System

Abstract

A system is provided.SOLUTION: A system including means for acquiring basic information of a user, means for performing aptitude diagnosis using a generated AI based on the basic information and specifying a learning style of the user, means for searching and selecting an appropriate learning program from an educational platform based on the aptitude diagnosis result and a desire for learning, means for presenting the selected learning program to the user, means for generating and customizing a learning plan according to a life schedule of the user, and means for periodically monitoring a learning progress status of the user and evaluating and feeding back a result.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 modern society, many people need to acquire new skills and knowledge while continuing their work and daily lives. However, finding the best learning method and educational institution for them is often difficult. Finding the optimal learning environment and program for efficiently acquiring new knowledge and experience is a major challenge, especially for the second generation of baby boomers and the Ice Age generation. There is also the problem of a frequent mismatch between what people want to learn and what they can actually learn, resulting in wasted effort, time, and money. This invention aims to solve these problems and provide the optimal learning environment while maintaining motivation to learn. [Means for solving the problem]

[0005] The present invention is a system that includes a means for acquiring basic information about a user, a means for using a generative AI to perform an aptitude test based on the basic information and identify the user's learning style, a means for searching for and selecting an appropriate learning program from an educational platform based on the results of the aptitude test and the user's learning preferences, a means for presenting the selected learning program to the user, a means for generating and customizing a learning plan that fits the user's schedule, and a means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results. Furthermore, by including a means for using a generative AI to perform an aptitude test that includes audio and image data, and a means for sending reminders and advice based on the user's learning progress, the system achieves efficient learning and effective progress management. This prevents mismatches between what a user wants to learn and what they can learn, providing an optimal learning environment.

[0006] "User" refers to an individual who uses this system to enter basic information, select a learning program, and have a learning plan created.

[0007] "Basic information" refers to the user's personal attribute data, such as the user's name, age, occupation, and desired learning content, which are necessary for aptitude testing and selecting a learning program.

[0008] "Generative AI" refers to algorithms and systems that use artificial intelligence technology to analyze data, generate aptitude test questions, score learning programs, and more.

[0009] "Aptitude test" refers to an assessment process that identifies a user's best learning style and format based on basic user information.

[0010] "Learning style" refers to the methods and formats in which users effectively study (e.g., online / offline, individual / group learning, etc.).

[0011] "Learning aspirations" refers to the knowledge or skills a user wants to acquire, or the areas of study in which they are interested.

[0012] An "educational platform" refers to a service that aggregates and provides online or offline educational programs and courses to users.

[0013] A "learning program" is a series of lectures or training designed to acquire specific knowledge or skills.

[0014] "Study Plan" refers to a plan that includes a learning schedule and goals customized to fit the user's life schedule.

[0015] "Progress" refers to the user's current achievement level and learning progress in their learning plan.

[0016] "Feedback" refers to the evaluation and comments provided to users on their learning outcomes, as well as advice on their next learning steps.

[0017] "Reminder" refers to notifications or reminders sent to users based on their study plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0026] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0039] The present invention is a system that provides an optimal learning environment, motivating users to "study" while continuing their daily work and other activities. This system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. Specific embodiments of this system are described below.

[0040] User registration and aptitude test

[0041] 1. Enter basic information

[0042] Terminal: The user enters basic information such as name, age, occupation, and desired study content.

[0043] Terminal: The entered information is sent to the server.

[0044] 2. Conducting aptitude tests

[0045] Server: Uses generative AI to perform aptitude diagnosis based on the user's basic information.

[0046] Server: Generates questions for the user to diagnose their learning style and sends them to the terminal.

[0047] User: Answers the aptitude test questions and submits the answers.

[0048] Server: Analyzes the responses and identifies the user's learning style (online / offline, individual / group).

[0049] Study program recommendation and application

[0050] 3. Searching for and selecting a study program

[0051] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[0052] Server: Scores the search results and selects the best program.

[0053] Server: Sends detailed information about the selected learning program to the terminal.

[0054] 4. Presentation of the study program

[0055] Device: Displays a list of selected learning programs to the user and provides detailed information about each program (content, duration, cost, etc.).

[0056] User: Choose the program that you think is best.

[0057] Planning and managing your study plan

[0058] 5. Generate a learning plan

[0059] Server: Automatically generates a specific study plan based on the study program selected by the user.

[0060] Server: Customize a plan to fit your schedule, for example, scheduling a six-week online course around your work schedule.

[0061] 6. Managing learning progress

[0062] Device: Displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[0063] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[0064] Assessment and feedback of learning outcomes

[0065] 7. Periodic evaluation

[0066] Device: Presents users with surveys and self-assessment forms as they progress through the learning process.

[0067] Users: Complete and submit surveys and self-assessment forms.

[0068] 8. Analysis of evaluation data and feedback

[0069] Server: Analyzes assessment data and evaluates learning outcomes.

[0070] Server: Generates feedback and performance reports for users and sends them to the terminal.

[0071] Device: Displaying feedback and performance reports to users.

[0072] 9. Suggested next steps

[0073] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[0074] Specific examples

[0075] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[0076] 1. Enter basic information

[0077] Device: Person A enters his / her name, age, occupation, and desired study subject (marketing).

[0078] Terminal: Information is sent to the server.

[0079] 2. Conducting aptitude tests

[0080] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[0081] User: Answers the question and sends it to the server.

[0082] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[0083] 3. Study Program Recommendations

[0084] Server: Based on the results of the aptitude test, search for online marketing programs and select the most suitable program.

[0085] Server: Sends program information to the terminal.

[0086] Terminal: Display a list of programs to Person A.

[0087] 4. Planning your study plan

[0088] Server: Generates a study plan that fits Mr. A's schedule based on the program he selected.

[0089] Device: Provides a dashboard where you can view your learning plan.

[0090] 5. Learning progress management

[0091] Device: Study according to your study plan and track your progress.

[0092] 6. Performance evaluation and feedback

[0093] Terminal: Periodically present self-assessment forms and collect responses.

[0094] Server: Analyzes the evaluation data and generates feedback.

[0095] Server: Sends a result report to the terminal.

[0096] This system allows Mr. A to efficiently acquire marketing skills. It also identifies learning styles based on aptitude tests and creates customized learning plans to eliminate mismatches between what he wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[0097] The processing flow will be explained below.

[0098] Step 1:

[0099] Terminal: The user enters basic information (name, age, occupation, desired study content, etc.).

[0100] Step 2:

[0101] Terminal: Sends the entered basic information to the server.

[0102] Step 3:

[0103] Server: Based on the received basic information, the server uses a generative AI to generate aptitude test questions appropriate for the user.

[0104] Step 4:

[0105] Server: Sends the generated aptitude test questions to the terminal.

[0106] Step 5:

[0107] Terminal: Presents the user with aptitude test questions.

[0108] Step 6:

[0109] User: Answers the aptitude test questions.

[0110] Step 7:

[0111] Terminal: Sends the user's answer to the server.

[0112] Step 8:

[0113] Server: Analyzes the aptitude test answers and identifies the user's learning style (online / offline, individual / group).

[0114] Step 9:

[0115] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[0116] Step 10:

[0117] Server: Scores the learning programs in the search results and selects the most suitable program.

[0118] Step 11:

[0119] Server: Sends information about the selected learning program (content, duration, cost, etc.) to the terminal.

[0120] Step 12:

[0121] Terminal: Shows the user a list of learning programs.

[0122] Step 13:

[0123] User: Select the most suitable learning program from the displayed list.

[0124] Step 14:

[0125] Terminal: Sends the user's selection to the server.

[0126] Step 15:

[0127] Server: Automatically generates a personalized learning plan based on user selections.

[0128] Step 16:

[0129] Server: Customizes learning plans to fit the user's schedule.

[0130] Step 17:

[0131] Server: Sends the generated learning plan to the device.

[0132] Step 18:

[0133] Device: Shows users their learning plan and provides a dashboard where they can track their progress.

[0134] Step 19:

[0135] User: Proceed with learning based on the generated learning plan.

[0136] Step 20:

[0137] Device: Sends learning progress to the server.

[0138] Step 21:

[0139] Server: Regularly monitors learning progress and generates reminders and advice as needed.

[0140] Step 22:

[0141] Server: Sends reminders and advice to the device.

[0142] Step 23:

[0143] Device: Display reminders and advice to the user.

[0144] Step 24:

[0145] Terminal: Periodically present users with surveys and self-assessment forms.

[0146] Step 25:

[0147] Users: Complete surveys and self-assessment forms.

[0148] Step 26:

[0149] Terminal: Sends the user's answer to the server.

[0150] Step 27:

[0151] Server: Analyzes the assessment data and evaluates the user's learning outcomes.

[0152] Step 28:

[0153] Server: Sends generated feedback and performance reports to the device.

