] Step 13:

[0577] The device displays a dashboard to show the user their current learning status.

[0578] Setting levels and objectives

[0579] Step 1:

[0580] On the dashboard, users click buttons to set their "learning level" and "learning objectives."

[0581] Step 2:

[0582] The terminal displays a drop-down menu, presenting the choices to the user.

[0583] Step 3:

[0584] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[0585] Step 4:

[0586] The terminal transmits the selected information to the server.

[0587] Step 5:

[0588] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[0589] Step 6:

[0590] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[0591] Step 7:

[0592] The terminal displays the generated teaching materials and problem sets to the user.

[0593] Provision of teaching materials

[0594] Step 1:

[0595] The user clicks on the link or file provided.

[0596] Step 2:

[0597] The device will display the linked educational material (PDF, video, etc.) or download the file.

[0598] Step 3:

[0599] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[0600] Questions and feedback

[0601] Step 1:

[0602] The server generates questions at regular intervals according to the user's learning progress.

[0603] Step 2:

[0604] The server sends the generated questions to the terminal.

[0605] Step 3:

[0606] The terminal displays the received problem to the user.

[0607] Step 4:

[0608] The user solves the problem, enters the answer, and clicks the "Submit" button.

[0609] Step 5:

[0610] The terminal transmits the user's answer to the server.

[0611] Step 6:

[0612] The server evaluates the received answers (e.g., determining whether they are correct or incorrect).

[0613] Step 7:

[0614] The server generates evaluation results and feedback messages and sends them to the terminal.

[0615] Step 8:

[0616] The terminal displays the feedback to the user.

[0617] Track your progress and increase motivation

[0618] Step 1:

[0619] The server records progress information for each learning session in a database.

[0620] Step 2:

[0621] The server periodically analyzes the progress and generates reports.

[0622] Step 3:

[0623] The server sends the generated report and motivation message to the terminal.

[0624] Step 4:

[0625] The terminal displays the received reports and messages to the user.

[0626] Step 5:

[0627] Users can check their progress and be motivated to move on to the next learning stage.

[0628] Example 1

[0629] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0630] Conventional online learning systems have limited means for efficiently tracking users' learning progress and providing appropriate feedback, making it difficult for users to effectively acquire knowledge. Furthermore, they often lack the ability to provide customized learning materials tailored to the user's learning level and goals, resulting in a lack of motivation to learn. The present invention aims to solve these problems and improve users' learning experience.

[0631] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0632] In this invention, the server includes: means for providing an interface accessible to a user via a terminal; means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives; means for providing the generated learning materials and questions to the user's terminal; means for receiving and evaluating the user's answers and generating feedback; means for recording and analyzing the user's learning progress information and generating motivational messages; means for recording the user's browsing history and managing the learning progress; and means for transmitting the learning materials and question sets generated based on the user's selected learning level and objectives to the user's terminal and displaying them to the user. This allows the user to receive individually tailored learning materials and feedback, enabling efficient learning and skill acquisition.

[0633] A "terminal" is a device that a user uses to access the system, and includes devices such as PCs, tablets, and smartphones.

[0634] "Interface" refers to the screen and input means that allow a user to interact with a system, and is a user interface that includes on-screen buttons, menus, etc.

[0635] A "generative AI model" refers to artificial intelligence technology that automatically generates customized teaching materials and questions based on user input.

[0636] "Learning Materials" means content provided for learning purposes, including formats such as PDFs, videos, and slide presentations.

[0637] "Questions" refer to questions or tasks that users answer to check their level of understanding, and come in multiple choice, written, and other formats.

[0638] "Feedback" refers to information that includes evaluation results and advice on the user's answers, and is intended to improve the user's learning.

[0639] "Study progress information" refers to data that records the user's learning progress, such as how much learning material the user has viewed and how many questions the user has answered.

[0640] A "motivational message" is a message containing encouragement or advice provided to increase the user's motivation to learn.

[0641] "Generated learning materials and problem sets" refers to learning materials and a set of problems that a generative AI model automatically creates based on the user's level and learning objectives.

[0642] A "study progress report" is a report that is periodically generated summarizing a user's learning progress.

[0643] "Analysis" refers to the process of evaluating the learning progress information collected by the server and identifying the user's learning situation and areas for improvement.

[0644] The present invention is an online learning system configured as follows, which performs various processes using a terminal, a server, and a generative AI model to enable users to effectively acquire knowledge.

[0645] System Configuration

[0646] 1. Hardware Configuration

[0647] Terminal: The device used by the user, including common devices such as PCs, tablets, and smartphones.

[0648] Server: A computer system that processes and manages data for the entire system. Includes database servers, application servers, etc.

[0649] 2. Software Configuration

[0650] Browser or dedicated app: A tool that provides an interface for users to access the system.

[0651] Generative AI model: Built using Python, TensorFlow, etc., it generates customized learning materials and questions based on the user's learning level and goals.

[0652] System action

[0653] User Registration and Login

[0654] Users access the system through a browser or a dedicated app. First, the device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information and verifies the input. Once verification is complete, the server saves the user information in a database and sends a confirmation email to the user. When a user logs in, they enter an email address and password, which the server verifies. If verification is successful, the user is taken to the home screen.

[0655] Setting levels and objectives

[0656] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0657] Providing learning materials and learning

[0658] The user selects and clicks on the material they want to study from the list of materials displayed on the device. For example, this could be a PDF or video. The server records the user's browsing history of the materials and stores it in a database. The user can then browse the materials and proceed with their learning.

[0659] Questions and feedback

[0660] As the learning process progresses, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers these questions and sends the results from the device to the server. The server receives the answers and evaluates them. For example, if the answer is correct, it displays "That's correct. You did a great job." If the answer is incorrect, it generates feedback such as "The correct answer is XX. Please check this part again."

[0661] Track your progress and increase motivation

[0662] The server records the user's answer history and learning material access history in a database. This data is periodically analyzed and a learning progress report is generated. Along with the progress report, the server also generates a motivational message to motivate the user to study and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[0663] Examples and prompts

[0664] If a user selects "Intermediate Level 5G Troubleshooting," the system will:

[0665] 1. From the home screen, the user navigates to the Learning Settings menu and selects the Intermediate level and 5G Troubleshooting.

[0666] 2. The server uses the generative AI model to generate intermediate-level troubleshooting materials (PDFs, videos, etc.) and problem sets.

[0667] 3. The terminal displays the generated teaching materials and problem sets to the user.

[0668] 4. The user clicks on the learning material and begins viewing it. The server records the user's browsing history.

[0669] 5. When the user has finished studying the learning material, the server sends the generated questions to the terminal, and the user answers the questions.

[0670] 6. The server evaluates the answer and provides feedback such as, "The correct answer is XX. Please check this part again."

[0671] 7. Learning progress information is stored on the server and progress reports are generated periodically.

[0672] This invention enables users to learn and master skills efficiently, and by using a generative AI model, it is possible to provide customized learning materials according to the user's learning needs, thereby enhancing the effectiveness of learning.

[0673] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0674] Step 1: User Registration

[0675] Users access the system using a browser or a dedicated app. The device displays the home screen, and when the user clicks the "Register" button, a screen for entering required information such as name, email address, and password is displayed. When the user enters the information and clicks the "Submit" button, the device sends the information to the server. The server receives the entered information and verifies the format of the email address and the strength of the password. Once this verification is complete, the server saves the user information in a database and generates and sends a confirmation email. When the user clicks the link in the email, registration is complete.

[0676] Input: User's name, email address, and password

[0677] Output: Sending a confirmation email, saving user information to a database

[0678] Step 2: Log in

[0679] The user enters their email address and password on the login screen. The device receives this and sends it to the server. The server checks the database to verify the information entered, and if it is correct, starts a session. The device displays a login success message and the user is taken to the home screen.

[0680] Input: User's email address, password

[0681] Output: Login success message, home screen displayed

[0682] Step 3: Set your level and objectives

[0683] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0684] Input: User's learning level, learning objectives

[0685] Output: Display of generated teaching materials and problem sets

[0686] Step 4: Provide learning materials and learn

[0687] The user clicks on the learning material (PDF, video, etc.) they want to learn from the list of learning materials displayed on the device and views it. The device displays the learning material and notifies the server of the user's click. The server records the user's browsing history of the learning materials accessed and saves it in a database. The user views the learning material and progresses with their learning.

[0688] Input: User's material selection (click)

[0689] Output: Display of teaching materials, saving of browsing history in database

[0690] Step 5: Questions and Answers

[0691] Once the user has finished viewing the learning material, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers the questions and sends the answers from the device to the server. The server receives the answers and evaluates them. Based on the results, feedback is generated and sent to the device. For example, if the answer is correct, it will display "That's correct. You did a great job." If the answer is incorrect, it will display "The correct answer is XX. Please check this part again."

[0692] Input: Content of the teaching material, user's answers

[0693] Output: Generated questions, feedback

[0694] Step 6: Track your progress and stay motivated

[0695] The server records the user's answer history and learning material access history in a database. It periodically analyzes this data and generates a learning progress report. The server also generates a motivational message along with the progress report and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[0696] Input: Answer history, learning material access history

[0697] Output: progress reports, motivational messages

[0698] (Application example 1)

[0699] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] Currently, online learning systems for efficiently learning about communications technology and mobile phone base stations are limited, and they lack the ability to provide learning materials optimized for individual users, track learning progress, and provide learning support for troubleshooting, especially for 5G technology. This reduces users' learning efficiency and makes it difficult to maintain motivation.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0702] In this invention, the server includes means for providing a user interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, and means for providing the generated learning materials and questions to the user's terminal, thereby enabling the user to effectively advance their learning related to communication technologies.

[0703] The server also includes a means for displaying and managing customized learning materials for users to advance their learning related to communication technologies, and a means for providing online troubleshooting and practical scenario-based questions and tallying up answers, thereby enabling more practical and interactive learning.

[0704] Furthermore, the generative AI model selects optimal learning materials and questions based on the learning level and learning goals entered by the user, making it possible to provide learning resources optimized for each individual user.The system also includes a means for periodically analyzing the user's learning progress information, generating learning progress reports, and sending these reports to the user's device, allowing the user to always be aware of their own learning progress, making it easier to maintain motivation.

[0705] "User interface" refers to the screen or operation panel that a user uses to access the system via a terminal.

[0706] "Generative AI model" refers to the artificial intelligence algorithm used to generate optimal learning materials and questions based on the user's learning level and goals.

[0707] "Customized learning materials" refers to learning resources created by generative AI models to suit a user's specific learning needs.

[0708] "Troubleshooting" refers to the diagnosis and resolution of system or technology related problems.

[0709] "Study progress information" refers to data that records the learning progress achieved by a user within a specific period of time.

[0710] "Motivational messages" refer to messages of encouragement and evaluation that are generated to increase the user's motivation to learn.

[0711] "Study Progress Report" refers to a document or digital file that compiles and analyzes a user's learning progress information.

[0712] "Terminal" refers to the electronic device (smartphone, tablet, PC, etc.) that a user uses to access the system.

[0713] The present invention is an online learning system for enabling users to efficiently learn about communication technologies and mobile phone base stations. A specific embodiment of this system will be described below.

[0714] System Overview

[0715] The server provides a user interface accessible to users via their terminals, through which users can access the system and perform operations such as registration, login, and learning progress.

[0716] The server generates customized learning materials and questions using a generative AI model based on the user's learning level and goals. This generative AI model selects the optimal learning materials and questions based on user input and provides them to the user's device.

[0717] Hardware and software used

[0718] Hardware: Devices (smartphones, tablets, PCs, etc.), servers

[0719] Software: User interface (browser or dedicated app), generative AI model, database, communication protocol (HTTP / HTTPS)

[0720] Data processing and calculation

[0721] 1. User Registration and Login:

[0722] A user accesses the user interface from a terminal and completes the registration process by entering their name, email address, and password. The server receives this information, stores it in a database, and sends a confirmation email. When logging in, the user enters their username and password, which the server verifies and starts a session.

[0723] 2. Setting learning levels and goals:

[0724] After logging in, users select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The selected information is sent to the server, and the generative AI model generates customized learning materials and questions.

[0725] 3. Providing learning materials and learning:

[0726] Users study using the provided learning materials (text, images, videos, etc.). The server records the user's learning progress in a database and manages the progress.

[0727] 4. Questions and feedback:

[0728] During the learning process, the server periodically generates questions and sends them to the user's device. The user answers the questions and sends them to the server. The server evaluates the answers, generates feedback, and provides it to the user.

[0729] 5. Track your progress and increase your motivation:

[0730] The server periodically analyzes the user's learning progress and generates progress reports and motivational messages, which are sent to the user's device, allowing the user to check their progress and stay motivated.

[0731] Example: User's intermediate level 5G troubleshooting training

[0732] If a user specifies that they want to learn "intermediate level 5G troubleshooting," the generative AI model will generate customized learning materials based on prompts like the following:

[0733] Example prompt sentence:

[0734] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[0735] 1. Basic troubleshooting techniques

[0736] 2. Common Problem Examples and Solutions

[0737] 3. Practical scenarios in the field

[0738] The generated learning materials include basic troubleshooting techniques, typical 5G trouble cases, and detailed explanations of their solutions. Users can use these learning materials to study and acquire practical knowledge by answering questions provided by the server.

[0739] In this way, users can learn about communication technologies at their own pace and the system can provide appropriate feedback and support.

[0740] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0741] Step 1:

[0742] User Registration and Login

[0743] A user accesses the user interface using a terminal.

