System

Abstract

A system is provided.SOLUTION: A system comprising: diagnostic means for diagnosing a technical level of a user; teaching material providing means for providing a teaching material and a task suitable for the user based on a result of the diagnostic means; evaluation means for evaluating a result of the task and generating feedback; and progress management means for managing learning progress of the user and providing a message and a reward for maintaining motivation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, with the spread of 5G technology, there has been a sharp increase in demand for personnel with advanced skills and knowledge related to mobile phone base stations. However, traditional learning methods for acquiring these skills and knowledge have made it difficult to efficiently acquire specialized knowledge and to provide optimal learning materials tailored to the level and progress of individual learners. As a result, there is an issue of not being able to sufficiently develop personnel with the technical skills required by telecommunications carriers and infrastructure providers. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a learning support system including a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user based on the diagnostic results, an evaluation means for evaluating the assignment results and generating feedback, and a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation. Specifically, the diagnostic means determines the skill level using a set of questions answered by the user, and the evaluation means uses an AI module to automatically score the assignments and generate feedback. Furthermore, the learning material provision means dynamically selects and provides optimal learning materials and assignments according to the user's skill level and learning progress. In this way, users can study flexibly regardless of location or time, and efficiently acquire advanced technical skills.

[0006] "User" refers to an individual who intends to acquire skills and knowledge by using this learning support system.

[0007] "Diagnostic" refers to a feature that includes a set of questions that the user answers, such as a quiz or test to assess the user's skill level.

[0008] The "teaching material providing means" refers to a function that dynamically selects and provides teaching materials and assignments that are optimal for the user's skill level based on the results of the diagnostic means.

[0009] "Assessment means" refers to functionality, including AI modules, used to automatically assess the results of user-submitted assignments and generate feedback.

[0010] "Progress management means" refers to a function that manages the user's learning progress and provides encouraging messages and rewards to maintain motivation. [Brief explanation of the drawings]

[0011] [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

[0012] 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.

[0013] First, the terms used in the following description will be explained.

[0014] 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).

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] The learning support system of the present invention is a system that supports users in efficiently acquiring advanced technology and knowledge related to mobile phone base stations. This system has the functions of user diagnosis, provision of learning materials, assignment evaluation, and learning progress management, and an embodiment thereof is shown below.

[0033] User Registration and Authentication

[0034] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks the link included in the confirmation email, and the device accesses the server to validate the registration.

[0035] User level assessment

[0036] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[0037] Providing teaching materials and assignments

[0038] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who can then read the learning materials and work on the assignments.

[0039] Submitting and grading assignments

[0040] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[0041] Manage your learning progress and maintain motivation

[0042] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating the user to continue learning.

[0043] Specific Examples

[0044] For example, user "A" registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "intermediate level." The server sends learning materials for "5G Technology Overview" and a basic problem set, which are ideal for intermediate level users, to A's device. A studies the materials, works on assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback such as "You need to learn more about the 5G base frequency band." Periodically, the server evaluates A's progress and sends a message saying, "Great progress, you've earned 50 points!" This motivates A to continue learning.

[0045] As described above, the learning support system of the present invention provides optimal learning materials and assignments according to the user's skill level, and supports the efficient acquisition of knowledge.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user enters their name, email address, and password on the new registration screen.

[0049] Step 2:

[0050] The terminal transmits this information to the server.

[0051] Step 3:

[0052] The server validates the information entered to ensure it is in the correct format.

[0053] Step 4:

[0054] The server saves the information that passes validation in the database.

[0055] Step 5:

[0056] The server sends a confirmation email to the user.

[0057] Step 6:

[0058] The user clicks on the link in the confirmation email they received.

[0059] Step 7:

[0060] The device sends the user's verification link to the server to confirm the registration.

[0061] Step 8:

[0062] The server validates the user's registration and updates the database.

[0063] Step 9:

[0064] The user enters their email address and password on the login screen and logs in.

[0065] Step 10:

[0066] The terminal sends this login information to the server.

[0067] Step 11:

[0068] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[0069] Step 12:

[0070] The server provides the user with a "level diagnostic test."

[0071] Step 13:

[0072] The user answers the questions in the diagnostic test.

[0073] Step 14:

[0074] The terminal sends the answer result to the server.

[0075] Step 15:

[0076] The server uses an AI module to determine the user's skill level.

[0077] Step 16:

[0078] The server stores the judgment results in a database.

[0079] Step 17:

[0080] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[0081] Step 18:

[0082] The server transmits the teaching materials and assignments to the terminal.

[0083] Step 19:

[0084] The terminal displays the received learning materials and assignments to the user.

[0085] Step 20:

[0086] Users study the materials and work on the assignments.

[0087] Step 21:

[0088] The user sends the completed assignments to the server via the terminal.

[0089] Step 22:

[0090] The server uses an AI module to automatically grade the assignments.

[0091] Step 23:

[0092] The server generates feedback and stores it in a database along with the scoring results.

[0093] Step 24:

[0094] The server notifies the user of the feedback and the score.

[0095] Step 25:

[0096] The server periodically evaluates the user's learning progress data.

[0097] Step 26:

[0098] The server generates encouraging messages and rewards to keep you motivated.

[0099] Step 27:

[0100] The server sends cheering messages and rewards to the device.

[0101] Step 28:

[0102] The device displays a message of encouragement and a reward notification to the user.

[0103] Example 1

[0104] 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."

[0105] With the rapid evolution of modern technology and knowledge, there is a demand for systems that allow users to efficiently learn advanced information in specific technical fields. However, conventional learning systems have difficulty providing optimal learning materials and assignments tailored to each user's skill level, and they lack means to manage users' learning progress and maintain their motivation. Furthermore, these systems often lack integrated user registration and authentication functions. This prevents users from properly understanding their own learning status, hindering effective learning.

[0106] 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.

[0107] In this invention, the server includes authentication means for user registration and authentication, diagnosis means for diagnosing the user's skill level, provision means for providing the user with educational materials and tasks appropriate for the user based on the results of the diagnosis means, evaluation means for evaluating the results of the tasks and generating feedback, and progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation. This makes it possible to provide optimal educational materials and tasks according to the skill level of each user, manage learning progress and maintain motivation, and provide efficient learning support that integrates user registration and authentication.

[0108] "User" refers to an individual who uses the system to learn.

[0109] "Authentication" refers to the process used to verify a user's registration information and grant access to the system.

[0110] "Diagnostic means" refers to the process of assessing the user's skill level and providing the most appropriate teaching materials and assignments based on the results.

[0111] "Educational materials" refers to teaching materials and reference materials that contain information and data for users to study.

[0112] "Work" refers to assignments and exercises that users undertake as part of their studies.

[0113] "Delivery means" refers to the method or process of providing and presenting educational materials and tasks to users.

[0114] "Evaluation means" refers to a system that evaluates the work submitted by a user and generates feedback based on the results.

[0115] "Progress management means" refers to the process of tracking and managing a user's learning progress and providing messages and rewards to maintain motivation as needed.

[0116] "Generative AI model" refers to an artificial intelligence program model used to diagnose a user's skill level and evaluate issues.

[0117] "Feedback" refers to evaluations and advice provided to users regarding their learning and work.

[0118] The learning support system of the present invention supports users in efficiently acquiring advanced skills and knowledge. This system includes functions for user registration and authentication, skill level assessment, provision of learning materials and tasks, task evaluation, learning progress management, and motivation maintenance.

[0119] Hardware and Software Configuration

[0120] This system operates using devices such as personal computers and smartphones. These devices communicate with the server via the Internet. Specifically, the following hardware and software are used:

[0121] Hardware

[0122] personal computer

[0123] Smartphone

[0124] software

[0125] A web browser (for users to access the system)

[0126] Email client (to receive the confirmation email)

[0127] Database (e.g. MySQL, PostgreSQL)

[0128] Server-side frameworks (e.g., Django, Node.js)

[0129] AI model (e.g. OpenAI GPT-3)

[0130] Detailed explanation of each function

[0131] User Registration and Authentication

[0132] When a user enters their name, email address, and password on the new registration screen, the device sends this data to the server. The server validates the data, stores it in a database, and then sends a confirmation email to the user. The user clicks the link in the confirmation email, and the device accesses the server to validate the registration.

[0133] Technical level diagnosis

[0134] When a user logs in for the first time, they answer a set of questions provided as a diagnostic tool. The device sends the answers to the server, which then uses an AI model (e.g., GPT-3) to determine the user's skill level. The results are stored in a database.

[0135] Examples:

[0136] The user answers diagnostic questions when logging in for the first time, and the terminal sends the results to the server.

[0137] The server inputs the diagnostic answers into the AI ​​module, which determines the level as "intermediate."

[0138] Example prompt for a generative AI model:

[0139] "The following diagnostic questions about 5G communication technology are provided, and you can use them to determine your technical level. Answer: {Answer}"

[0140] Providing materials and work

[0141] The server selects appropriate educational materials and tasks based on the user's skill level and sends them to the terminal, which displays them to the user, who then reads the educational materials and works on the tasks.

[0142] Submitting and grading your work

[0143] After the user completes their work, they click the submit button and their device sends the results to the server, which uses an AI model to automatically grade the work and generate feedback, which is stored in a database and notified to the user.

[0144] Manage your learning progress and maintain motivation

[0145] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards. As a means of progress management, the server sends these messages and rewards to the device, which then displays them to the user. This motivates the user to continue learning.

[0146] Examples:

[0147] The server evaluates your learning progress and sends you a message saying "Great progress, you've earned 50 points!"

[0148] Users receive messages and stay motivated to learn.

[0149] As described above, the learning support system of the present invention is designed to enable users to efficiently acquire advanced knowledge, providing optimal educational materials and tasks according to the user's technical level, managing learning progress, and maintaining motivation.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] Program processing steps

[0152] User Registration and Authentication

[0153] Step 1:

[0154] The user enters their name, email address, and password on the new registration screen.

[0155] Input: Name, Email Address, Password

[0156] Specific actions: Open the registration form in a web browser and enter information in the input fields.

[0157] Output: Data entered into the form

[0158] Step 2:

[0159] The terminal sends the input data to the server.

[0160] Input: Data entered into a form

[0161] Specific operation: Uses JavaScript's fetch API to send form data via a POST request.

[0162] Output: User data sent to the server

[0163] Step 3:

[0164] The server validates the data and saves it to the database.

[0165] Input: Submitted user data

[0166] Specific operation: Django view function receives data, performs form validation, and saves it to the database (MySQL).

[0167] Output: User data stored in the database

[0168] Step 4:

[0169] The server sends a confirmation email to the user.

[0170] Input: Saved user data

[0171] Specific operation: Sends a confirmation email using Django's send_mail function.

[0172] Output: Confirmation email sent

[0173] Step 5:

[0174] The user clicks on the link in the confirmation email and the device accesses the server.

[0175] Input: Link in confirmation email

[0176] What happens: A user opens their email client, clicks a link, and the browser sends a new request to the server.

[0177] Output: Request sent to the server

[0178] Step 6:

[0179] The server validates the registration.

[0180] Input: The request sent to the server

[0181] Specific behavior: The server verifies the token in the link and updates the user status in the database.

[0182] Output: Enabled user accounts

[0183] Technical level diagnosis

[0184] Step 1:

[0185] The user answers a level assessment question when they first log in.

[0186] Input: Answer to diagnostic question

[0187] Specific actions: Open a question form in a web browser and answer each question.

[0188] Output: Response data

[0189] Step 2:

[0190] The terminal transmits the response data to the server.

[0191] Input: Answer data

[0192] Specific operation: Uses JavaScript's fetch API to send the response data via a POST request.

[0193] Output: Response data sent to the server

[0194] Step 3:

[0195] The server analyzes the response data using an AI module.

[0196] Input: Submitted response data

[0197] Specific operation: Send data to the API of the AI ​​module (e.g., GPT-3) and receive the analysis results.

[0198] Output: Technical level as analysis result

[0199] Step 4:

[0200] The server determines the skill level and stores the results in a database.

[0201] Input: Analysis results of the AI ​​module

[0202] Specific operation: The results of the AI ​​module are saved in a database using an SQL query.

[0203] Output: Skill level stored in the database

[0204] Providing materials and work

[0205] Step 1:

[0206] The server selects materials and tasks based on the user's skill level.

[0207] Input: User skill level

[0208] Specific operation: The system retrieves the user's skill level from the database and executes an algorithm to select appropriate learning materials and tasks.

[0209] Output: Selected materials and tasks

[0210] Step 2:

[0211] The server sends the selected teaching materials (such as PDF files) and tasks to the terminal.

[0212] Input: Selected materials and tasks

[0213] Specific operation: Send teaching material data and tasks to the terminal in JSON format.

[0214] Output: Teaching materials and work data sent to the device

[0215] Step 3:

[0216] The terminal displays these to the user.

[0217] Input: Teaching materials and work data sent to the terminal

[0218] Specific operation: Use JavaScript to display teaching materials and tasks on HTML.

[0219] Output: The material and tasks displayed to the user

[0220] Submitting and grading your work

[0221] Step 1:

[0222] Once the user has completed their work, they click the submit button.

[0223] Input: Completed work data

[0224] Specific behavior: The user fills out a work form in a browser and clicks the submit button.

[0225] Output: Working data for submission

[0226] Step 2:

[0227] The terminal sends the submitted data to the server.

[0228] Input: Working data for submission

[0229] Specific operation: Uses JavaScript's fetch API to send the submitted data to the server via a POST request.

[0230] Output: The submission data sent to the server

[0231] Step 3:

[0232] The server grades the work using an AI module.

[0233] Input: Submitted submission data

[0234] Specific operation: Input the work data into the AI ​​module and obtain the scoring results.

[0235] Output:Scoring results

[0236] Step 4:

[0237] The server generates the feedback and stores it in a database.

[0238] Input:Scoring results

[0239] Specific behavior: Generate feedback and store it in a database using an SQL query.

[0240] Output: Generated feedback

[0241] Step 5:

[0242] The server notifies the user.

[0243] Input: Generated feedback

[0244] Specific operation: Generate notification data and send it to the user's device.

[0245] Output: User notification

[0246] Manage your learning progress and maintain motivation

[0247] Step 1:

[0248] The server periodically evaluates the learning progress data.

[0249] Input: User's learning progress data

[0250] What it does: Runs a script using a periodic task (Cron job) to evaluate progress data.

[0251] Output: Evaluation results

[0252] Step 2:

[0253] The server generates cheer messages and rewards.

[0254] Input: Evaluation result

[0255] Specific behavior: Runs an algorithm that generates messages and rewards based on progress data.

[0256] Output: Generated message and reward

[0257] Step 3:

[0258] The server sends these to the terminal.

[0259] Input: Generated message and reward

[0260] Specific operation: The generated message and reward data are sent to the terminal in JSON format.

[0261] Output: Message and reward sent to the terminal

[0262] Step 4:

[0263] The terminal displays it to the user.

[0264] Input: Message and reward sent to the terminal

[0265] Specific behavior: Use JavaScript to display messages and rewards on HTML.

[0266] Output: The message and reward displayed to the user

[0267] (Application example 1)

[0268] 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."

[0269] Conventional learning support systems are limited to specific technical fields, making it difficult to provide efficient and motivating training for factory workers and engineers to acquire the robot operation skills they require. In particular, when it comes to evaluating technical levels and managing progress, incentives to maintain user motivation are often lacking, resulting in issues with learning retention rates. However, if there were a system that could enable efficient learning in a managed environment, these issues could be resolved.

[0270] 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.

[0271] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user based on the results of the diagnostic means, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, a training means for factory workers and engineers to learn robot operation techniques, and incentives such as points and badges as rewards provided by the progress management means. This allows users to efficiently and continuously progress in their learning while receiving learning materials optimal for their skill level. Furthermore, the incentives maintain motivation and improve the effectiveness of training.

[0272] A "diagnostic means for diagnosing a user's technical level" is a device that runs a series of questions and tests to assess whether a user has a certain level of technical knowledge and skills, and determines the user's technical level based on the results.

[0273] The "teaching material providing means for providing teaching materials and exercises suitable for the user based on the results of the diagnostic means" is a device that selects optimal learning materials and exercises and provides them to the user based on the user's skill level determined by the diagnostic means.

[0274] The "assessment means for evaluating the results of the assignment and generating feedback" is a device that analyzes the content of the assignment submitted by the user and automatically generates feedback such as the results and areas for improvement.

[0275] A "progress management means for managing a user's learning progress and providing messages and rewards to maintain motivation" is a device that monitors the user's learning process and increases their motivation to learn by providing encouragement and rewards according to their progress.

[0276] A "training means for factory workers and engineers to learn robot operation techniques" is a device that provides a series of training programs for factory employees and engineers to efficiently learn the techniques necessary for operating and setting up robots.

[0277] "Incentives such as points and badges as rewards provided by the progress management means" are rewards such as points and badges given according to the user's learning progress and results, and are intended to increase motivation to learn.

[0278] The learning support system of the present invention provides a training platform for factory workers and engineers to efficiently learn robot operation techniques. Specific embodiments of the system are described below.

[0279] User Registration and Authentication

[0280] The user (factory worker or technician) enters the required information such as name, email address, and password, and the device (computer, smartphone, etc.) sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks on a link in the confirmation email, and the device accesses the server to activate the registration.

[0281] User level assessment

[0282] When a user logs in for the first time, they answer a set of questions prepared in advance. The device sends the answers to the server, which then uses AI modules (e.g., TensorFlow, scikit-learn) to determine the user's skill level. The results are stored in a database.

[0283] Providing teaching materials and assignments

[0284] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials are sent to the terminal, where the user can study them and work on the assignments.

[0285] Submitting and grading assignments

[0286] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[0287] Manage your learning progress and maintain motivation

[0288] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points and badges) and messages to maintain motivation according to the progress. These rewards and messages are also sent to the device, motivating the user to continue learning.

[0289] Specific Examples

[0290] For example, a user, Worker A, registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "beginner level." The server sends learning materials and practice questions for "basics of robot operation," which are optimal for beginners, to A's device. A studies the materials, works on the assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback that "you still need to understand basic operating procedures." The server also sends A a message saying, "Great progress, you've earned 50 points!", which motivates A to continue learning.

[0291] Prompt Sentence Examples

[0292] Design a program to apply a support system for learning technology and knowledge related to mobile phone base stations as a training system for operating factory robots, provide teaching materials and assignments according to the user's level, and develop an application that manages progress and performs automatic evaluation.

[0293] This allows users to efficiently acquire robot operation skills and maintain their motivation as they continue their studies.

[0294] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0295] Step 1: User Registration and Authentication

[0296] The user enters information such as name, email address, and password. The terminal sends this information to the server. The server validates the input data and, if there are no problems, saves the information in a database. A confirmation email is then sent to the user, who clicks on a link in the confirmation email to activate their registration. The input of this step is the user's registration information, and the output is the activation of the user's registration.

[0297] Step 2: User Level Assessment

[0298] When a user logs in for the first time, the system presents the user with a set of pre-prepared questions. The user answers these questions, and the device sends the answers to the server. The server then uses an AI module (e.g., TensorFlow, scikit-learn) to determine the user's skill level and saves the results in a database. The input of this step is the user's answer data, and the output is the user's skill level assessment result.

[0299] Step 3: Providing materials and assignments

[0300] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials and assignments are sent to the terminal, which displays them to the user. The user studies the learning materials and works on the assignments. The input to this step is the user's skill level, and the output is the most appropriate learning materials and assignments provided to the user.

[0301] Step 4: Submit and grade assignments

[0302] When a user completes an assignment, the device sends the submission results to the server. The server uses an AI module to automatically grade the assignment and generate feedback. This feedback is stored in a database and notified to the user via the device. The input of this step is the user's submitted assignment data, and the output is the automatic grading results and feedback.