[0154] Step 29:

[0155] Device: Shows feedback and performance reports to users.

[0156] Step 30:

[0157] Server: Based on the user's learning outcomes, suggests the next learning step or a new learning program.

[0158] Example 1

[0159] 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."

[0160] In today's world, it is difficult for users to find and continue learning the optimal learning program effectively and efficiently. Customization to fit the user's schedule and learning style is particularly important, but no system provides such a service. Therefore, a system is needed that can select the optimal learning program based on the results of a unique aptitude test and the user's individual needs, generate a specific learning plan, and track learning progress.

[0161] 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.

[0162] In this invention, the server includes means for acquiring basic information about the user, means for conducting an aptitude test using a generation AI based on the basic information and identifying the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the results of the aptitude test and the user's learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results, and means for suggesting the next content to be learned and new learning programs based on the user's basic information, learning style, and progress. This makes it possible to provide optimal learning programs that meet the individual needs of each user and provide continuous learning support.

[0163] "Basic user information" refers to basic data about the user, such as name, age, occupation, and desired learning content.

[0164] "Generative AI" is an artificial intelligence technology that uses natural language generation models to generate text and analyze data.

[0165] "Aptitude testing" is the process of using generative AI based on a user's basic information to identify the learning style and method that best suits the user.

[0166] "Learning style" refers to the learning format or method that a user finds most suitable (e.g., online learning, face-to-face classes, individual learning, group learning).

[0167] "Educational platform" is a general term for online services that provide educational services and learning programs available to users.

[0168] "Program of Study" means a series of lectures or courses designed to impart specific skills or knowledge.

[0169] A "study plan" is a daily study schedule created based on a study program selected by the user.

[0170] "Study progress" is a status that indicates how far a user has progressed according to their study plan.

[0171] "Result evaluation and feedback" is the process of analyzing the user's learning effectiveness and providing advice on areas for improvement and future learning based on the results.

[0172] "Suggesting next learning content or new learning programs" means recommending the next learning content or program that is best suited to further development based on the user's current learning outcomes.

[0173] The present invention is a system that provides users with an optimal learning environment. The system acquires basic information about the user, uses generative AI to conduct an aptitude test and identify their learning style. It then searches for and selects an appropriate learning program from an educational platform based on the user's learning preferences, and then generates and provides a customized learning plan for the user. It monitors learning progress, evaluates and provides feedback on results, and suggests next steps in learning.

[0174] Specifically, the system is constructed using the following hardware and software.

[0175] Hardware:

[0176] 1. Terminal: A device that accepts and displays user input (e.g., PC, smartphone, tablet)

[0177] 2. Server: A computer system that stores and processes data

[0178] software:

[0179] 1. Use a generative AI model (e.g., OpenAI's GPT-4)

[0180] 2. Program search function using educational platform APIs

[0181] 3. Aptitude test and learning progress evaluation function using data analysis algorithms

[0182] When a user first uses the system, they enter basic information through their terminal, such as their name, age, occupation, and desired course of study, which is then sent to the server via an HTTP POST request.

[0183] The server uses a generative AI to generate aptitude test questions and sends them to the device. The user answers the questions displayed on the device and sends the answers back to the server. The server then identifies the user's learning style based on the aptitude test results. For example, it determines whether online learning or face-to-face classes are more suitable.

[0184] Next, the server uses the API of educational platforms (e.g., Coursera, Udemy) to search for learning programs based on the user's learning preferences. The search results are scored and the most suitable program is selected. Information about the selected learning program is sent to the device and presented to the user.

[0185] After the user selects the most suitable learning program, the server generates a learning plan based on that program. This plan is customized to fit the user's schedule. The generated learning plan is displayed on a dashboard on the device, allowing the user to check their learning progress.

[0186] The server periodically monitors the user's learning progress and sends reminders and advice. It also periodically generates feedback and achievement reports and sends them to the device. The user can refer to this feedback to progress with their learning.

[0187] Finally, the system will suggest the next learning content or new learning program based on the user's learning outcomes, allowing users to efficiently acquire skills in a learning environment that is always optimal for them.

[0188] Example prompt sentence:

[0189] "I'm a 30-year-old office worker who wants to learn marketing skills. I'd like an aptitude test and a study plan created."

[0190] This system allows users to find the best learning program for them and progress through their studies effectively and efficiently.

[0191] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0192] Step 1:

[0193] Users enter basic information such as their name, age, occupation, and desired study content into the terminal.

[0194] Input: User's basic information (name, age, occupation, desired study content)

[0195] Output: Basic information sent to the server (e.g., JSON format)

[0196] Specific operation: When the user enters the required information into the form and clicks the submit button, the device sends this data to the server as an HTTP POST request.

[0197] Step 2:

[0198] The server uses a generative AI model to generate aptitude questions based on the user's basic information.

[0199] Input: User basic information

[0200] Output: Aptitude test questions (question data output by the generative AI model)

[0201] Specific operation: The server inputs basic information as a prompt to the generative AI model and sends a request saying, "Please generate aptitude test questions based on the user's basic information." The generative AI model then generates aptitude test questions.

[0202] Step 3:

[0203] The server transmits the generated aptitude test questions to the terminal.

[0204] Input: Aptitude test question

[0205] Output: Aptitude test questions displayed on the device (JSON format)

[0206] Specific operation: The server converts the question received from the generative AI model into an appropriate format and sends it to the device, which then displays the question on the screen.

[0207] Step 4:

[0208] The user answers questions for the aptitude test displayed on the terminal and sends the answers from the terminal to the server.

[0209] Input: User's answer

[0210] Output: Response data (JSON format)

[0211] Specific operation: When the user enters an answer to a question on the device screen and presses the send answer button, the device sends this data to the server as an HTTP POST request.

[0212] Step 5:

[0213] The server analyzes the user's responses and identifies the user's learning style.

[0214] Input: User response data

[0215] Output: Learning style (analysis results)

[0216] How it works: The server uses machine learning algorithms to analyze the response data and identify learning styles, such as online learning formats or individualized learning formats.

[0217] Step 6:

[0218] The server searches and selects relevant learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[0219] Input: Aptitude test results, user learning preferences

[0220] Output: List of study programs (with scores)

[0221] Specific operation: The server uses the educational platform's API to search for learning programs that meet the criteria, evaluates the results using a scoring algorithm, and selects the most suitable program.

[0222] Step 7:

[0223] The server sends detailed information about the selected learning program to the terminal.

[0224] Input: List of study programs

[0225] Output: A list of learning programs displayed on the device (JSON format)

[0226] Specific operation: The server obtains detailed information about the selected learning program and sends it to the terminal, which has a display interface and presents it to the user in list form.

[0227] Step 8:

[0228] The user selects the program they think is most suitable from a list of study programs displayed on the terminal.

[0229] Input: Select a study program

[0230] Output: Data for the selected study program

[0231] Specific operation: The user selects the most suitable learning program from the list and presses the selection button, and information about the selected program is sent to the server.

[0232] Step 9:

[0233] The server automatically generates a specific study plan based on the study program selected by the user.

[0234] Input: Data for the selected study program

[0235] Output: Learning plan data

[0236] Specific operation: The server executes a script to generate a learning plan that fits the user's schedule based on the content of the selected learning program.

[0237] Step 10:

[0238] The server transmits the generated study plan to the terminal.

[0239] Input: Learning plan data

[0240] Output: The lesson plan displayed on the device

[0241] Specific operation: The server sends the generated learning plan to the device, which displays it on the device's dashboard.

[0242] Step 11:

[0243] The device displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[0244] Input: Learning plan data

[0245] Output: Learning plan and progress displayed on the dashboard

[0246] Specific operation: The device analyzes the learning plan received from the server and creates a dashboard so that the user can check their progress.

[0247] Step 12:

[0248] The server periodically monitors the user's learning progress and sends reminders and advice.

[0249] Input: Learning progress data

[0250] Output: Reminders and advice

[0251] Specific operation: The server continuously collects the user's progress data and automatically generates reminders and advice based on the progress and sends them to the device.

[0252] Step 13:

[0253] The device presents users with surveys and self-assessment forms as they progress through the learning process.

[0254] Input: Evaluation request

[0255] Output: User self-assessment data

[0256] Specific operation: The device periodically displays evaluation forms and surveys to the user and accepts input.

[0257] Step 14:

[0258] The user answers the questionnaire and self-evaluation form and sends them to the server from the terminal.

[0259] Input: Self-assessment answers

[0260] Output: Response data sent to the server

[0261] Specific operation: When the user answers the questions in the self-assessment form or survey and presses the send button, the device sends the answer data to the server.

[0262] Step 15:

[0263] The server analyzes the assessment data and evaluates the learning outcomes.