[0744] Input: Name, Email Address, Password

[0745] Processing: The server receives the entered information and stores it in a database. Once registration is complete, the server sends a confirmation email. When a user logs in, they enter their username and password, which the server checks against the database, and if authentication is successful, a session begins.

[0746] Output: Login confirmation message, user session started

[0747] Step 2:

[0748] Setting learning levels and goals

[0749] After logging in, the user proceeds to a screen where they can set their learning level and goals.

[0750] Input: Learning level (beginner, intermediate, advanced), learning objectives (e.g., 5G basic knowledge, troubleshooting)

[0751] Processing: The device sends the selected information to the server, which uses a generative AI model to generate customized learning materials and questions based on the input learning level and objectives.

[0752] Output: A customized set of study materials and questions

[0753] Step 3:

[0754] Providing learning materials and learning

[0755] Users can view the provided learning materials (text, images, videos, etc.) on their devices and progress through their studies.

[0756] Input: Generated teaching materials

[0757] Processing: The server records the user's access history and learning progress in a database.

[0758] Output: Teaching material viewing screen, learning progress information update

[0759] Step 4:

[0760] Questions and feedback

[0761] As the user progresses with their studies, the server periodically generates questions and sends them to the device. The user answers the questions and sends them to the server.

[0762] Input: User's answer

[0763] Processing: The server evaluates the answer and generates feedback such as "That's right. You did a great job." if the answer is correct, or "The correct answer is XX. Please check this part again." if the answer is incorrect, and sends it to the device.

[0764] Output: Feedback message

[0765] Step 5:

[0766] Tracking learning progress and generating motivational messages

[0767] The server periodically analyzes the user's learning progress information and generates progress reports and motivational messages, which are provided to the user via the terminal.

[0768] Input: Learning progress information

[0769] Processing: The server analyzes the learning progress information recorded in the database, generates a progress report and a motivation message, and sends the generated report and message to the terminal.

[0770] Output: progress reports, motivational messages

[0771] Example: User's intermediate level 5G troubleshooting training

[0772] If a user wants to learn "Intermediate Level 5G Troubleshooting," the server will provide the generative AI model with prompts like this:

[0773] Example prompt sentence:

[0774] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[0775] 1. Basic troubleshooting techniques

[0776] 2. Common Problem Examples and Solutions

[0777] 3. Practical scenarios in the field

[0778] Based on this prompt, the generative AI model generates customized learning materials and provides them to the user's device. The user can then view the materials and answer questions to acquire practical knowledge.

[0779] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0780] The present invention provides an online learning system that allows users to effectively acquire knowledge about communication technologies and mobile phone base stations by providing an interface accessible via a terminal. It also combines an emotion engine that recognizes the user's emotions and adjusts the learning content and feedback accordingly, providing a more personalized educational experience.

[0781] User Registration and Login

[0782] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0783] Setting levels and objectives

[0784] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0785] Emotion engine integration

[0786] The system integrates an emotion engine to recognize the user's emotional state in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotional data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[0787] Providing learning materials and learning

[0788] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed.

[0789] Questions and feedback

[0790] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[0791] Track your progress and increase motivation

[0792] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[0793] Specific examples

[0794] For example, if a user selects "Intermediate-level 5G troubleshooting," the generative AI model will generate intermediate-level learning materials and specific troubleshooting problems based on that selection. While the user is answering the questions, the emotion engine analyzes the user's facial expressions and voice. If confusion or stress is detected, the server will provide additional explanations or simplified questions. If the answer is correct, the server will provide positive feedback, such as "Great! Keep it up!"

[0795] In this way, the system recognizes the user's emotional state and provides appropriate feedback and learning resources accordingly, greatly improving the user's learning efficiency and motivation.

[0796] The processing flow will be explained below.

[0797] User Registration and Login

[0798] Step 1:

[0799] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[0800] Step 2:

[0801] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[0802] Step 3:

[0803] The user enters the required information and clicks the "Submit" button.

[0804] Step 4:

[0805] The terminal sends the entered information to the server in JSON format.

[0806] Step 5:

[0807] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[0808] Step 6:

[0809] The terminal displays a notification of successful registration to the user.

[0810] Step 7:

[0811] The user clicks the "Login" button on the home screen.

[0812] Step 8:

[0813] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[0814] Step 9:

[0815] The user enters the required information and clicks the "Login" button.

[0816] Step 10:

[0817] The terminal transmits the input information to the server.

[0818] Step 11:

[0819] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[0820] Step 12:

[0821] The server returns a login success message and dashboard data to the terminal.

[0822] Step 13:

[0823] The device displays a dashboard to show the user their current learning status.

[0824] Setting levels and objectives

[0825] Step 1:

[0826] Users click buttons on the dashboard to set their "learning level" and "learning objectives."

[0827] Step 2:

[0828] The terminal displays a drop-down menu, presenting the choices to the user.

[0829] Step 3:

[0830] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[0831] Step 4:

[0832] The terminal transmits the selected information to the server.

[0833] Step 5:

[0834] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[0835] Step 6:

[0836] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[0837] Step 7:

[0838] The terminal displays the generated teaching materials and problem sets to the user.

[0839] Emotion engine integration

[0840] Step 1:

[0841] The server collects the user's facial expression, voice and input data in real time through the emotion engine.

[0842] Step 2:

[0843] The server analyzes the data obtained from the emotion engine to determine the user's emotional state (e.g., confusion, stress, concentration).

[0844] Step 3:

[0845] The server takes into account the user's emotional state and adjusts the learning content and feedback provided to the user.

[0846] Providing learning materials and learning

[0847] Step 1:

[0848] Users click on the provided link or file to view the materials.

[0849] Step 2:

[0850] The device will display the linked educational material (PDF, video, etc.) or download the file.

[0851] Step 3:

[0852] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[0853] Step 4:

[0854] The emotion engine assesses the user's level of attention and comprehension and adjusts the learning content as needed.

[0855] Step 5:

[0856] If the user is feeling stressed, the server provides supplementary materials to help them relax.

[0857] Questions and feedback

[0858] Step 1:

[0859] The server periodically generates questions according to the user's learning progress.

[0860] Step 2:

[0861] The server sends the generated questions to the terminal.

[0862] Step 3:

[0863] The terminal displays the received problem to the user.

[0864] Step 4:

[0865] The user solves the problem, enters the answer, and clicks the "Submit" button.

[0866] Step 5:

[0867] The terminal transmits the user's answer to the server.

[0868] Step 6:

[0869] The server evaluates the received answers (e.g., determining whether they are correct or incorrect) and generates feedback.

[0870] Step 7:

[0871] Feedback includes advice and motivational messages based on data obtained from the emotion engine.

[0872] Step 8:

[0873] The terminal displays the feedback to the user.

[0874] Track your progress and increase motivation

[0875] Step 1:

[0876] The server records progress information for each learning session in a database.

[0877] Step 2:

[0878] The server periodically analyzes the progress and generates a progress report.

[0879] Step 3:

[0880] The server sends the generated report and motivation message to the terminal.

[0881] Step 4:

[0882] The terminal displays the received reports and messages to the user.

[0883] Step 5:

[0884] Users can check their progress and be motivated to move on to the next learning stage.

[0885] Specific examples

[0886] Step 1:

[0887] A user selects "Intermediate Level 5G Troubleshooting."

[0888] Step 2:

[0889] The generative AI model generates intermediate-level learning materials and specific troubleshooting problems.

[0890] Step 3:

[0891] As the user browses the learning materials and answers the questions, the server analyzes the user's facial expressions and voice using an emotion engine.

[0892] Step 4:

[0893] If confusion or stress is detected, the server will provide additional explanations or simplified questions.

[0894] Step 5:

[0895] If you get it right, the server generates positive feedback like "Great! Keep it up next time!"

[0896] Step 6:

[0897] To provide motivational messages as appropriate so that users can maintain their motivation while studying.

[0898] This is the specific processing flow of a system that integrates an emotion engine. By recognizing the user's emotional state in real time and providing appropriate feedback and learning resources accordingly, it is possible to significantly improve the user's learning efficiency and motivation.

[0899] Example 2

[0900] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0901] Conventional online learning systems have the problem of being difficult to respond to the learning level and emotional state of individual users. As a result, it is difficult to expect improvements in user learning efficiency and motivation. In addition, learning progress and feedback are often simplified, making it difficult to provide a fully personalized learning experience.

[0902] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0903] In this invention, the server includes means for providing a user with an interface accessible via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for recognizing the user's emotional state in real time, and means for adjusting learning content and feedback based on the recognized emotions, thereby enabling a personalized learning experience according to each user's emotional state and learning progress.

[0904] "User" refers to an individual or corporation that accesses the system and engages in learning activities.

[0905] "Terminal" refers to a device, such as a computer, smartphone, or tablet, that a user uses to access the system.

[0906] "Interface" refers to the software and hardware components that provide the screens and controls for a user to interact with a system.

[0907] A "generative AI model" refers to an artificial intelligence model that automatically generates optimal teaching materials and questions based on user input information (e.g., learning level, learning objectives).

[0908] "Teaching materials" refers to educational content such as PDFs, videos, and texts that users can use to study.

[0909] "Questions" refer to questions or exercises that are used to assess whether the user has understood the learning content.

[0910] "Feedback" refers to the evaluation and advice provided to the user based on the answers they provide.

[0911] "Study progress information" refers to data about how far a user has progressed in their studies (e.g., the learning materials accessed, the questions answered, the level of understanding, etc.).

[0912] "Motivational messages" refer to messages of encouragement or advice provided to increase a user's motivation to learn.

[0913] "Emotional state" refers to the user's current emotion (e.g., joy, confusion, stress, etc.).

[0914] An "emotion engine" refers to a system that recognizes a user's emotional state in real time from facial expressions, voice, input data, etc.

[0915] A "personalized learning experience" refers to a learning experience that is customized according to the learning level, learning goals, and emotional state of each individual user.

[0916] The system of the present invention is an online learning system that provides users with an interface accessible via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. The system is implemented using the following hardware and software.

[0917] Hardware and Software

[0918] Hardware: The devices used by users (e.g., computers, smartphones, tablets) and servers.

[0919] Software: A browser or dedicated app to provide the interface, a generative AI model (e.g., GPT-3), and an emotion engine (e.g., Emotion API).

[0920] User Registration and Login

[0921] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information, verifies it, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0922] Setting levels and objectives

[0923] Once a user logs in, they are directed to a menu where they can set their learning level and goal. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[0924] An example prompt is:

[0925] "Create intermediate level teaching materials and questions on the basics of 5G."

[0926] Examples include:

[0927] Emotion engine integration

[0928] The system integrates an emotion engine to recognize the user's emotions in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotion data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[0929] Providing learning materials and learning

[0930] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension, and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed. This creates a personalized learning experience.

[0931] Questions and feedback

[0932] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[0933] Track your progress and increase motivation

[0934] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[0935] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0936] Step 1: User Registration

[0937] Input: The user launches a browser or a dedicated app and accesses the home screen. The user clicks the "Register" button and enters information such as their name, email address, and password.

[0938] Specific operation: The terminal displays an input form into which the user enters information. Once the information is entered, the terminal sends it to the server.

[0939] Data processing and calculation: The server verifies the received information and checks whether the information is correct. If it is correct, it stores it in the database.

[0940] Output: The server sends a confirmation email to the user informing them that their registration is complete.

[0941] Step 2: Log in

[0942] Input: The user opens the login screen and enters their username and password.

[0943] Specific operation: The terminal displays a login form, the user enters information, and the terminal sends the input information to the server.

[0944] Data processing and calculation: The server checks the received information against the database and determines whether the authentication was successful.

[0945] Output: The server notifies the terminal that the login was successful and redirects the user to the dashboard screen.

[0946] Step 3: Set your learning level and goals

[0947] Input: After logging in, the user selects the learning level (beginner, intermediate, advanced) and purpose (e.g., 5G basic knowledge, troubleshooting).

[0948] Specific operation: The device displays a drop-down menu, the user selects an option, and the device sends the selected information to the server.

[0949] Data processing and calculation: Based on the received information, the server sends prompts to the generative AI model, requesting it to generate appropriate teaching materials and questions.

[0950] Output: The server receives the teaching materials and problem sets generated by the generative AI model and sends them to the user's device for display.

[0951] Step 4: Emotion Recognition

[0952] Input: Facial expressions and voice data while the user is using the system.

[0953] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice, and transmits the data to the server in real time.

[0954] Data processing and calculation: The server uses an emotion engine to analyze the data and recognize the user's emotional state.

[0955] Output: The server generates emotion-based learning feedback and motivational messages and sends them to the device.

[0956] Step 5: Provide learning materials and learn

[0957] Input: The user selects and views the provided learning materials (PDF, video, etc.).

[0958] Specific operation: The terminal displays a link to the teaching material, and when the user clicks on it, the teaching material is displayed.

[0959] Data processing and calculation: The server records which learning materials the user has viewed and tracks the user's browsing history. The emotion engine evaluates the user's level of concentration and comprehension.

[0960] Output: The server adjusts the learning content or provides supplementary material as needed.

[0961] Step 6: Questions and feedback

[0962] Input: Questions presented to the user as they progress through the learning process.

[0963] Specific operation: The server sends prompts to the generative AI model to generate appropriate questions. The device displays the questions and the user inputs the answers.

[0964] Data processing and calculation: The server evaluates the received answers and determines whether they are correct or not. It also refers to the emotion engine to consider the user's emotions.

[0965] Output: The server generates evaluation results and feedback and sends them to the device.

[0966] Step 7: Track your progress and stay motivated

[0967] Input: User's learning progress data.