[0303] Step 5: Manage your learning progress and stay motivated

[0304] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points or badges) and messages to maintain motivation according to the progress. These rewards and messages are sent to the device and notified to the user. The input of this step is the user's learning progress data, and the output is the rewards and motivation messages.

[0305] These processing steps enable the system to efficiently support the user in acquiring robot operation skills and maintain their motivation to learn.

[0306] 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.

[0307] The learning support system of the present invention was developed to enable users to efficiently acquire advanced technology and knowledge related to mobile phone base stations. This system diagnoses the user's skill level, provides learning materials, evaluates assignments and generates feedback, manages learning progress, and provides encouraging messages and rewards to maintain motivation. In addition, the system has the ability to recognize the user's emotions and dynamically adjust learning support based on their state.

[0308] User Registration and Authentication

[0309] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, confirms that it is in the correct format, and then saves it in a database. A confirmation email is then sent to the user. The user clicks the link contained in the confirmation email, and the device accesses the server and validates the registration.

[0310] User level assessment

[0311] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[0312] Providing teaching materials and assignments

[0313] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who then studies the learning materials and works on the assignments.

[0314] Submitting and grading assignments

[0315] Once a user completes an assignment, the device sends the submission to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[0316] Manage your learning progress and maintain motivation

[0317] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating them to continue learning.

[0318] Introducing emotion recognition methods

[0319] The device is equipped with an emotion recognition means that analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state. This emotion recognition means can grasp in real time the stress, frustration, joy, and other feelings the user feels while studying. Based on the data obtained from the emotion recognition means, the server dynamically adjusts the optimal support messages and teaching methods according to the user's emotional state.

[0320] Specific Examples

[0321] For example, suppose user "B" registers with the system and is judged to be at an "intermediate" level through a level assessment. The server provides B with the learning materials and a basic problem set on "5G Technology Overview" that are optimal for him. B studies the materials and submits assignments. The server uses an AI module to grade the assignments and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message such as "take a break." This allows B to take breaks at appropriate times and continue studying effectively.

[0322] As described above, the learning support system of the present invention provides an optimal learning environment according to the user's skill level, learning progress, and emotional state, thereby realizing efficient learning support.

[0323] The processing flow will be explained below.

[0324] Step 1:

[0325] The user enters their name, email address, and password on the new registration screen.

[0326] Step 2:

[0327] The terminal transmits this information to the server.

[0328] Step 3:

[0329] The server validates the information entered to ensure it is in the correct format.

[0330] Step 4:

[0331] The server saves the information that passes validation in the database.

[0332] Step 5:

[0333] The server sends a confirmation email to the user.

[0334] Step 6:

[0335] The user clicks on the link in the confirmation email they received.

[0336] Step 7:

[0337] The device sends the user's verification link to the server to confirm the registration.

[0338] Step 8:

[0339] The server validates the user's registration and updates the database.

[0340] Step 9:

[0341] The user enters their email address and password on the login screen and logs in.

[0342] Step 10:

[0343] The terminal sends this login information to the server.

[0344] Step 11:

[0345] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[0346] Step 12:

[0347] The server provides the user with a "level diagnostic test."

[0348] Step 13:

[0349] The user answers the questions in the diagnostic test.

[0350] Step 14:

[0351] The terminal sends the answer result to the server.

[0352] Step 15:

[0353] The server uses an AI module to determine the user's skill level.

[0354] Step 16:

[0355] The server stores the judgment results in a database.

[0356] Step 17:

[0357] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[0358] Step 18:

[0359] The server transmits the teaching materials and assignments to the terminal.

[0360] Step 19:

[0361] The terminal displays the received learning materials and assignments to the user.

[0362] Step 20:

[0363] Users study the materials and work on the assignments.

[0364] Step 21:

[0365] The user sends the completed assignments to the server via the terminal.

[0366] Step 22:

[0367] The server uses an AI module to automatically grade the assignments.

[0368] Step 23:

[0369] The server generates feedback and stores it in a database along with the scoring results.

[0370] Step 24:

[0371] The server notifies the user of the feedback and the score.

[0372] Step 25:

[0373] The server periodically evaluates the user's learning progress data.

[0374] Step 26:

[0375] The server generates encouraging messages and rewards to keep you motivated.

[0376] Step 27:

[0377] The server sends cheering messages and rewards to the device.

[0378] Step 28:

[0379] The device displays a message of encouragement and a reward notification to the user.

[0380] Step 29:

[0381] The terminal analyzes the user's facial expressions, voice, and biometric signals and determines the user's emotional state using emotion recognition means.

[0382] Step 30:

[0383] The server evaluates variables that may have an impact based on the emotional data obtained from the emotion recognition means, and adjusts the delivery of conventional teaching materials and task allocation.

[0384] Step 31:

[0385] The server dynamically generates optimal support messages and instruction methods based on the user's emotional state.

[0386] Step 32:

[0387] The server transmits the generated message and instruction method to the terminal.

[0388] Step 33:

[0389] The device displays these messages and teaching methods to the user, further promoting improved learning efficiency.

[0390] Example 2

[0391] 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."

[0392] Conventional learning support systems can provide learning materials and assess tasks based on the user's skill level and learning progress, but they cannot adjust learning support to take the user's emotional state into account. As a result, they are unable to provide appropriate support when the user feels stressed or frustrated, resulting in reduced learning efficiency. Furthermore, feedback and support messages are generated uniformly, without dynamic responses tailored to the individual user's state. Therefore, the objective of this invention is to improve learning efficiency and maintain motivation by recognizing the user's emotional state in real time and providing optimal learning support based on this.

[0393] 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.

[0394] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition and adjustment means for recognizing the user's emotional state and adjusting learning support in accordance with that state. This makes it possible to grasp the user's emotional state in real time during the learning process and provide support and feedback at appropriate times, thereby improving the user's learning efficiency and maintaining motivation.

[0395] The "diagnostic means" is a system for measuring the user's skill level, including a set of questions to be answered by the user and an evaluation method.

[0396] The "means for providing learning materials" is a system that selects appropriate learning materials and assignments according to the user's skill level and learning progress, and provides them to the user.

[0397] The "evaluation means" is a mechanism for analyzing and evaluating the results of assignments submitted by users and generating feedback, and is a system that includes an AI module.

[0398] The "progress management means" is a mechanism for managing the user's learning progress and providing encouraging messages and rewards to maintain the user's motivation.

[0399] The "emotion recognition and adjustment means" is a mechanism that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state and dynamically adjusts learning support according to that state.

[0400] An "AI module" is a part of the artificial intelligence used within the learning support system, and is a system that analyzes users' answers and task results, determines their skill level, and generates feedback.

[0401] A "question set" is a series of questions to assess a user's skill level, and is answered by the user when they log in for the first time.

[0402] "Teaching materials" are materials for users to study, and include formats such as PDF files and online content.

[0403] MODE FOR CARRYING OUT THE INVENTION

[0404] The purpose of the learning support system of the present invention is to help users efficiently acquire advanced technology and knowledge related to mobile phone base stations. The system assesses the user's skill level, provides appropriate learning materials and assignments, manages the user's learning progress, and provides various encouraging messages and rewards to maintain motivation. It also has the ability to recognize the user's emotional state and dynamically adjust learning support based on that state.

[0405] User Registration and Authentication

[0406] The information provided by the user on the new registration screen, such as "Name," "Email address," and "Password," is sent from the terminal to the server. The server validates the information and stores it in a database. A confirmation email is then sent to the user, and the user validates their registration by clicking the link in the confirmation email.

[0407] User level assessment

[0408] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[0409] Providing teaching materials and assignments

[0410] The server selects the most appropriate learning materials and assignments based on the user's technical level. For example, the PDF learning material "5G Technology Overview" may be selected. The server then sends the selected learning materials and assignments to the device, which then displays them to the user.

[0411] Submitting and grading assignments

[0412] The user completes the assignment and the device sends the submission results to the server, which uses an AI module to automatically grade and generate feedback, which is stored in a database and notified to the user.

[0413] Manage your learning progress and maintain motivation

[0414] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards, which are then sent to the device and displayed to the user, encouraging them to continue learning.

[0415] Introducing emotion recognition methods

[0416] The device analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state in real time. Cameras, microphones, and sensors are used for emotion recognition. The server dynamically adjusts cheering messages and teaching methods based on the data obtained from the emotion recognition means.

[0417] Specific Examples

[0418] For example, suppose user "B" registers with the system and is assessed as "intermediate" in the level assessment. The server provides B with the optimal "5G Technology Overview" study materials and a basic problem set. B studies these and submits the assignment. The server grades the assignment using an AI module and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message saying "take a break." This allows B to continue studying efficiently while taking appropriate breaks.

[0419] Example prompts for generative AI models

[0420] "Please describe a learning support system for mobile phone base stations. Please include the following points: user registration and authentication procedures, methods for assessing user levels, provision of learning materials and assignments, assignment submission and evaluation, learning progress management and motivation maintenance, and emotion recognition methods."

[0421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0422] Step 1:

[0423] The user enters their name, email address, and password on the new registration screen. The input data is entered into the terminal form. This signal data becomes the input for the terminal.

[0424] Step 2:

[0425] The device sends the entered information to the server. The data sent is "Name," "Email address," and "Password," and becomes the server's input. Specifically, the data is sent to the server when the send button is pressed.

[0426] Step 3:

[0427] The format of the information received by the server is validated to check for invalid data. Data processing involves checking the format of email addresses using regular expressions. The output is the validation results and the input data.

[0428] Step 4:

[0429] If the server passes validation, it saves the user information to the database. The data is inserted into the database using an SQL query. The output is a message that the data was saved successfully.

[0430] Step 5:

[0431] The server will auto-generate a confirmation email and send it to the email address provided, containing a confirmation link, which is the output sent to the user.

[0432] Step 6:

[0433] The user clicks on the link in the confirmation email they received. This causes the device to access the linked server. This action constitutes the user's confirmation input.

[0434] Step 7:

[0435] The device sends a link access request to the server. The request data includes the user ID and a confirmation token. This becomes the server's input.

[0436] Step 8:

[0437] The server receives the link access request and validates the confirmation information. The data operation includes the process of verifying the confirmation token. The output is the user registration validation result.

[0438] Step 9:

[0439] The server updates the user's status to "confirmed" and saves it in the database. Data calculation is the process of updating the status using an SQL query. The output is a successful update message.

[0440] Step 10:

[0441] When a user logs in for the first time, they answer a set of diagnostic questions. The questions are selected and entered as input data by the user.

[0442] Step 11:

[0443] The terminal converts the user's answer into a data format and sends it to the server. The converted data is the answer content and becomes the input for the server.

[0444] Step 12:

[0445] The server uses an AI module to determine the technical level based on the user's answers using numerical values ​​and categories. Data processing involves using natural language processing and machine learning models to evaluate the level. The output is the technical level assessment result.

[0446] Step 13:

[0447] The server saves the result of the decision to a database. Data calculation is the process of writing to the database using an SQL query, and the output is a save success message.

[0448] Step 14:

[0449] The server selects the most appropriate learning materials and assignments based on the user's skill level. Specifically, it applies a learning material selection algorithm based on the evaluation results to generate the selection results. The output is the selected learning material information.

[0450] Step 15:

[0451] The server sends the selected learning materials to the terminal. The learning material data includes PDF files and link information, which are input to the terminal.

[0452] Step 16:

[0453] The terminal displays the received learning materials and assignments to the user. For example, it opens the learning materials in a PDF viewer and displays the assignment input form. The output is that the user confirms the display.

[0454] Step 17:

[0455] Users complete the assignment and submit it through an online form, which becomes input data and is saved on the device.

[0456] Step 18:

[0457] The terminal sends the submitted results to the server in real time. The sent data is the answer to the assignment and becomes the input for the server.

[0458] Step 19:

[0459] The server uses an AI module to automatically grade submitted assignments and generate feedback. Data processing involves generating the accuracy rate and feedback content through a model evaluation process. The output is feedback information.

[0460] Step 20:

[0461] The server saves the generated feedback in a database and notifies the user. The saving process uses SQL queries. The output is a save success message and a notification.

[0462] Step 21:

[0463] The server periodically retrieves the user's learning progress data from the database and evaluates it. This results in the application of a progress evaluation algorithm. The input is the progress data, and the output is the evaluation result.

[0464] Step 22:

[0465] The server generates encouragement messages and rewards to encourage users to continue learning. The encouragement messages are dynamically created based on the user's progress. The output is the encouragement message content and reward information.

[0466] Step 23:

[0467] The server sends the generated message and reward to the device. The sent content includes the support message and reward information, which are input to the device.

[0468] Step 24:

[0469] The device displays these messages and rewards to the user, for example, in a notification banner or on a dedicated page. The output is the user confirming the display.

[0470] Step 25:

[0471] The device analyzes the user's facial expressions, voice, and biometric signals in real time through a camera and microphone, and emotional data is acquired as input to the device.

[0472] Step 26:

[0473] The server analyzes the emotion data obtained from the emotion recognition means and adjusts dynamic learning support according to the user's state. An emotion analysis algorithm is used for data calculation. The output is the learning support adjustment result.

[0474] In this way, learning support based on the user's skill level and emotional state is realized.

[0475] (Application example 2)

[0476] 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."

[0477] While conventional learning support systems provide learning materials tailored to the user's technical level and automatically evaluate assignments and provide feedback, they do not dynamically adjust based on the user's emotional state. This can lead to problems such as users feeling stressed or losing motivation, reducing the effectiveness of their learning. Furthermore, it is difficult to provide real-time technical support in the actual work environment of a factory.

[0478] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0479] In this invention, the server includes a diagnostic means for diagnosing the user's skill level, a learning material providing means for providing learning materials and assignments suitable for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition means for recognizing the user's emotional state and dynamically adjusting learning support based on that state. This not only improves the user's learning efficiency but also enables real-time technical support in an actual working environment.

[0480] A "user" is an individual who wishes to learn about the technology and knowledge related to mobile phone base stations.

[0481] The "diagnosis means" is a means for diagnosing the user's skill level, and determines the user's skill level based on the answers to a set of questions.

[0482] The "teaching material providing means" is a means for dynamically selecting and providing optimal teaching materials and assignments according to the user's skill level and learning progress.

[0483] "Evaluation means" is a means for evaluating user-submitted assignments and generating feedback using an AI module.

[0484] The "progress management means" is a means for periodically evaluating the user's learning progress and providing encouraging messages and rewards to maintain motivation.

[0485] The "emotion recognition means" is a means for analyzing the user's facial expressions, voice, biometric signals, etc., and recognizing the user's emotional state in real time.

[0486] A "factory robot" is an automated machine used to perform manufacturing and assembly tasks in a factory.

[0487] "Smart glasses" are glasses worn by a user that include a display device that provides visual information.

[0488] "Real time" refers to a state in which processing is carried out instantaneously in synchronization with real-world time.

[0489] An "AI module" is a software module that uses artificial intelligence technology to perform data analysis and judgment.

[0490] The learning support system of the present invention is implemented in a form that can be used by engineers who operate and maintain factory robots while wearing smart glasses.

[0491] Overall system configuration

[0492] 1. User Registration and Authentication

[0493] The server receives the information entered by the user on the new registration screen (e.g., "Name", "Email address", "Password"), validates it, saves it in the database and sends a confirmation email to the user. When the user clicks the link included in the confirmation email, the server validates the registration.

[0494] 2. User Level Assessment

[0495] A set of questions that the user answers when logging in for the first time is provided to the device (smart glasses), and the server receives the answers. The AI ​​module determines the user's skill level and stores it in a database.

[0496] 3. Providing study materials and assignments

[0497] The server selects the most appropriate learning materials (e.g., "PDF Learning Materials 1") and assignments based on the user's skill level and sends them to the device. The device displays them, and the user works on the learning or assignments.

[0498] 4. Submitting and grading assignments

[0499] When a user submits an assignment, the results are sent from the device to the server, where the server automatically grades the assignment using an AI module, generates feedback, stores it in a database, and notifies the user.

[0500] 5. Manage your learning progress and maintain motivation

[0501] The server periodically evaluates the user's learning progress data, generates encouraging messages and rewards to maintain motivation, and sends them to the terminal for display to the user.

[0502] 6. Introduction of emotion recognition methods

[0503] The device is equipped with an emotion recognition unit that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state. Based on the data obtained from the emotion recognition unit, the server dynamically adjusts the support messages and teaching methods according to the user's emotional state.

[0504] Hardware and software used

[0505] Server: A central processing unit that manages user data, learning materials, assignments, progress data, and provides assessment and feedback.

[0506] Terminal (smart glasses): A device that provides the user with real-time visual information, diagnoses their skill level, displays teaching materials and assignments, and recognizes emotions.

[0507] AI module: Artificial intelligence software that automatically grades assignments and assesses skill levels.

[0508] Emotion recognition means: A set of software and sensors that analyze the user's facial expressions, voice, and biometric signals.

[0509] Specific examples

[0510] For example, when a user named "Engineer A" logs in for the first time, he answers a set of questions, and the server determines his skill level as "intermediate." The server then provides learning materials (e.g., "PDF Learning Materials 2") and assignments suitable for an "intermediate" user. Engineer A continues his studies through the smart glasses and submits the assignments. The server's AI module generates feedback and notifies the user that "you need to learn more about the 5G base frequency band." If Engineer A feels stressed while studying, the emotion recognition means detects this, and the server sends a supportive message such as "take a break."

[0511] Generative AI model prompt example

[0512] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[0513] Output: User's skill level (e.g. "Intermediate")

[0514] Input: User's skill level (e.g., "Intermediate")

[0515] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[0516] Input: The assignment submitted by the user (e.g., "Assignment 1")

[0517] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[0518] Input: User emotion recognition result (e.g., "stress")

[0519] Output: A suitable encouraging message (e.g., "You're almost there!")

[0520] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0521] Step 1:

[0522] User Registration and Authentication

[0523] Input: Name, email address, and password entered by the user on the sign-up screen

[0524] The device sends the registration information entered by the user to the server. The server validates this information to ensure it is in the correct format. It then stores it in a database and sends a confirmation email to the user. The user clicks on a link in the confirmation email, which causes the device to contact the server and validate the registration.

[0525] Output: User registration completion notification

[0526] Step 2:

[0527] User level assessment

[0528] Input: A set of questions that the user answers when they first log in

[0529] The server provides a set of questions to the user through the device, and the user answers them. The device then sends the answers back to the server, which then uses an AI module to diagnose the user's skill level and stores it in a database.

[0530] Output: User's skill level (e.g., "Beginner", "Intermediate", "Advanced")

[0531] Step 3:

[0532] Providing teaching materials and assignments

[0533] Input: User skill level assessment result

[0534] The server selects the most appropriate learning materials and assignments based on the user's skill level and sends them to the terminal. The terminal displays these learning materials (e.g., "PDF learning materials") and assignments to the user. The user studies the learning materials and works on the assignments.

[0535] Output: A list of materials and assignments provided to the user

[0536] Step 4:

[0537] Submitting and grading assignments

[0538] Input: The task the user completed

[0539] Once the user completes the assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback, which is stored in a database and notified to the user.

[0540] Output: Assignment evaluation results and feedback (e.g., "passed," "failed," "what needs further study").

[0541] Step 5:

[0542] Manage your learning progress and maintain motivation

[0543] Input: User's learning progress data

[0544] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. The progress management results are sent to the device, which then displays the encouraging messages and reward notifications to the user.

[0545] Output: Cheer message and reward notification

[0546] Step 6:

[0547] Introducing emotion recognition methods

[0548] Input: Data such as user facial expressions, voice, and biometric signals

[0549] The device analyzes the user's facial expressions, voice, and biometric signals in real time to recognize their emotional state. The recognized emotional state data is sent to the server, which then uses this data to generate and dynamically adjust the optimal cheering message according to the user's emotional state.