[0264] Input: Self-assessment response data

[0265] Output: Analysis results (learning outcome evaluation)

[0266] Specific operation: The server uses machine learning algorithms to analyze the self-assessment data and generate a report assessing learning outcomes.

[0267] Step 16:

[0268] The server generates feedback and performance reports for the user and sends them to the terminal.

[0269] Input: Analysis results

[0270] Output: Feedback and performance reports sent to your device

[0271] Specific operation: The server sends the generated feedback and performance report to the terminal, which then displays it to the user.

[0272] Step 17:

[0273] The device displays feedback and performance reports to the user.

[0274] Input: Feedback and performance reports

[0275] Output: Displayed feedback and performance report

[0276] Specific operation: The device displays the received feedback and performance report on the screen.

[0277] Step 18:

[0278] Based on the user's learning outcomes, the server suggests what to learn next and new learning programs.

[0279] Input: Learning outcome data

[0280] Output: Next learning program suggestion

[0281] Specific operation: The server generates new learning content and programs based on the user's performance data and sends suggestions for the next step to the terminal.

[0282] (Application example 1)

[0283] 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."

[0284] Conventional learning support systems have difficulty providing optimal learning programs tailored to individual users' learning styles and life schedules, and do not adequately manage learning progress or provide appropriate evaluations and feedback. Furthermore, they lacked reminder and advice functions that utilize smart devices, making it difficult to effectively support users' continuity of learning.

[0285] 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.

[0286] In this invention, the server includes means for acquiring basic information about the user, means for performing an aptitude test using a generation AI to identify the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for sending reminders and advice to the user using a smart device and managing and displaying learning progress, means for periodically monitoring the user's learning progress and evaluating and providing feedback on results, and means for using a content distribution service to push notifications of optimal learning content to the user. This enables the provision of optimal learning programs tailored to the user's individual learning style, as well as learning progress management and effective feedback.

[0287] "Basic information" refers to basic data about a person, such as the user's name, age, occupation, and desired learning.

[0288] "Generative AI" is a technology that uses artificial intelligence to analyze data and diagnose users' characteristics and aptitudes.

[0289] "Aptitude test" is a diagnostic process that evaluates a user's learning style and aptitude based on their basic information and response data.

[0290] "Educational platform" refers to a platform or system that provides online or offline learning programs.

[0291] A "program of study" is a series of educational courses or materials designed to develop specific skills or knowledge.

[0292] A "study plan" is a specific study schedule or progress plan that is set to fit the user's daily schedule.

[0293] A "smart device" is a portable electronic device that is connected to a network, such as a smartphone, tablet, or smartwatch.

[0294] "Reminders" is a feature that sends users notifications about specific dates and times or tasks.

[0295] "Advice" refers to providing users with advice and information about their learning progress and areas for improvement.

[0296] "Learning Progress" is the status or data that indicates how far a user has progressed in a learning program.

[0297] "Monitoring" means the act of regularly and continuously observing and recording a user's learning progress.

[0298] "Outcome assessment" is the process of evaluating users' learning outcomes and progress and providing feedback.

[0299] "Feedback" is information that communicates to users the results of evaluations of their learning outcomes and progress, as well as areas for improvement.

[0300] A "content distribution service" is a service that provides users with various content such as educational and entertainment content via the Internet.

[0301] "Push notifications" are a feature that sends information and notifications to smart devices in real time.

[0302] This invention relates to a learning support system that allows users to study efficiently according to their daily schedule. This system acquires basic information about the user, performs aptitude tests using a generative AI, selects an appropriate learning program, and creates a learning plan and manages progress.

[0303] Overall overview

[0304] The system mainly consists of a server and a smart device (such as a smartphone). The server uses generative AI to diagnose the user's aptitude and select a program, and sends reminders and advice to the smart device to manage learning progress.

[0305] Hardware and software used

[0306] Hardware:

[0307] Smart devices (e.g. smartphones, tablets)

[0308] server

[0309] software:

[0310] Smart device side: Native application using React Native etc.

[0311] Server side: Python, Flask (web framework), generative AI (e.g., OpenAI's GPT-4)

[0312] Processing flow

[0313] The server first obtains the user's basic information. The user enters their name, age, occupation, and desired learning content through an application on their smart device, which is then sent to the server. The server then uses generative AI to generate the basic information and a list of questions for aptitude assessment, which are then sent to the smart device. The smart device then presents these questions to the user, collects their answers, and sends them to the server. The server then analyzes the response data to identify the user's learning style.

[0314] Next, the server searches for and selects an appropriate learning program from the educational platform based on the aptitude test results. This information is sent to the smart device, which presents the user with a list of learning programs and detailed information. Once the user selects the desired program, the server generates an automatic learning plan that takes into account the user's daily schedule and displays it on the smart device.

[0315] The server also automatically sends reminders and advice to smart devices, and manages and displays learning progress. As users progress through their learning, the server periodically monitors their progress, evaluates their progress, and provides feedback. Furthermore, the server pushes the most appropriate learning content to users via a content distribution service.

[0316] Specific examples

[0317] For example, if a 40-year-old professional wants to learn digital marketing skills, the system works as follows: The user enters basic information into the smartphone app and sends it to the server. The generation AI processes the information, conducts an aptitude test, and identifies the user's optimal learning style as "online personalized learning." The server then searches for suitable online programs related to digital marketing and presents them to the user. After the user selects an appropriate program, the server generates a learning plan incorporating two online courses per week based on the user's schedule and displays it on the smartphone app.

[0318] In addition, periodic reminders and advice are sent via push notifications to the smartphone to manage learning progress, allowing users to continue their studies efficiently. Furthermore, surveys and self-assessment forms are provided via the smart device as needed, and the server analyzes them to evaluate results and provide feedback.

[0319] Prompt Sentence Examples

[0320] "Please answer the following questions: What is your age, what is your occupation, and what do you want to study?"

[0321] "We'll take a learning style aptitude test. Answer the following questions: Do you prefer online or offline learning?"

[0322] "Which do you find more effective: individual learning or group learning?"

[0323] "Thank you for your response. An online, tutored learning format would be ideal for you."

[0324] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0325] Step 1:

[0326] User registration and basic information acquisition

[0327] The terminal (smart device) prompts the user to enter basic information such as name, age, occupation, desired study content, etc. Once the user has completed the input and pressed the send button, the terminal sends the information to the server.

[0328] Input: Name, age, occupation, desired study content

[0329] Server Output: Save basic information

[0330] Specific operation: The terminal displays a user interface and prompts the user to enter information. After the user enters the information, the terminal sends the information to the server. The server receives the information and stores it in a database.

[0331] Step 2:

[0332] Aptitude test using generative AI

[0333] The server uses a generation AI to generate a list of aptitude test questions based on the user's basic information and sends them to the device. The user answers the questions on the device, and the answers are sent from the device to the server. The generation AI analyzes these to identify the user's learning style.

[0334] Input: Basic information, aptitude test questions

[0335] Server Output: Identifying the user's learning style

[0336] How it works: A generative AI (e.g., GPT-4) generates aptitude test questions based on the user's basic information and sends them to the device. The device displays the questions to the user, who answers them. The answer data is sent from the device to a server, where it is analyzed. As a result, the user's optimal learning style is identified.

[0337] Step 3:

[0338] Searching and selecting a study program

[0339] The server searches for and scores appropriate learning programs from educational platforms based on the aptitude test results and the user's learning preferences. The optimal program is selected and the information is sent to the device.

[0340] Input: Aptitude test results, user learning preferences

[0341] Server output: List of suitable study programs

[0342] Specific operation: The server accesses the educational platform using APIs and searches for learning programs that match the user's aptitude. The search results are scored to select the most suitable program. The selection results are sent to the device and displayed to the user.

[0343] Step 4:

[0344] Presentation and selection of study programs

[0345] The device displays a list of selected learning programs to the user and provides detailed information (content, duration, cost, etc.). The user selects the appropriate program.

[0346] Input: List of study programs

[0347] Output: User's choice of program

[0348] Specific operation: The terminal displays the details of the learning program through the user interface. The user selects the appropriate program and sends the selection result to the server.

[0349] Step 5:

[0350] Generate and customize a learning plan

[0351] The server automatically generates a specific study plan based on the user's selected study program and taking into account the user's daily schedule. The generated study plan is sent to the terminal and displayed to the user.

[0352] Input: User-selected learning program, user's daily schedule

[0353] Server output: Generated lesson plan

[0354] Specific operation: The server automatically generates a learning program schedule based on the user's schedule. The generated learning plan is sent to the device and displayed to the user.

[0355] Step 6:

[0356] Track your progress and receive reminders

[0357] The server manages the user's learning progress and periodically sends reminders and advice to the smart device, which then displays these notifications to the user.

[0358] Input: User's learning progress data

[0359] Server output: reminder and advice notifications

[0360] Specific operation: The server monitors the user's learning progress and periodically generates and sends reminders and advice to the device, which then displays these notifications to the user.