[0968] Specific operation: The server records the user's learning progress information in a database.

[0969] Data Processing and Computation: The server periodically analyzes the progress data and uses the generative AI model to generate progress reports and motivational messages.

[0970] Output: The generated reports and motivation messages are sent to the terminal and presented to the user.

[0971] (Application example 2)

[0972] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0973] Conventional online learning systems can provide learning materials based on a user's learning progress and level of understanding, but lack the functionality to adjust learning content and feedback taking into account the user's emotional state. As a result, users often become discouraged or lose motivation to learn, making efficient learning difficult. Furthermore, because there is no mechanism for recognizing emotions in real time and adjusting learning content and feedback based on that, it is not possible to provide an optimal educational experience for each individual user. This problem needed to be solved.

[0974] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for using an emotion engine that recognizes the user's emotional state in real time using the terminal's camera and microphone, and means for adjusting learning feedback and motivational messages based on the emotional state. This allows the server to provide optimal learning feedback and motivational messages according to the user's emotional state, enabling efficient and effective learning.

[0975] A "terminal" is a digital device that allows a user to access and operate the system.

[0976] An "interface" is a screen or operating means that allows a user to access and operate the system via a terminal.

[0977] A "generative AI model" is an artificial intelligence algorithm that generates customized learning materials and questions based on the user's level and learning goals.

[0978] "Teaching materials" refers to information and materials for users to study, and includes formats such as e-books, videos, and PDFs.

[0979] "Questions" are questions or tasks that allow users to check their learning content and measure their level of understanding.

[0980] "Feedback" refers to evaluations and advice provided based on the user's answers and learning progress.

[0981] "Motivational messages" are messages of encouragement or encouragement provided to increase the user's motivation to learn.

[0982] The "emotion engine" is a system that uses the device's camera and microphone to recognize and analyze the user's emotional state in real time.

[0983] "Emotional state" refers to the state or change of an emotion a user is feeling, including, for example, confusion, stress, excitement, joy, and the like.

[0984] The present invention provides an online learning system that provides users with an interface that can be accessed via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. Specific methods for implementing the present invention are described below.

[0985] User Registration and Login

[0986] Users access the application through a device such as a smartphone or tablet. A "Register" button appears on the device screen, and the user enters the required information. The server then verifies this information and stores it in a database. Once registration is complete, a confirmation email is sent. When logging in, the user enters their username and password, which the server verifies and starts the session.

[0987] Setting levels and objectives

[0988] After logging in, users are directed to a screen where they can select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The device displays these options, and the user selects the appropriate option. This information is sent to the server, and the generative AI model uses this information to generate optimal learning materials and problem sets. The generated learning materials and problem sets are then sent to the device and displayed to the user.

[0989] Providing learning materials and learning

[0990] Users click on the provided learning materials (e.g., PDFs, videos) to view them. The server records the user's browsing history and manages their learning progress. The content and display of the learning materials are handled by HTML5, PDF viewers, video streaming players, etc. The emotion engine uses the device's camera and microphone to recognize the user's emotional state in real time and adjust the learning content accordingly.

[0991] Emotion engine integration

[0992] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice to recognize the user's emotional state. This allows it to provide additional hints or simplified explanations if the user is confused, and positive feedback if the user is successful. This is done using emotion recognition technologies such as Google Cloud Vision API and Microsoft Azure Emotion API.

[0993] Questions and feedback

[0994] The server periodically generates questions based on the user's learning progress using a generative AI model and sends them to the device. When the user answers the questions, the results are sent to the server, which evaluates the answers, determines whether they are correct, and generates feedback. For example, it may display an encouraging message such as "You'll succeed next time," motivating the user to continue learning.

[0995] Track your progress and increase motivation

[0996] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates progress reports and motivational messages, which are then provided to the user via their device. The server generates the most effective motivational messages by taking into account the data from the emotion engine.

[0997] Specific examples

[0998] For example, if the user selects "Intermediate level 5G troubleshooting," the following prompt sentence is input to the generative AI model:

[0999] Example prompt sentence:

[1000] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[1001] Based on this prompt, the generative AI model generates intermediate-level learning materials and specific questions for the user, providing appropriate feedback based on the user's emotional state, significantly improving learning efficiency and motivation.

[1002] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1003] Step 1:

[1004] A user accesses the application using a terminal, clicks the "Register" button, and enters the required information (email address, password, etc.). The server receives the entered information and validates it before saving it to the database. If the validation passes, a confirmation email is sent to the user, notifying them that registration is complete. The input is the user's registration information, and the output is the registration information that will be saved in the database.

[1005] Step 2:

[1006] The user enters a username and password on the login screen. The device sends this information to the server, which checks it against information in a database. If the check is successful, the server starts a session and displays the user's home screen on the device. The input is the user's login information, and the output is the start of a session and the display of the home screen.

[1007] Step 3:

[1008] The user sets their learning level and learning goals on the home screen. The device displays these options, and the user selects the appropriate option. The selection information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. The generated learning materials and problem sets are sent to the device and displayed to the user. The input is the user's learning level and goals, and the output is a display of the customized learning materials and problem sets.

[1009] Step 4:

[1010] Users click on the provided learning materials to view them and progress through their studies. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time and evaluates the user's level of concentration and understanding. The input is the browsing history and emotion data, and the output is learning progress management and evaluation data.

[1011] Step 5:

[1012] As the user progresses with their learning, the server periodically generates questions using the generative AI model and sends them to the device. The user answers the questions and sends the answers to the server. The server evaluates the answers, determines whether they are correct or not, and generates the necessary feedback, which is sent to the device and displayed to the user. The input is the user's answer, and the output is the evaluation result and feedback.

[1013] Step 6:

[1014] The server periodically analyzes the user's learning progress information. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the terminal. Data from the emotion engine is also taken into consideration, and if the user is confused, the server provides additional explanations or simplified problems, and if the user is successful, it provides positive feedback. The input is learning progress information and emotion data, and the output is a progress report and motivational messages.

[1015] Specific examples

[1016] For example, if a user selects "Intermediate Level 5G Troubleshooting," the following prompt sentences will be input into the generative AI model to generate appropriate learning materials and questions:

[1017] Example prompt sentence:

[1018] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[1019] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1020] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1021] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1022] [Third embodiment]

[1023] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1024] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1026] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1027] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1028] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1030] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1031] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1033] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1034] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1035] The present invention is an online learning system that provides users with learning resources via their terminals, enabling them to effectively acquire knowledge about communication technologies and mobile phone base stations. Below, we will explain the specific program processing of this system in natural language.

[1036] User Registration and Login

[1037] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1038] Setting levels and objectives

[1039] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1040] Providing learning materials and learning

[1041] Users click on the provided learning materials (PDF, video, etc.) to view them. The server records the user's browsing history of the learning materials and manages their learning progress.

[1042] Questions and feedback

[1043] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback is displayed to the user via the device, and the user uses it as a guide to proceed to the next learning step.

[1044] Track your progress and increase motivation

[1045] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages and provides them to the user via their device. The user can check these to increase their motivation for learning.

[1046] Specific examples

[1047] If a user selects "Intermediate-level 5G troubleshooting," the generative AI model generates intermediate-level learning materials and specific troubleshooting problems based on that selection. For example, it includes case studies of typical 5G issues and techniques for improving performance. When the user answers the questions, the server receives the answer and provides feedback such as "That's right! You did a great job!" if the answer is correct, or "The correct answer is ____. Please check this part again." if the answer is incorrect.

[1048] In this way, the system supports efficient learning and skill acquisition by providing learning resources tailored to the user and appropriate feedback.

[1049] The processing flow will be explained below.

[1050] User Registration and Login

[1051] Step 1:

[1052] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[1053] Step 2:

[1054] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[1055] Step 3:

[1056] The user enters the required information and clicks the "Submit" button.

[1057] Step 4:

[1058] The terminal sends the entered information to the server in JSON format.

[1059] Step 5:

[1060] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[1061] Step 6:

[1062] The terminal displays a notification of successful registration to the user.

[1063] Step 7:

[1064] The user clicks the "Login" button on the home screen.

[1065] Step 8:

[1066] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[1067] Step 9:

[1068] The user enters the required information and clicks the "Login" button.

[1069] Step 10:

[1070] The terminal transmits the input information to the server.

[1071] Step 11:

[1072] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[1073] Step 12:

[1074] The server returns a login success message and dashboard data to the terminal.

[1075] Step 13:

[1076] The device displays a dashboard to show the user their current learning status.

[1077] Setting levels and objectives

[1078] Step 1:

[1079] On the dashboard, users click buttons to set their "learning level" and "learning objectives."

[1080] Step 2:

[1081] The terminal displays a drop-down menu, presenting the choices to the user.

[1082] Step 3:

[1083] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[1084] Step 4:

[1085] The terminal transmits the selected information to the server.

[1086] Step 5:

[1087] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[1088] Step 6:

[1089] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[1090] Step 7:

[1091] The terminal displays the generated teaching materials and problem sets to the user.

[1092] Provision of teaching materials

[1093] Step 1:

[1094] The user clicks on the link or file provided.

[1095] Step 2:

[1096] The device will display the linked educational material (PDF, video, etc.) or download the file.

[1097] Step 3:

[1098] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[1099] Questions and feedback

[1100] Step 1:

[1101] The server generates questions at regular intervals according to the user's learning progress.

[1102] Step 2:

[1103] The server sends the generated questions to the terminal.

[1104] Step 3:

[1105] The terminal displays the received problem to the user.

[1106] Step 4:

[1107] The user solves the problem, enters the answer, and clicks the "Submit" button.

[1108] Step 5:

[1109] The terminal transmits the user's answer to the server.

[1110] Step 6:

[1111] The server evaluates the received answers (e.g., determining whether they are correct or incorrect).

[1112] Step 7:

[1113] The server generates evaluation results and feedback messages and sends them to the terminal.

[1114] Step 8:

[1115] The terminal displays the feedback to the user.

[1116] Track your progress and increase motivation

[1117] Step 1:

[1118] The server records progress information for each learning session in a database.

[1119] Step 2:

[1120] The server periodically analyzes the progress and generates reports.

[1121] Step 3:

[1122] The server sends the generated report and motivation message to the terminal.

[1123] Step 4:

[1124] The terminal displays the received reports and messages to the user.

[1125] Step 5:

[1126] Users can check their progress and be motivated to move on to the next learning stage.

[1127] Example 1

[1128] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1129] Conventional online learning systems have limited means for efficiently tracking users' learning progress and providing appropriate feedback, making it difficult for users to effectively acquire knowledge. Furthermore, they often lack the ability to provide customized learning materials tailored to the user's learning level and goals, resulting in a lack of motivation to learn. The present invention aims to solve these problems and improve users' learning experience.

[1130] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1131] In this invention, the server includes: means for providing an interface accessible to a user via a terminal; means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives; means for providing the generated learning materials and questions to the user's terminal; means for receiving and evaluating the user's answers and generating feedback; means for recording and analyzing the user's learning progress information and generating motivational messages; means for recording the user's browsing history and managing the learning progress; and means for transmitting the learning materials and question sets generated based on the user's selected learning level and objectives to the user's terminal and displaying them to the user. This allows the user to receive individually tailored learning materials and feedback, enabling efficient learning and skill acquisition.

[1132] A "terminal" is a device that a user uses to access the system, and includes devices such as PCs, tablets, and smartphones.

[1133] "Interface" refers to the screen and input means that allow a user to interact with a system, and is a user interface that includes on-screen buttons, menus, etc.

[1134] A "generative AI model" refers to artificial intelligence technology that automatically generates customized teaching materials and questions based on user input.

[1135] "Learning Materials" means content provided for learning purposes, including formats such as PDFs, videos, and slide presentations.

[1136] "Questions" refer to questions or tasks that users answer to check their level of understanding, and come in multiple choice, written, and other formats.

[1137] "Feedback" refers to information that includes evaluation results and advice on the user's answers, and is intended to improve the user's learning.

[1138] "Study progress information" refers to data that records the user's learning progress, such as how much learning material the user has viewed and how many questions the user has answered.

[1139] A "motivational message" is a message containing encouragement or advice provided to increase the user's motivation to learn.

[1140] "Generated learning materials and problem sets" refers to learning materials and a set of problems that a generative AI model automatically creates based on the user's level and learning objectives.

[1141] A "study progress report" is a report that is periodically generated summarizing a user's learning progress.

[1142] "Analysis" refers to the process of evaluating the learning progress information collected by the server and identifying the user's learning situation and areas for improvement.

[1143] The present invention is an online learning system configured as follows, which performs various processes using a terminal, a server, and a generative AI model to enable users to effectively acquire knowledge.

[1144] System Configuration

[1145] 1. Hardware Configuration

[1146] Terminal: The device used by the user, including common devices such as PCs, tablets, and smartphones.

[1147] Server: A computer system that processes and manages data for the entire system. Includes database servers, application servers, etc.

[1148] 2. Software Configuration

[1149] Browser or dedicated app: A tool that provides an interface for users to access the system.

[1150] Generative AI model: Built using Python, TensorFlow, etc., it generates customized learning materials and questions based on the user's learning level and goals.

[1151] System action

[1152] User Registration and Login

[1153] Users access the system through a browser or a dedicated app. First, the device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information and verifies the input. Once verification is complete, the server saves the user information in a database and sends a confirmation email to the user. When a user logs in, they enter an email address and password, which the server verifies. If verification is successful, the user is taken to the home screen.