[0550] Output: A cheering message based on the emotional state

[0551] Generative AI model prompt example

[0552] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[0553] Output: User's skill level (e.g. "Intermediate")

[0554] Input: User's skill level (e.g., "Intermediate")

[0555] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[0556] Input: The assignment submitted by the user (e.g., "Assignment 1")

[0557] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[0558] Input: User emotion recognition result (e.g., "stress")

[0559] Output: A suitable encouraging message (e.g., "You're almost there!")

[0560] 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.

[0561] 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.

[0562] 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.

[0563] [Second embodiment]

[0564] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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."

[0576] The learning support system of the present invention is a system that supports users in efficiently acquiring advanced technology and knowledge related to mobile phone base stations. This system has the functions of user diagnosis, provision of learning materials, assignment evaluation, and learning progress management, and an embodiment thereof is shown below.

[0577] User Registration and Authentication

[0578] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks the link included in the confirmation email, and the device accesses the server to validate the registration.

[0579] User level assessment

[0580] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[0581] Providing teaching materials and assignments

[0582] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who can then read the learning materials and work on the assignments.

[0583] Submitting and grading assignments

[0584] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[0585] Manage your learning progress and maintain motivation

[0586] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating the user to continue learning.

[0587] Specific Examples

[0588] For example, user "A" registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "intermediate level." The server sends learning materials for "5G Technology Overview" and a basic problem set, which are ideal for intermediate level users, to A's device. A studies the materials, works on assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback such as "You need to learn more about the 5G base frequency band." Periodically, the server evaluates A's progress and sends a message saying, "Great progress, you've earned 50 points!" This motivates A to continue learning.

[0589] As described above, the learning support system of the present invention provides optimal learning materials and assignments according to the user's skill level, and supports the efficient acquisition of knowledge.

[0590] The processing flow will be explained below.

[0591] Step 1:

[0592] The user enters their name, email address, and password on the new registration screen.

[0593] Step 2:

[0594] The terminal transmits this information to the server.

[0595] Step 3:

[0596] The server validates the information entered to ensure it is in the correct format.

[0597] Step 4:

[0598] The server saves the information that passes validation in the database.

[0599] Step 5:

[0600] The server sends a confirmation email to the user.

[0601] Step 6:

[0602] The user clicks on the link in the confirmation email they received.

[0603] Step 7:

[0604] The device sends the user's verification link to the server to confirm the registration.

[0605] Step 8:

[0606] The server validates the user's registration and updates the database.

[0607] Step 9:

[0608] The user enters their email address and password on the login screen and logs in.

[0609] Step 10:

[0610] The terminal sends this login information to the server.

[0611] Step 11:

[0612] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[0613] Step 12:

[0614] The server provides the user with a "level diagnostic test."

[0615] Step 13:

[0616] The user answers the questions in the diagnostic test.

[0617] Step 14:

[0618] The terminal sends the answer result to the server.

[0619] Step 15:

[0620] The server uses an AI module to determine the user's skill level.

[0621] Step 16:

[0622] The server stores the judgment results in a database.

[0623] Step 17:

[0624] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[0625] Step 18:

[0626] The server transmits the teaching materials and assignments to the terminal.

[0627] Step 19:

[0628] The terminal displays the received learning materials and assignments to the user.

[0629] Step 20:

[0630] Users study the materials and work on the assignments.

[0631] Step 21:

[0632] The user sends the completed assignments to the server via the terminal.

[0633] Step 22:

[0634] The server uses an AI module to automatically grade the assignments.

[0635] Step 23:

[0636] The server generates feedback and stores it in a database along with the scoring results.

[0637] Step 24:

[0638] The server notifies the user of the feedback and the score.

[0639] Step 25:

[0640] The server periodically evaluates the user's learning progress data.

[0641] Step 26:

[0642] The server generates encouraging messages and rewards to keep you motivated.

[0643] Step 27:

[0644] The server sends cheering messages and rewards to the device.

[0645] Step 28:

[0646] The device displays a message of encouragement and a reward notification to the user.

[0647] Example 1

[0648] 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."

[0649] With the rapid evolution of modern technology and knowledge, there is a demand for systems that allow users to efficiently learn advanced information in specific technical fields. However, conventional learning systems have difficulty providing optimal learning materials and assignments tailored to each user's skill level, and they lack means to manage users' learning progress and maintain their motivation. Furthermore, these systems often lack integrated user registration and authentication functions. This prevents users from properly understanding their own learning status, hindering effective learning.

[0650] 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.

[0651] In this invention, the server includes authentication means for user registration and authentication, diagnosis means for diagnosing the user's skill level, provision means for providing the user with educational materials and tasks appropriate for the user based on the results of the diagnosis means, evaluation means for evaluating the results of the tasks and generating feedback, and progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation. This makes it possible to provide optimal educational materials and tasks according to the skill level of each user, manage learning progress and maintain motivation, and provide efficient learning support that integrates user registration and authentication.

[0652] "User" refers to an individual who uses the system to learn.

[0653] "Authentication" refers to the process used to verify a user's registration information and grant access to the system.

[0654] "Diagnostic means" refers to the process of assessing the user's skill level and providing the most appropriate teaching materials and assignments based on the results.

[0655] "Educational materials" refers to teaching materials and reference materials that contain information and data for users to study.

[0656] "Work" refers to assignments and exercises that users undertake as part of their studies.

[0657] "Delivery means" refers to the method or process of providing and presenting educational materials and tasks to users.

[0658] "Evaluation means" refers to a system that evaluates the work submitted by a user and generates feedback based on the results.

[0659] "Progress management means" refers to the process of tracking and managing a user's learning progress and providing messages and rewards to maintain motivation as needed.

[0660] "Generative AI model" refers to an artificial intelligence program model used to diagnose a user's skill level and evaluate issues.

[0661] "Feedback" refers to evaluations and advice provided to users regarding their learning and work.

[0662] The learning support system of the present invention supports users in efficiently acquiring advanced skills and knowledge. This system includes functions for user registration and authentication, skill level assessment, provision of learning materials and tasks, task evaluation, learning progress management, and motivation maintenance.

[0663] Hardware and Software Configuration

[0664] This system operates using devices such as personal computers and smartphones. These devices communicate with the server via the Internet. Specifically, the following hardware and software are used:

[0665] Hardware

[0666] personal computer

[0667] Smartphone

[0668] software

[0669] A web browser (for users to access the system)

[0670] Email client (to receive the confirmation email)

[0671] Database (e.g. MySQL, PostgreSQL)

[0672] Server-side frameworks (e.g., Django, Node.js)

[0673] AI model (e.g. OpenAI GPT-3)

[0674] Detailed explanation of each function

[0675] User Registration and Authentication

[0676] When a user enters their name, email address, and password on the new registration screen, the device sends this data to the server. The server validates the data, stores it in a database, and then sends a confirmation email to the user. The user clicks the link in the confirmation email, and the device accesses the server to validate the registration.

[0677] Technical level diagnosis

[0678] When a user logs in for the first time, they answer a set of questions provided as a diagnostic tool. The device sends the answers to the server, which then uses an AI model (e.g., GPT-3) to determine the user's skill level. The results are stored in a database.

[0679] Examples:

[0680] The user answers diagnostic questions when logging in for the first time, and the terminal sends the results to the server.

[0681] The server inputs the diagnostic answers into the AI ​​module, which determines the level as "intermediate."

[0682] Example prompt for a generative AI model:

[0683] "The following diagnostic questions about 5G communication technology are provided, and you can use them to determine your technical level. Answer: {Answer}"

[0684] Providing materials and work

[0685] The server selects appropriate educational materials and tasks based on the user's skill level and sends them to the terminal, which displays them to the user, who then reads the educational materials and works on the tasks.

[0686] Submitting and grading your work

[0687] After the user completes their work, they click the submit button and their device sends the results to the server, which uses an AI model to automatically grade the work and generate feedback, which is stored in a database and notified to the user.

[0688] Manage your learning progress and maintain motivation

[0689] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards. As a means of progress management, the server sends these messages and rewards to the device, which then displays them to the user. This motivates the user to continue learning.

[0690] Examples:

[0691] The server evaluates your learning progress and sends you a message saying "Great progress, you've earned 50 points!"

[0692] Users receive messages and stay motivated to learn.

[0693] As described above, the learning support system of the present invention is designed to enable users to efficiently acquire advanced knowledge, providing optimal educational materials and tasks according to the user's technical level, managing learning progress, and maintaining motivation.

[0694] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0695] Program processing steps

[0696] User Registration and Authentication

[0697] Step 1:

[0698] The user enters their name, email address, and password on the new registration screen.

[0699] Input: Name, Email Address, Password

[0700] Specific actions: Open the registration form in a web browser and enter information in the input fields.

[0701] Output: Data entered into the form

[0702] Step 2:

[0703] The terminal sends the input data to the server.

[0704] Input: Data entered into a form

[0705] Specific operation: Uses JavaScript's fetch API to send form data via a POST request.

[0706] Output: User data sent to the server

[0707] Step 3:

[0708] The server validates the data and saves it to the database.

[0709] Input: Submitted user data

[0710] Specific operation: Django view function receives data, performs form validation, and saves it to the database (MySQL).

[0711] Output: User data stored in the database

[0712] Step 4:

[0713] The server sends a confirmation email to the user.

[0714] Input: Saved user data

[0715] Specific operation: Sends a confirmation email using Django's send_mail function.

[0716] Output: Confirmation email sent

[0717] Step 5:

[0718] The user clicks on the link in the confirmation email and the device accesses the server.

[0719] Input: Link in confirmation email

[0720] What happens: A user opens their email client, clicks a link, and the browser sends a new request to the server.

[0721] Output: Request sent to the server

[0722] Step 6:

[0723] The server validates the registration.

[0724] Input: The request sent to the server

[0725] Specific behavior: The server verifies the token in the link and updates the user status in the database.

[0726] Output: Enabled user accounts

[0727] Technical level diagnosis

[0728] Step 1:

[0729] The user answers a level assessment question when they first log in.

[0730] Input: Answer to diagnostic question

[0731] Specific actions: Open a question form in a web browser and answer each question.

[0732] Output: Response data

[0733] Step 2:

[0734] The terminal transmits the response data to the server.

[0735] Input: Answer data

[0736] Specific operation: Uses JavaScript's fetch API to send the response data via a POST request.

[0737] Output: Response data sent to the server

[0738] Step 3:

[0739] The server analyzes the response data using an AI module.

[0740] Input: Submitted response data

[0741] Specific operation: Send data to the API of the AI ​​module (e.g., GPT-3) and receive the analysis results.

[0742] Output: Technical level as analysis result

[0743] Step 4:

[0744] The server determines the skill level and stores the results in a database.

[0745] Input: Analysis results of the AI ​​module

[0746] Specific operation: The results of the AI ​​module are saved in a database using an SQL query.

[0747] Output: Skill level stored in the database

[0748] Providing materials and work

[0749] Step 1:

[0750] The server selects materials and tasks based on the user's skill level.

[0751] Input: User skill level

[0752] Specific operation: The system retrieves the user's skill level from the database and executes an algorithm to select appropriate learning materials and tasks.

[0753] Output: Selected materials and tasks

[0754] Step 2:

[0755] The server sends the selected teaching materials (such as PDF files) and tasks to the terminal.

[0756] Input: Selected materials and tasks

[0757] Specific operation: Send teaching material data and tasks to the terminal in JSON format.

[0758] Output: Teaching materials and work data sent to the device

[0759] Step 3:

[0760] The terminal displays these to the user.

[0761] Input: Teaching materials and work data sent to the terminal

[0762] Specific operation: Use JavaScript to display teaching materials and tasks on HTML.

[0763] Output: The material and tasks displayed to the user

[0764] Submitting and grading your work

[0765] Step 1:

[0766] Once the user has completed their work, they click the submit button.

[0767] Input: Completed work data

[0768] Specific behavior: The user fills out a work form in a browser and clicks the submit button.

[0769] Output: Working data for submission

[0770] Step 2:

[0771] The terminal sends the submitted data to the server.

[0772] Input: Working data for submission

[0773] Specific operation: Uses JavaScript's fetch API to send the submitted data to the server via a POST request.

[0774] Output: The submission data sent to the server

[0775] Step 3:

[0776] The server grades the work using an AI module.

[0777] Input: Submitted submission data

[0778] Specific operation: Input the work data into the AI ​​module and obtain the scoring results.

[0779] Output:Scoring results

[0780] Step 4:

[0781] The server generates the feedback and stores it in a database.

[0782] Input:Scoring results

[0783] Specific behavior: Generate feedback and store it in a database using an SQL query.

[0784] Output: Generated feedback

[0785] Step 5:

[0786] The server notifies the user.

[0787] Input: Generated feedback

[0788] Specific operation: Generate notification data and send it to the user's device.

[0789] Output: User notification

[0790] Manage your learning progress and maintain motivation

[0791] Step 1:

[0792] The server periodically evaluates the learning progress data.

[0793] Input: User's learning progress data

[0794] What it does: Runs a script using a periodic task (Cron job) to evaluate progress data.

[0795] Output: Evaluation results

[0796] Step 2:

[0797] The server generates cheer messages and rewards.

[0798] Input: Evaluation result

[0799] Specific behavior: Runs an algorithm that generates messages and rewards based on progress data.

[0800] Output: Generated message and reward

[0801] Step 3:

[0802] The server sends these to the terminal.

[0803] Input: Generated message and reward

[0804] Specific operation: The generated message and reward data are sent to the terminal in JSON format.

[0805] Output: Message and reward sent to the terminal

[0806] Step 4:

[0807] The terminal displays it to the user.

[0808] Input: Message and reward sent to the terminal

[0809] Specific behavior: Use JavaScript to display messages and rewards on HTML.

[0810] Output: The message and reward displayed to the user

[0811] (Application example 1)

[0812] 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."

[0813] Conventional learning support systems are limited to specific technical fields, making it difficult to provide efficient and motivating training for factory workers and engineers to acquire the robot operation skills they require. In particular, when it comes to evaluating technical levels and managing progress, incentives to maintain user motivation are often lacking, resulting in issues with learning retention rates. However, if there were a system that could enable efficient learning in a managed environment, these issues could be resolved.

[0814] 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.

[0815] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user based on the results of the diagnostic means, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, a training means for factory workers and engineers to learn robot operation techniques, and incentives such as points and badges as rewards provided by the progress management means. This allows users to efficiently and continuously progress in their learning while receiving learning materials optimal for their skill level. Furthermore, the incentives maintain motivation and improve the effectiveness of training.

[0816] A "diagnostic means for diagnosing a user's technical level" is a device that runs a series of questions and tests to assess whether a user has a certain level of technical knowledge and skills, and determines the user's technical level based on the results.

[0817] The "teaching material providing means for providing teaching materials and exercises suitable for the user based on the results of the diagnostic means" is a device that selects optimal learning materials and exercises and provides them to the user based on the user's skill level determined by the diagnostic means.

[0818] The "assessment means for evaluating the results of the assignment and generating feedback" is a device that analyzes the content of the assignment submitted by the user and automatically generates feedback such as the results and areas for improvement.

[0819] A "progress management means for managing a user's learning progress and providing messages and rewards to maintain motivation" is a device that monitors the user's learning process and increases their motivation to learn by providing encouragement and rewards according to their progress.

[0820] A "training means for factory workers and engineers to learn robot operation techniques" is a device that provides a series of training programs for factory employees and engineers to efficiently learn the techniques necessary for operating and setting up robots.

[0821] "Incentives such as points and badges as rewards provided by the progress management means" are rewards such as points and badges given according to the user's learning progress and results, and are intended to increase motivation to learn.

[0822] The learning support system of the present invention provides a training platform for factory workers and engineers to efficiently learn robot operation techniques. Specific embodiments of the system are described below.

[0823] User Registration and Authentication

[0824] The user (factory worker or technician) enters the required information such as name, email address, and password, and the device (computer, smartphone, etc.) sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks on a link in the confirmation email, and the device accesses the server to activate the registration.

[0825] User level assessment

[0826] When a user logs in for the first time, they answer a set of questions prepared in advance. The device sends the answers to the server, which then uses AI modules (e.g., TensorFlow, scikit-learn) to determine the user's skill level. The results are stored in a database.

[0827] Providing teaching materials and assignments

[0828] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials are sent to the terminal, where the user can study them and work on the assignments.

[0829] Submitting and grading assignments

[0830] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[0831] Manage your learning progress and maintain motivation

[0832] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points and badges) and messages to maintain motivation according to the progress. These rewards and messages are also sent to the device, motivating the user to continue learning.

[0833] Specific Examples

[0834] For example, a user, Worker A, registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "beginner level." The server sends learning materials and practice questions for "basics of robot operation," which are optimal for beginners, to A's device. A studies the materials, works on the assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback that "you still need to understand basic operating procedures." The server also sends A a message saying, "Great progress, you've earned 50 points!", which motivates A to continue learning.

[0835] Prompt Sentence Examples

[0836] Design a program to apply a support system for learning technology and knowledge related to mobile phone base stations as a training system for operating factory robots, provide teaching materials and assignments according to the user's level, and develop an application that manages progress and performs automatic evaluation.

[0837] This allows users to efficiently acquire robot operation skills and maintain their motivation as they continue their studies.

[0838] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0839] Step 1: User Registration and Authentication

[0840] The user enters information such as name, email address, and password. The terminal sends this information to the server. The server validates the input data and, if there are no problems, saves the information in a database. A confirmation email is then sent to the user, who clicks on a link in the confirmation email to activate their registration. The input of this step is the user's registration information, and the output is the activation of the user's registration.

[0841] Step 2: User Level Assessment

[0842] When a user logs in for the first time, the system presents the user with a set of pre-prepared questions. The user answers these questions, and the device sends the answers to the server. The server then uses an AI module (e.g., TensorFlow, scikit-learn) to determine the user's skill level and saves the results in a database. The input of this step is the user's answer data, and the output is the user's skill level assessment result.

[0843] Step 3: Providing materials and assignments

[0844] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials and assignments are sent to the terminal, which displays them to the user. The user studies the learning materials and works on the assignments. The input to this step is the user's skill level, and the output is the most appropriate learning materials and assignments provided to the user.

[0845] Step 4: Submit and grade assignments

[0846] When a user completes an assignment, the device sends the submission results to the server. The server uses an AI module to automatically grade the assignment and generate feedback. This feedback is stored in a database and notified to the user via the device. The input of this step is the user's submitted assignment data, and the output is the automatic grading results and feedback.

[0847] Step 5: Manage your learning progress and stay motivated

[0848] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points or badges) and messages to maintain motivation according to the progress. These rewards and messages are sent to the device and notified to the user. The input of this step is the user's learning progress data, and the output is the rewards and motivation messages.

[0849] These processing steps enable the system to efficiently support the user in acquiring robot operation skills and maintain their motivation to learn.

[0850] 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.

[0851] The learning support system of the present invention was developed to enable users to efficiently acquire advanced technology and knowledge related to mobile phone base stations. This system diagnoses the user's skill level, provides learning materials, evaluates assignments and generates feedback, manages learning progress, and provides encouraging messages and rewards to maintain motivation. In addition, the system has the ability to recognize the user's emotions and dynamically adjust learning support based on their state.

[0852] User Registration and Authentication

[0853] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, confirms that it is in the correct format, and then saves it in a database. A confirmation email is then sent to the user. The user clicks the link contained in the confirmation email, and the device accesses the server and validates the registration.

[0854] User level assessment

[0855] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[0856] Providing teaching materials and assignments

[0857] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who then studies the learning materials and works on the assignments.

[0858] Submitting and grading assignments

[0859] Once a user completes an assignment, the device sends the submission to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[0860] Manage your learning progress and maintain motivation

[0861] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating them to continue learning.