[0361] Step 7:

[0362] Regular assessment and feedback of learning outcomes

[0363] The server periodically evaluates the user's learning progress, generates feedback, and sends it to the device, where the user can view the feedback and progress report.

[0364] Input: User learning progress data, survey data

[0365] Server output: feedback and performance reports

[0366] Specific operation: The server analyzes the learning progress data and evaluates the user's performance. Based on the evaluation results, it generates feedback and performance reports and sends them to the device. The device displays them to the user.

[0367] Step 8:

[0368] Push notifications for the best learning content

[0369] Using the content delivery service, the server pushes the most suitable learning content to the user's smart device, allowing the user to receive new learning content.

[0370] Input: User's learning history, aptitude test results

[0371] Server output: Pushed learning content

[0372] Specific operation: The server selects new learning content based on the user's learning history and aptitude test results through the content distribution service and pushes it to the smart device, which then displays the new learning content to the user.

[0373] 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.

[0374] This invention is a system that provides an optimal learning environment, motivating users to "study" while continuing their daily work and other activities. This system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. In addition, by using an emotion engine that recognizes the user's emotions, the accuracy and efficiency of learning are further improved.

[0375] User registration and aptitude test

[0376] 1. Enter basic information

[0377] Terminal: The user enters basic information such as name, age, occupation, and desired study content.

[0378] Terminal: The entered information is sent to the server.

[0379] 2. Conducting aptitude tests

[0380] Server: Uses generative AI to perform aptitude diagnosis based on the user's basic information.

[0381] Server: Generates questions for the user to diagnose their learning style and sends them to the terminal.

[0382] User: Answers the aptitude test questions and submits the answers.

[0383] Server: Analyzes the responses and identifies the user's learning style (online / offline, individual / group).

[0384] Emotion recognition by emotion engine

[0385] 3. Acquiring Emotion Data

[0386] Device: Using the emotion engine, emotional data such as the user's facial expressions, voice, and body movements is acquired.

[0387] Terminal: Sends the acquired emotion data to the server.

[0388] 4. Emotion Data Analysis

[0389] Server: Analyzes emotional data and understands the user's emotional state in real time.

[0390] Server: Improve the accuracy of aptitude tests based on emotional data.

[0391] Study program recommendation and application

[0392] 5. Searching for and selecting a study program

[0393] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[0394] Server: Scores the search results and selects the best program.

[0395] Server: Sends detailed information about the selected learning program to the terminal.

[0396] 6. Presentation of the study program

[0397] Device: Displays a list of selected learning programs to the user and provides detailed information about each program (content, duration, cost, etc.).

[0398] User: Choose the program that you think is best.

[0399] Planning and managing your study plan

[0400] 7. Generate a learning plan

[0401] Server: Automatically generates a specific study plan based on the study program selected by the user.

[0402] Server: Customizes the plan to fit the user's schedule, for example, scheduling classes so they can be taken during work breaks.

[0403] 8. Managing learning progress

[0404] Device: Displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[0405] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[0406] 9. Use of Emotional Data

[0407] Server: Monitors emotional data during learning in real time and adjusts learning plans based on that data.

[0408] Server: Analyzes the user's emotional data and generates appropriate feedback and advice.

[0409] Assessment and feedback of learning outcomes

[0410] 10. Periodic evaluation

[0411] Device: Presents users with surveys and self-assessment forms as they progress through the learning process.

[0412] Users: Complete and submit surveys and self-assessment forms.

[0413] 11. Analysis of evaluation data and feedback

[0414] Server: Analyzes assessment data and evaluates learning outcomes.

[0415] Server: Generates feedback and performance reports for users and sends them to the terminal.

[0416] Device: Displaying feedback and performance reports to users.

[0417] 12. Suggested next steps for learning

[0418] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[0419] Specific examples

[0420] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[0421] 1. Enter basic information

[0422] Device: Person A enters his / her name, age, occupation, and desired study subject (marketing).

[0423] Terminal: Information is sent to the server.

[0424] 2. Conducting aptitude tests

[0425] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[0426] User: Answers the question and sends it to the server.

[0427] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[0428] 3. Acquiring Emotion Data

[0429] Terminal: Uses the emotion engine to record A's facial expressions and voice, and sends the emotion data to the server.

[0430] 4. Emotion Data Analysis

[0431] Server: Analyzes A's emotional data and improves the accuracy of the learning style diagnosis results.

[0432] 5. Study Program Recommendations

[0433] Server: Searches for marketing-related online programs based on aptitude tests and emotional data, and selects the most suitable program.

[0434] Server: Sends program information to the terminal.

[0435] Terminal: Display a list of programs to Person A.

[0436] 6. Planning your study plan

[0437] Server: Generates a study plan that fits Mr. A's schedule based on the program he selected.

[0438] Device: Provides a dashboard where you can view your learning plan.

[0439] 7. Learning progress management

[0440] Device: Study according to your study plan and track your progress.

[0441] Server: Monitors emotional data in real time and sends reminders to adjust learning plans.

[0442] 8. Performance evaluation and feedback

[0443] Terminal: Periodically present self-assessment forms and collect responses.

[0444] Server: Analyzes the rating and sentiment data and generates feedback.

[0445] Server: Sends a result report to the terminal.

[0446] This system allows A to efficiently acquire marketing skills. Real-time feedback and progress adjustments from the emotion engine also eliminate mismatches between what A wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[0447] The processing flow will be explained below.

[0448] Step 1:

[0449] Terminal: The user enters basic information (name, age, occupation, desired study content, etc.).

[0450] Step 2:

[0451] Terminal: Sends the entered basic information to the server.

[0452] Step 3:

[0453] Server: Based on the received basic information, the server uses a generative AI to generate aptitude test questions appropriate for the user.

[0454] Step 4:

[0455] Server: Sends the generated aptitude test questions to the terminal.

[0456] Step 5:

[0457] Terminal: Presents the user with aptitude test questions.

[0458] Step 6:

[0459] User: Answers the aptitude test questions.

[0460] Step 7:

[0461] Terminal: Sends the user's answer to the server.

[0462] Step 8:

[0463] Server: Analyzes the aptitude test answers and identifies the user's learning style (online / offline, individual / group).

[0464] Step 9:

[0465] Terminal: Uses an emotion engine to acquire user emotion data (e.g., facial expressions, voice, and movements).

[0466] Step 10:

[0467] Terminal: Sends the acquired emotion data to the server.

[0468] Step 11:

[0469] Server: Analyzes emotional data and understands the user's emotional state.

[0470] Step 12:

[0471] Server: Considers emotional state to improve the accuracy of aptitude test results.

[0472] Step 13:

[0473] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[0474] Step 14:

[0475] Server: Scores the learning programs from the search results and selects the most suitable program, taking into account sentiment data.

[0476] Step 15:

[0477] Server: Sends information about the selected learning program (content, duration, cost, etc.) to the terminal.

[0478] Step 16:

[0479] Terminal: Shows the user a list of learning programs.

[0480] Step 17:

[0481] User: Select the most suitable learning program from the displayed list.

[0482] Step 18:

[0483] Terminal: Sends the user's selection to the server.

[0484] Step 19:

[0485] Server: Automatically generates a personalized learning plan based on user selections.

[0486] Step 20:

[0487] Server: Customize plans to fit your schedule.

[0488] Step 21:

[0489] Server: Sends the generated learning plan to the device.

[0490] Step 22:

[0491] Device: Shows users their learning plan and provides a dashboard where they can track their progress.

[0492] Step 23:

[0493] User: Proceed with learning based on the generated learning plan.

[0494] Step 24:

[0495] Device: Sends learning progress to the server.

[0496] Step 25:

[0497] Server: Regularly monitors learning progress and generates reminders and advice as needed.

[0498] Step 26:

[0499] Server: Sends reminders and advice to the device.

[0500] Step 27:

[0501] Device: Display reminders and advice to the user.

[0502] Step 28:

[0503] Server: Monitors emotional data in real time and adjusts learning plans as needed.

[0504] Step 29:

[0505] Terminal: Periodically present users with surveys and self-assessment forms.

[0506] Step 30:

[0507] Users: Complete surveys and self-assessment forms.

[0508] Step 31:

[0509] Terminal: Sends the user's answer to the server.

[0510] Step 32:

[0511] Server: Analyzes the assessment data and evaluates the user's learning outcomes.

[0512] Step 33:

[0513] Server: Sends generated feedback and performance reports to the device.

[0514] Step 34:

[0515] Device: Shows feedback and performance reports to users.

[0516] Step 35:

[0517] Server: Based on the user's learning outcomes, suggests the next learning step or a new learning program.

[0518] Step 36:

[0519] Server: Sends proposed programs and feedback to the device.