[1154] Setting levels and objectives

[1155] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1156] Providing learning materials and learning

[1157] The user selects and clicks on the material they want to study from the list of materials displayed on the device. For example, this could be a PDF or video. The server records the user's browsing history of the materials and stores it in a database. The user can then browse the materials and proceed with their learning.

[1158] Questions and feedback

[1159] As the learning process progresses, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers these questions and sends the results from the device to the server. The server receives the answers and evaluates them. For example, if the answer is correct, it displays "That's correct. You did a great job." If the answer is incorrect, it generates feedback such as "The correct answer is XX. Please check this part again."

[1160] Track your progress and increase motivation

[1161] The server records the user's answer history and learning material access history in a database. This data is periodically analyzed and a learning progress report is generated. Along with the progress report, the server also generates a motivational message to motivate the user to study and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[1162] Examples and prompts

[1163] If a user selects "Intermediate Level 5G Troubleshooting," the system will:

[1164] 1. From the home screen, the user navigates to the Learning Settings menu and selects the Intermediate level and 5G Troubleshooting.

[1165] 2. The server uses the generative AI model to generate intermediate-level troubleshooting materials (PDFs, videos, etc.) and problem sets.

[1166] 3. The terminal displays the generated teaching materials and problem sets to the user.

[1167] 4. The user clicks on the learning material and begins viewing it. The server records the user's browsing history.

[1168] 5. When the user has finished studying the learning material, the server sends the generated questions to the terminal, and the user answers the questions.

[1169] 6. The server evaluates the answer and provides feedback such as, "The correct answer is XX. Please check this part again."

[1170] 7. Learning progress information is stored on the server and progress reports are generated periodically.

[1171] This invention enables users to learn and master skills efficiently, and by using a generative AI model, it is possible to provide customized learning materials according to the user's learning needs, thereby enhancing the effectiveness of learning.

[1172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1173] Step 1: User Registration

[1174] Users access the system using a browser or a dedicated app. The device displays the home screen, and when the user clicks the "Register" button, a screen for entering required information such as name, email address, and password is displayed. When the user enters the information and clicks the "Submit" button, the device sends the information to the server. The server receives the entered information and verifies the format of the email address and the strength of the password. Once this verification is complete, the server saves the user information in a database and generates and sends a confirmation email. When the user clicks the link in the email, registration is complete.

[1175] Input: User's name, email address, and password

[1176] Output: Sending a confirmation email, saving user information to a database

[1177] Step 2: Log in

[1178] The user enters their email address and password on the login screen. The device receives this and sends it to the server. The server checks the database to verify the information entered, and if it is correct, starts a session. The device displays a login success message and the user is taken to the home screen.

[1179] Input: User's email address, password

[1180] Output: Login success message, home screen displayed

[1181] Step 3: Set your level and objectives

[1182] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1183] Input: User's learning level, learning objectives

[1184] Output: Display of generated teaching materials and problem sets

[1185] Step 4: Provide learning materials and learn

[1186] The user clicks on the learning material (PDF, video, etc.) they want to learn from the list of learning materials displayed on the device and views it. The device displays the learning material and notifies the server of the user's click. The server records the user's browsing history of the learning materials accessed and saves it in a database. The user views the learning material and progresses with their learning.

[1187] Input: User's material selection (click)

[1188] Output: Display of teaching materials, saving of browsing history in database

[1189] Step 5: Questions and Answers

[1190] Once the user has finished viewing the learning material, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers the questions and sends the answers from the device to the server. The server receives the answers and evaluates them. Based on the results, feedback is generated and sent to the device. For example, if the answer is correct, it will display "That's correct. You did a great job." If the answer is incorrect, it will display "The correct answer is XX. Please check this part again."

[1191] Input: Content of the teaching material, user's answers

[1192] Output: Generated questions, feedback

[1193] Step 6: Track your progress and stay motivated

[1194] The server records the user's answer history and learning material access history in a database. It periodically analyzes this data and generates a learning progress report. The server also generates a motivational message along with the progress report and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[1195] Input: Answer history, learning material access history

[1196] Output: progress reports, motivational messages

[1197] (Application example 1)

[1198] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1199] Currently, online learning systems for efficiently learning about communications technology and mobile phone base stations are limited, and they lack the ability to provide learning materials optimized for individual users, track learning progress, and provide learning support for troubleshooting, especially for 5G technology. This reduces users' learning efficiency and makes it difficult to maintain motivation.

[1200] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1201] In this invention, the server includes means for providing a user interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, and means for providing the generated learning materials and questions to the user's terminal, thereby enabling the user to effectively advance their learning related to communication technologies.

[1202] The server also includes a means for displaying and managing customized learning materials for users to advance their learning related to communication technologies, and a means for providing online troubleshooting and practical scenario-based questions and tallying up answers, thereby enabling more practical and interactive learning.

[1203] Furthermore, the generative AI model selects optimal learning materials and questions based on the learning level and learning goals entered by the user, making it possible to provide learning resources optimized for each individual user.The system also includes a means for periodically analyzing the user's learning progress information, generating learning progress reports, and sending these reports to the user's device, allowing the user to always be aware of their own learning progress, making it easier to maintain motivation.

[1204] "User interface" refers to the screen or operation panel that a user uses to access the system via a terminal.

[1205] "Generative AI model" refers to the artificial intelligence algorithm used to generate optimal learning materials and questions based on the user's learning level and goals.

[1206] "Customized learning materials" refers to learning resources created by generative AI models to suit a user's specific learning needs.

[1207] "Troubleshooting" refers to the diagnosis and resolution of system or technology related problems.

[1208] "Study progress information" refers to data that records the learning progress achieved by a user within a specific period of time.

[1209] "Motivational messages" refer to messages of encouragement and evaluation that are generated to increase the user's motivation to learn.

[1210] "Study Progress Report" refers to a document or digital file that compiles and analyzes a user's learning progress information.

[1211] "Terminal" refers to the electronic device (smartphone, tablet, PC, etc.) that a user uses to access the system.

[1212] The present invention is an online learning system for enabling users to efficiently learn about communication technologies and mobile phone base stations. A specific embodiment of this system will be described below.

[1213] System Overview

[1214] The server provides a user interface accessible to users via their terminals, through which users can access the system and perform operations such as registration, login, and learning progress.

[1215] The server generates customized learning materials and questions using a generative AI model based on the user's learning level and goals. This generative AI model selects the optimal learning materials and questions based on user input and provides them to the user's device.

[1216] Hardware and software used

[1217] Hardware: Devices (smartphones, tablets, PCs, etc.), servers

[1218] Software: User interface (browser or dedicated app), generative AI model, database, communication protocol (HTTP / HTTPS)

[1219] Data processing and calculation

[1220] 1. User Registration and Login:

[1221] A user accesses the user interface from a terminal and completes the registration process by entering their name, email address, and password. The server receives this information, stores it in a database, and sends a confirmation email. When logging in, the user enters their username and password, which the server verifies and starts a session.

[1222] 2. Setting learning levels and goals:

[1223] After logging in, users select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The selected information is sent to the server, and the generative AI model generates customized learning materials and questions.

[1224] 3. Providing learning materials and learning:

[1225] Users study using the provided learning materials (text, images, videos, etc.). The server records the user's learning progress in a database and manages the progress.

[1226] 4. Questions and feedback:

[1227] During the learning process, the server periodically generates questions and sends them to the user's device. The user answers the questions and sends them to the server. The server evaluates the answers, generates feedback, and provides it to the user.

[1228] 5. Track your progress and increase your motivation:

[1229] The server periodically analyzes the user's learning progress and generates progress reports and motivational messages, which are sent to the user's device, allowing the user to check their progress and stay motivated.

[1230] Example: User's intermediate level 5G troubleshooting training

[1231] If a user specifies that they want to learn "intermediate level 5G troubleshooting," the generative AI model will generate customized learning materials based on prompts like the following:

[1232] Example prompt sentence:

[1233] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[1234] 1. Basic troubleshooting techniques

[1235] 2. Common Problem Examples and Solutions

[1236] 3. Practical scenarios in the field

[1237] The generated learning materials include basic troubleshooting techniques, typical 5G trouble cases, and detailed explanations of their solutions. Users can use these learning materials to study and acquire practical knowledge by answering questions provided by the server.

[1238] In this way, users can learn about communication technologies at their own pace and the system can provide appropriate feedback and support.

[1239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1240] Step 1:

[1241] User Registration and Login

[1242] A user accesses the user interface using a terminal.

[1243] Input: Name, Email Address, Password

[1244] Processing: The server receives the entered information and stores it in a database. Once registration is complete, the server sends a confirmation email. When a user logs in, they enter their username and password, which the server checks against the database, and if authentication is successful, a session begins.

[1245] Output: Login confirmation message, user session started

[1246] Step 2:

[1247] Setting learning levels and goals

[1248] After logging in, the user proceeds to a screen where they can set their learning level and goals.

[1249] Input: Learning level (beginner, intermediate, advanced), learning objectives (e.g., 5G basic knowledge, troubleshooting)

[1250] Processing: The device sends the selected information to the server, which uses a generative AI model to generate customized learning materials and questions based on the input learning level and objectives.

[1251] Output: A customized set of study materials and questions

[1252] Step 3:

[1253] Providing learning materials and learning

[1254] Users can view the provided learning materials (text, images, videos, etc.) on their devices and progress through their studies.

[1255] Input: Generated teaching materials

[1256] Processing: The server records the user's access history and learning progress in a database.

[1257] Output: Teaching material viewing screen, learning progress information update

[1258] Step 4:

[1259] Questions and feedback

[1260] As the user progresses with their studies, the server periodically generates questions and sends them to the device. The user answers the questions and sends them to the server.

[1261] Input: User's answer

[1262] Processing: The server evaluates the answer and generates feedback such as "That's right. You did a great job." if the answer is correct, or "The correct answer is XX. Please check this part again." if the answer is incorrect, and sends it to the device.

[1263] Output: Feedback message

[1264] Step 5:

[1265] Tracking learning progress and generating motivational messages

[1266] The server periodically analyzes the user's learning progress information and generates progress reports and motivational messages, which are provided to the user via the terminal.

[1267] Input: Learning progress information

[1268] Processing: The server analyzes the learning progress information recorded in the database, generates a progress report and a motivation message, and sends the generated report and message to the terminal.

[1269] Output: progress reports, motivational messages

[1270] Example: User's intermediate level 5G troubleshooting training

[1271] If a user wants to learn "Intermediate Level 5G Troubleshooting," the server will provide the generative AI model with prompts like this:

[1272] Example prompt sentence:

[1273] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[1274] 1. Basic troubleshooting techniques

[1275] 2. Common Problem Examples and Solutions

[1276] 3. Practical scenarios in the field

[1277] Based on this prompt, the generative AI model generates customized learning materials and provides them to the user's device. The user can then view the materials and answer questions to acquire practical knowledge.

[1278] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1279] The present invention provides an online learning system that allows users to effectively acquire knowledge about communication technologies and mobile phone base stations by providing an interface accessible via a terminal. It also combines an emotion engine that recognizes the user's emotions and adjusts the learning content and feedback accordingly, providing a more personalized educational experience.

[1280] User Registration and Login

[1281] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1282] Setting levels and objectives

[1283] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1284] Emotion engine integration

[1285] The system integrates an emotion engine to recognize the user's emotional state in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotional data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[1286] Providing learning materials and learning

[1287] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed.

[1288] Questions and feedback

[1289] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[1290] Track your progress and increase motivation

[1291] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[1292] Specific examples

[1293] For example, if a user selects "Intermediate-level 5G troubleshooting," the generative AI model will generate intermediate-level learning materials and specific troubleshooting problems based on that selection. While the user is answering the questions, the emotion engine analyzes the user's facial expressions and voice. If confusion or stress is detected, the server will provide additional explanations or simplified questions. If the answer is correct, the server will provide positive feedback, such as "Great! Keep it up!"

[1294] In this way, the system recognizes the user's emotional state and provides appropriate feedback and learning resources accordingly, greatly improving the user's learning efficiency and motivation.

[1295] The processing flow will be explained below.

[1296] User Registration and Login

[1297] Step 1:

[1298] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[1299] Step 2:

[1300] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[1301] Step 3:

[1302] The user enters the required information and clicks the "Submit" button.

[1303] Step 4:

[1304] The terminal sends the entered information to the server in JSON format.

[1305] Step 5:

[1306] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[1307] Step 6:

[1308] The terminal displays a notification of successful registration to the user.

[1309] Step 7:

[1310] The user clicks the "Login" button on the home screen.

[1311] Step 8:

[1312] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[1313] Step 9:

[1314] The user enters the required information and clicks the "Login" button.

[1315] Step 10:

[1316] The terminal transmits the input information to the server.

[1317] Step 11:

[1318] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[1319] Step 12:

[1320] The server returns a login success message and dashboard data to the terminal.

[1321] Step 13:

[1322] The device displays a dashboard to show the user their current learning status.

[1323] Setting levels and objectives

[1324] Step 1:

[1325] Users click buttons on the dashboard to set their "learning level" and "learning objectives."

[1326] Step 2:

[1327] The terminal displays a drop-down menu, presenting the choices to the user.

[1328] Step 3:

[1329] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[1330] Step 4:

[1331] The terminal transmits the selected information to the server.

[1332] Step 5:

[1333] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[1334] Step 6:

[1335] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[1336] Step 7:

[1337] The terminal displays the generated teaching materials and problem sets to the user.

[1338] Emotion engine integration

[1339] Step 1:

[1340] The server collects the user's facial expression, voice and input data in real time through the emotion engine.