[0862] Introducing emotion recognition methods

[0863] The device is equipped with an emotion recognition means that analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state. This emotion recognition means can grasp in real time the stress, frustration, joy, and other feelings the user feels while studying. Based on the data obtained from the emotion recognition means, the server dynamically adjusts the optimal support messages and teaching methods according to the user's emotional state.

[0864] Specific Examples

[0865] For example, suppose user "B" registers with the system and is judged to be at an "intermediate" level through a level assessment. The server provides B with the learning materials and a basic problem set on "5G Technology Overview" that are optimal for him. B studies the materials and submits assignments. The server uses an AI module to grade the assignments and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message such as "take a break." This allows B to take breaks at appropriate times and continue studying effectively.

[0866] As described above, the learning support system of the present invention provides an optimal learning environment according to the user's skill level, learning progress, and emotional state, thereby realizing efficient learning support.

[0867] The processing flow will be explained below.

[0868] Step 1:

[0869] The user enters their name, email address, and password on the new registration screen.

[0870] Step 2:

[0871] The terminal transmits this information to the server.

[0872] Step 3:

[0873] The server validates the information entered to ensure it is in the correct format.

[0874] Step 4:

[0875] The server saves the information that passes validation in the database.

[0876] Step 5:

[0877] The server sends a confirmation email to the user.

[0878] Step 6:

[0879] The user clicks on the link in the confirmation email they received.

[0880] Step 7:

[0881] The device sends the user's verification link to the server to confirm the registration.

[0882] Step 8:

[0883] The server validates the user's registration and updates the database.

[0884] Step 9:

[0885] The user enters their email address and password on the login screen and logs in.

[0886] Step 10:

[0887] The terminal sends this login information to the server.

[0888] Step 11:

[0889] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[0890] Step 12:

[0891] The server provides the user with a "level diagnostic test."

[0892] Step 13:

[0893] The user answers the questions in the diagnostic test.

[0894] Step 14:

[0895] The terminal sends the answer result to the server.

[0896] Step 15:

[0897] The server uses an AI module to determine the user's skill level.

[0898] Step 16:

[0899] The server stores the judgment results in a database.

[0900] Step 17:

[0901] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[0902] Step 18:

[0903] The server transmits the teaching materials and assignments to the terminal.

[0904] Step 19:

[0905] The terminal displays the received learning materials and assignments to the user.

[0906] Step 20:

[0907] Users study the materials and work on the assignments.

[0908] Step 21:

[0909] The user sends the completed assignments to the server via the terminal.

[0910] Step 22:

[0911] The server uses an AI module to automatically grade the assignments.

[0912] Step 23:

[0913] The server generates feedback and stores it in a database along with the scoring results.

[0914] Step 24:

[0915] The server notifies the user of the feedback and the score.

[0916] Step 25:

[0917] The server periodically evaluates the user's learning progress data.

[0918] Step 26:

[0919] The server generates encouraging messages and rewards to keep you motivated.

[0920] Step 27:

[0921] The server sends cheering messages and rewards to the device.

[0922] Step 28:

[0923] The device displays a message of encouragement and a reward notification to the user.

[0924] Step 29:

[0925] The terminal analyzes the user's facial expressions, voice, and biometric signals and determines the user's emotional state using emotion recognition means.

[0926] Step 30:

[0927] The server evaluates variables that may have an impact based on the emotional data obtained from the emotion recognition means, and adjusts the delivery of conventional teaching materials and task allocation.

[0928] Step 31:

[0929] The server dynamically generates optimal support messages and instruction methods based on the user's emotional state.

[0930] Step 32:

[0931] The server transmits the generated message and instruction method to the terminal.

[0932] Step 33:

[0933] The device displays these messages and teaching methods to the user, further promoting improved learning efficiency.

[0934] Example 2

[0935] 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."

[0936] Conventional learning support systems can provide learning materials and assess tasks based on the user's skill level and learning progress, but they cannot adjust learning support to take the user's emotional state into account. As a result, they are unable to provide appropriate support when the user feels stressed or frustrated, resulting in reduced learning efficiency. Furthermore, feedback and support messages are generated uniformly, without dynamic responses tailored to the individual user's state. Therefore, the objective of this invention is to improve learning efficiency and maintain motivation by recognizing the user's emotional state in real time and providing optimal learning support based on this.

[0937] 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.

[0938] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition and adjustment means for recognizing the user's emotional state and adjusting learning support in accordance with that state. This makes it possible to grasp the user's emotional state in real time during the learning process and provide support and feedback at appropriate times, thereby improving the user's learning efficiency and maintaining motivation.

[0939] The "diagnostic means" is a system for measuring the user's skill level, including a set of questions to be answered by the user and an evaluation method.

[0940] The "means for providing learning materials" is a system that selects appropriate learning materials and assignments according to the user's skill level and learning progress, and provides them to the user.

[0941] The "evaluation means" is a mechanism for analyzing and evaluating the results of assignments submitted by users and generating feedback, and is a system that includes an AI module.

[0942] The "progress management means" is a mechanism for managing the user's learning progress and providing encouraging messages and rewards to maintain the user's motivation.

[0943] The "emotion recognition and adjustment means" is a mechanism that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state and dynamically adjusts learning support according to that state.

[0944] An "AI module" is a part of the artificial intelligence used within the learning support system, and is a system that analyzes users' answers and task results, determines their skill level, and generates feedback.

[0945] A "question set" is a series of questions to assess a user's skill level, and is answered by the user when they log in for the first time.

[0946] "Teaching materials" are materials for users to study, and include formats such as PDF files and online content.

[0947] MODE FOR CARRYING OUT THE INVENTION

[0948] The purpose of the learning support system of the present invention is to help users efficiently acquire advanced technology and knowledge related to mobile phone base stations. The system assesses the user's skill level, provides appropriate learning materials and assignments, manages the user's learning progress, and provides various encouraging messages and rewards to maintain motivation. It also has the ability to recognize the user's emotional state and dynamically adjust learning support based on that state.

[0949] User Registration and Authentication

[0950] The information provided by the user on the new registration screen, such as "Name," "Email address," and "Password," is sent from the terminal to the server. The server validates the information and stores it in a database. A confirmation email is then sent to the user, and the user validates their registration by clicking the link in the confirmation email.

[0951] User level assessment

[0952] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[0953] Providing teaching materials and assignments

[0954] The server selects the most appropriate learning materials and assignments based on the user's technical level. For example, the PDF learning material "5G Technology Overview" may be selected. The server then sends the selected learning materials and assignments to the device, which then displays them to the user.

[0955] Submitting and grading assignments

[0956] The user completes the assignment and the device sends the submission results to the server, which uses an AI module to automatically grade and generate feedback, which is stored in a database and notified to the user.

[0957] Manage your learning progress and maintain motivation

[0958] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards, which are then sent to the device and displayed to the user, encouraging them to continue learning.

[0959] Introducing emotion recognition methods

[0960] The device analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state in real time. Cameras, microphones, and sensors are used for emotion recognition. The server dynamically adjusts cheering messages and teaching methods based on the data obtained from the emotion recognition means.

[0961] Specific Examples

[0962] For example, suppose user "B" registers with the system and is assessed as "intermediate" in the level assessment. The server provides B with the optimal "5G Technology Overview" study materials and a basic problem set. B studies these and submits the assignment. The server grades the assignment using an AI module and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message saying "take a break." This allows B to continue studying efficiently while taking appropriate breaks.

[0963] Example prompts for generative AI models

[0964] "Please describe a learning support system for mobile phone base stations. Please include the following points: user registration and authentication procedures, methods for assessing user levels, provision of learning materials and assignments, assignment submission and evaluation, learning progress management and motivation maintenance, and emotion recognition methods."

[0965] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0966] Step 1:

[0967] The user enters their name, email address, and password on the new registration screen. The input data is entered into the terminal form. This signal data becomes the input for the terminal.

[0968] Step 2:

[0969] The device sends the entered information to the server. The data sent is "Name," "Email address," and "Password," and becomes the server's input. Specifically, the data is sent to the server when the send button is pressed.

[0970] Step 3:

[0971] The format of the information received by the server is validated to check for invalid data. Data processing involves checking the format of email addresses using regular expressions. The output is the validation results and the input data.

[0972] Step 4:

[0973] If the server passes validation, it saves the user information to the database. The data is inserted into the database using an SQL query. The output is a message that the data was saved successfully.

[0974] Step 5:

[0975] The server will auto-generate a confirmation email and send it to the email address provided, containing a confirmation link, which is the output sent to the user.

[0976] Step 6:

[0977] The user clicks on the link in the confirmation email they received. This causes the device to access the linked server. This action constitutes the user's confirmation input.

[0978] Step 7:

[0979] The device sends a link access request to the server. The request data includes the user ID and a confirmation token. This becomes the server's input.

[0980] Step 8:

[0981] The server receives the link access request and validates the confirmation information. The data operation includes the process of verifying the confirmation token. The output is the user registration validation result.

[0982] Step 9:

[0983] The server updates the user's status to "confirmed" and saves it in the database. Data calculation is the process of updating the status using an SQL query. The output is a successful update message.

[0984] Step 10:

[0985] When a user logs in for the first time, they answer a set of diagnostic questions. The questions are selected and entered as input data by the user.

[0986] Step 11:

[0987] The terminal converts the user's answer into a data format and sends it to the server. The converted data is the answer content and becomes the input for the server.

[0988] Step 12:

[0989] The server uses an AI module to determine the technical level based on the user's answers using numerical values ​​and categories. Data processing involves using natural language processing and machine learning models to evaluate the level. The output is the technical level assessment result.

[0990] Step 13:

[0991] The server saves the result of the decision to a database. Data calculation is the process of writing to the database using an SQL query, and the output is a save success message.

[0992] Step 14:

[0993] The server selects the most appropriate learning materials and assignments based on the user's skill level. Specifically, it applies a learning material selection algorithm based on the evaluation results to generate the selection results. The output is the selected learning material information.

[0994] Step 15:

[0995] The server sends the selected learning materials to the terminal. The learning material data includes PDF files and link information, which are input to the terminal.

[0996] Step 16:

[0997] The terminal displays the received learning materials and assignments to the user. For example, it opens the learning materials in a PDF viewer and displays the assignment input form. The output is that the user confirms the display.

[0998] Step 17:

[0999] Users complete the assignment and submit it through an online form, which becomes input data and is saved on the device.

[1000] Step 18:

[1001] The terminal sends the submitted results to the server in real time. The sent data is the answer to the assignment and becomes the input for the server.

[1002] Step 19:

[1003] The server uses an AI module to automatically grade submitted assignments and generate feedback. Data processing involves generating the accuracy rate and feedback content through a model evaluation process. The output is feedback information.

[1004] Step 20:

[1005] The server saves the generated feedback in a database and notifies the user. The saving process uses SQL queries. The output is a save success message and a notification.

[1006] Step 21:

[1007] The server periodically retrieves the user's learning progress data from the database and evaluates it. This results in the application of a progress evaluation algorithm. The input is the progress data, and the output is the evaluation result.

[1008] Step 22:

[1009] The server generates encouragement messages and rewards to encourage users to continue learning. The encouragement messages are dynamically created based on the user's progress. The output is the encouragement message content and reward information.

[1010] Step 23:

[1011] The server sends the generated message and reward to the device. The sent content includes the support message and reward information, which are input to the device.

[1012] Step 24:

[1013] The device displays these messages and rewards to the user, for example, in a notification banner or on a dedicated page. The output is the user confirming the display.

[1014] Step 25:

[1015] The device analyzes the user's facial expressions, voice, and biometric signals in real time through a camera and microphone, and emotional data is acquired as input to the device.

[1016] Step 26:

[1017] The server analyzes the emotion data obtained from the emotion recognition means and adjusts dynamic learning support according to the user's state. An emotion analysis algorithm is used for data calculation. The output is the learning support adjustment result.

[1018] In this way, learning support based on the user's skill level and emotional state is realized.

[1019] (Application example 2)

[1020] 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."

[1021] While conventional learning support systems provide learning materials tailored to the user's technical level and automatically evaluate assignments and provide feedback, they do not dynamically adjust based on the user's emotional state. This can lead to problems such as users feeling stressed or losing motivation, reducing the effectiveness of their learning. Furthermore, it is difficult to provide real-time technical support in the actual work environment of a factory.

[1022] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1023] In this invention, the server includes a diagnostic means for diagnosing the user's skill level, a learning material providing means for providing learning materials and assignments suitable for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition means for recognizing the user's emotional state and dynamically adjusting learning support based on that state. This not only improves the user's learning efficiency but also enables real-time technical support in an actual working environment.

[1024] A "user" is an individual who wishes to learn about the technology and knowledge related to mobile phone base stations.

[1025] The "diagnosis means" is a means for diagnosing the user's skill level, and determines the user's skill level based on the answers to a set of questions.

[1026] The "teaching material providing means" is a means for dynamically selecting and providing optimal teaching materials and assignments according to the user's skill level and learning progress.

[1027] "Evaluation means" is a means for evaluating user-submitted assignments and generating feedback using an AI module.

[1028] The "progress management means" is a means for periodically evaluating the user's learning progress and providing encouraging messages and rewards to maintain motivation.

[1029] The "emotion recognition means" is a means for analyzing the user's facial expressions, voice, biometric signals, etc., and recognizing the user's emotional state in real time.

[1030] A "factory robot" is an automated machine used to perform manufacturing and assembly tasks in a factory.

[1031] "Smart glasses" are glasses worn by a user that include a display device that provides visual information.

[1032] "Real time" refers to a state in which processing is carried out instantaneously in synchronization with real-world time.

[1033] An "AI module" is a software module that uses artificial intelligence technology to perform data analysis and judgment.

[1034] The learning support system of the present invention is implemented in a form that can be used by engineers who operate and maintain factory robots while wearing smart glasses.

[1035] Overall system configuration

[1036] 1. User Registration and Authentication

[1037] The server receives the information entered by the user on the new registration screen (e.g., "Name", "Email address", "Password"), validates it, saves it in the database and sends a confirmation email to the user. When the user clicks the link included in the confirmation email, the server validates the registration.

[1038] 2. User Level Assessment

[1039] A set of questions that the user answers when logging in for the first time is provided to the device (smart glasses), and the server receives the answers. The AI ​​module determines the user's skill level and stores it in a database.

[1040] 3. Providing study materials and assignments

[1041] The server selects the most appropriate learning materials (e.g., "PDF Learning Materials 1") and assignments based on the user's skill level and sends them to the device. The device displays them, and the user works on the learning or assignments.

[1042] 4. Submitting and grading assignments

[1043] When a user submits an assignment, the results are sent from the device to the server, where the server automatically grades the assignment using an AI module, generates feedback, stores it in a database, and notifies the user.

[1044] 5. Manage your learning progress and maintain motivation

[1045] The server periodically evaluates the user's learning progress data, generates encouraging messages and rewards to maintain motivation, and sends them to the terminal for display to the user.

[1046] 6. Introduction of emotion recognition methods

[1047] The device is equipped with an emotion recognition unit that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state. Based on the data obtained from the emotion recognition unit, the server dynamically adjusts the support messages and teaching methods according to the user's emotional state.

[1048] Hardware and software used

[1049] Server: A central processing unit that manages user data, learning materials, assignments, progress data, and provides assessment and feedback.

[1050] Terminal (smart glasses): A device that provides the user with real-time visual information, diagnoses their skill level, displays teaching materials and assignments, and recognizes emotions.

[1051] AI module: Artificial intelligence software that automatically grades assignments and assesses skill levels.

[1052] Emotion recognition means: A set of software and sensors that analyze the user's facial expressions, voice, and biometric signals.

[1053] Specific examples

[1054] For example, when a user named "Engineer A" logs in for the first time, he answers a set of questions, and the server determines his skill level as "intermediate." The server then provides learning materials (e.g., "PDF Learning Materials 2") and assignments suitable for an "intermediate" user. Engineer A continues his studies through the smart glasses and submits the assignments. The server's AI module generates feedback and notifies the user that "you need to learn more about the 5G base frequency band." If Engineer A feels stressed while studying, the emotion recognition means detects this, and the server sends a supportive message such as "take a break."

[1055] Generative AI model prompt example

[1056] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[1057] Output: User's skill level (e.g. "Intermediate")

[1058] Input: User's skill level (e.g., "Intermediate")

[1059] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[1060] Input: The assignment submitted by the user (e.g., "Assignment 1")

[1061] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[1062] Input: User emotion recognition result (e.g., "stress")

[1063] Output: A suitable encouraging message (e.g., "You're almost there!")

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

[1065] Step 1:

[1066] User Registration and Authentication

[1067] Input: Name, email address, and password entered by the user on the sign-up screen

[1068] The device sends the registration information entered by the user to the server. The server validates this information to ensure it is in the correct format. It then stores it in a database and sends a confirmation email to the user. The user clicks on a link in the confirmation email, which causes the device to contact the server and validate the registration.

[1069] Output: User registration completion notification

[1070] Step 2:

[1071] User level assessment

[1072] Input: A set of questions that the user answers when they first log in

[1073] The server provides a set of questions to the user through the device, and the user answers them. The device then sends the answers back to the server, which then uses an AI module to diagnose the user's skill level and stores it in a database.

[1074] Output: User's skill level (e.g., "Beginner", "Intermediate", "Advanced")

[1075] Step 3:

[1076] Providing teaching materials and assignments

[1077] Input: User skill level assessment result

[1078] The server selects the most appropriate learning materials and assignments based on the user's skill level and sends them to the terminal. The terminal displays these learning materials (e.g., "PDF learning materials") and assignments to the user. The user studies the learning materials and works on the assignments.

[1079] Output: A list of materials and assignments provided to the user

[1080] Step 4:

[1081] Submitting and grading assignments

[1082] Input: The task the user completed

[1083] Once the user completes the assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback, which is stored in a database and notified to the user.

[1084] Output: Assignment evaluation results and feedback (e.g., "passed," "failed," "what needs further study").

[1085] Step 5:

[1086] Manage your learning progress and maintain motivation

[1087] Input: User's learning progress data

[1088] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. The progress management results are sent to the device, which then displays the encouraging messages and reward notifications to the user.

[1089] Output: Cheer message and reward notification

[1090] Step 6:

[1091] Introducing emotion recognition methods

[1092] Input: Data such as user facial expressions, voice, and biometric signals

[1093] The device analyzes the user's facial expressions, voice, and biometric signals in real time to recognize their emotional state. The recognized emotional state data is sent to the server, which then uses this data to generate and dynamically adjust the optimal cheering message according to the user's emotional state.

[1094] Output: A cheering message based on the emotional state

[1095] Generative AI model prompt example

[1096] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[1097] Output: User's skill level (e.g. "Intermediate")

[1098] Input: User's skill level (e.g., "Intermediate")

[1099] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[1100] Input: The assignment submitted by the user (e.g., "Assignment 1")

[1101] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[1102] Input: User emotion recognition result (e.g., "stress")

[1103] Output: A suitable encouraging message (e.g., "You're almost there!")

[1104] 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.

[1105] 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.

[1106] 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.

[1107] [Third embodiment]

[1108] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1109] 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.

[1110] 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).

[1111] 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.

[1112] 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.

[1113] 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).

[1114] 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.

[1115] 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.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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."

[1120] The learning support system of the present invention is a system that supports users in efficiently acquiring advanced technology and knowledge related to mobile phone base stations. This system has the functions of user diagnosis, provision of learning materials, assignment evaluation, and learning progress management, and an embodiment thereof is shown below.

[1121] User Registration and Authentication

[1122] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks the link included in the confirmation email, and the device accesses the server to validate the registration.

[1123] User level assessment

[1124] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[1125] Providing teaching materials and assignments

[1126] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who can then read the learning materials and work on the assignments.

[1127] Submitting and grading assignments

[1128] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[1129] Manage your learning progress and maintain motivation

[1130] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating the user to continue learning.