[0520] Step 37:

[0521] Device: Shows the user next learning steps and feedback.

[0522] Example 2

[0523] 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."

[0524] Conventional learning systems provide a uniform learning plan without fully considering the individual learning style or emotional state of the user, which makes learning ineffective. In addition, the lack of real-time feedback based on the user's emotional state or progress can easily lead to a decline in learning motivation and stagnation of progress.

[0525] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring basic information about the user, means for performing an aptitude test using a generation AI based on the basic information and identifying the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan tailored to the user's daily schedule, means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results, means for acquiring and analyzing the user's emotional data in real time, and means for adjusting the learning plan based on the emotional data. This provides an optimal learning environment tailored to the user's individual learning style and emotional state, enabling effective learning and maintaining motivation.

[0526] "Basic user information" refers to information that indicates the individual characteristics of a user, such as name, age, occupation, and desired study content.

[0527] "Generative AI" is an artificial intelligence algorithm that performs aptitude tests, generates questions, analyzes data, and more based on user input.

[0528] Aptitude testing is the process of identifying the best learning style for a user based on basic information about them.

[0529] "Learning style" refers to the method and environment in which a user learns most effectively (online / offline, individual / group, etc.).

[0530] "Educational platform" is a general term for websites and applications that provide online learning content.

[0531] A "program of study" is a series of courses or lectures designed to teach a particular skill or knowledge.

[0532] A "life schedule" is a user's daily schedule and activity plan.

[0533] A "study plan" is a plan that indicates the specific timing and method of study based on the study program selected by the user.

[0534] "Monitoring" refers to the act of regularly observing a user's learning progress and collecting necessary data.

[0535] "Outcomes" refers to the progress or goals a user achieves through a learning program.

[0536] "Feedback" means evaluations and advice provided to users based on their learning progress and achievements.

[0537] "Emotional data" is data that indicates the emotional state of a user, obtained from facial expressions, voice, body movements, etc.

[0538] "Real-time analysis" refers to the process of analyzing emotion data as soon as it is acquired and reflecting the results.

[0539] "Adjusting a study plan" refers to the act of changing an existing study schedule or content depending on the user's emotional state and progress.

[0540] This invention is a system that provides an optimal learning environment while a user continues their daily work or other activities. The system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. It also uses an emotion engine that recognizes the user's emotions to improve the accuracy and efficiency of learning.

[0541] User registration and aptitude test

[0542] 1. Enter basic information

[0543] Device: The user enters basic information such as name, age, occupation, and desired study topic. This is done through a web form or a mobile app interface.

[0544] Terminal: The entered information is encoded and sent to the server via HTTPS. The software used here is Apache (registered trademark) or NGINX as a web server, and Node.js or Python frameworks (Django, Flask) as a backend.

[0545] 2. Conducting aptitude tests

[0546] Server: The server receives the user's basic information and generates aptitude test questions using a generative AI model, which uses a general-purpose AI algorithm.

[0547] Server: The generated question is sent to the terminal in JSON format.

[0548] User: The user answers questions for the aptitude test and sends the answers from the device to the server.

[0549] Server: Analyzes the answers and uses machine learning models (e.g., using scikit-learn or TENSORFLOW®) to identify the user's learning style.

[0550] Emotion recognition by emotion engine

[0551] 3. Acquiring Emotion Data

[0552] Device: The device uses an emotion engine (e.g., OpenCV for facial recognition and Google® Speech-to-Text API for voice analysis) to acquire emotion data such as the user's facial expressions, voice, and body movements.

[0553] Terminal: Encodes the acquired emotion data and sends it to the server.

[0554] 4. Emotion Data Analysis

[0555] Server: Analyzes emotional data and understands the user's emotional state in real time. For example, if the user is determined to be tired, the AI ​​model will adjust its learning pace accordingly.

[0556] Server: Stores the analysis results in a database (for example, MySQL (registered trademark) or PostgreSQL).

[0557] Study program recommendation and application

[0558] 5. Searching for and selecting a study program

[0559] Server: Based on the aptitude test results and learning preferences, the server searches for appropriate learning programs from the educational platform via API. It sends API requests using Python's Requests library, etc.

[0560] Server: Evaluates search results using a scoring algorithm and selects the most suitable program.

[0561] Server: Sends detailed information about the selected learning program to the terminal in JSON format.

[0562] 6. Presentation of the study program

[0563] Terminal: Displays a list of learning programs selected by the user. This screen is constructed using HTML / CSS / JavaScript (registered trademark).

[0564] User: Choose the program that you think is best.

[0565] Planning and managing your study plan

[0566] 7. Generate a learning plan

[0567] Server: Automatically generates a specific learning plan based on the user's chosen learning program. The plan is created using an AI algorithm.

[0568] Server: Customizes the plan to fit the user's schedule. For example, schedules the course so that it can be taken during work breaks.

[0569] 8. Managing learning progress

[0570] Terminal: Displays the generated learning plan to the user and provides a dashboard where the user can check their progress. Utilizing front-end frameworks such as React.

[0571] Server: Regularly monitors learning progress and sends reminders and motivational messages as needed. Uses Twilio or Firebase Cloud Messaging.

[0572] Assessment and feedback of learning outcomes

[0573] 9. Use of Emotional Data

[0574] Server: Monitors emotional data in real time during learning and adjusts learning plans based on that data, for example, if it determines that a break is needed after a long period of study.

[0575] Server: Analyzes user emotional data and generates appropriate feedback and advice. The AI ​​model suggests appropriate actions.

[0576] 10. Periodic evaluation

[0577] Device: Present surveys and self-assessment forms to users during the learning process. Use Google Forms or Typeform.

[0578] Users: Complete and submit surveys and self-assessment forms.

[0579] 11. Analysis of evaluation data and feedback

[0580] Server: Analyzes assessment data and evaluates learning outcomes. Machine learning algorithms are used.

[0581] Server: Generates feedback and performance reports for users and sends them to the device in JSON format.

[0582] Device: Displaying feedback and performance reports to users.

[0583] 12. Suggested next steps for learning

[0584] Server: Based on the user's learning outcomes, suggests what to study next and new learning programs. This is done using AI models.

[0585] Specific examples

[0586] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[0587] 1. Enter basic information

[0588] Device: Person A enters his / her name, age, occupation, and desired study content (marketing). For example, "Taro Tanaka, 30 years old, company employee, wants to study marketing."

[0589] Terminal: Information is sent to the server.

[0590] 2. Conducting aptitude tests

[0591] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[0592] User: Answers the question, for example, "I only have 30 minutes a day to study."

[0593] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[0594] 3. Acquiring Emotion Data

[0595] Device: Uses the emotion engine to record A's facial expressions and voice, and sends the emotion data to the server. For example, "A's facial expression looks tired."

[0596] 4. Emotion Data Analysis

[0597] Server: Analyzes A's emotional data and improves the accuracy of the learning style diagnosis results. For example, it determines that "A feels fatigued during the learning process."

[0598] 5. Study Program Recommendations

[0599] Server: Based on aptitude tests and emotional data, the server searches for online marketing programs and selects the most suitable one. For example, it selects the "Basic Marketing Course" as the most suitable one.

[0600] Server: Sends program information to the terminal. For example, "Duration: 3 months, Cost: Free."

[0601] Terminal: Display a list of programs to Person A.

[0602] 6. Planning your study plan

[0603] Server: Based on the program selected by Mr. A, a study plan is generated that fits his / her daily schedule, for example, "attend lectures from 8:00 PM to 9:00 PM every day."

[0604] Device: Provides a dashboard where you can view your learning plan.

[0605] 7. Learning progress management

[0606] Device: Study based on the study plan and record your progress. For example, it might record "I progressed three pages today."

[0607] Server: Monitors emotional data in real time, for example, "sends a reminder to take a break if you feel tired while studying."

[0608] 8. Performance evaluation and feedback

[0609] Device: Periodically present students with a self-evaluation form and ask them to answer questions such as, "Are you satisfied with what you have learned?"

[0610] Server: Analyzes the evaluation data and emotion data and generates feedback such as, "Person A is satisfied with the learning content, but there is a possibility that he or she could increase the study time a little more."

[0611] Server: Sends a performance report to the terminal. For example, the report may say, "Mr. A is very promising."

[0612] This system allows A to efficiently acquire marketing skills. Real-time feedback and progress adjustments from the emotion engine also eliminate mismatches between what A wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[0613] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0614] The flow of this system's program processing

[0615] Step 1: Enter basic information

[0616] Device: The user enters basic information such as name, age, occupation, and desired study content. This is done through a web form or a mobile app interface.

[0617] Input: User information such as name, age, occupation, desired study content, etc.

[0618] Output: User basic information data in JSON format.

[0619] What happens: When a user enters information into a form and presses the Submit button, the data is encoded and sent to the server via the HTTPS protocol.