[1341] Step 2:

[1342] The server analyzes the data obtained from the emotion engine to determine the user's emotional state (e.g., confusion, stress, concentration).

[1343] Step 3:

[1344] The server takes into account the user's emotional state and adjusts the learning content and feedback provided to the user.

[1345] Providing learning materials and learning

[1346] Step 1:

[1347] Users click on the provided link or file to view the materials.

[1348] Step 2:

[1349] The device will display the linked educational material (PDF, video, etc.) or download the file.

[1350] Step 3:

[1351] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[1352] Step 4:

[1353] The emotion engine assesses the user's level of attention and comprehension and adjusts the learning content as needed.

[1354] Step 5:

[1355] If the user is feeling stressed, the server provides supplementary materials to help them relax.

[1356] Questions and feedback

[1357] Step 1:

[1358] The server periodically generates questions according to the user's learning progress.

[1359] Step 2:

[1360] The server sends the generated questions to the terminal.

[1361] Step 3:

[1362] The terminal displays the received problem to the user.

[1363] Step 4:

[1364] The user solves the problem, enters the answer, and clicks the "Submit" button.

[1365] Step 5:

[1366] The terminal transmits the user's answer to the server.

[1367] Step 6:

[1368] The server evaluates the received answers (e.g., determining whether they are correct or incorrect) and generates feedback.

[1369] Step 7:

[1370] Feedback includes advice and motivational messages based on data obtained from the emotion engine.

[1371] Step 8:

[1372] The terminal displays the feedback to the user.

[1373] Track your progress and increase motivation

[1374] Step 1:

[1375] The server records progress information for each learning session in a database.

[1376] Step 2:

[1377] The server periodically analyzes the progress and generates a progress report.

[1378] Step 3:

[1379] The server sends the generated report and motivation message to the terminal.

[1380] Step 4:

[1381] The terminal displays the received reports and messages to the user.

[1382] Step 5:

[1383] Users can check their progress and be motivated to move on to the next learning stage.

[1384] Specific examples

[1385] Step 1:

[1386] A user selects "Intermediate Level 5G Troubleshooting."

[1387] Step 2:

[1388] The generative AI model generates intermediate-level learning materials and specific troubleshooting problems.

[1389] Step 3:

[1390] As the user browses the learning materials and answers the questions, the server analyzes the user's facial expressions and voice using an emotion engine.

[1391] Step 4:

[1392] If confusion or stress is detected, the server will provide additional explanations or simplified questions.

[1393] Step 5:

[1394] If you get it right, the server generates positive feedback like "Great! Keep it up next time!"

[1395] Step 6:

[1396] To provide motivational messages as appropriate so that users can maintain their motivation while studying.

[1397] This is the specific processing flow of a system that integrates an emotion engine. By recognizing the user's emotional state in real time and providing appropriate feedback and learning resources accordingly, it is possible to significantly improve the user's learning efficiency and motivation.

[1398] Example 2

[1399] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1400] Conventional online learning systems have the problem of being difficult to respond to the learning level and emotional state of individual users. As a result, it is difficult to expect improvements in user learning efficiency and motivation. In addition, learning progress and feedback are often simplified, making it difficult to provide a fully personalized learning experience.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1402] In this invention, the server includes means for providing a user with an interface accessible via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for recognizing the user's emotional state in real time, and means for adjusting learning content and feedback based on the recognized emotions, thereby enabling a personalized learning experience according to each user's emotional state and learning progress.

[1403] "User" refers to an individual or corporation that accesses the system and engages in learning activities.

[1404] "Terminal" refers to a device, such as a computer, smartphone, or tablet, that a user uses to access the system.

[1405] "Interface" refers to the software and hardware components that provide the screens and controls for a user to interact with a system.

[1406] A "generative AI model" refers to an artificial intelligence model that automatically generates optimal teaching materials and questions based on user input information (e.g., learning level, learning objectives).

[1407] "Teaching materials" refers to educational content such as PDFs, videos, and texts that users can use to study.

[1408] "Questions" refer to questions or exercises that are used to assess whether the user has understood the learning content.

[1409] "Feedback" refers to the evaluation and advice provided to the user based on the answers they provide.

[1410] "Study progress information" refers to data about how far a user has progressed in their studies (e.g., the learning materials accessed, the questions answered, the level of understanding, etc.).

[1411] "Motivational messages" refer to messages of encouragement or advice provided to increase a user's motivation to learn.

[1412] "Emotional state" refers to the user's current emotion (e.g., joy, confusion, stress, etc.).

[1413] An "emotion engine" refers to a system that recognizes a user's emotional state in real time from facial expressions, voice, input data, etc.

[1414] A "personalized learning experience" refers to a learning experience that is customized according to the learning level, learning goals, and emotional state of each individual user.

[1415] The system of the present invention is an online learning system that provides users with an interface accessible via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. The system is implemented using the following hardware and software.

[1416] Hardware and Software

[1417] Hardware: The devices used by users (e.g., computers, smartphones, tablets) and servers.

[1418] Software: A browser or dedicated app to provide the interface, a generative AI model (e.g., GPT-3), and an emotion engine (e.g., Emotion API).

[1419] User Registration and Login

[1420] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information, verifies it, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1421] Setting levels and objectives

[1422] Once a user logs in, they are directed to a menu where they can set their learning level and goal. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1423] An example prompt is:

[1424] "Create intermediate level teaching materials and questions on the basics of 5G."

[1425] Examples include:

[1426] Emotion engine integration

[1427] The system integrates an emotion engine to recognize the user's emotions in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotion data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[1428] Providing learning materials and learning

[1429] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension, and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed. This creates a personalized learning experience.

[1430] Questions and feedback

[1431] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[1432] Track your progress and increase motivation

[1433] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[1434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1435] Step 1: User Registration

[1436] Input: The user launches a browser or a dedicated app and accesses the home screen. The user clicks the "Register" button and enters information such as their name, email address, and password.

[1437] Specific operation: The terminal displays an input form into which the user enters information. Once the information is entered, the terminal sends it to the server.

[1438] Data processing and calculation: The server verifies the received information and checks whether the information is correct. If it is correct, it stores it in the database.

[1439] Output: The server sends a confirmation email to the user informing them that their registration is complete.

[1440] Step 2: Log in

[1441] Input: The user opens the login screen and enters their username and password.

[1442] Specific operation: The terminal displays a login form, the user enters information, and the terminal sends the input information to the server.

[1443] Data processing and calculation: The server checks the received information against the database and determines whether the authentication was successful.

[1444] Output: The server notifies the terminal that the login was successful and redirects the user to the dashboard screen.

[1445] Step 3: Set your learning level and goals

[1446] Input: After logging in, the user selects the learning level (beginner, intermediate, advanced) and purpose (e.g., 5G basic knowledge, troubleshooting).

[1447] Specific operation: The device displays a drop-down menu, the user selects an option, and the device sends the selected information to the server.

[1448] Data processing and calculation: Based on the received information, the server sends prompts to the generative AI model, requesting it to generate appropriate teaching materials and questions.

[1449] Output: The server receives the teaching materials and problem sets generated by the generative AI model and sends them to the user's device for display.

[1450] Step 4: Emotion Recognition

[1451] Input: Facial expressions and voice data while the user is using the system.

[1452] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice, and transmits the data to the server in real time.

[1453] Data processing and calculation: The server uses an emotion engine to analyze the data and recognize the user's emotional state.

[1454] Output: The server generates emotion-based learning feedback and motivational messages and sends them to the device.

[1455] Step 5: Provide learning materials and learn

[1456] Input: The user selects and views the provided learning materials (PDF, video, etc.).

[1457] Specific operation: The terminal displays a link to the teaching material, and when the user clicks on it, the teaching material is displayed.

[1458] Data processing and calculation: The server records which learning materials the user has viewed and tracks the user's browsing history. The emotion engine evaluates the user's level of concentration and comprehension.

[1459] Output: The server adjusts the learning content or provides supplementary material as needed.

[1460] Step 6: Questions and feedback

[1461] Input: Questions presented to the user as they progress through the learning process.

[1462] Specific operation: The server sends prompts to the generative AI model to generate appropriate questions. The device displays the questions and the user inputs the answers.

[1463] Data processing and calculation: The server evaluates the received answers and determines whether they are correct or not. It also refers to the emotion engine to consider the user's emotions.

[1464] Output: The server generates evaluation results and feedback and sends them to the device.

[1465] Step 7: Track your progress and stay motivated

[1466] Input: User's learning progress data.

[1467] Specific operation: The server records the user's learning progress information in a database.

[1468] Data Processing and Computation: The server periodically analyzes the progress data and uses the generative AI model to generate progress reports and motivational messages.

[1469] Output: The generated reports and motivation messages are sent to the terminal and presented to the user.

[1470] (Application example 2)

[1471] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1472] Conventional online learning systems can provide learning materials based on a user's learning progress and level of understanding, but lack the functionality to adjust learning content and feedback taking into account the user's emotional state. As a result, users often become discouraged or lose motivation to learn, making efficient learning difficult. Furthermore, because there is no mechanism for recognizing emotions in real time and adjusting learning content and feedback based on that, it is not possible to provide an optimal educational experience for each individual user. This problem needed to be solved.

[1473] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for using an emotion engine that recognizes the user's emotional state in real time using the terminal's camera and microphone, and means for adjusting learning feedback and motivational messages based on the emotional state. This allows the server to provide optimal learning feedback and motivational messages according to the user's emotional state, enabling efficient and effective learning.

[1474] A "terminal" is a digital device that allows a user to access and operate the system.

[1475] An "interface" is a screen or operating means that allows a user to access and operate the system via a terminal.

[1476] A "generative AI model" is an artificial intelligence algorithm that generates customized learning materials and questions based on the user's level and learning goals.

[1477] "Teaching materials" refers to information and materials for users to study, and includes formats such as e-books, videos, and PDFs.

[1478] "Questions" are questions or tasks that allow users to check their learning content and measure their level of understanding.

[1479] "Feedback" refers to evaluations and advice provided based on the user's answers and learning progress.

[1480] "Motivational messages" are messages of encouragement or encouragement provided to increase the user's motivation to learn.

[1481] The "emotion engine" is a system that uses the device's camera and microphone to recognize and analyze the user's emotional state in real time.

[1482] "Emotional state" refers to the state or change of an emotion a user is feeling, including, for example, confusion, stress, excitement, joy, and the like.

[1483] The present invention provides an online learning system that provides users with an interface that can be accessed via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. Specific methods for implementing the present invention are described below.

[1484] User Registration and Login

[1485] Users access the application through a device such as a smartphone or tablet. A "Register" button appears on the device screen, and the user enters the required information. The server then verifies this information and stores it in a database. Once registration is complete, a confirmation email is sent. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1486] Setting levels and objectives

[1487] After logging in, users are directed to a screen where they can select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The device displays these options, and the user selects the appropriate option. This information is sent to the server, and the generative AI model uses this information to generate optimal learning materials and problem sets. The generated learning materials and problem sets are then sent to the device and displayed to the user.

[1488] Providing learning materials and learning

[1489] Users click on the provided learning materials (e.g., PDFs, videos) to view them. The server records the user's browsing history and manages their learning progress. The content and display of the learning materials are handled by HTML5, PDF viewers, video streaming players, etc. The emotion engine uses the device's camera and microphone to recognize the user's emotional state in real time and adjust the learning content accordingly.

[1490] Emotion engine integration

[1491] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice to recognize the user's emotional state. This allows it to provide additional hints or simplified explanations if the user is confused, and positive feedback if the user is successful. This is done using emotion recognition technologies such as Google Cloud Vision API and Microsoft Azure Emotion API.

[1492] Questions and feedback

[1493] The server periodically generates questions based on the user's learning progress using a generative AI model and sends them to the device. When the user answers the questions, the results are sent to the server, which evaluates the answers, determines whether they are correct, and generates feedback. For example, it may display an encouraging message such as "You'll succeed next time," motivating the user to continue learning.

[1494] Track your progress and increase motivation

[1495] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates progress reports and motivational messages, which are then provided to the user via their device. The server generates the most effective motivational messages by taking into account the data from the emotion engine.

[1496] Specific examples

[1497] For example, if the user selects "Intermediate level 5G troubleshooting," the following prompt sentence is input to the generative AI model:

[1498] Example prompt sentence:

[1499] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[1500] Based on this prompt, the generative AI model generates intermediate-level learning materials and specific questions for the user, providing appropriate feedback based on the user's emotional state, significantly improving learning efficiency and motivation.

[1501] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1502] Step 1:

[1503] A user accesses the application using a terminal, clicks the "Register" button, and enters the required information (email address, password, etc.). The server receives the entered information and validates it before saving it to the database. If the validation passes, a confirmation email is sent to the user, notifying them that registration is complete. The input is the user's registration information, and the output is the registration information that will be saved in the database.

[1504] Step 2:

[1505] The user enters a username and password on the login screen. The device sends this information to the server, which checks it against information in a database. If the check is successful, the server starts a session and displays the user's home screen on the device. The input is the user's login information, and the output is the start of a session and the display of the home screen.

[1506] Step 3:

[1507] The user sets their learning level and learning goals on the home screen. The device displays these options, and the user selects the appropriate option. The selection information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. The generated learning materials and problem sets are sent to the device and displayed to the user. The input is the user's learning level and goals, and the output is a display of the customized learning materials and problem sets.

[1508] Step 4:

[1509] Users click on the provided learning materials to view them and progress through their studies. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time and evaluates the user's level of concentration and understanding. The input is the browsing history and emotion data, and the output is learning progress management and evaluation data.