[1131] Specific Examples

[1132] For example, user "A" registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "intermediate level." The server sends learning materials for "5G Technology Overview" and a basic problem set, which are ideal for intermediate level users, to A's device. A studies the materials, works on assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback such as "You need to learn more about the 5G base frequency band." Periodically, the server evaluates A's progress and sends a message saying, "Great progress, you've earned 50 points!" This motivates A to continue learning.

[1133] As described above, the learning support system of the present invention provides optimal learning materials and assignments according to the user's skill level, and supports the efficient acquisition of knowledge.

[1134] The processing flow will be explained below.

[1135] Step 1:

[1136] The user enters their name, email address, and password on the new registration screen.

[1137] Step 2:

[1138] The terminal transmits this information to the server.

[1139] Step 3:

[1140] The server validates the information entered to ensure it is in the correct format.

[1141] Step 4:

[1142] The server saves the information that passes validation in the database.

[1143] Step 5:

[1144] The server sends a confirmation email to the user.

[1145] Step 6:

[1146] The user clicks on the link in the confirmation email they received.

[1147] Step 7:

[1148] The device sends the user's verification link to the server to confirm the registration.

[1149] Step 8:

[1150] The server validates the user's registration and updates the database.

[1151] Step 9:

[1152] The user enters their email address and password on the login screen and logs in.

[1153] Step 10:

[1154] The terminal sends this login information to the server.

[1155] Step 11:

[1156] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[1157] Step 12:

[1158] The server provides the user with a "level diagnostic test."

[1159] Step 13:

[1160] The user answers the questions in the diagnostic test.

[1161] Step 14:

[1162] The terminal sends the answer result to the server.

[1163] Step 15:

[1164] The server uses an AI module to determine the user's skill level.

[1165] Step 16:

[1166] The server stores the judgment results in a database.

[1167] Step 17:

[1168] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[1169] Step 18:

[1170] The server transmits the teaching materials and assignments to the terminal.

[1171] Step 19:

[1172] The terminal displays the received learning materials and assignments to the user.

[1173] Step 20:

[1174] Users study the materials and work on the assignments.

[1175] Step 21:

[1176] The user sends the completed assignments to the server via the terminal.

[1177] Step 22:

[1178] The server uses an AI module to automatically grade the assignments.

[1179] Step 23:

[1180] The server generates feedback and stores it in a database along with the scoring results.

[1181] Step 24:

[1182] The server notifies the user of the feedback and the score.

[1183] Step 25:

[1184] The server periodically evaluates the user's learning progress data.

[1185] Step 26:

[1186] The server generates encouraging messages and rewards to keep you motivated.

[1187] Step 27:

[1188] The server sends cheering messages and rewards to the device.

[1189] Step 28:

[1190] The device displays a message of encouragement and a reward notification to the user.

[1191] Example 1

[1192] 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."

[1193] With the rapid evolution of modern technology and knowledge, there is a demand for systems that allow users to efficiently learn advanced information in specific technical fields. However, conventional learning systems have difficulty providing optimal learning materials and assignments tailored to each user's skill level, and they lack means to manage users' learning progress and maintain their motivation. Furthermore, these systems often lack integrated user registration and authentication functions. This prevents users from properly understanding their own learning status, hindering effective learning.

[1194] 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.

[1195] In this invention, the server includes authentication means for user registration and authentication, diagnosis means for diagnosing the user's skill level, provision means for providing the user with educational materials and tasks appropriate for the user based on the results of the diagnosis means, evaluation means for evaluating the results of the tasks and generating feedback, and progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation. This makes it possible to provide optimal educational materials and tasks according to the skill level of each user, manage learning progress and maintain motivation, and provide efficient learning support that integrates user registration and authentication.

[1196] "User" refers to an individual who uses the system to learn.

[1197] "Authentication" refers to the process used to verify a user's registration information and grant access to the system.

[1198] "Diagnostic means" refers to the process of assessing the user's skill level and providing the most appropriate teaching materials and assignments based on the results.

[1199] "Educational materials" refers to teaching materials and reference materials that contain information and data for users to study.

[1200] "Work" refers to assignments and exercises that users undertake as part of their studies.

[1201] "Delivery means" refers to the method or process of providing and presenting educational materials and tasks to users.

[1202] "Evaluation means" refers to a system that evaluates the work submitted by a user and generates feedback based on the results.

[1203] "Progress management means" refers to the process of tracking and managing a user's learning progress and providing messages and rewards to maintain motivation as needed.

[1204] "Generative AI model" refers to an artificial intelligence program model used to diagnose a user's skill level and evaluate issues.

[1205] "Feedback" refers to evaluations and advice provided to users regarding their learning and work.

[1206] The learning support system of the present invention supports users in efficiently acquiring advanced skills and knowledge. This system includes functions for user registration and authentication, skill level assessment, provision of learning materials and tasks, task evaluation, learning progress management, and motivation maintenance.

[1207] Hardware and Software Configuration

[1208] This system operates using devices such as personal computers and smartphones. These devices communicate with the server via the Internet. Specifically, the following hardware and software are used:

[1209] Hardware

[1210] personal computer

[1211] Smartphone

[1212] software

[1213] A web browser (for users to access the system)

[1214] Email client (to receive the confirmation email)

[1215] Database (e.g. MySQL, PostgreSQL)

[1216] Server-side frameworks (e.g., Django, Node.js)

[1217] AI model (e.g. OpenAI GPT-3)

[1218] Detailed explanation of each function

[1219] User Registration and Authentication

[1220] When a user enters their name, email address, and password on the new registration screen, the device sends this data to the server. The server validates the data, stores it in a database, and then sends a confirmation email to the user. The user clicks the link in the confirmation email, and the device accesses the server to validate the registration.

[1221] Technical level diagnosis

[1222] When a user logs in for the first time, they answer a set of questions provided as a diagnostic tool. The device sends the answers to the server, which then uses an AI model (e.g., GPT-3) to determine the user's skill level. The results are stored in a database.

[1223] Examples:

[1224] The user answers diagnostic questions when logging in for the first time, and the terminal sends the results to the server.

[1225] The server inputs the diagnostic answers into the AI ​​module, which determines the level as "intermediate."

[1226] Example prompt for a generative AI model:

[1227] "The following diagnostic questions about 5G communication technology are provided, and you can use them to determine your technical level. Answer: {Answer}"

[1228] Providing materials and work

[1229] The server selects appropriate educational materials and tasks based on the user's skill level and sends them to the terminal, which displays them to the user, who then reads the educational materials and works on the tasks.

[1230] Submitting and grading your work

[1231] After the user completes their work, they click the submit button and their device sends the results to the server, which uses an AI model to automatically grade the work and generate feedback, which is stored in a database and notified to the user.

[1232] Manage your learning progress and maintain motivation

[1233] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards. As a means of progress management, the server sends these messages and rewards to the device, which then displays them to the user. This motivates the user to continue learning.

[1234] Examples:

[1235] The server evaluates your learning progress and sends you a message saying "Great progress, you've earned 50 points!"

[1236] Users receive messages and stay motivated to learn.

[1237] As described above, the learning support system of the present invention is designed to enable users to efficiently acquire advanced knowledge, providing optimal educational materials and tasks according to the user's technical level, managing learning progress, and maintaining motivation.

[1238] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1239] Program processing steps

[1240] User Registration and Authentication

[1241] Step 1:

[1242] The user enters their name, email address, and password on the new registration screen.

[1243] Input: Name, Email Address, Password

[1244] Specific actions: Open the registration form in a web browser and enter information in the input fields.

[1245] Output: Data entered into the form

[1246] Step 2:

[1247] The terminal sends the input data to the server.

[1248] Input: Data entered into a form

[1249] Specific operation: Uses JavaScript's fetch API to send form data via a POST request.

[1250] Output: User data sent to the server

[1251] Step 3:

[1252] The server validates the data and saves it to the database.

[1253] Input: Submitted user data

[1254] Specific operation: Django view function receives data, performs form validation, and saves it to the database (MySQL).

[1255] Output: User data stored in the database

[1256] Step 4:

[1257] The server sends a confirmation email to the user.

[1258] Input: Saved user data

[1259] Specific operation: Sends a confirmation email using Django's send_mail function.

[1260] Output: Confirmation email sent

[1261] Step 5:

[1262] The user clicks on the link in the confirmation email and the device accesses the server.

[1263] Input: Link in confirmation email

[1264] What happens: A user opens their email client, clicks a link, and the browser sends a new request to the server.

[1265] Output: Request sent to the server

[1266] Step 6:

[1267] The server validates the registration.

[1268] Input: The request sent to the server

[1269] Specific behavior: The server verifies the token in the link and updates the user status in the database.

[1270] Output: Enabled user accounts

[1271] Technical level diagnosis

[1272] Step 1:

[1273] The user answers a level assessment question when they first log in.

[1274] Input: Answer to diagnostic question

[1275] Specific actions: Open a question form in a web browser and answer each question.

[1276] Output: Response data

[1277] Step 2:

[1278] The terminal transmits the response data to the server.

[1279] Input: Answer data

[1280] Specific operation: Uses JavaScript's fetch API to send the response data via a POST request.

[1281] Output: Response data sent to the server

[1282] Step 3:

[1283] The server analyzes the response data using an AI module.

[1284] Input: Submitted response data

[1285] Specific operation: Send data to the API of the AI ​​module (e.g., GPT-3) and receive the analysis results.

[1286] Output: Technical level as analysis result

[1287] Step 4:

[1288] The server determines the skill level and stores the results in a database.

[1289] Input: Analysis results of the AI ​​module

[1290] Specific operation: The results of the AI ​​module are saved in a database using an SQL query.

[1291] Output: Skill level stored in the database

[1292] Providing materials and work

[1293] Step 1:

[1294] The server selects materials and tasks based on the user's skill level.

[1295] Input: User skill level

[1296] Specific operation: The system retrieves the user's skill level from the database and executes an algorithm to select appropriate learning materials and tasks.

[1297] Output: Selected materials and tasks

[1298] Step 2:

[1299] The server sends the selected teaching materials (such as PDF files) and tasks to the terminal.

[1300] Input: Selected materials and tasks

[1301] Specific operation: Send teaching material data and tasks to the terminal in JSON format.

[1302] Output: Teaching materials and work data sent to the device

[1303] Step 3:

[1304] The terminal displays these to the user.

[1305] Input: Teaching materials and work data sent to the terminal

[1306] Specific operation: Use JavaScript to display teaching materials and tasks on HTML.

[1307] Output: The material and tasks displayed to the user

[1308] Submitting and grading your work

[1309] Step 1:

[1310] Once the user has completed their work, they click the submit button.

[1311] Input: Completed work data

[1312] Specific behavior: The user fills out a work form in a browser and clicks the submit button.

[1313] Output: Working data for submission

[1314] Step 2:

[1315] The terminal sends the submitted data to the server.

[1316] Input: Working data for submission

[1317] Specific operation: Uses JavaScript's fetch API to send the submitted data to the server via a POST request.

[1318] Output: The submission data sent to the server

[1319] Step 3:

[1320] The server grades the work using an AI module.

[1321] Input: Submitted submission data

[1322] Specific operation: Input the work data into the AI ​​module and obtain the scoring results.

[1323] Output:Scoring results

[1324] Step 4:

[1325] The server generates the feedback and stores it in a database.

[1326] Input:Scoring results

[1327] Specific behavior: Generate feedback and store it in a database using an SQL query.

[1328] Output: Generated feedback

[1329] Step 5:

[1330] The server notifies the user.

[1331] Input: Generated feedback

[1332] Specific operation: Generate notification data and send it to the user's device.

[1333] Output: User notification

[1334] Manage your learning progress and maintain motivation

[1335] Step 1:

[1336] The server periodically evaluates the learning progress data.

[1337] Input: User's learning progress data

[1338] What it does: Runs a script using a periodic task (Cron job) to evaluate progress data.

[1339] Output: Evaluation results

[1340] Step 2:

[1341] The server generates cheer messages and rewards.

[1342] Input: Evaluation result

[1343] Specific behavior: Runs an algorithm that generates messages and rewards based on progress data.

[1344] Output: Generated message and reward

[1345] Step 3:

[1346] The server sends these to the terminal.

[1347] Input: Generated message and reward

[1348] Specific operation: The generated message and reward data are sent to the terminal in JSON format.

[1349] Output: Message and reward sent to the terminal

[1350] Step 4:

[1351] The terminal displays it to the user.

[1352] Input: Message and reward sent to the terminal

[1353] Specific behavior: Use JavaScript to display messages and rewards on HTML.

[1354] Output: The message and reward displayed to the user

[1355] (Application example 1)

[1356] 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."

[1357] Conventional learning support systems are limited to specific technical fields, making it difficult to provide efficient and motivating training for factory workers and engineers to acquire the robot operation skills they require. In particular, when it comes to evaluating technical levels and managing progress, incentives to maintain user motivation are often lacking, resulting in issues with learning retention rates. However, if there were a system that could enable efficient learning in a managed environment, these issues could be resolved.

[1358] 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.

[1359] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user based on the results of the diagnostic means, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, a training means for factory workers and engineers to learn robot operation techniques, and incentives such as points and badges as rewards provided by the progress management means. This allows users to efficiently and continuously progress in their learning while receiving learning materials optimal for their skill level. Furthermore, the incentives maintain motivation and improve the effectiveness of training.

[1360] A "diagnostic means for diagnosing a user's technical level" is a device that runs a series of questions and tests to assess whether a user has a certain level of technical knowledge and skills, and determines the user's technical level based on the results.

[1361] The "teaching material providing means for providing teaching materials and exercises suitable for the user based on the results of the diagnostic means" is a device that selects optimal learning materials and exercises and provides them to the user based on the user's skill level determined by the diagnostic means.

[1362] The "assessment means for evaluating the results of the assignment and generating feedback" is a device that analyzes the content of the assignment submitted by the user and automatically generates feedback such as the results and areas for improvement.

[1363] A "progress management means for managing a user's learning progress and providing messages and rewards to maintain motivation" is a device that monitors the user's learning process and increases their motivation to learn by providing encouragement and rewards according to their progress.

[1364] A "training means for factory workers and engineers to learn robot operation techniques" is a device that provides a series of training programs for factory employees and engineers to efficiently learn the techniques necessary for operating and setting up robots.

[1365] "Incentives such as points and badges as rewards provided by the progress management means" are rewards such as points and badges given according to the user's learning progress and results, and are intended to increase motivation to learn.

[1366] The learning support system of the present invention provides a training platform for factory workers and engineers to efficiently learn robot operation techniques. Specific embodiments of the system are described below.

[1367] User Registration and Authentication

[1368] The user (factory worker or technician) enters the required information such as name, email address, and password, and the device (computer, smartphone, etc.) sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks on a link in the confirmation email, and the device accesses the server to activate the registration.

[1369] User level assessment

[1370] When a user logs in for the first time, they answer a set of questions prepared in advance. The device sends the answers to the server, which then uses AI modules (e.g., TensorFlow, scikit-learn) to determine the user's skill level. The results are stored in a database.

[1371] Providing teaching materials and assignments

[1372] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials are sent to the terminal, where the user can study them and work on the assignments.

[1373] Submitting and grading assignments

[1374] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[1375] Manage your learning progress and maintain motivation

[1376] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points and badges) and messages to maintain motivation according to the progress. These rewards and messages are also sent to the device, motivating the user to continue learning.

[1377] Specific Examples

[1378] For example, a user, Worker A, registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "beginner level." The server sends learning materials and practice questions for "basics of robot operation," which are optimal for beginners, to A's device. A studies the materials, works on the assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback that "you still need to understand basic operating procedures." The server also sends A a message saying, "Great progress, you've earned 50 points!", which motivates A to continue learning.

[1379] Prompt Sentence Examples

[1380] Design a program to apply a support system for learning technology and knowledge related to mobile phone base stations as a training system for operating factory robots, provide teaching materials and assignments according to the user's level, and develop an application that manages progress and performs automatic evaluation.

[1381] This allows users to efficiently acquire robot operation skills and maintain their motivation as they continue their studies.

[1382] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1383] Step 1: User Registration and Authentication

[1384] The user enters information such as name, email address, and password. The terminal sends this information to the server. The server validates the input data and, if there are no problems, saves the information in a database. A confirmation email is then sent to the user, who clicks on a link in the confirmation email to activate their registration. The input of this step is the user's registration information, and the output is the activation of the user's registration.

[1385] Step 2: User Level Assessment

[1386] When a user logs in for the first time, the system presents the user with a set of pre-prepared questions. The user answers these questions, and the device sends the answers to the server. The server then uses an AI module (e.g., TensorFlow, scikit-learn) to determine the user's skill level and saves the results in a database. The input of this step is the user's answer data, and the output is the user's skill level assessment result.

[1387] Step 3: Providing materials and assignments

[1388] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials and assignments are sent to the terminal, which displays them to the user. The user studies the learning materials and works on the assignments. The input to this step is the user's skill level, and the output is the most appropriate learning materials and assignments provided to the user.

[1389] Step 4: Submit and grade assignments

[1390] When a user completes an assignment, the device sends the submission results to the server. The server uses an AI module to automatically grade the assignment and generate feedback. This feedback is stored in a database and notified to the user via the device. The input of this step is the user's submitted assignment data, and the output is the automatic grading results and feedback.

[1391] Step 5: Manage your learning progress and stay motivated

[1392] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points or badges) and messages to maintain motivation according to the progress. These rewards and messages are sent to the device and notified to the user. The input of this step is the user's learning progress data, and the output is the rewards and motivation messages.

[1393] These processing steps enable the system to efficiently support the user in acquiring robot operation skills and maintain their motivation to learn.

[1394] 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.

[1395] The learning support system of the present invention was developed to enable users to efficiently acquire advanced technology and knowledge related to mobile phone base stations. This system diagnoses the user's skill level, provides learning materials, evaluates assignments and generates feedback, manages learning progress, and provides encouraging messages and rewards to maintain motivation. In addition, the system has the ability to recognize the user's emotions and dynamically adjust learning support based on their state.

[1396] User Registration and Authentication

[1397] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, confirms that it is in the correct format, and then saves it in a database. A confirmation email is then sent to the user. The user clicks the link contained in the confirmation email, and the device accesses the server and validates the registration.

[1398] User level assessment

[1399] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[1400] Providing teaching materials and assignments

[1401] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who then studies the learning materials and works on the assignments.

[1402] Submitting and grading assignments

[1403] Once a user completes an assignment, the device sends the submission to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[1404] Manage your learning progress and maintain motivation

[1405] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating them to continue learning.

[1406] Introducing emotion recognition methods

[1407] The device is equipped with an emotion recognition means that analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state. This emotion recognition means can grasp in real time the stress, frustration, joy, and other feelings the user feels while studying. Based on the data obtained from the emotion recognition means, the server dynamically adjusts the optimal support messages and teaching methods according to the user's emotional state.

[1408] Specific Examples

[1409] For example, suppose user "B" registers with the system and is judged to be at an "intermediate" level through a level assessment. The server provides B with the learning materials and a basic problem set on "5G Technology Overview" that are optimal for him. B studies the materials and submits assignments. The server uses an AI module to grade the assignments and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message such as "take a break." This allows B to take breaks at appropriate times and continue studying effectively.

[1410] As described above, the learning support system of the present invention provides an optimal learning environment according to the user's skill level, learning progress, and emotional state, thereby realizing efficient learning support.

[1411] The processing flow will be explained below.

[1412] Step 1:

[1413] The user enters their name, email address, and password on the new registration screen.

[1414] Step 2:

[1415] The terminal transmits this information to the server.

[1416] Step 3:

[1417] The server validates the information entered to ensure it is in the correct format.

[1418] Step 4:

[1419] The server saves the information that passes validation in the database.

[1420] Step 5:

[1421] The server sends a confirmation email to the user.

[1422] Step 6:

[1423] The user clicks on the link in the confirmation email they received.