[0620] Step 2: Submit basic information and prepare for the aptitude test

[0621] Server: Receives the encoded basic information and uses the generative AI model to analyze the user's basic information and generate questions for aptitude tests.

[0622] Input: Basic user information data sent from the device.

[0623] Output: Generated aptitude test question data.

[0624] Specific operation: The server uses the generative AI model to analyze the user's basic information, generate questions for aptitude testing, and send the questions to the terminal in JSON format.

[0625] Step 3: Answer the aptitude test questions

[0626] Terminal: Displays aptitude test questions to the user, who then answers the questions.

[0627] Input: Generated aptitude test question data.

[0628] Output: User's answers to the aptitude test questions.

[0629] Specific operation: When the user enters an answer to the displayed question and presses the "Submit" button, the answer data is encoded and sent back to the server.

[0630] Step 4: Assess your aptitude and identify your learning style

[0631] Server: Analyzes user response data and uses machine learning models to identify the best learning style (online / offline, individual / group).

[0632] Input: User's answers to the aptitude test questions.

[0633] Output: Aptitude test results and identified learning style data.

[0634] How it works: The server analyzes the response data using machine learning algorithms to identify the learning style that best suits the user and stores the results in a database.

[0635] Step 5: Obtaining emotion data

[0636] Device: Using the emotion engine, emotional data such as the user's facial expressions, voice, and body movements is acquired.

[0637] Input: Real-time facial expressions, voice, and body movements of the user.

[0638] Output: Encoded emotion data.

[0639] Specific operation: Using the device's camera and microphone, emotion data is acquired using an emotion engine (e.g., OpenCV, Google Speech-to-Text API) and sent to the server.

[0640] Step 6: Analyze the sentiment data

[0641] Server: Analyzes emotional data and understands the user's emotional state in real time.

[0642] Input: Emotion data sent from the device.

[0643] Output: Parsed emotional state data.

[0644] Specific operation: Analyzes emotional data, determines whether the user is in an emotional state such as "tired," and stores the results in a database.

[0645] Step 7: Search and select a study program

[0646] Server: Based on the aptitude test results and learning preferences, searches for appropriate learning programs from educational platforms and selects the most suitable program.

[0647] Input: Aptitude test results and desired learning content.

[0648] Output: A list of scored learning programs.

[0649] Specific operation: The server sends an API request to the educational platform to obtain appropriate learning programs, evaluates them using a scoring algorithm, and selects the most suitable program.

[0650] Step 8: Present your learning program

[0651] Terminal: A list of selected learning programs is displayed to the user, and the user selects a program.

[0652] Input: Details of the selected study program.

[0653] Output: Data on the study program selected by the user.

[0654] Specific operation: Display a list of programs and detailed information on the terminal screen, and provide an interface for users to select the desired program.

[0655] Step 9: Generate and customize your lesson plan

[0656] Server: Automatically generates a specific study plan based on the study program selected by the user.

[0657] Input: Data of the study program selected by the user.

[0658] Output: A customized study plan.

[0659] How it works: The server uses AI algorithms to generate a learning plan based on the learning program, customizing it to fit the user's schedule and available time.

[0660] Step 10: Managing your learning progress

[0661] Device: Shows the generated learning plan to the user and allows them to see their progress on a dashboard.

[0662] Input: Customized study plan.

[0663] Output: A dashboard display of your learning progress.

[0664] How it works: Users can check their learning plans and progress in real time through the dashboard. The device records their learning progress and sends the data to the server as needed.

[0665] Step 11: Monitor learning progress and send reminders

[0666] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[0667] Input: Learning progress data.

[0668] Output: Reminder and advice messages.

[0669] Specific operation: The server analyzes the user's learning progress and sends reminder messages and advice when progress falls behind or when goals are achieved.

[0670] Step 12: Assessment and feedback of learning outcomes

[0671] Server: Presents questionnaires and self-assessment forms to users during the learning process and collects evaluation data.

[0672] Input: Survey responses and self-assessment data about the user's learning status.

[0673] Output: Assessed learning outcomes and feedback report.

[0674] Specific operation: Based on the collected evaluation data, the server evaluates the learning outcomes, generates an appropriate feedback report, and sends it to the terminal.

[0675] Step 13: Use emotional data to adjust your learning plan

[0676] Server: Monitors emotional data during learning in real time and adjusts learning plans based on that data.

[0677] Input: Real-time emotion data.

[0678] Output: A tailored learning plan.

[0679] Specific operation: The server analyzes the emotional data and makes appropriate adjustments to the learning plan, for example, if the user is tired.

[0680] Step 14: Suggest next learning steps

[0681] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[0682] Input: Learning outcome data.

[0683] Output: Suggested next learning steps.

[0684] Specific operation: The server analyzes the learning outcome data, suggests the next learning course or program to the user, and sends this information to the terminal.

[0685] These steps enable the system to monitor users' individual learning needs and progress in real time and provide an optimal learning environment.

[0686] (Application example 2)

[0687] 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."

[0688] Conventional learning support systems select and customize learning programs based on the user's basic information and aptitude, but this alone has the problem of making it difficult to maximize the learning effect of the user. Also, the learning plan does not adequately take into account the user's emotional state and concentration level, and there is a need to optimize the learning environment, especially for security personnel who are prone to stress.

[0689] 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.

[0690] In this invention, the server includes a means for acquiring basic information about the user, a means for performing an aptitude test using a generative AI model to identify the user's learning style, a means for searching and selecting learning programs from an educational platform, an emotion recognition means for acquiring and analyzing emotion data in real time, and a means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results. This makes it possible to customize a learning plan to suit the user's emotional state and daily schedule, thereby maximizing the effectiveness of learning.

[0691] "Means of obtaining basic information about users" refers to means of collecting information such as the user's name, age, occupation, and desired learning content.

[0692] A "generative AI model" is an artificial intelligence model that analyzes a user's basic information and aptitude test data to generate optimal questions and learning programs.

[0693] "Means for conducting aptitude tests and identifying a user's learning style" refers to a means for conducting a diagnosis based on the user's basic information and finding the optimal learning method for the user (online, offline, individual, group).

[0694] "Means for searching and selecting learning programs from educational platforms" refers to means for finding and selecting appropriate educational programs based on the user's learning preferences and aptitude test results.

[0695] "Emotion recognition means for acquiring and analyzing emotional data in real time" refers to a means for collecting a user's facial expressions, voice, body movements, etc., and analyzing that data to understand the user's emotional state.

[0696] "Means for regularly monitoring users' learning progress and evaluating and providing feedback on results" refers to means for tracking users' learning status, evaluating their progress, and providing appropriate advice and reminders.

[0697] "Means for adjusting the study plan" refers to means for changing the study schedule and content based on the user's emotional data and study progress, thereby providing an optimal study environment.

[0698] This invention is a learning support system that helps users maintain their motivation to learn while continuing their daily work and life. This system acquires basic information about the user, performs aptitude diagnosis using a generative AI model, and provides an optimal learning program. It also maximizes the user's learning effect by using emotion recognition means.

[0699] Hardware and software used

[0700] Hardware:

[0701] Smart glasses (e.g., general smart glasses devices)

[0702] software:

[0703] Emotion recognition engine (e.g. Affectiva SDK)

[0704] Generative AI models (e.g., OpenAI GPT-4)

[0705] Cloud services (e.g., AWS (registered trademark))

[0706] Data analysis tools (e.g. TensorFlow)

[0707] DETAILED DESCRIPTION OF THE EMBODIMENTS

[0708] 1. Obtaining basic information

[0709] The user puts on the smart glasses and enters basic information such as name, age, occupation, and desired study subject, which is then sent to a cloud server via the smart glasses.

[0710] 2. Conducting aptitude tests

[0711] The cloud server uses a generative AI model to generate aptitude test questions based on the received basic information and sends them to the smart glasses. The user answers the questions, and the results are sent back to the server for analysis.

[0712] 3. Acquisition and Analysis of Emotion Data

[0713] The smart glasses' emotion recognition engine captures the user's facial expressions and voice in real time, and the captured emotion data is sent to a cloud server for analysis.

[0714] 4. Study Program Recommendations

[0715] The cloud server searches for the most suitable learning program from the educational platform based on the aptitude test results and emotional data, scores the search results, and presents the most suitable program to the user through the smart glasses.

[0716] 5. Create and customize your study plan

[0717] Based on the user's selected learning program, the cloud server generates an optimal learning plan, which is customized to fit the user's daily schedule and displayed on the smart glasses dashboard.

[0718] 6. Progress monitoring and feedback

[0719] The cloud server periodically monitors the user's learning progress and sends reminders and advice to the smart glasses as needed. It also adjusts learning plans and provides feedback based on emotional data.

[0720] Specific examples

[0721] A 30-year-old security professional wants to learn about a new intrusion detection system.