[1510] Step 5:

[1511] As the user progresses with their learning, the server periodically generates questions using the generative AI model and sends them to the device. The user answers the questions and sends the answers to the server. The server evaluates the answers, determines whether they are correct or not, and generates the necessary feedback, which is sent to the device and displayed to the user. The input is the user's answer, and the output is the evaluation result and feedback.

[1512] Step 6:

[1513] The server periodically analyzes the user's learning progress information. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the terminal. Data from the emotion engine is also taken into consideration, and if the user is confused, the server provides additional explanations or simplified problems, and if the user is successful, it provides positive feedback. The input is learning progress information and emotion data, and the output is a progress report and motivational messages.

[1514] Specific examples

[1515] For example, if a user selects "Intermediate Level 5G Troubleshooting," the following prompt sentences will be input into the generative AI model to generate appropriate learning materials and questions:

[1516] Example prompt sentence:

[1517] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[1518] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1519] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1520] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1521] [Fourth embodiment]

[1522] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1523] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1524] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1525] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1526] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1527] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1528] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1529] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1530] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1531] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1532] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1533] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1534] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1535] The present invention is an online learning system that provides users with learning resources via their terminals, enabling them to effectively acquire knowledge about communication technologies and mobile phone base stations. Below, we will explain the specific program processing of this system in natural language.

[1536] User Registration and Login

[1537] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1538] Setting levels and objectives

[1539] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1540] Providing learning materials and learning

[1541] Users click on the provided learning materials (PDF, video, etc.) to view them. The server records the user's browsing history of the learning materials and manages their learning progress.

[1542] Questions and feedback

[1543] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback is displayed to the user via the device, and the user uses it as a guide to proceed to the next learning step.

[1544] Track your progress and increase motivation

[1545] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages and provides them to the user via their device. The user can check these to increase their motivation for learning.

[1546] Specific examples

[1547] If a user selects "Intermediate-level 5G troubleshooting," the generative AI model generates intermediate-level learning materials and specific troubleshooting problems based on that selection. For example, it includes case studies of typical 5G issues and techniques for improving performance. When the user answers the questions, the server receives the answer and provides feedback such as "That's right! You did a great job!" if the answer is correct, or "The correct answer is ____. Please check this part again." if the answer is incorrect.

[1548] In this way, the system supports efficient learning and skill acquisition by providing learning resources tailored to the user and appropriate feedback.

[1549] The processing flow will be explained below.

[1550] User Registration and Login

[1551] Step 1:

[1552] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[1553] Step 2:

[1554] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[1555] Step 3:

[1556] The user enters the required information and clicks the "Submit" button.

[1557] Step 4:

[1558] The terminal sends the entered information to the server in JSON format.

[1559] Step 5:

[1560] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[1561] Step 6:

[1562] The terminal displays a notification of successful registration to the user.

[1563] Step 7:

[1564] The user clicks the "Login" button on the home screen.

[1565] Step 8:

[1566] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[1567] Step 9:

[1568] The user enters the required information and clicks the "Login" button.

[1569] Step 10:

[1570] The terminal transmits the input information to the server.

[1571] Step 11:

[1572] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[1573] Step 12:

[1574] The server returns a login success message and dashboard data to the terminal.

[1575] Step 13:

[1576] The device displays a dashboard to show the user their current learning status.

[1577] Setting levels and objectives

[1578] Step 1:

[1579] On the dashboard, users click buttons to set their "learning level" and "learning objectives."

[1580] Step 2:

[1581] The terminal displays a drop-down menu, presenting the choices to the user.

[1582] Step 3:

[1583] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[1584] Step 4:

[1585] The terminal transmits the selected information to the server.

[1586] Step 5:

[1587] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[1588] Step 6:

[1589] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[1590] Step 7:

[1591] The terminal displays the generated teaching materials and problem sets to the user.

[1592] Provision of teaching materials

[1593] Step 1:

[1594] The user clicks on the link or file provided.

[1595] Step 2:

[1596] The device will display the linked educational material (PDF, video, etc.) or download the file.

[1597] Step 3:

[1598] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[1599] Questions and feedback

[1600] Step 1:

[1601] The server generates questions at regular intervals according to the user's learning progress.

[1602] Step 2:

[1603] The server sends the generated questions to the terminal.

[1604] Step 3:

[1605] The terminal displays the received problem to the user.

[1606] Step 4:

[1607] The user solves the problem, enters the answer, and clicks the "Submit" button.

[1608] Step 5:

[1609] The terminal transmits the user's answer to the server.

[1610] Step 6:

[1611] The server evaluates the received answers (e.g., determining whether they are correct or incorrect).

[1612] Step 7:

[1613] The server generates evaluation results and feedback messages and sends them to the terminal.

[1614] Step 8:

[1615] The terminal displays the feedback to the user.

[1616] Track your progress and increase motivation

[1617] Step 1:

[1618] The server records progress information for each learning session in a database.

[1619] Step 2:

[1620] The server periodically analyzes the progress and generates reports.

[1621] Step 3:

[1622] The server sends the generated report and motivation message to the terminal.

[1623] Step 4:

[1624] The terminal displays the received reports and messages to the user.

[1625] Step 5:

[1626] Users can check their progress and be motivated to move on to the next learning stage.

[1627] Example 1

[1628] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1629] Conventional online learning systems have limited means for efficiently tracking users' learning progress and providing appropriate feedback, making it difficult for users to effectively acquire knowledge. Furthermore, they often lack the ability to provide customized learning materials tailored to the user's learning level and goals, resulting in a lack of motivation to learn. The present invention aims to solve these problems and improve users' learning experience.

[1630] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1631] In this invention, the server includes: means for providing an interface accessible to a user via a terminal; means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives; means for providing the generated learning materials and questions to the user's terminal; means for receiving and evaluating the user's answers and generating feedback; means for recording and analyzing the user's learning progress information and generating motivational messages; means for recording the user's browsing history and managing the learning progress; and means for transmitting the learning materials and question sets generated based on the user's selected learning level and objectives to the user's terminal and displaying them to the user. This allows the user to receive individually tailored learning materials and feedback, enabling efficient learning and skill acquisition.

[1632] A "terminal" is a device that a user uses to access the system, and includes devices such as PCs, tablets, and smartphones.

[1633] "Interface" refers to the screen and input means that allow a user to interact with a system, and is a user interface that includes on-screen buttons, menus, etc.

[1634] A "generative AI model" refers to artificial intelligence technology that automatically generates customized teaching materials and questions based on user input.

[1635] "Learning Materials" means content provided for learning purposes, including formats such as PDFs, videos, and slide presentations.

[1636] "Questions" refer to questions or tasks that users answer to check their level of understanding, and come in multiple choice, written, and other formats.

[1637] "Feedback" refers to information that includes evaluation results and advice on the user's answers, and is intended to improve the user's learning.

[1638] "Study progress information" refers to data that records the user's learning progress, such as how much learning material the user has viewed and how many questions the user has answered.

[1639] A "motivational message" is a message containing encouragement or advice provided to increase the user's motivation to learn.

[1640] "Generated learning materials and problem sets" refers to learning materials and a set of problems that a generative AI model automatically creates based on the user's level and learning objectives.

[1641] A "study progress report" is a report that is periodically generated summarizing a user's learning progress.

[1642] "Analysis" refers to the process of evaluating the learning progress information collected by the server and identifying the user's learning situation and areas for improvement.

[1643] The present invention is an online learning system configured as follows, which performs various processes using a terminal, a server, and a generative AI model to enable users to effectively acquire knowledge.

[1644] System Configuration

[1645] 1. Hardware Configuration

[1646] Terminal: The device used by the user, including common devices such as PCs, tablets, and smartphones.

[1647] Server: A computer system that processes and manages data for the entire system. Includes database servers, application servers, etc.

[1648] 2. Software Configuration

[1649] Browser or dedicated app: A tool that provides an interface for users to access the system.

[1650] Generative AI model: Built using Python, TensorFlow, etc., it generates customized learning materials and questions based on the user's learning level and goals.

[1651] System action

[1652] User Registration and Login

[1653] Users access the system through a browser or a dedicated app. First, the device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information and verifies the input. Once verification is complete, the server saves the user information in a database and sends a confirmation email to the user. When a user logs in, they enter an email address and password, which the server verifies. If verification is successful, the user is taken to the home screen.

[1654] Setting levels and objectives

[1655] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1656] Providing learning materials and learning

[1657] The user selects and clicks on the material they want to study from the list of materials displayed on the device. For example, this could be a PDF or video. The server records the user's browsing history of the materials and stores it in a database. The user can then browse the materials and proceed with their learning.

[1658] Questions and feedback

[1659] As the learning process progresses, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers these questions and sends the results from the device to the server. The server receives the answers and evaluates them. For example, if the answer is correct, it displays "That's correct. You did a great job." If the answer is incorrect, it generates feedback such as "The correct answer is XX. Please check this part again."

[1660] Track your progress and increase motivation

[1661] The server records the user's answer history and learning material access history in a database. This data is periodically analyzed and a learning progress report is generated. Along with the progress report, the server also generates a motivational message to motivate the user to study and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[1662] Examples and prompts

[1663] If a user selects "Intermediate Level 5G Troubleshooting," the system will:

[1664] 1. From the home screen, the user navigates to the Learning Settings menu and selects the Intermediate level and 5G Troubleshooting.

[1665] 2. The server uses the generative AI model to generate intermediate-level troubleshooting materials (PDFs, videos, etc.) and problem sets.

[1666] 3. The terminal displays the generated teaching materials and problem sets to the user.

[1667] 4. The user clicks on the learning material and begins viewing it. The server records the user's browsing history.

[1668] 5. When the user has finished studying the learning material, the server sends the generated questions to the terminal, and the user answers the questions.

[1669] 6. The server evaluates the answer and provides feedback such as, "The correct answer is XX. Please check this part again."

[1670] 7. Learning progress information is stored on the server and progress reports are generated periodically.

[1671] This invention enables users to learn and master skills efficiently, and by using a generative AI model, it is possible to provide customized learning materials according to the user's learning needs, thereby enhancing the effectiveness of learning.

[1672] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1673] Step 1: User Registration

[1674] Users access the system using a browser or a dedicated app. The device displays the home screen, and when the user clicks the "Register" button, a screen for entering required information such as name, email address, and password is displayed. When the user enters the information and clicks the "Submit" button, the device sends the information to the server. The server receives the entered information and verifies the format of the email address and the strength of the password. Once this verification is complete, the server saves the user information in a database and generates and sends a confirmation email. When the user clicks the link in the email, registration is complete.

[1675] Input: User's name, email address, and password

[1676] Output: Sending a confirmation email, saving user information to a database

[1677] Step 2: Log in

[1678] The user enters their email address and password on the login screen. The device receives this and sends it to the server. The server checks the database to verify the information entered, and if it is correct, starts a session. The device displays a login success message and the user is taken to the home screen.

[1679] Input: User's email address, password

[1680] Output: Login success message, home screen displayed

[1681] Step 3: Set your level and objectives

[1682] After logging in, the user navigates to the "Learning Settings" menu from the home screen. The device displays options for learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). When the user makes these selections, the device sends the information to the server. The server uses a generative AI model to generate learning materials and problem sets tailored to the user's learning level and objectives. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1683] Input: User's learning level, learning objectives

[1684] Output: Display of generated teaching materials and problem sets

[1685] Step 4: Provide learning materials and learn

[1686] The user clicks on the learning material (PDF, video, etc.) they want to learn from the list of learning materials displayed on the device and views it. The device displays the learning material and notifies the server of the user's click. The server records the user's browsing history of the learning materials accessed and saves it in a database. The user views the learning material and progresses with their learning.

[1687] Input: User's material selection (click)

[1688] Output: Display of teaching materials, saving of browsing history in database

[1689] Step 5: Questions and Answers

[1690] Once the user has finished viewing the learning material, the server uses the generative AI model to generate customized questions and sends them to the device. The user answers the questions and sends the answers from the device to the server. The server receives the answers and evaluates them. Based on the results, feedback is generated and sent to the device. For example, if the answer is correct, it will display "That's correct. You did a great job." If the answer is incorrect, it will display "The correct answer is XX. Please check this part again."

[1691] Input: Content of the teaching material, user's answers

[1692] Output: Generated questions, feedback

[1693] Step 6: Track your progress and stay motivated

[1694] The server records the user's answer history and learning material access history in a database. It periodically analyzes this data and generates a learning progress report. The server also generates a motivational message along with the progress report and sends it to the device. For example, it displays a message such as, "You're almost done with the intermediate level! Keep up the great work!"

[1695] Input: Answer history, learning material access history

[1696] Output: progress reports, motivational messages

[1697] (Application example 1)

[1698] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1699] Currently, online learning systems for efficiently learning about communications technology and mobile phone base stations are limited, and they lack the ability to provide learning materials optimized for individual users, track learning progress, and provide learning support for troubleshooting, especially for 5G technology. This reduces users' learning efficiency and makes it difficult to maintain motivation.

[1700] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1701] In this invention, the server includes means for providing a user interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, and means for providing the generated learning materials and questions to the user's terminal, thereby enabling the user to effectively advance their learning related to communication technologies.

[1702] The server also includes a means for displaying and managing customized learning materials for users to advance their learning related to communication technologies, and a means for providing online troubleshooting and practical scenario-based questions and tallying up answers, thereby enabling more practical and interactive learning.

[1703] Furthermore, the generative AI model selects optimal learning materials and questions based on the learning level and learning goals entered by the user, making it possible to provide learning resources optimized for each individual user.The system also includes a means for periodically analyzing the user's learning progress information, generating learning progress reports, and sending these reports to the user's device, allowing the user to always be aware of their own learning progress, making it easier to maintain motivation.