[1424] Step 7:

[1425] The device sends the user's verification link to the server to confirm the registration.

[1426] Step 8:

[1427] The server validates the user's registration and updates the database.

[1428] Step 9:

[1429] The user enters their email address and password on the login screen and logs in.

[1430] Step 10:

[1431] The terminal sends this login information to the server.

[1432] Step 11:

[1433] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[1434] Step 12:

[1435] The server provides the user with a "level diagnostic test."

[1436] Step 13:

[1437] The user answers the questions in the diagnostic test.

[1438] Step 14:

[1439] The terminal sends the answer result to the server.

[1440] Step 15:

[1441] The server uses an AI module to determine the user's skill level.

[1442] Step 16:

[1443] The server stores the judgment results in a database.

[1444] Step 17:

[1445] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[1446] Step 18:

[1447] The server transmits the teaching materials and assignments to the terminal.

[1448] Step 19:

[1449] The terminal displays the received learning materials and assignments to the user.

[1450] Step 20:

[1451] Users study the materials and work on the assignments.

[1452] Step 21:

[1453] The user sends the completed assignments to the server via the terminal.

[1454] Step 22:

[1455] The server uses an AI module to automatically grade the assignments.

[1456] Step 23:

[1457] The server generates feedback and stores it in a database along with the scoring results.

[1458] Step 24:

[1459] The server notifies the user of the feedback and the score.

[1460] Step 25:

[1461] The server periodically evaluates the user's learning progress data.

[1462] Step 26:

[1463] The server generates encouraging messages and rewards to keep you motivated.

[1464] Step 27:

[1465] The server sends cheering messages and rewards to the device.

[1466] Step 28:

[1467] The device displays a message of encouragement and a reward notification to the user.

[1468] Step 29:

[1469] The terminal analyzes the user's facial expressions, voice, and biometric signals and determines the user's emotional state using emotion recognition means.

[1470] Step 30:

[1471] The server evaluates variables that may have an impact based on the emotional data obtained from the emotion recognition means, and adjusts the delivery of conventional teaching materials and task allocation.

[1472] Step 31:

[1473] The server dynamically generates optimal support messages and instruction methods based on the user's emotional state.

[1474] Step 32:

[1475] The server transmits the generated message and instruction method to the terminal.

[1476] Step 33:

[1477] The device displays these messages and teaching methods to the user, further promoting improved learning efficiency.

[1478] Example 2

[1479] 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."

[1480] Conventional learning support systems can provide learning materials and assess tasks based on the user's skill level and learning progress, but they cannot adjust learning support to take the user's emotional state into account. As a result, they are unable to provide appropriate support when the user feels stressed or frustrated, resulting in reduced learning efficiency. Furthermore, feedback and support messages are generated uniformly, without dynamic responses tailored to the individual user's state. Therefore, the objective of this invention is to improve learning efficiency and maintain motivation by recognizing the user's emotional state in real time and providing optimal learning support based on this.

[1481] 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.

[1482] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition and adjustment means for recognizing the user's emotional state and adjusting learning support in accordance with that state. This makes it possible to grasp the user's emotional state in real time during the learning process and provide support and feedback at appropriate times, thereby improving the user's learning efficiency and maintaining motivation.

[1483] The "diagnostic means" is a system for measuring the user's skill level, including a set of questions to be answered by the user and an evaluation method.

[1484] The "means for providing learning materials" is a system that selects appropriate learning materials and assignments according to the user's skill level and learning progress, and provides them to the user.

[1485] The "evaluation means" is a mechanism for analyzing and evaluating the results of assignments submitted by users and generating feedback, and is a system that includes an AI module.

[1486] The "progress management means" is a mechanism for managing the user's learning progress and providing encouraging messages and rewards to maintain the user's motivation.

[1487] The "emotion recognition and adjustment means" is a mechanism that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state and dynamically adjusts learning support according to that state.

[1488] An "AI module" is a part of the artificial intelligence used within the learning support system, and is a system that analyzes users' answers and task results, determines their skill level, and generates feedback.

[1489] A "question set" is a series of questions to assess a user's skill level, and is answered by the user when they log in for the first time.

[1490] "Teaching materials" are materials for users to study, and include formats such as PDF files and online content.

[1491] MODE FOR CARRYING OUT THE INVENTION

[1492] The purpose of the learning support system of the present invention is to help users efficiently acquire advanced technology and knowledge related to mobile phone base stations. The system assesses the user's skill level, provides appropriate learning materials and assignments, manages the user's learning progress, and provides various encouraging messages and rewards to maintain motivation. It also has the ability to recognize the user's emotional state and dynamically adjust learning support based on that state.

[1493] User Registration and Authentication

[1494] The information provided by the user on the new registration screen, such as "Name," "Email address," and "Password," is sent from the terminal to the server. The server validates the information and stores it in a database. A confirmation email is then sent to the user, and the user validates their registration by clicking the link in the confirmation email.

[1495] User level assessment

[1496] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[1497] Providing teaching materials and assignments

[1498] The server selects the most appropriate learning materials and assignments based on the user's technical level. For example, the PDF learning material "5G Technology Overview" may be selected. The server then sends the selected learning materials and assignments to the device, which then displays them to the user.

[1499] Submitting and grading assignments

[1500] The user completes the assignment and the device sends the submission results to the server, which uses an AI module to automatically grade and generate feedback, which is stored in a database and notified to the user.

[1501] Manage your learning progress and maintain motivation

[1502] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards, which are then sent to the device and displayed to the user, encouraging them to continue learning.

[1503] Introducing emotion recognition methods

[1504] The device analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state in real time. Cameras, microphones, and sensors are used for emotion recognition. The server dynamically adjusts cheering messages and teaching methods based on the data obtained from the emotion recognition means.

[1505] Specific Examples

[1506] For example, suppose user "B" registers with the system and is assessed as "intermediate" in the level assessment. The server provides B with the optimal "5G Technology Overview" study materials and a basic problem set. B studies these and submits the assignment. The server grades the assignment using an AI module and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message saying "take a break." This allows B to continue studying efficiently while taking appropriate breaks.

[1507] Example prompts for generative AI models

[1508] "Please describe a learning support system for mobile phone base stations. Please include the following points: user registration and authentication procedures, methods for assessing user levels, provision of learning materials and assignments, assignment submission and evaluation, learning progress management and motivation maintenance, and emotion recognition methods."

[1509] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1510] Step 1:

[1511] The user enters their name, email address, and password on the new registration screen. The input data is entered into the terminal form. This signal data becomes the input for the terminal.

[1512] Step 2:

[1513] The device sends the entered information to the server. The data sent is "Name," "Email address," and "Password," and becomes the server's input. Specifically, the data is sent to the server when the send button is pressed.

[1514] Step 3:

[1515] The format of the information received by the server is validated to check for invalid data. Data processing involves checking the format of email addresses using regular expressions. The output is the validation results and the input data.

[1516] Step 4:

[1517] If the server passes validation, it saves the user information to the database. The data is inserted into the database using an SQL query. The output is a message that the data was saved successfully.

[1518] Step 5:

[1519] The server will auto-generate a confirmation email and send it to the email address provided, containing a confirmation link, which is the output sent to the user.

[1520] Step 6:

[1521] The user clicks on the link in the confirmation email they received. This causes the device to access the linked server. This action constitutes the user's confirmation input.

[1522] Step 7:

[1523] The device sends a link access request to the server. The request data includes the user ID and a confirmation token. This becomes the server's input.

[1524] Step 8:

[1525] The server receives the link access request and validates the confirmation information. The data operation includes the process of verifying the confirmation token. The output is the user registration validation result.

[1526] Step 9:

[1527] The server updates the user's status to "confirmed" and saves it in the database. Data calculation is the process of updating the status using an SQL query. The output is a successful update message.

[1528] Step 10:

[1529] When a user logs in for the first time, they answer a set of diagnostic questions. The questions are selected and entered as input data by the user.

[1530] Step 11:

[1531] The terminal converts the user's answer into a data format and sends it to the server. The converted data is the answer content and becomes the input for the server.

[1532] Step 12:

[1533] The server uses an AI module to determine the technical level based on the user's answers using numerical values ​​and categories. Data processing involves using natural language processing and machine learning models to evaluate the level. The output is the technical level assessment result.

[1534] Step 13:

[1535] The server saves the result of the decision to a database. Data calculation is the process of writing to the database using an SQL query, and the output is a save success message.

[1536] Step 14:

[1537] The server selects the most appropriate learning materials and assignments based on the user's skill level. Specifically, it applies a learning material selection algorithm based on the evaluation results to generate the selection results. The output is the selected learning material information.

[1538] Step 15:

[1539] The server sends the selected learning materials to the terminal. The learning material data includes PDF files and link information, which are input to the terminal.

[1540] Step 16:

[1541] The terminal displays the received learning materials and assignments to the user. For example, it opens the learning materials in a PDF viewer and displays the assignment input form. The output is that the user confirms the display.

[1542] Step 17:

[1543] Users complete the assignment and submit it through an online form, which becomes input data and is saved on the device.

[1544] Step 18:

[1545] The terminal sends the submitted results to the server in real time. The sent data is the answer to the assignment and becomes the input for the server.

[1546] Step 19:

[1547] The server uses an AI module to automatically grade submitted assignments and generate feedback. Data processing involves generating the accuracy rate and feedback content through a model evaluation process. The output is feedback information.

[1548] Step 20:

[1549] The server saves the generated feedback in a database and notifies the user. The saving process uses SQL queries. The output is a save success message and a notification.

[1550] Step 21:

[1551] The server periodically retrieves the user's learning progress data from the database and evaluates it. This results in the application of a progress evaluation algorithm. The input is the progress data, and the output is the evaluation result.

[1552] Step 22:

[1553] The server generates encouragement messages and rewards to encourage users to continue learning. The encouragement messages are dynamically created based on the user's progress. The output is the encouragement message content and reward information.

[1554] Step 23:

[1555] The server sends the generated message and reward to the device. The sent content includes the support message and reward information, which are input to the device.

[1556] Step 24:

[1557] The device displays these messages and rewards to the user, for example, in a notification banner or on a dedicated page. The output is the user confirming the display.

[1558] Step 25:

[1559] The device analyzes the user's facial expressions, voice, and biometric signals in real time through a camera and microphone, and emotional data is acquired as input to the device.

[1560] Step 26:

[1561] The server analyzes the emotion data obtained from the emotion recognition means and adjusts dynamic learning support according to the user's state. An emotion analysis algorithm is used for data calculation. The output is the learning support adjustment result.

[1562] In this way, learning support based on the user's skill level and emotional state is realized.

[1563] (Application example 2)

[1564] 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."

[1565] While conventional learning support systems provide learning materials tailored to the user's technical level and automatically evaluate assignments and provide feedback, they do not dynamically adjust based on the user's emotional state. This can lead to problems such as users feeling stressed or losing motivation, reducing the effectiveness of their learning. Furthermore, it is difficult to provide real-time technical support in the actual work environment of a factory.

[1566] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1567] In this invention, the server includes a diagnostic means for diagnosing the user's skill level, a learning material providing means for providing learning materials and assignments suitable for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition means for recognizing the user's emotional state and dynamically adjusting learning support based on that state. This not only improves the user's learning efficiency but also enables real-time technical support in an actual working environment.

[1568] A "user" is an individual who wishes to learn about the technology and knowledge related to mobile phone base stations.

[1569] The "diagnosis means" is a means for diagnosing the user's skill level, and determines the user's skill level based on the answers to a set of questions.

[1570] The "teaching material providing means" is a means for dynamically selecting and providing optimal teaching materials and assignments according to the user's skill level and learning progress.

[1571] "Evaluation means" is a means for evaluating user-submitted assignments and generating feedback using an AI module.

[1572] The "progress management means" is a means for periodically evaluating the user's learning progress and providing encouraging messages and rewards to maintain motivation.

[1573] The "emotion recognition means" is a means for analyzing the user's facial expressions, voice, biometric signals, etc., and recognizing the user's emotional state in real time.

[1574] A "factory robot" is an automated machine used to perform manufacturing and assembly tasks in a factory.

[1575] "Smart glasses" are glasses worn by a user that include a display device that provides visual information.

[1576] "Real time" refers to a state in which processing is carried out instantaneously in synchronization with real-world time.

[1577] An "AI module" is a software module that uses artificial intelligence technology to perform data analysis and judgment.

[1578] The learning support system of the present invention is implemented in a form that can be used by engineers who operate and maintain factory robots while wearing smart glasses.

[1579] Overall system configuration

[1580] 1. User Registration and Authentication

[1581] The server receives the information entered by the user on the new registration screen (e.g., "Name", "Email address", "Password"), validates it, saves it in the database and sends a confirmation email to the user. When the user clicks the link included in the confirmation email, the server validates the registration.

[1582] 2. User Level Assessment

[1583] A set of questions that the user answers when logging in for the first time is provided to the device (smart glasses), and the server receives the answers. The AI ​​module determines the user's skill level and stores it in a database.

[1584] 3. Providing study materials and assignments

[1585] The server selects the most appropriate learning materials (e.g., "PDF Learning Materials 1") and assignments based on the user's skill level and sends them to the device. The device displays them, and the user works on the learning or assignments.

[1586] 4. Submitting and grading assignments

[1587] When a user submits an assignment, the results are sent from the device to the server, where the server automatically grades the assignment using an AI module, generates feedback, stores it in a database, and notifies the user.

[1588] 5. Manage your learning progress and maintain motivation

[1589] The server periodically evaluates the user's learning progress data, generates encouraging messages and rewards to maintain motivation, and sends them to the terminal for display to the user.

[1590] 6. Introduction of emotion recognition methods

[1591] The device is equipped with an emotion recognition unit that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state. Based on the data obtained from the emotion recognition unit, the server dynamically adjusts the support messages and teaching methods according to the user's emotional state.

[1592] Hardware and software used

[1593] Server: A central processing unit that manages user data, learning materials, assignments, progress data, and provides assessment and feedback.

[1594] Terminal (smart glasses): A device that provides the user with real-time visual information, diagnoses their skill level, displays teaching materials and assignments, and recognizes emotions.

[1595] AI module: Artificial intelligence software that automatically grades assignments and assesses skill levels.

[1596] Emotion recognition means: A set of software and sensors that analyze the user's facial expressions, voice, and biometric signals.

[1597] Specific examples

[1598] For example, when a user named "Engineer A" logs in for the first time, he answers a set of questions, and the server determines his skill level as "intermediate." The server then provides learning materials (e.g., "PDF Learning Materials 2") and assignments suitable for an "intermediate" user. Engineer A continues his studies through the smart glasses and submits the assignments. The server's AI module generates feedback and notifies the user that "you need to learn more about the 5G base frequency band." If Engineer A feels stressed while studying, the emotion recognition means detects this, and the server sends a supportive message such as "take a break."

[1599] Generative AI model prompt example

[1600] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[1601] Output: User's skill level (e.g. "Intermediate")

[1602] Input: User's skill level (e.g., "Intermediate")

[1603] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[1604] Input: The assignment submitted by the user (e.g., "Assignment 1")

[1605] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[1606] Input: User emotion recognition result (e.g., "stress")

[1607] Output: A suitable encouraging message (e.g., "You're almost there!")

[1608] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1609] Step 1:

[1610] User Registration and Authentication

[1611] Input: Name, email address, and password entered by the user on the sign-up screen

[1612] The device sends the registration information entered by the user to the server. The server validates this information to ensure it is in the correct format. It then stores it in a database and sends a confirmation email to the user. The user clicks on a link in the confirmation email, which causes the device to contact the server and validate the registration.

[1613] Output: User registration completion notification

[1614] Step 2:

[1615] User level assessment

[1616] Input: A set of questions that the user answers when they first log in

[1617] The server provides a set of questions to the user through the device, and the user answers them. The device then sends the answers back to the server, which then uses an AI module to diagnose the user's skill level and stores it in a database.

[1618] Output: User's skill level (e.g., "Beginner", "Intermediate", "Advanced")

[1619] Step 3:

[1620] Providing teaching materials and assignments

[1621] Input: User skill level assessment result

[1622] The server selects the most appropriate learning materials and assignments based on the user's skill level and sends them to the terminal. The terminal displays these learning materials (e.g., "PDF learning materials") and assignments to the user. The user studies the learning materials and works on the assignments.

[1623] Output: A list of materials and assignments provided to the user

[1624] Step 4:

[1625] Submitting and grading assignments

[1626] Input: The task the user completed

[1627] Once the user completes the assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback, which is stored in a database and notified to the user.

[1628] Output: Assignment evaluation results and feedback (e.g., "passed," "failed," "what needs further study").

[1629] Step 5:

[1630] Manage your learning progress and maintain motivation

[1631] Input: User's learning progress data

[1632] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. The progress management results are sent to the device, which then displays the encouraging messages and reward notifications to the user.

[1633] Output: Cheer message and reward notification

[1634] Step 6:

[1635] Introducing emotion recognition methods

[1636] Input: Data such as user facial expressions, voice, and biometric signals

[1637] The device analyzes the user's facial expressions, voice, and biometric signals in real time to recognize their emotional state. The recognized emotional state data is sent to the server, which then uses this data to generate and dynamically adjust the optimal cheering message according to the user's emotional state.

[1638] Output: A cheering message based on the emotional state

[1639] Generative AI model prompt example

[1640] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[1641] Output: User's skill level (e.g. "Intermediate")

[1642] Input: User's skill level (e.g., "Intermediate")

[1643] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[1644] Input: The assignment submitted by the user (e.g., "Assignment 1")

[1645] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[1646] Input: User emotion recognition result (e.g., "stress")

[1647] Output: A suitable encouraging message (e.g., "You're almost there!")

[1648] 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.

[1649] 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.

[1650] 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.

[1651] [Fourth embodiment]

[1652] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1653] 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.

[1654] 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).

[1655] 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.

[1656] 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.

[1657] 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).

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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."

[1665] The learning support system of the present invention is a system that supports users in efficiently acquiring advanced technology and knowledge related to mobile phone base stations. This system has the functions of user diagnosis, provision of learning materials, assignment evaluation, and learning progress management, and an embodiment thereof is shown below.

[1666] User Registration and Authentication

[1667] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks the link included in the confirmation email, and the device accesses the server to validate the registration.

[1668] User level assessment

[1669] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[1670] Providing teaching materials and assignments

[1671] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who can then read the learning materials and work on the assignments.

[1672] Submitting and grading assignments

[1673] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[1674] Manage your learning progress and maintain motivation

[1675] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating the user to continue learning.

[1676] Specific Examples

[1677] For example, user "A" registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "intermediate level." The server sends learning materials for "5G Technology Overview" and a basic problem set, which are ideal for intermediate level users, to A's device. A studies the materials, works on assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback such as "You need to learn more about the 5G base frequency band." Periodically, the server evaluates A's progress and sends a message saying, "Great progress, you've earned 50 points!" This motivates A to continue learning.

[1678] As described above, the learning support system of the present invention provides optimal learning materials and assignments according to the user's skill level, and supports the efficient acquisition of knowledge.

[1679] The processing flow will be explained below.

[1680] Step 1:

[1681] The user enters their name, email address, and password on the new registration screen.

[1682] Step 2:

[1683] The terminal transmits this information to the server.

[1684] Step 3:

[1685] The server validates the information entered to ensure it is in the correct format.

[1686] Step 4:

[1687] The server saves the information that passes validation in the database.

[1688] Step 5:

[1689] The server sends a confirmation email to the user.

[1690] Step 6:

[1691] The user clicks on the link in the confirmation email they received.

[1692] Step 7:

[1693] The device sends the user's verification link to the server to confirm the registration.

[1694] Step 8:

[1695] The server validates the user's registration and updates the database.

[1696] Step 9:

[1697] The user enters their email address and password on the login screen and logs in.