[0722] 1. Enter basic information:

[0723] Users enter their name, age, occupation, and desired learning content into the smart glasses.

[0724] "Name: Mr. A, Age: 30, Occupation: Security Personnel, Learning Content: Latest Intrusion Detection Systems"

[0725] 2. Conducting aptitude tests:

[0726] The server uses a generative AI model to generate aptitude test questions.

[0727] "User information: {name: "Mr. A", age: 30, role: "Security Personnel", trainingRequest: "Latest Intrusion Detection System"}"

[0728] 3. Acquiring and analyzing emotion data:

[0729] The smart glasses collect the user's facial expressions and voice in real time and send them to a server.

[0730] 4. Study Program Recommendation:

[0731] The server selects the optimal learning program based on the aptitude test results and emotional data.

[0732] "User aptitude test results: {answers: [...]}"

[0733] 5. Create and customize your learning plan:

[0734] The server creates a study plan tailored to the user's schedule and displays it on the smart glasses.

[0735] 6. Progress monitoring and feedback:

[0736] The server adjusts the learning plan and provides feedback based on progress and emotional data.

[0737] These details allow security personnel to effectively learn the latest technologies while continuing to work.

[0738] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0739] Step 1:

[0740] Enter basic information

[0741] Input: The user enters their name, age, occupation, and desired study content through the smart glasses.

[0742] Processing: The smart glasses send the input information to the server.

[0743] Output: The server receives the user's basic information and stores it in a database.

[0744] Step 2:

[0745] Preparation for the aptitude test

[0746] Input: The user's basic information received by the server.

[0747] Processing: The server sends a prompt to the generative AI model to generate questions for the aptitude test.

[0748] Output: The server sends the generated question list to the smart glasses.

[0749] Specific operation: A prompt sentence is generated: "User information: {name: "Mr. A", age: 30, role: "Security Personnel", trainingRequest: "Latest Intrusion Detection System"}", and the AI ​​model generates questions for aptitude assessment.

[0750] Step 3:

[0751] Conducting aptitude tests

[0752] Input: A list of questions displayed on smart glasses.

[0753] Processing: The user answers the questions and sends the answers to the server via the smart glasses.

[0754] Output: The server analyzes the response data and generates the user's aptitude test results.

[0755] Specific operation: The generative AI model analyzes the user's answers and generates the data "User aptitude test results: {answers: [...]}".

[0756] Step 4:

[0757] Acquiring emotion data

[0758] Input: Real-time facial and voice data of the user.

[0759] Processing: The smart glasses' emotion recognition engine analyzes these data and obtains the emotional state in real time.

[0760] Output: The emotion data is sent to the server and stored in a database.

[0761] How it works: The smart glasses capture the user's facial expressions and voice in real time, and the emotion recognition engine analyzes them to generate emotional data.

[0762] Step 5:

[0763] Study program recommendations

[0764] Input: Aptitude test results and user emotion data.

[0765] Processing: The server uses the generative AI model to find and score the optimal learning program.

[0766] Output: The server sends the optimal learning program to the smart glasses.

[0767] Specific operation: The data "User's aptitude test results: {answers: [...]}" is input into the AI ​​model, and the optimal learning program is selected.

[0768] Step 6:

[0769] Create and customize a lesson plan

[0770] Input: The study program selected by the user.

[0771] Processing: The server creates and customizes a study plan based on the user's life schedule.

[0772] Output: The generated learning plan is displayed on the dashboard of the smart glasses.

[0773] What it does: Uses a generative AI model to tailor a learning plan based on the data "User's schedule: {...}".

[0774] Step 7:

[0775] Progress monitoring and feedback

[0776] Input: User's learning progress and emotion data.

[0777] Processing: The server analyzes the progress data and emotion data in an integrated manner and generates reminders and advice.

[0778] Output: Feedback and reminders are sent to the smart glasses.

[0779] How it works: Using TensorFlow, it analyzes progress data and emotion data to generate optimal feedback. "User progress data: {...}, emotion data: {...}" are combined to generate a reminder.

[0780] 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.

[0781] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0782] 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.

[0783] [Second embodiment]

[0784] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0785] 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.

[0786] 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).

[0787] 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.

[0788] 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.

[0789] 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).

[0790] 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. 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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."

[0796] The present invention is a system that provides an optimal learning environment, motivating users to "study" while continuing their daily work and other activities. This system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. Specific embodiments of this system are described below.

[0797] User registration and aptitude test

[0798] 1. Enter basic information

[0799] Terminal: The user enters basic information such as name, age, occupation, and desired study content.

[0800] Terminal: The entered information is sent to the server.

[0801] 2. Conducting aptitude tests

[0802] Server: Uses generative AI to perform aptitude diagnosis based on the user's basic information.

[0803] Server: Generates questions for the user to diagnose their learning style and sends them to the terminal.

[0804] User: Answers the aptitude test questions and submits the answers.

[0805] Server: Analyzes the responses and identifies the user's learning style (online / offline, individual / group).

[0806] Study program recommendation and application

[0807] 3. Searching for and selecting a study program

[0808] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[0809] Server: Scores the search results and selects the best program.

[0810] Server: Sends detailed information about the selected learning program to the terminal.

[0811] 4. Presentation of the study program

[0812] Device: Displays a list of selected learning programs to the user and provides detailed information about each program (content, duration, cost, etc.).

[0813] User: Choose the program that you think is best.

[0814] Planning and managing your study plan

[0815] 5. Generate a learning plan

[0816] Server: Automatically generates a specific study plan based on the study program selected by the user.

[0817] Server: Customize a plan to fit your schedule, for example, scheduling a six-week online course around your work schedule.

[0818] 6. Managing learning progress

[0819] Device: Displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[0820] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[0821] Assessment and feedback of learning outcomes

[0822] 7. Periodic evaluation

[0823] Device: Presents users with surveys and self-assessment forms as they progress through the learning process.

[0824] Users: Complete and submit surveys and self-assessment forms.

[0825] 8. Analysis of evaluation data and feedback

[0826] Server: Analyzes assessment data and evaluates learning outcomes.

[0827] Server: Generates feedback and performance reports for users and sends them to the terminal.

[0828] Device: Displaying feedback and performance reports to users.

[0829] 9. Suggested next steps

[0830] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[0831] Specific examples

[0832] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[0833] 1. Enter basic information

[0834] Device: Person A enters his / her name, age, occupation, and desired study subject (marketing).

[0835] Terminal: Information is sent to the server.

[0836] 2. Conducting aptitude tests

[0837] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[0838] User: Answers the question and sends it to the server.

[0839] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[0840] 3. Study Program Recommendations

[0841] Server: Based on the results of the aptitude test, search for online marketing programs and select the most suitable program.

[0842] Server: Sends program information to the terminal.

[0843] Terminal: Display a list of programs to Person A.

[0844] 4. Planning your study plan

[0845] Server: Generates a study plan that fits Mr. A's schedule based on the program he selected.

[0846] Device: Provides a dashboard where you can view your learning plan.

[0847] 5. Learning progress management

[0848] Device: Study according to your study plan and track your progress.

[0849] 6. Performance evaluation and feedback

[0850] Terminal: Periodically present self-assessment forms and collect responses.

[0851] Server: Analyzes the evaluation data and generates feedback.

[0852] Server: Sends a result report to the terminal.

[0853] This system allows Mr. A to efficiently acquire marketing skills. It also identifies learning styles based on aptitude tests and creates customized learning plans to eliminate mismatches between what he wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[0854] The processing flow will be explained below.

[0855] Step 1:

[0856] Terminal: The user enters basic information (name, age, occupation, desired study content, etc.).

[0857] Step 2:

[0858] Terminal: Sends the entered basic information to the server.

[0859] Step 3:

[0860] Server: Based on the received basic information, the server uses a generative AI to generate aptitude test questions appropriate for the user.

[0861] Step 4:

[0862] Server: Sends the generated aptitude test questions to the terminal.

[0863] Step 5:

[0864] Terminal: Presents the user with aptitude test questions.

[0865] Step 6:

[0866] User: Answers the aptitude test questions.

[0867] Step 7:

[0868] Terminal: Sends the user's answer to the server.

[0869] Step 8:

[0870] Server: Analyzes the aptitude test answers and identifies the user's learning style (online / offline, individual / group).

[0871] Step 9:

[0872] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[0873] Step 10:

[0874] Server: Scores the learning programs in the search results and selects the most suitable program.

[0875] Step 11:

[0876] Server: Sends information about the selected learning program (content, duration, cost, etc.) to the terminal.

[0877] Step 12:

[0878] Terminal: Shows the user a list of learning programs.

[0879] Step 13:

[0880] User: Select the most suitable learning program from the displayed list.

[0881] Step 14:

[0882] Terminal: Sends the user's selection to the server.