[1704] "User interface" refers to the screen or operation panel that a user uses to access the system via a terminal.

[1705] "Generative AI model" refers to the artificial intelligence algorithm used to generate optimal learning materials and questions based on the user's learning level and goals.

[1706] "Customized learning materials" refers to learning resources created by generative AI models to suit a user's specific learning needs.

[1707] "Troubleshooting" refers to the diagnosis and resolution of system or technology related problems.

[1708] "Study progress information" refers to data that records the learning progress achieved by a user within a specific period of time.

[1709] "Motivational messages" refer to messages of encouragement and evaluation that are generated to increase the user's motivation to learn.

[1710] "Study Progress Report" refers to a document or digital file that compiles and analyzes a user's learning progress information.

[1711] "Terminal" refers to the electronic device (smartphone, tablet, PC, etc.) that a user uses to access the system.

[1712] The present invention is an online learning system for enabling users to efficiently learn about communication technologies and mobile phone base stations. A specific embodiment of this system will be described below.

[1713] System Overview

[1714] The server provides a user interface accessible to users via their terminals, through which users can access the system and perform operations such as registration, login, and learning progress.

[1715] The server generates customized learning materials and questions using a generative AI model based on the user's learning level and goals. This generative AI model selects the optimal learning materials and questions based on user input and provides them to the user's device.

[1716] Hardware and software used

[1717] Hardware: Devices (smartphones, tablets, PCs, etc.), servers

[1718] Software: User interface (browser or dedicated app), generative AI model, database, communication protocol (HTTP / HTTPS)

[1719] Data processing and calculation

[1720] 1. User Registration and Login:

[1721] A user accesses the user interface from a terminal and completes the registration process by entering their name, email address, and password. The server receives this information, stores it in a database, and sends a confirmation email. When logging in, the user enters their username and password, which the server verifies and starts a session.

[1722] 2. Setting learning levels and goals:

[1723] After logging in, users select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The selected information is sent to the server, and the generative AI model generates customized learning materials and questions.

[1724] 3. Providing learning materials and learning:

[1725] Users study using the provided learning materials (text, images, videos, etc.). The server records the user's learning progress in a database and manages the progress.

[1726] 4. Questions and feedback:

[1727] During the learning process, the server periodically generates questions and sends them to the user's device. The user answers the questions and sends them to the server. The server evaluates the answers, generates feedback, and provides it to the user.

[1728] 5. Track your progress and increase your motivation:

[1729] The server periodically analyzes the user's learning progress and generates progress reports and motivational messages, which are sent to the user's device, allowing the user to check their progress and stay motivated.

[1730] Example: User's intermediate level 5G troubleshooting training

[1731] If a user specifies that they want to learn "intermediate level 5G troubleshooting," the generative AI model will generate customized learning materials based on prompts like the following:

[1732] Example prompt sentence:

[1733] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[1734] 1. Basic troubleshooting techniques

[1735] 2. Common Problem Examples and Solutions

[1736] 3. Practical scenarios in the field

[1737] The generated learning materials include basic troubleshooting techniques, typical 5G trouble cases, and detailed explanations of their solutions. Users can use these learning materials to study and acquire practical knowledge by answering questions provided by the server.

[1738] In this way, users can learn about communication technologies at their own pace and the system can provide appropriate feedback and support.

[1739] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1740] Step 1:

[1741] User Registration and Login

[1742] A user accesses the user interface using a terminal.

[1743] Input: Name, Email Address, Password

[1744] Processing: The server receives the entered information and stores it in a database. Once registration is complete, the server sends a confirmation email. When a user logs in, they enter their username and password, which the server checks against the database, and if authentication is successful, a session begins.

[1745] Output: Login confirmation message, user session started

[1746] Step 2:

[1747] Setting learning levels and goals

[1748] After logging in, the user proceeds to a screen where they can set their learning level and goals.

[1749] Input: Learning level (beginner, intermediate, advanced), learning objectives (e.g., 5G basic knowledge, troubleshooting)

[1750] Processing: The device sends the selected information to the server, which uses a generative AI model to generate customized learning materials and questions based on the input learning level and objectives.

[1751] Output: A customized set of study materials and questions

[1752] Step 3:

[1753] Providing learning materials and learning

[1754] Users can view the provided learning materials (text, images, videos, etc.) on their devices and progress through their studies.

[1755] Input: Generated teaching materials

[1756] Processing: The server records the user's access history and learning progress in a database.

[1757] Output: Teaching material viewing screen, learning progress information update

[1758] Step 4:

[1759] Questions and feedback

[1760] As the user progresses with their studies, the server periodically generates questions and sends them to the device. The user answers the questions and sends them to the server.

[1761] Input: User's answer

[1762] Processing: The server evaluates the answer and generates feedback such as "That's right. You did a great job." if the answer is correct, or "The correct answer is XX. Please check this part again." if the answer is incorrect, and sends it to the device.

[1763] Output: Feedback message

[1764] Step 5:

[1765] Tracking learning progress and generating motivational messages

[1766] The server periodically analyzes the user's learning progress information and generates progress reports and motivational messages, which are provided to the user via the terminal.

[1767] Input: Learning progress information

[1768] Processing: The server analyzes the learning progress information recorded in the database, generates a progress report and a motivation message, and sends the generated report and message to the terminal.

[1769] Output: progress reports, motivational messages

[1770] Example: User's intermediate level 5G troubleshooting training

[1771] If a user wants to learn "Intermediate Level 5G Troubleshooting," the server will provide the generative AI model with prompts like this:

[1772] Example prompt sentence:

[1773] "Generate intermediate level 5G troubleshooting materials, focusing specifically on the following topics:

[1774] 1. Basic troubleshooting techniques

[1775] 2. Common Problem Examples and Solutions

[1776] 3. Practical scenarios in the field

[1777] Based on this prompt, the generative AI model generates customized learning materials and provides them to the user's device. The user can then view the materials and answer questions to acquire practical knowledge.

[1778] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1779] The present invention provides an online learning system that allows users to effectively acquire knowledge about communication technologies and mobile phone base stations by providing an interface accessible via a terminal. It also combines an emotion engine that recognizes the user's emotions and adjusts the learning content and feedback accordingly, providing a more personalized educational experience.

[1780] User Registration and Login

[1781] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this, verifies the entered information, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1782] Setting levels and objectives

[1783] After logging in, users navigate to a menu where they can set their learning level and goals. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1784] Emotion engine integration

[1785] The system integrates an emotion engine to recognize the user's emotional state in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotional data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[1786] Providing learning materials and learning

[1787] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed.

[1788] Questions and feedback

[1789] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[1790] Track your progress and increase motivation

[1791] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[1792] Specific examples

[1793] For example, if a user selects "Intermediate-level 5G troubleshooting," the generative AI model will generate intermediate-level learning materials and specific troubleshooting problems based on that selection. While the user is answering the questions, the emotion engine analyzes the user's facial expressions and voice. If confusion or stress is detected, the server will provide additional explanations or simplified questions. If the answer is correct, the server will provide positive feedback, such as "Great! Keep it up!"

[1794] In this way, the system recognizes the user's emotional state and provides appropriate feedback and learning resources accordingly, greatly improving the user's learning efficiency and motivation.

[1795] The processing flow will be explained below.

[1796] User Registration and Login

[1797] Step 1:

[1798] The user opens a browser or dedicated app and clicks the "Register" button on the home screen.

[1799] Step 2:

[1800] The terminal displays a user registration form, which includes input fields for "Username," "Email address," and "Password."

[1801] Step 3:

[1802] The user enters the required information and clicks the "Submit" button.

[1803] Step 4:

[1804] The terminal sends the entered information to the server in JSON format.

[1805] Step 5:

[1806] The server validates the received information (e.g., email address format, password strength), and if validation is successful, stores the user information in the database and sends a confirmation email to the user.

[1807] Step 6:

[1808] The terminal displays a notification of successful registration to the user.

[1809] Step 7:

[1810] The user clicks the "Login" button on the home screen.

[1811] Step 8:

[1812] The terminal will display a login form, which contains input fields for "Username" or "Email address" and "Password".

[1813] Step 9:

[1814] The user enters the required information and clicks the "Login" button.

[1815] Step 10:

[1816] The terminal transmits the input information to the server.

[1817] Step 11:

[1818] The server verifies the received information (e.g., whether there is matching user information in the database, whether the password is correct), and if the verification is successful, starts the session.

[1819] Step 12:

[1820] The server returns a login success message and dashboard data to the terminal.

[1821] Step 13:

[1822] The device displays a dashboard to show the user their current learning status.

[1823] Setting levels and objectives

[1824] Step 1:

[1825] Users click buttons on the dashboard to set their "learning level" and "learning objectives."

[1826] Step 2:

[1827] The terminal displays a drop-down menu, presenting the choices to the user.

[1828] Step 3:

[1829] The user selects their level (e.g., "Beginner," "Intermediate," or "Advanced") and purpose (e.g., "5G Basic Knowledge" or "Troubleshooting") and clicks the "Submit" button.

[1830] Step 4:

[1831] The terminal transmits the selected information to the server.

[1832] Step 5:

[1833] Based on the received level and objectives, the server invokes a generative AI model to generate customized learning materials and problem sets.

[1834] Step 6:

[1835] The server stores the generated teaching materials and problem sets in a database and transmits them to the terminal.

[1836] Step 7:

[1837] The terminal displays the generated teaching materials and problem sets to the user.

[1838] Emotion engine integration

[1839] Step 1:

[1840] The server collects the user's facial expression, voice and input data in real time through the emotion engine.

[1841] Step 2:

[1842] The server analyzes the data obtained from the emotion engine to determine the user's emotional state (e.g., confusion, stress, concentration).

[1843] Step 3:

[1844] The server takes into account the user's emotional state and adjusts the learning content and feedback provided to the user.

[1845] Providing learning materials and learning

[1846] Step 1:

[1847] Users click on the provided link or file to view the materials.

[1848] Step 2:

[1849] The device will display the linked educational material (PDF, video, etc.) or download the file.

[1850] Step 3:

[1851] The server records the browsing history of the educational materials accessed by the user and manages the user's learning progress.

[1852] Step 4:

[1853] The emotion engine assesses the user's level of attention and comprehension and adjusts the learning content as needed.

[1854] Step 5:

[1855] If the user is feeling stressed, the server provides supplementary materials to help them relax.

[1856] Questions and feedback

[1857] Step 1:

[1858] The server periodically generates questions according to the user's learning progress.

[1859] Step 2:

[1860] The server sends the generated questions to the terminal.

[1861] Step 3:

[1862] The terminal displays the received problem to the user.

[1863] Step 4:

[1864] The user solves the problem, enters the answer, and clicks the "Submit" button.

[1865] Step 5:

[1866] The terminal transmits the user's answer to the server.

[1867] Step 6:

[1868] The server evaluates the received answers (e.g., determining whether they are correct or incorrect) and generates feedback.

[1869] Step 7:

[1870] Feedback includes advice and motivational messages based on data obtained from the emotion engine.

[1871] Step 8:

[1872] The terminal displays the feedback to the user.

[1873] Track your progress and increase motivation

[1874] Step 1:

[1875] The server records progress information for each learning session in a database.

[1876] Step 2:

[1877] The server periodically analyzes the progress and generates a progress report.

[1878] Step 3:

[1879] The server sends the generated report and motivation message to the terminal.

[1880] Step 4:

[1881] The terminal displays the received reports and messages to the user.

[1882] Step 5:

[1883] Users can check their progress and be motivated to move on to the next learning stage.

[1884] Specific examples

[1885] Step 1:

[1886] A user selects "Intermediate Level 5G Troubleshooting."

[1887] Step 2:

[1888] The generative AI model generates intermediate-level learning materials and specific troubleshooting problems.

[1889] Step 3:

[1890] As the user browses the learning materials and answers the questions, the server analyzes the user's facial expressions and voice using an emotion engine.

[1891] Step 4:

[1892] If confusion or stress is detected, the server will provide additional explanations or simplified questions.

[1893] Step 5:

[1894] If you get it right, the server generates positive feedback like "Great! Keep it up next time!"

[1895] Step 6:

[1896] To provide motivational messages as appropriate so that users can maintain their motivation while studying.

[1897] This is the specific processing flow of a system that integrates an emotion engine. By recognizing the user's emotional state in real time and providing appropriate feedback and learning resources accordingly, it is possible to significantly improve the user's learning efficiency and motivation.

[1898] Example 2

[1899] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1900] Conventional online learning systems have the problem of being difficult to respond to the learning level and emotional state of individual users. As a result, it is difficult to expect improvements in user learning efficiency and motivation. In addition, learning progress and feedback are often simplified, making it difficult to provide a fully personalized learning experience.

[1901] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1902] In this invention, the server includes means for providing a user with an interface accessible via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for recognizing the user's emotional state in real time, and means for adjusting learning content and feedback based on the recognized emotions, thereby enabling a personalized learning experience according to each user's emotional state and learning progress.

[1903] "User" refers to an individual or corporation that accesses the system and engages in learning activities.

[1904] "Terminal" refers to a device, such as a computer, smartphone, or tablet, that a user uses to access the system.

[1905] "Interface" refers to the software and hardware components that provide the screens and controls for a user to interact with a system.

[1906] A "generative AI model" refers to an artificial intelligence model that automatically generates optimal teaching materials and questions based on user input information (e.g., learning level, learning objectives).