[1698] Step 10:

[1699] The terminal sends this login information to the server.

[1700] Step 11:

[1701] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[1702] Step 12:

[1703] The server provides the user with a "level diagnostic test."

[1704] Step 13:

[1705] The user answers the questions in the diagnostic test.

[1706] Step 14:

[1707] The terminal sends the answer result to the server.

[1708] Step 15:

[1709] The server uses an AI module to determine the user's skill level.

[1710] Step 16:

[1711] The server stores the judgment results in a database.

[1712] Step 17:

[1713] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[1714] Step 18:

[1715] The server transmits the teaching materials and assignments to the terminal.

[1716] Step 19:

[1717] The terminal displays the received learning materials and assignments to the user.

[1718] Step 20:

[1719] Users study the materials and work on the assignments.

[1720] Step 21:

[1721] The user sends the completed assignments to the server via the terminal.

[1722] Step 22:

[1723] The server uses an AI module to automatically grade the assignments.

[1724] Step 23:

[1725] The server generates feedback and stores it in a database along with the scoring results.

[1726] Step 24:

[1727] The server notifies the user of the feedback and the score.

[1728] Step 25:

[1729] The server periodically evaluates the user's learning progress data.

[1730] Step 26:

[1731] The server generates encouraging messages and rewards to keep you motivated.

[1732] Step 27:

[1733] The server sends cheering messages and rewards to the device.

[1734] Step 28:

[1735] The device displays a message of encouragement and a reward notification to the user.

[1736] Example 1

[1737] 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."

[1738] With the rapid evolution of modern technology and knowledge, there is a demand for systems that allow users to efficiently learn advanced information in specific technical fields. However, conventional learning systems have difficulty providing optimal learning materials and assignments tailored to each user's skill level, and they lack means to manage users' learning progress and maintain their motivation. Furthermore, these systems often lack integrated user registration and authentication functions. This prevents users from properly understanding their own learning status, hindering effective learning.

[1739] 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.

[1740] In this invention, the server includes authentication means for user registration and authentication, diagnosis means for diagnosing the user's skill level, provision means for providing the user with educational materials and tasks appropriate for the user based on the results of the diagnosis means, evaluation means for evaluating the results of the tasks and generating feedback, and progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation. This makes it possible to provide optimal educational materials and tasks according to the skill level of each user, manage learning progress and maintain motivation, and provide efficient learning support that integrates user registration and authentication.

[1741] "User" refers to an individual who uses the system to learn.

[1742] "Authentication" refers to the process used to verify a user's registration information and grant access to the system.

[1743] "Diagnostic means" refers to the process of assessing the user's skill level and providing the most appropriate teaching materials and assignments based on the results.

[1744] "Educational materials" refers to teaching materials and reference materials that contain information and data for users to study.

[1745] "Work" refers to assignments and exercises that users undertake as part of their studies.

[1746] "Delivery means" refers to the method or process of providing and presenting educational materials and tasks to users.

[1747] "Evaluation means" refers to a system that evaluates the work submitted by a user and generates feedback based on the results.

[1748] "Progress management means" refers to the process of tracking and managing a user's learning progress and providing messages and rewards to maintain motivation as needed.

[1749] "Generative AI model" refers to an artificial intelligence program model used to diagnose a user's skill level and evaluate issues.

[1750] "Feedback" refers to evaluations and advice provided to users regarding their learning and work.

[1751] The learning support system of the present invention supports users in efficiently acquiring advanced skills and knowledge. This system includes functions for user registration and authentication, skill level assessment, provision of learning materials and tasks, task evaluation, learning progress management, and motivation maintenance.

[1752] Hardware and Software Configuration

[1753] This system operates using devices such as personal computers and smartphones. These devices communicate with the server via the Internet. Specifically, the following hardware and software are used:

[1754] Hardware

[1755] personal computer

[1756] Smartphone

[1757] software

[1758] A web browser (for users to access the system)

[1759] Email client (to receive the confirmation email)

[1760] Database (e.g. MySQL, PostgreSQL)

[1761] Server-side frameworks (e.g., Django, Node.js)

[1762] AI model (e.g. OpenAI GPT-3)

[1763] Detailed explanation of each function

[1764] User Registration and Authentication

[1765] When a user enters their name, email address, and password on the new registration screen, the device sends this data to the server. The server validates the data, stores it in a database, and then sends a confirmation email to the user. The user clicks the link in the confirmation email, and the device accesses the server to validate the registration.

[1766] Technical level diagnosis

[1767] When a user logs in for the first time, they answer a set of questions provided as a diagnostic tool. The device sends the answers to the server, which then uses an AI model (e.g., GPT-3) to determine the user's skill level. The results are stored in a database.

[1768] Examples:

[1769] The user answers diagnostic questions when logging in for the first time, and the terminal sends the results to the server.

[1770] The server inputs the diagnostic answers into the AI ​​module, which determines the level as "intermediate."

[1771] Example prompt for a generative AI model:

[1772] "The following diagnostic questions about 5G communication technology are provided, and you can use them to determine your technical level. Answer: {Answer}"

[1773] Providing materials and work

[1774] The server selects appropriate educational materials and tasks based on the user's skill level and sends them to the terminal, which displays them to the user, who then reads the educational materials and works on the tasks.

[1775] Submitting and grading your work

[1776] After the user completes their work, they click the submit button and their device sends the results to the server, which uses an AI model to automatically grade the work and generate feedback, which is stored in a database and notified to the user.

[1777] Manage your learning progress and maintain motivation

[1778] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards. As a means of progress management, the server sends these messages and rewards to the device, which then displays them to the user. This motivates the user to continue learning.

[1779] Examples:

[1780] The server evaluates your learning progress and sends you a message saying "Great progress, you've earned 50 points!"

[1781] Users receive messages and stay motivated to learn.

[1782] As described above, the learning support system of the present invention is designed to enable users to efficiently acquire advanced knowledge, providing optimal educational materials and tasks according to the user's technical level, managing learning progress, and maintaining motivation.

[1783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1784] Program processing steps

[1785] User Registration and Authentication

[1786] Step 1:

[1787] The user enters their name, email address, and password on the new registration screen.

[1788] Input: Name, Email Address, Password

[1789] Specific actions: Open the registration form in a web browser and enter information in the input fields.

[1790] Output: Data entered into the form

[1791] Step 2:

[1792] The terminal sends the input data to the server.

[1793] Input: Data entered into a form

[1794] Specific operation: Uses JavaScript's fetch API to send form data via a POST request.

[1795] Output: User data sent to the server

[1796] Step 3:

[1797] The server validates the data and saves it to the database.

[1798] Input: Submitted user data

[1799] Specific operation: Django view function receives data, performs form validation, and saves it to the database (MySQL).

[1800] Output: User data stored in the database

[1801] Step 4:

[1802] The server sends a confirmation email to the user.

[1803] Input: Saved user data

[1804] Specific operation: Sends a confirmation email using Django's send_mail function.

[1805] Output: Confirmation email sent

[1806] Step 5:

[1807] The user clicks on the link in the confirmation email and the device accesses the server.

[1808] Input: Link in confirmation email

[1809] What happens: A user opens their email client, clicks a link, and the browser sends a new request to the server.

[1810] Output: Request sent to the server

[1811] Step 6:

[1812] The server validates the registration.

[1813] Input: The request sent to the server

[1814] Specific behavior: The server verifies the token in the link and updates the user status in the database.

[1815] Output: Enabled user accounts

[1816] Technical level diagnosis

[1817] Step 1:

[1818] The user answers a level assessment question when they first log in.

[1819] Input: Answer to diagnostic question

[1820] Specific actions: Open a question form in a web browser and answer each question.

[1821] Output: Response data

[1822] Step 2:

[1823] The terminal transmits the response data to the server.

[1824] Input: Answer data

[1825] Specific operation: Uses JavaScript's fetch API to send the response data via a POST request.

[1826] Output: Response data sent to the server

[1827] Step 3:

[1828] The server analyzes the response data using an AI module.

[1829] Input: Submitted response data

[1830] Specific operation: Send data to the API of the AI ​​module (e.g., GPT-3) and receive the analysis results.

[1831] Output: Technical level as analysis result

[1832] Step 4:

[1833] The server determines the skill level and stores the results in a database.

[1834] Input: Analysis results of the AI ​​module

[1835] Specific operation: The results of the AI ​​module are saved in a database using an SQL query.

[1836] Output: Skill level stored in the database

[1837] Providing materials and work

[1838] Step 1:

[1839] The server selects materials and tasks based on the user's skill level.

[1840] Input: User skill level

[1841] Specific operation: The system retrieves the user's skill level from the database and executes an algorithm to select appropriate learning materials and tasks.

[1842] Output: Selected materials and tasks

[1843] Step 2:

[1844] The server sends the selected teaching materials (such as PDF files) and tasks to the terminal.

[1845] Input: Selected materials and tasks

[1846] Specific operation: Send teaching material data and tasks to the terminal in JSON format.

[1847] Output: Teaching materials and work data sent to the device

[1848] Step 3:

[1849] The terminal displays these to the user.

[1850] Input: Teaching materials and work data sent to the terminal

[1851] Specific operation: Use JavaScript to display teaching materials and tasks on HTML.

[1852] Output: The material and tasks displayed to the user

[1853] Submitting and grading your work

[1854] Step 1:

[1855] Once the user has completed their work, they click the submit button.

[1856] Input: Completed work data

[1857] Specific behavior: The user fills out a work form in a browser and clicks the submit button.

[1858] Output: Working data for submission

[1859] Step 2:

[1860] The terminal sends the submitted data to the server.

[1861] Input: Working data for submission

[1862] Specific operation: Uses JavaScript's fetch API to send the submitted data to the server via a POST request.

[1863] Output: The submission data sent to the server

[1864] Step 3:

[1865] The server grades the work using an AI module.

[1866] Input: Submitted submission data

[1867] Specific operation: Input the work data into the AI ​​module and obtain the scoring results.

[1868] Output:Scoring results

[1869] Step 4:

[1870] The server generates the feedback and stores it in a database.

[1871] Input:Scoring results

[1872] Specific behavior: Generate feedback and store it in a database using an SQL query.

[1873] Output: Generated feedback

[1874] Step 5:

[1875] The server notifies the user.

[1876] Input: Generated feedback

[1877] Specific operation: Generate notification data and send it to the user's device.

[1878] Output: User notification

[1879] Manage your learning progress and maintain motivation

[1880] Step 1:

[1881] The server periodically evaluates the learning progress data.

[1882] Input: User's learning progress data

[1883] What it does: Runs a script using a periodic task (Cron job) to evaluate progress data.

[1884] Output: Evaluation results

[1885] Step 2:

[1886] The server generates cheer messages and rewards.

[1887] Input: Evaluation result

[1888] Specific behavior: Runs an algorithm that generates messages and rewards based on progress data.

[1889] Output: Generated message and reward

[1890] Step 3:

[1891] The server sends these to the terminal.

[1892] Input: Generated message and reward

[1893] Specific operation: The generated message and reward data are sent to the terminal in JSON format.

[1894] Output: Message and reward sent to the terminal

[1895] Step 4:

[1896] The terminal displays it to the user.

[1897] Input: Message and reward sent to the terminal

[1898] Specific behavior: Use JavaScript to display messages and rewards on HTML.

[1899] Output: The message and reward displayed to the user

[1900] (Application example 1)

[1901] 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."

[1902] Conventional learning support systems are limited to specific technical fields, making it difficult to provide efficient and motivating training for factory workers and engineers to acquire the robot operation skills they require. In particular, when it comes to evaluating technical levels and managing progress, incentives to maintain user motivation are often lacking, resulting in issues with learning retention rates. However, if there were a system that could enable efficient learning in a managed environment, these issues could be resolved.

[1903] 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.

[1904] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user based on the results of the diagnostic means, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, a training means for factory workers and engineers to learn robot operation techniques, and incentives such as points and badges as rewards provided by the progress management means. This allows users to efficiently and continuously progress in their learning while receiving learning materials optimal for their skill level. Furthermore, the incentives maintain motivation and improve the effectiveness of training.

[1905] A "diagnostic means for diagnosing a user's technical level" is a device that runs a series of questions and tests to assess whether a user has a certain level of technical knowledge and skills, and determines the user's technical level based on the results.

[1906] The "teaching material providing means for providing teaching materials and exercises suitable for the user based on the results of the diagnostic means" is a device that selects optimal learning materials and exercises and provides them to the user based on the user's skill level determined by the diagnostic means.

[1907] The "assessment means for evaluating the results of the assignment and generating feedback" is a device that analyzes the content of the assignment submitted by the user and automatically generates feedback such as the results and areas for improvement.

[1908] A "progress management means for managing a user's learning progress and providing messages and rewards to maintain motivation" is a device that monitors the user's learning process and increases their motivation to learn by providing encouragement and rewards according to their progress.

[1909] A "training means for factory workers and engineers to learn robot operation techniques" is a device that provides a series of training programs for factory employees and engineers to efficiently learn the techniques necessary for operating and setting up robots.

[1910] "Incentives such as points and badges as rewards provided by the progress management means" are rewards such as points and badges given according to the user's learning progress and results, and are intended to increase motivation to learn.

[1911] The learning support system of the present invention provides a training platform for factory workers and engineers to efficiently learn robot operation techniques. Specific embodiments of the system are described below.

[1912] User Registration and Authentication

[1913] The user (factory worker or technician) enters the required information such as name, email address, and password, and the device (computer, smartphone, etc.) sends this information to the server. The server validates the information, stores it in a database, and then sends a confirmation email to the user. The user clicks on a link in the confirmation email, and the device accesses the server to activate the registration.

[1914] User level assessment

[1915] When a user logs in for the first time, they answer a set of questions prepared in advance. The device sends the answers to the server, which then uses AI modules (e.g., TensorFlow, scikit-learn) to determine the user's skill level. The results are stored in a database.

[1916] Providing teaching materials and assignments

[1917] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials are sent to the terminal, where the user can study them and work on the assignments.

[1918] Submitting and grading assignments

[1919] Once a user completes an assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[1920] Manage your learning progress and maintain motivation

[1921] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points and badges) and messages to maintain motivation according to the progress. These rewards and messages are also sent to the device, motivating the user to continue learning.

[1922] Specific Examples

[1923] For example, a user, Worker A, registers with the system and activates his / her registration by clicking the link in a confirmation email. Next, when A logs in for the first time, he / she takes a level assessment and is determined to be at "beginner level." The server sends learning materials and practice questions for "basics of robot operation," which are optimal for beginners, to A's device. A studies the materials, works on the assignments, and submits the completed assignments. The server uses an AI module to grade the assignments and generates feedback that "you still need to understand basic operating procedures." The server also sends A a message saying, "Great progress, you've earned 50 points!", which motivates A to continue learning.

[1924] Prompt Sentence Examples

[1925] Design a program to apply a support system for learning technology and knowledge related to mobile phone base stations as a training system for operating factory robots, provide teaching materials and assignments according to the user's level, and develop an application that manages progress and performs automatic evaluation.

[1926] This allows users to efficiently acquire robot operation skills and maintain their motivation as they continue their studies.

[1927] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1928] Step 1: User Registration and Authentication

[1929] The user enters information such as name, email address, and password. The terminal sends this information to the server. The server validates the input data and, if there are no problems, saves the information in a database. A confirmation email is then sent to the user, who clicks on a link in the confirmation email to activate their registration. The input of this step is the user's registration information, and the output is the activation of the user's registration.

[1930] Step 2: User Level Assessment

[1931] When a user logs in for the first time, the system presents the user with a set of pre-prepared questions. The user answers these questions, and the device sends the answers to the server. The server then uses an AI module (e.g., TensorFlow, scikit-learn) to determine the user's skill level and saves the results in a database. The input of this step is the user's answer data, and the output is the user's skill level assessment result.

[1932] Step 3: Providing materials and assignments

[1933] The server selects the most appropriate learning materials (e.g., PDF materials on the basics of robot operation) and assignments based on the user's skill level. The selected learning materials and assignments are sent to the terminal, which displays them to the user. The user studies the learning materials and works on the assignments. The input to this step is the user's skill level, and the output is the most appropriate learning materials and assignments provided to the user.

[1934] Step 4: Submit and grade assignments

[1935] When a user completes an assignment, the device sends the submission results to the server. The server uses an AI module to automatically grade the assignment and generate feedback. This feedback is stored in a database and notified to the user via the device. The input of this step is the user's submitted assignment data, and the output is the automatic grading results and feedback.

[1936] Step 5: Manage your learning progress and stay motivated

[1937] The server periodically evaluates the user's learning progress data and generates rewards (e.g., points or badges) and messages to maintain motivation according to the progress. These rewards and messages are sent to the device and notified to the user. The input of this step is the user's learning progress data, and the output is the rewards and motivation messages.

[1938] These processing steps enable the system to efficiently support the user in acquiring robot operation skills and maintain their motivation to learn.

[1939] 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.

[1940] The learning support system of the present invention was developed to enable users to efficiently acquire advanced technology and knowledge related to mobile phone base stations. This system diagnoses the user's skill level, provides learning materials, evaluates assignments and generates feedback, manages learning progress, and provides encouraging messages and rewards to maintain motivation. In addition, the system has the ability to recognize the user's emotions and dynamically adjust learning support based on their state.

[1941] User Registration and Authentication

[1942] The user enters the required information (e.g., "Name," "Email address," and "Password") on the new registration screen, and the device sends this information to the server. The server validates the information, confirms that it is in the correct format, and then saves it in a database. A confirmation email is then sent to the user. The user clicks the link contained in the confirmation email, and the device accesses the server and validates the registration.

[1943] User level assessment

[1944] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[1945] Providing teaching materials and assignments

[1946] The server selects the most appropriate learning materials and assignments based on the user's technical level. As a means of providing the learning materials, the server sends the selected learning materials (e.g., PDF learning material "5G Technology Overview") and assignments to the terminal. The terminal displays these to the user, who then studies the learning materials and works on the assignments.

[1947] Submitting and grading assignments

[1948] Once a user completes an assignment, the device sends the submission to the server, which uses an AI module to automatically grade the assignment and generate feedback that is stored in a database and notified to the user.

[1949] Manage your learning progress and maintain motivation

[1950] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. As a means of progress management, the server sends these messages and rewards to the device. The device displays the encouraging messages and reward notifications to the user, motivating them to continue learning.

[1951] Introducing emotion recognition methods

[1952] The device is equipped with an emotion recognition means that analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state. This emotion recognition means can grasp in real time the stress, frustration, joy, and other feelings the user feels while studying. Based on the data obtained from the emotion recognition means, the server dynamically adjusts the optimal support messages and teaching methods according to the user's emotional state.

[1953] Specific Examples

[1954] For example, suppose user "B" registers with the system and is judged to be at an "intermediate" level through a level assessment. The server provides B with the learning materials and a basic problem set on "5G Technology Overview" that are optimal for him. B studies the materials and submits assignments. The server uses an AI module to grade the assignments and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message such as "take a break." This allows B to take breaks at appropriate times and continue studying effectively.

[1955] As described above, the learning support system of the present invention provides an optimal learning environment according to the user's skill level, learning progress, and emotional state, thereby realizing efficient learning support.

[1956] The processing flow will be explained below.

[1957] Step 1:

[1958] The user enters their name, email address, and password on the new registration screen.

[1959] Step 2:

[1960] The terminal transmits this information to the server.

[1961] Step 3:

[1962] The server validates the information entered to ensure it is in the correct format.

[1963] Step 4:

[1964] The server saves the information that passes validation in the database.

[1965] Step 5:

[1966] The server sends a confirmation email to the user.

[1967] Step 6:

[1968] The user clicks on the link in the confirmation email they received.

[1969] Step 7:

[1970] The device sends the user's verification link to the server to confirm the registration.

[1971] Step 8:

[1972] The server validates the user's registration and updates the database.