[1907] "Teaching materials" refers to educational content such as PDFs, videos, and texts that users can use to study.

[1908] "Questions" refer to questions or exercises that are used to assess whether the user has understood the learning content.

[1909] "Feedback" refers to the evaluation and advice provided to the user based on the answers they provide.

[1910] "Study progress information" refers to data about how far a user has progressed in their studies (e.g., the learning materials accessed, the questions answered, the level of understanding, etc.).

[1911] "Motivational messages" refer to messages of encouragement or advice provided to increase a user's motivation to learn.

[1912] "Emotional state" refers to the user's current emotion (e.g., joy, confusion, stress, etc.).

[1913] An "emotion engine" refers to a system that recognizes a user's emotional state in real time from facial expressions, voice, input data, etc.

[1914] A "personalized learning experience" refers to a learning experience that is customized according to the learning level, learning goals, and emotional state of each individual user.

[1915] The system of the present invention is an online learning system that provides users with an interface accessible via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. The system is implemented using the following hardware and software.

[1916] Hardware and Software

[1917] Hardware: The devices used by users (e.g., computers, smartphones, tablets) and servers.

[1918] Software: A browser or dedicated app to provide the interface, a generative AI model (e.g., GPT-3), and an emotion engine (e.g., Emotion API).

[1919] User Registration and Login

[1920] First, the user accesses the system using a browser or a dedicated app. The device displays the home screen, and the user clicks the "Register" button to enter the required information. The server receives this information, verifies it, saves it in the database, and sends a confirmation email to the user. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1921] Setting levels and objectives

[1922] Once a user logs in, they are directed to a menu where they can set their learning level and goal. The device displays these options, and the user selects their level (beginner, intermediate, advanced) and goal (e.g., 5G basics, troubleshooting). The selected information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. These generated learning materials and problem sets are sent to the device and displayed to the user.

[1923] An example prompt is:

[1924] "Create intermediate level teaching materials and questions on the basics of 5G."

[1925] Examples include:

[1926] Emotion engine integration

[1927] The system integrates an emotion engine to recognize the user's emotions in real time. This includes facial expression recognition, voice analysis, and input data analysis. The server generates appropriate learning feedback and motivational messages based on the emotion data obtained through the emotion engine. For example, if the user is confused, the server generates feedback that provides additional hints and support.

[1928] Providing learning materials and learning

[1929] Users click on the provided learning materials (PDFs, videos, etc.) to view them. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine evaluates the user's level of concentration and comprehension, and adjusts the learning content as necessary. It can also provide supplementary materials to help users relax if they are feeling stressed. This creates a personalized learning experience.

[1930] Questions and feedback

[1931] As the user progresses through their learning, the server periodically generates questions and sends them to the device. The user answers the questions and sends the results to the server. The server evaluates the answers, determines whether they are correct or incorrect, and generates feedback. The feedback includes advice and motivational messages based on data obtained from the emotion engine. For example, if the user is feeling unsure, an encouraging message such as "You will definitely succeed next time" will be displayed.

[1932] Track your progress and increase motivation

[1933] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the device. The emotion engine considers the user's emotional state to generate the most effective motivational messages. For example, if the user feels fatigued, the server will provide a message saying, "Take a short break and resume learning."

[1934] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1935] Step 1: User Registration

[1936] Input: The user launches a browser or a dedicated app and accesses the home screen. The user clicks the "Register" button and enters information such as their name, email address, and password.

[1937] Specific operation: The terminal displays an input form into which the user enters information. Once the information is entered, the terminal sends it to the server.

[1938] Data processing and calculation: The server verifies the received information and checks whether the information is correct. If it is correct, it stores it in the database.

[1939] Output: The server sends a confirmation email to the user informing them that their registration is complete.

[1940] Step 2: Log in

[1941] Input: The user opens the login screen and enters their username and password.

[1942] Specific operation: The terminal displays a login form, the user enters information, and the terminal sends the input information to the server.

[1943] Data processing and calculation: The server checks the received information against the database and determines whether the authentication was successful.

[1944] Output: The server notifies the terminal that the login was successful and redirects the user to the dashboard screen.

[1945] Step 3: Set your learning level and goals

[1946] Input: After logging in, the user selects the learning level (beginner, intermediate, advanced) and purpose (e.g., 5G basic knowledge, troubleshooting).

[1947] Specific operation: The device displays a drop-down menu, the user selects an option, and the device sends the selected information to the server.

[1948] Data processing and calculation: Based on the received information, the server sends prompts to the generative AI model, requesting it to generate appropriate teaching materials and questions.

[1949] Output: The server receives the teaching materials and problem sets generated by the generative AI model and sends them to the user's device for display.

[1950] Step 4: Emotion Recognition

[1951] Input: Facial expressions and voice data while the user is using the system.

[1952] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice, and transmits the data to the server in real time.

[1953] Data processing and calculation: The server uses an emotion engine to analyze the data and recognize the user's emotional state.

[1954] Output: The server generates emotion-based learning feedback and motivational messages and sends them to the device.

[1955] Step 5: Provide learning materials and learn

[1956] Input: The user selects and views the provided learning materials (PDF, video, etc.).

[1957] Specific operation: The terminal displays a link to the teaching material, and when the user clicks on it, the teaching material is displayed.

[1958] Data processing and calculation: The server records which learning materials the user has viewed and tracks the user's browsing history. The emotion engine evaluates the user's level of concentration and comprehension.

[1959] Output: The server adjusts the learning content or provides supplementary material as needed.

[1960] Step 6: Questions and feedback

[1961] Input: Questions presented to the user as they progress through the learning process.

[1962] Specific operation: The server sends prompts to the generative AI model to generate appropriate questions. The device displays the questions and the user inputs the answers.

[1963] Data processing and calculation: The server evaluates the received answers and determines whether they are correct or not. It also refers to the emotion engine to consider the user's emotions.

[1964] Output: The server generates evaluation results and feedback and sends them to the device.

[1965] Step 7: Track your progress and stay motivated

[1966] Input: User's learning progress data.

[1967] Specific operation: The server records the user's learning progress information in a database.

[1968] Data Processing and Computation: The server periodically analyzes the progress data and uses the generative AI model to generate progress reports and motivational messages.

[1969] Output: The generated reports and motivation messages are sent to the terminal and presented to the user.

[1970] (Application example 2)

[1971] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1972] Conventional online learning systems can provide learning materials based on a user's learning progress and level of understanding, but lack the functionality to adjust learning content and feedback taking into account the user's emotional state. As a result, users often become discouraged or lose motivation to learn, making efficient learning difficult. Furthermore, because there is no mechanism for recognizing emotions in real time and adjusting learning content and feedback based on that, it is not possible to provide an optimal educational experience for each individual user. This problem needed to be solved.

[1973] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface accessible to the user via a terminal, means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning objectives, means for providing the generated learning materials and questions to the user's terminal, means for receiving and evaluating the user's answers and generating feedback, means for recording and analyzing the user's learning progress information and generating motivational messages, means for using an emotion engine that recognizes the user's emotional state in real time using the terminal's camera and microphone, and means for adjusting learning feedback and motivational messages based on the emotional state. This allows the server to provide optimal learning feedback and motivational messages according to the user's emotional state, enabling efficient and effective learning.

[1974] A "terminal" is a digital device that allows a user to access and operate the system.

[1975] An "interface" is a screen or operating means that allows a user to access and operate the system via a terminal.

[1976] A "generative AI model" is an artificial intelligence algorithm that generates customized learning materials and questions based on the user's level and learning goals.

[1977] "Teaching materials" refers to information and materials for users to study, and includes formats such as e-books, videos, and PDFs.

[1978] "Questions" are questions or tasks that allow users to check their learning content and measure their level of understanding.

[1979] "Feedback" refers to evaluations and advice provided based on the user's answers and learning progress.

[1980] "Motivational messages" are messages of encouragement or encouragement provided to increase the user's motivation to learn.

[1981] The "emotion engine" is a system that uses the device's camera and microphone to recognize and analyze the user's emotional state in real time.

[1982] "Emotional state" refers to the state or change of an emotion a user is feeling, including, for example, confusion, stress, excitement, joy, and the like.

[1983] The present invention provides an online learning system that provides users with an interface that can be accessed via a terminal, allowing them to effectively acquire knowledge about communication technologies and mobile phone base stations. Specific methods for implementing the present invention are described below.

[1984] User Registration and Login

[1985] Users access the application through a device such as a smartphone or tablet. A "Register" button appears on the device screen, and the user enters the required information. The server then verifies this information and stores it in a database. Once registration is complete, a confirmation email is sent. When logging in, the user enters their username and password, which the server verifies and starts the session.

[1986] Setting levels and objectives

[1987] After logging in, users are directed to a screen where they can select their learning level (beginner, intermediate, advanced) and learning objectives (e.g., 5G basics, troubleshooting). The device displays these options, and the user selects the appropriate option. This information is sent to the server, and the generative AI model uses this information to generate optimal learning materials and problem sets. The generated learning materials and problem sets are then sent to the device and displayed to the user.

[1988] Providing learning materials and learning

[1989] Users click on the provided learning materials (e.g., PDFs, videos) to view them. The server records the user's browsing history and manages their learning progress. The content and display of the learning materials are handled by HTML5, PDF viewers, video streaming players, etc. The emotion engine uses the device's camera and microphone to recognize the user's emotional state in real time and adjust the learning content accordingly.

[1990] Emotion engine integration

[1991] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice to recognize the user's emotional state. This allows it to provide additional hints or simplified explanations if the user is confused, and positive feedback if the user is successful. This is done using emotion recognition technologies such as Google Cloud Vision API and Microsoft Azure Emotion API.

[1992] Questions and feedback

[1993] The server periodically generates questions based on the user's learning progress using a generative AI model and sends them to the device. When the user answers the questions, the results are sent to the server, which evaluates the answers, determines whether they are correct, and generates feedback. For example, it may display an encouraging message such as "You'll succeed next time," motivating the user to continue learning.

[1994] Track your progress and increase motivation

[1995] The server records the user's learning progress information in a database and periodically analyzes it. Based on the results of this analysis, the server generates progress reports and motivational messages, which are then provided to the user via their device. The server generates the most effective motivational messages by taking into account the data from the emotion engine.

[1996] Specific examples

[1997] For example, if the user selects "Intermediate level 5G troubleshooting," the following prompt sentence is input to the generative AI model:

[1998] Example prompt sentence:

[1999] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[2000] Based on this prompt, the generative AI model generates intermediate-level learning materials and specific questions for the user, providing appropriate feedback based on the user's emotional state, significantly improving learning efficiency and motivation.

[2001] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2002] Step 1:

[2003] A user accesses the application using a terminal, clicks the "Register" button, and enters the required information (email address, password, etc.). The server receives the entered information and validates it before saving it to the database. If the validation passes, a confirmation email is sent to the user, notifying them that registration is complete. The input is the user's registration information, and the output is the registration information that will be saved in the database.

[2004] Step 2:

[2005] The user enters a username and password on the login screen. The device sends this information to the server, which checks it against information in a database. If the check is successful, the server starts a session and displays the user's home screen on the device. The input is the user's login information, and the output is the start of a session and the display of the home screen.

[2006] Step 3:

[2007] The user sets their learning level and learning goals on the home screen. The device displays these options, and the user selects the appropriate option. The selection information is sent to the server, which uses a generative AI model to generate customized learning materials and problem sets. The generated learning materials and problem sets are sent to the device and displayed to the user. The input is the user's learning level and goals, and the output is a display of the customized learning materials and problem sets.

[2008] Step 4:

[2009] Users click on the provided learning materials to view them and progress through their studies. The server records the user's browsing history of the materials they have accessed and manages their learning progress. The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time and evaluates the user's level of concentration and understanding. The input is the browsing history and emotion data, and the output is learning progress management and evaluation data.

[2010] Step 5:

[2011] As the user progresses with their learning, the server periodically generates questions using the generative AI model and sends them to the device. The user answers the questions and sends the answers to the server. The server evaluates the answers, determines whether they are correct or not, and generates the necessary feedback, which is sent to the device and displayed to the user. The input is the user's answer, and the output is the evaluation result and feedback.

[2012] Step 6:

[2013] The server periodically analyzes the user's learning progress information. Based on the results of this analysis, the server generates a progress report and motivational messages, which are then provided to the user via the terminal. Data from the emotion engine is also taken into consideration, and if the user is confused, the server provides additional explanations or simplified problems, and if the user is successful, it provides positive feedback. The input is learning progress information and emotion data, and the output is a progress report and motivational messages.

[2014] Specific examples

[2015] For example, if a user selects "Intermediate Level 5G Troubleshooting," the following prompt sentences will be input into the generative AI model to generate appropriate learning materials and questions:

[2016] Example prompt sentence:

[2017] Taro Tanaka wants to learn "Intermediate Level 5G Troubleshooting." He already has basic knowledge and wants to hone his more specific troubleshooting skills. Please generate the best study materials and practice questions for him.

[2018] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2019] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or su...

Claims

1. means for providing a user with an interface accessible via a terminal; A means for using a generative AI model to generate customized learning materials and questions based on the user's level and learning goals; means for providing the generated teaching materials and questions to a user's terminal; means for receiving, evaluating and generating feedback from the user's answers; means for recording and analyzing user learning progress information and generating motivational messages; A system including:

2. The system of claim 1, wherein the generative AI model selects optimal teaching materials and questions based on the learning level and learning objectives input by the user.

3. 2. The system of claim 1, further comprising means for periodically analyzing the user's learning progress information and generating a learning progress report.

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