[1973] Step 9:

[1974] The user enters their email address and password on the login screen and logs in.

[1975] Step 10:

[1976] The terminal sends this login information to the server.

[1977] Step 11:

[1978] The server authenticates the user based on the information entered and verifies that this is the first time the user has logged in.

[1979] Step 12:

[1980] The server provides the user with a "level diagnostic test."

[1981] Step 13:

[1982] The user answers the questions in the diagnostic test.

[1983] Step 14:

[1984] The terminal sends the answer result to the server.

[1985] Step 15:

[1986] The server uses an AI module to determine the user's skill level.

[1987] Step 16:

[1988] The server stores the judgment results in a database.

[1989] Step 17:

[1990] The server selects the most appropriate learning materials and assignments based on the user's skill level.

[1991] Step 18:

[1992] The server transmits the teaching materials and assignments to the terminal.

[1993] Step 19:

[1994] The terminal displays the received learning materials and assignments to the user.

[1995] Step 20:

[1996] Users study the materials and work on the assignments.

[1997] Step 21:

[1998] The user sends the completed assignments to the server via the terminal.

[1999] Step 22:

[2000] The server uses an AI module to automatically grade the assignments.

[2001] Step 23:

[2002] The server generates feedback and stores it in a database along with the scoring results.

[2003] Step 24:

[2004] The server notifies the user of the feedback and the score.

[2005] Step 25:

[2006] The server periodically evaluates the user's learning progress data.

[2007] Step 26:

[2008] The server generates encouraging messages and rewards to keep you motivated.

[2009] Step 27:

[2010] The server sends cheering messages and rewards to the device.

[2011] Step 28:

[2012] The device displays a message of encouragement and a reward notification to the user.

[2013] Step 29:

[2014] The terminal analyzes the user's facial expressions, voice, and biometric signals and determines the user's emotional state using emotion recognition means.

[2015] Step 30:

[2016] The server evaluates variables that may have an impact based on the emotional data obtained from the emotion recognition means, and adjusts the delivery of conventional teaching materials and task allocation.

[2017] Step 31:

[2018] The server dynamically generates optimal support messages and instruction methods based on the user's emotional state.

[2019] Step 32:

[2020] The server transmits the generated message and instruction method to the terminal.

[2021] Step 33:

[2022] The device displays these messages and teaching methods to the user, further promoting improved learning efficiency.

[2023] Example 2

[2024] 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."

[2025] Conventional learning support systems can provide learning materials and assess tasks based on the user's skill level and learning progress, but they cannot adjust learning support to take the user's emotional state into account. As a result, they are unable to provide appropriate support when the user feels stressed or frustrated, resulting in reduced learning efficiency. Furthermore, feedback and support messages are generated uniformly, without dynamic responses tailored to the individual user's state. Therefore, the objective of this invention is to improve learning efficiency and maintain motivation by recognizing the user's emotional state in real time and providing optimal learning support based on this.

[2026] 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.

[2027] In this invention, the server includes a diagnostic means for diagnosing a user's skill level, a learning material provision means for providing learning materials and assignments appropriate for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition and adjustment means for recognizing the user's emotional state and adjusting learning support in accordance with that state. This makes it possible to grasp the user's emotional state in real time during the learning process and provide support and feedback at appropriate times, thereby improving the user's learning efficiency and maintaining motivation.

[2028] The "diagnostic means" is a system for measuring the user's skill level, including a set of questions to be answered by the user and an evaluation method.

[2029] The "means for providing learning materials" is a system that selects appropriate learning materials and assignments according to the user's skill level and learning progress, and provides them to the user.

[2030] The "evaluation means" is a mechanism for analyzing and evaluating the results of assignments submitted by users and generating feedback, and is a system that includes an AI module.

[2031] The "progress management means" is a mechanism for managing the user's learning progress and providing encouraging messages and rewards to maintain the user's motivation.

[2032] The "emotion recognition and adjustment means" is a mechanism that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state and dynamically adjusts learning support according to that state.

[2033] An "AI module" is a part of the artificial intelligence used within the learning support system, and is a system that analyzes users' answers and task results, determines their skill level, and generates feedback.

[2034] A "question set" is a series of questions to assess a user's skill level, and is answered by the user when they log in for the first time.

[2035] "Teaching materials" are materials for users to study, and include formats such as PDF files and online content.

[2036] MODE FOR CARRYING OUT THE INVENTION

[2037] The purpose of the learning support system of the present invention is to help users efficiently acquire advanced technology and knowledge related to mobile phone base stations. The system assesses the user's skill level, provides appropriate learning materials and assignments, manages the user's learning progress, and provides various encouraging messages and rewards to maintain motivation. It also has the ability to recognize the user's emotional state and dynamically adjust learning support based on that state.

[2038] User Registration and Authentication

[2039] The information provided by the user on the new registration screen, such as "Name," "Email address," and "Password," is sent from the terminal to the server. The server validates the information and stores it in a database. A confirmation email is then sent to the user, and the user validates their registration by clicking the link in the confirmation email.

[2040] User level assessment

[2041] When a user logs in for the first time, they answer a set of questions prepared as a diagnostic tool. The device sends the answers to the server, which uses an AI module to determine the user's skill level. The results are stored in a database.

[2042] Providing teaching materials and assignments

[2043] The server selects the most appropriate learning materials and assignments based on the user's technical level. For example, the PDF learning material "5G Technology Overview" may be selected. The server then sends the selected learning materials and assignments to the device, which then displays them to the user.

[2044] Submitting and grading assignments

[2045] The user completes the assignment and the device sends the submission results to the server, which uses an AI module to automatically grade and generate feedback, which is stored in a database and notified to the user.

[2046] Manage your learning progress and maintain motivation

[2047] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards, which are then sent to the device and displayed to the user, encouraging them to continue learning.

[2048] Introducing emotion recognition methods

[2049] The device analyzes the user's facial expressions, voice, and biometric signals to recognize the user's emotional state in real time. Cameras, microphones, and sensors are used for emotion recognition. The server dynamically adjusts cheering messages and teaching methods based on the data obtained from the emotion recognition means.

[2050] Specific Examples

[2051] For example, suppose user "B" registers with the system and is assessed as "intermediate" in the level assessment. The server provides B with the optimal "5G Technology Overview" study materials and a basic problem set. B studies these and submits the assignment. The server grades the assignment using an AI module and provides feedback that "you need to learn more about the 5G basic frequency band." If B becomes stressed while studying, the emotion recognition means detects this and the server sends a supportive message saying "take a break." This allows B to continue studying efficiently while taking appropriate breaks.

[2052] Example prompts for generative AI models

[2053] "Please describe a learning support system for mobile phone base stations. Please include the following points: user registration and authentication procedures, methods for assessing user levels, provision of learning materials and assignments, assignment submission and evaluation, learning progress management and motivation maintenance, and emotion recognition methods."

[2054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2055] Step 1:

[2056] The user enters their name, email address, and password on the new registration screen. The input data is entered into the terminal form. This signal data becomes the input for the terminal.

[2057] Step 2:

[2058] The device sends the entered information to the server. The data sent is "Name," "Email address," and "Password," and becomes the server's input. Specifically, the data is sent to the server when the send button is pressed.

[2059] Step 3:

[2060] The format of the information received by the server is validated to check for invalid data. Data processing involves checking the format of email addresses using regular expressions. The output is the validation results and the input data.

[2061] Step 4:

[2062] If the server passes validation, it saves the user information to the database. The data is inserted into the database using an SQL query. The output is a message that the data was saved successfully.

[2063] Step 5:

[2064] The server will auto-generate a confirmation email and send it to the email address provided, containing a confirmation link, which is the output sent to the user.

[2065] Step 6:

[2066] The user clicks on the link in the confirmation email they received. This causes the device to access the linked server. This action constitutes the user's confirmation input.

[2067] Step 7:

[2068] The device sends a link access request to the server. The request data includes the user ID and a confirmation token. This becomes the server's input.

[2069] Step 8:

[2070] The server receives the link access request and validates the confirmation information. The data operation includes the process of verifying the confirmation token. The output is the user registration validation result.

[2071] Step 9:

[2072] The server updates the user's status to "confirmed" and saves it in the database. Data calculation is the process of updating the status using an SQL query. The output is a successful update message.

[2073] Step 10:

[2074] When a user logs in for the first time, they answer a set of diagnostic questions. The questions are selected and entered as input data by the user.

[2075] Step 11:

[2076] The terminal converts the user's answer into a data format and sends it to the server. The converted data is the answer content and becomes the input for the server.

[2077] Step 12:

[2078] The server uses an AI module to determine the technical level based on the user's answers using numerical values ​​and categories. Data processing involves using natural language processing and machine learning models to evaluate the level. The output is the technical level assessment result.

[2079] Step 13:

[2080] The server saves the result of the decision to a database. Data calculation is the process of writing to the database using an SQL query, and the output is a save success message.

[2081] Step 14:

[2082] The server selects the most appropriate learning materials and assignments based on the user's skill level. Specifically, it applies a learning material selection algorithm based on the evaluation results to generate the selection results. The output is the selected learning material information.

[2083] Step 15:

[2084] The server sends the selected learning materials to the terminal. The learning material data includes PDF files and link information, which are input to the terminal.

[2085] Step 16:

[2086] The terminal displays the received learning materials and assignments to the user. For example, it opens the learning materials in a PDF viewer and displays the assignment input form. The output is that the user confirms the display.

[2087] Step 17:

[2088] Users complete the assignment and submit it through an online form, which becomes input data and is saved on the device.

[2089] Step 18:

[2090] The terminal sends the submitted results to the server in real time. The sent data is the answer to the assignment and becomes the input for the server.

[2091] Step 19:

[2092] The server uses an AI module to automatically grade submitted assignments and generate feedback. Data processing involves generating the accuracy rate and feedback content through a model evaluation process. The output is feedback information.

[2093] Step 20:

[2094] The server saves the generated feedback in a database and notifies the user. The saving process uses SQL queries. The output is a save success message and a notification.

[2095] Step 21:

[2096] The server periodically retrieves the user's learning progress data from the database and evaluates it. This results in the application of a progress evaluation algorithm. The input is the progress data, and the output is the evaluation result.

[2097] Step 22:

[2098] The server generates encouragement messages and rewards to encourage users to continue learning. The encouragement messages are dynamically created based on the user's progress. The output is the encouragement message content and reward information.

[2099] Step 23:

[2100] The server sends the generated message and reward to the device. The sent content includes the support message and reward information, which are input to the device.

[2101] Step 24:

[2102] The device displays these messages and rewards to the user, for example, in a notification banner or on a dedicated page. The output is the user confirming the display.

[2103] Step 25:

[2104] The device analyzes the user's facial expressions, voice, and biometric signals in real time through a camera and microphone, and emotional data is acquired as input to the device.

[2105] Step 26:

[2106] The server analyzes the emotion data obtained from the emotion recognition means and adjusts dynamic learning support according to the user's state. An emotion analysis algorithm is used for data calculation. The output is the learning support adjustment result.

[2107] In this way, learning support based on the user's skill level and emotional state is realized.

[2108] (Application example 2)

[2109] 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."

[2110] While conventional learning support systems provide learning materials tailored to the user's technical level and automatically evaluate assignments and provide feedback, they do not dynamically adjust based on the user's emotional state. This can lead to problems such as users feeling stressed or losing motivation, reducing the effectiveness of their learning. Furthermore, it is difficult to provide real-time technical support in the actual work environment of a factory.

[2111] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2112] In this invention, the server includes a diagnostic means for diagnosing the user's skill level, a learning material providing means for providing learning materials and assignments suitable for the user, an evaluation means for evaluating the results of the assignments and generating feedback, a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation, and an emotion recognition means for recognizing the user's emotional state and dynamically adjusting learning support based on that state. This not only improves the user's learning efficiency but also enables real-time technical support in an actual working environment.

[2113] A "user" is an individual who wishes to learn about the technology and knowledge related to mobile phone base stations.

[2114] The "diagnosis means" is a means for diagnosing the user's skill level, and determines the user's skill level based on the answers to a set of questions.

[2115] The "teaching material providing means" is a means for dynamically selecting and providing optimal teaching materials and assignments according to the user's skill level and learning progress.

[2116] "Evaluation means" is a means for evaluating user-submitted assignments and generating feedback using an AI module.

[2117] The "progress management means" is a means for periodically evaluating the user's learning progress and providing encouraging messages and rewards to maintain motivation.

[2118] The "emotion recognition means" is a means for analyzing the user's facial expressions, voice, biometric signals, etc., and recognizing the user's emotional state in real time.

[2119] A "factory robot" is an automated machine used to perform manufacturing and assembly tasks in a factory.

[2120] "Smart glasses" are glasses worn by a user that include a display device that provides visual information.

[2121] "Real time" refers to a state in which processing is carried out instantaneously in synchronization with real-world time.

[2122] An "AI module" is a software module that uses artificial intelligence technology to perform data analysis and judgment.

[2123] The learning support system of the present invention is implemented in a form that can be used by engineers who operate and maintain factory robots while wearing smart glasses.

[2124] Overall system configuration

[2125] 1. User Registration and Authentication

[2126] The server receives the information entered by the user on the new registration screen (e.g., "Name", "Email address", "Password"), validates it, saves it in the database and sends a confirmation email to the user. When the user clicks the link included in the confirmation email, the server validates the registration.

[2127] 2. User Level Assessment

[2128] A set of questions that the user answers when logging in for the first time is provided to the device (smart glasses), and the server receives the answers. The AI ​​module determines the user's skill level and stores it in a database.

[2129] 3. Providing study materials and assignments

[2130] The server selects the most appropriate learning materials (e.g., "PDF Learning Materials 1") and assignments based on the user's skill level and sends them to the device. The device displays them, and the user works on the learning or assignments.

[2131] 4. Submitting and grading assignments

[2132] When a user submits an assignment, the results are sent from the device to the server, where the server automatically grades the assignment using an AI module, generates feedback, stores it in a database, and notifies the user.

[2133] 5. Manage your learning progress and maintain motivation

[2134] The server periodically evaluates the user's learning progress data, generates encouraging messages and rewards to maintain motivation, and sends them to the terminal for display to the user.

[2135] 6. Introduction of emotion recognition methods

[2136] The device is equipped with an emotion recognition unit that analyzes the user's facial expressions, voice, and biometric signals to recognize their emotional state. Based on the data obtained from the emotion recognition unit, the server dynamically adjusts the support messages and teaching methods according to the user's emotional state.

[2137] Hardware and software used

[2138] Server: A central processing unit that manages user data, learning materials, assignments, progress data, and provides assessment and feedback.

[2139] Terminal (smart glasses): A device that provides the user with real-time visual information, diagnoses their skill level, displays teaching materials and assignments, and recognizes emotions.

[2140] AI module: Artificial intelligence software that automatically grades assignments and assesses skill levels.

[2141] Emotion recognition means: A set of software and sensors that analyze the user's facial expressions, voice, and biometric signals.

[2142] Specific examples

[2143] For example, when a user named "Engineer A" logs in for the first time, he answers a set of questions, and the server determines his skill level as "intermediate." The server then provides learning materials (e.g., "PDF Learning Materials 2") and assignments suitable for an "intermediate" user. Engineer A continues his studies through the smart glasses and submits the assignments. The server's AI module generates feedback and notifies the user that "you need to learn more about the 5G base frequency band." If Engineer A feels stressed while studying, the emotion recognition means detects this, and the server sends a supportive message such as "take a break."

[2144] Generative AI model prompt example

[2145] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[2146] Output: User's skill level (e.g. "Intermediate")

[2147] Input: User's skill level (e.g., "Intermediate")

[2148] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[2149] Input: The assignment submitted by the user (e.g., "Assignment 1")

[2150] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[2151] Input: User emotion recognition result (e.g., "stress")

[2152] Output: A suitable encouraging message (e.g., "You're almost there!")

[2153] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2154] Step 1:

[2155] User Registration and Authentication

[2156] Input: Name, email address, and password entered by the user on the sign-up screen

[2157] The device sends the registration information entered by the user to the server. The server validates this information to ensure it is in the correct format. It then stores it in a database and sends a confirmation email to the user. The user clicks on a link in the confirmation email, which causes the device to contact the server and validate the registration.

[2158] Output: User registration completion notification

[2159] Step 2:

[2160] User level assessment

[2161] Input: A set of questions that the user answers when they first log in

[2162] The server provides a set of questions to the user through the device, and the user answers them. The device then sends the answers back to the server, which then uses an AI module to diagnose the user's skill level and stores it in a database.

[2163] Output: User's skill level (e.g., "Beginner", "Intermediate", "Advanced")

[2164] Step 3:

[2165] Providing teaching materials and assignments

[2166] Input: User skill level assessment result

[2167] The server selects the most appropriate learning materials and assignments based on the user's skill level and sends them to the terminal. The terminal displays these learning materials (e.g., "PDF learning materials") and assignments to the user. The user studies the learning materials and works on the assignments.

[2168] Output: A list of materials and assignments provided to the user

[2169] Step 4:

[2170] Submitting and grading assignments

[2171] Input: The task the user completed

[2172] Once the user completes the assignment, the device sends the results to the server, which uses an AI module to automatically grade the assignment and generate feedback, which is stored in a database and notified to the user.

[2173] Output: Assignment evaluation results and feedback (e.g., "passed," "failed," "what needs further study").

[2174] Step 5:

[2175] Manage your learning progress and maintain motivation

[2176] Input: User's learning progress data

[2177] The server periodically evaluates the user's learning progress data and generates encouraging messages and rewards to maintain motivation. The progress management results are sent to the device, which then displays the encouraging messages and reward notifications to the user.

[2178] Output: Cheer message and reward notification

[2179] Step 6:

[2180] Introducing emotion recognition methods

[2181] Input: Data such as user facial expressions, voice, and biometric signals

[2182] The device analyzes the user's facial expressions, voice, and biometric signals in real time to recognize their emotional state. The recognized emotional state data is sent to the server, which then uses this data to generate and dynamically adjust the optimal cheering message according to the user's emotional state.

[2183] Output: A cheering message based on the emotional state

[2184] Generative AI model prompt example

[2185] Input: Answers to a set of questions submitted by the user (e.g., 4, 5, 4)

[2186] Output: User's skill level (e.g. "Intermediate")

[2187] Input: User's skill level (e.g., "Intermediate")

[2188] Output: A list of suitable learning materials (e.g. "PDF Learning Materials 2")

[2189] Input: The assignment submitted by the user (e.g., "Assignment 1")

[2190] Output: Assignment evaluation results and feedback (e.g., "Failed," "You need to learn about 5G fundamental frequency bands")

[2191] Input: User emotion recognition result (e.g., "stress")

[2192] Output: A suitable encouraging message (e.g., "You're almost there!")

[2193] 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.

[2194] 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.

[2195] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2196] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2197] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2198] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2199] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2200] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2201] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2203] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2204] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2205] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2206] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2207] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2208] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2209] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2210] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2211] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2213] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2214] The following is further disclosed regarding the above embodiment.

[2215] (Claim 1)

[2216] A diagnostic means for diagnosing the skill level of a user;

[2217] a learning material providing means for providing learning materials and assignments suitable for...

Claims

1. A diagnostic means for diagnosing the skill level of a user; a learning material providing means for providing learning materials and assignments suitable for the user based on the results of the diagnostic means; evaluation means for evaluating the results of the task and generating feedback; a progress management means for managing the user's learning progress and providing messages and rewards to maintain motivation; A system including:

2. The system of claim 1, wherein the diagnostic means is a means for determining a skill level using a set of questions answered by a user, and the evaluation means is a means for automatically scoring submitted assignments and generating feedback using an AI module.

3. 2. The system according to claim 1, wherein said learning material providing means is means for dynamically selecting and providing optimal learning materials and assignments according to the user's skill level and learning progress.

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