[0883] Step 15:

[0884] Server: Automatically generates a personalized learning plan based on user selections.

[0885] Step 16:

[0886] Server: Customizes learning plans to fit the user's schedule.

[0887] Step 17:

[0888] Server: Sends the generated learning plan to the device.

[0889] Step 18:

[0890] Device: Shows users their learning plan and provides a dashboard where they can track their progress.

[0891] Step 19:

[0892] User: Proceed with learning based on the generated learning plan.

[0893] Step 20:

[0894] Device: Sends learning progress to the server.

[0895] Step 21:

[0896] Server: Regularly monitors learning progress and generates reminders and advice as needed.

[0897] Step 22:

[0898] Server: Sends reminders and advice to the device.

[0899] Step 23:

[0900] Device: Display reminders and advice to the user.

[0901] Step 24:

[0902] Terminal: Periodically present users with surveys and self-assessment forms.

[0903] Step 25:

[0904] Users: Complete surveys and self-assessment forms.

[0905] Step 26:

[0906] Terminal: Sends the user's answer to the server.

[0907] Step 27:

[0908] Server: Analyzes the assessment data and evaluates the user's learning outcomes.

[0909] Step 28:

[0910] Server: Sends generated feedback and performance reports to the device.

[0911] Step 29:

[0912] Device: Shows feedback and performance reports to users.

[0913] Step 30:

[0914] Server: Based on the user's learning outcomes, suggests the next learning step or a new learning program.

[0915] Example 1

[0916] 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."

[0917] In today's world, it is difficult for users to find and continue learning the optimal learning program effectively and efficiently. Customization to fit the user's schedule and learning style is particularly important, but no system provides such a service. Therefore, a system is needed that can select the optimal learning program based on the results of a unique aptitude test and the user's individual needs, generate a specific learning plan, and track learning progress.

[0918] 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.

[0919] In this invention, the server includes means for acquiring basic information about the user, means for conducting an aptitude test using a generation AI based on the basic information and identifying the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the results of the aptitude test and the user's learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results, and means for suggesting the next content to be learned and new learning programs based on the user's basic information, learning style, and progress. This makes it possible to provide optimal learning programs that meet the individual needs of each user and provide continuous learning support.

[0920] "Basic user information" refers to basic data about the user, such as name, age, occupation, and desired learning content.

[0921] "Generative AI" is an artificial intelligence technology that uses natural language generation models to generate text and analyze data.

[0922] "Aptitude testing" is the process of using generative AI based on a user's basic information to identify the learning style and method that best suits the user.

[0923] "Learning style" refers to the learning format or method that a user finds most suitable (e.g., online learning, face-to-face classes, individual learning, group learning).

[0924] "Educational platform" is a general term for online services that provide educational services and learning programs available to users.

[0925] "Program of Study" means a series of lectures or courses designed to impart specific skills or knowledge.

[0926] A "study plan" is a daily study schedule created based on a study program selected by the user.

[0927] "Study progress" is a status that indicates how far a user has progressed according to their study plan.

[0928] "Result evaluation and feedback" is the process of analyzing the user's learning effectiveness and providing advice on areas for improvement and future learning based on the results.

[0929] "Suggesting next learning content or new learning programs" means recommending the next learning content or program that is best suited to further development based on the user's current learning outcomes.

[0930] The present invention is a system that provides users with an optimal learning environment. The system acquires basic information about the user, uses generative AI to conduct an aptitude test and identify their learning style. It then searches for and selects an appropriate learning program from an educational platform based on the user's learning preferences, and then generates and provides a customized learning plan for the user. It monitors learning progress, evaluates and provides feedback on results, and suggests next steps in learning.

[0931] Specifically, the system is constructed using the following hardware and software.

[0932] Hardware:

[0933] 1. Terminal: A device that accepts and displays user input (e.g., PC, smartphone, tablet)

[0934] 2. Server: A computer system that stores and processes data

[0935] software:

[0936] 1. Use a generative AI model (e.g., OpenAI's GPT-4)

[0937] 2. Program search function using educational platform APIs

[0938] 3. Aptitude test and learning progress evaluation function using data analysis algorithms

[0939] When a user first uses the system, they enter basic information through their terminal, such as their name, age, occupation, and desired course of study, which is then sent to the server via an HTTP POST request.

[0940] The server uses a generative AI to generate aptitude test questions and sends them to the device. The user answers the questions displayed on the device and sends the answers back to the server. The server then identifies the user's learning style based on the aptitude test results. For example, it determines whether online learning or face-to-face classes are more suitable.

[0941] Next, the server uses the API of educational platforms (e.g., Coursera, Udemy) to search for learning programs based on the user's learning preferences. The search results are scored and the most suitable program is selected. Information about the selected learning program is sent to the device and presented to the user.

[0942] After the user selects the most suitable learning program, the server generates a learning plan based on that program. This plan is customized to fit the user's schedule. The generated learning plan is displayed on a dashboard on the device, allowing the user to check their learning progress.

[0943] The server periodically monitors the user's learning progress and sends reminders and advice. It also periodically generates feedback and achievement reports and sends them to the device. The user can refer to this feedback to progress with their learning.

[0944] Finally, the system will suggest the next learning content or new learning program based on the user's learning outcomes, allowing users to efficiently acquire skills in a learning environment that is always optimal for them.

[0945] Example prompt sentence:

[0946] "I'm a 30-year-old office worker who wants to learn marketing skills. I'd like an aptitude test and a study plan created."

[0947] This system allows users to find the best learning program for them and progress through their studies effectively and efficiently.

[0948] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] Users enter basic information such as their name, age, occupation, and desired study content into the terminal.

[0951] Input: User's basic information (name, age, occupation, desired study content)

[0952] Output: Basic information sent to the server (e.g., JSON format)

[0953] Specific operation: When the user enters the required information into the form and clicks the submit button, the device sends this data to the server as an HTTP POST request.

[0954] Step 2:

[0955] The server uses a generative AI model to generate aptitude questions based on the user's basic information.

[0956] Input: User basic information

[0957] Output: Aptitude test questions (question data output by the generative AI model)

[0958] Specific operation: The server inputs basic information as a prompt to the generative AI model and sends a request saying, "Please generate aptitude test questions based on the user's basic information." The generative AI model then generates aptitude test questions.

[0959] Step 3:

[0960] The server transmits the generated aptitude test questions to the terminal.

[0961] Input: Aptitude test question

[0962] Output: Aptitude test questions displayed on the device (JSON format)

[0963] Specific operation: The server converts the question received from the generative AI model into an appropriate format and sends it to the device, which then displays the question on the screen.

[0964] Step 4:

[0965] The user answers questions for the aptitude test displayed on the terminal and sends the answers from the terminal to the server.

[0966] Input: User's answer

[0967] Output: Response data (JSON format)

[0968] Specific operation: When the user enters an answer to a question on the device screen and presses the send answer button, the device sends this data to the server as an HTTP POST request.

[0969] Step 5:

[0970] The server analyzes the user's responses and identifies the user's learning style.

[0971] Input: User response data

[0972] Output: Learning style (analysis results)

[0973] How it works: The server uses machine learning algorithms to analyze the response data and identify learning styles, such as online learning formats or individualized learning formats.

[0974] Step 6:

[0975] The server searches and selects relevant learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[0976] Input: Aptitude test results, user learning preferences

[0977] Output: List of study programs (with scores)

[0978] Specific operation: The server uses the educational platform's API to search for learning programs that meet the criteria, evaluates the results using a scoring algorithm, and selects the most suitable program.

[0979] Step 7:

[0980] The server sends detailed information about the selected learning program to the terminal.

[0981] Input: List of study programs

[0982] Output: A list of learning programs displayed on the device (JSON format)

[0983] Specific operation: The server obtains detailed information about the selected learning program and sends it to the terminal, which has a display interface and presents it to the user in list form.

[0984] Step 8:

[0985] The user selects the program they think is most suitable from a list of study programs displayed on the terminal.

[0986] Input: Select a study program

[0987] Output: Data for the selected study program

[0988] Specific operation: The user selects the most suitable learning program from the list and presses the selection button, and information about the selected program is sent to the server.

[0989] Step 9:

[0990] The server automatically generates a specific study plan based on the study program selected by the user.

[0991] Input: Data for the selected study program

[0992] Output: Learning plan data

[0993] Specific operation: The server executes a script to generate a learning plan that fits the user's schedule based on the content of the selected learning program.

[0994] Step 10:

[0995] The server transmits the generated study plan to the terminal.

[0996] Input: Learning plan data

[0997] Output: The lesson plan displayed on the device

[0998] Specific operation: The server sends the generated learning plan to the device, which displays it on the device's dashboard.

[0999] Step 11:

[1000] The device displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[1001] Input: Learning plan data

[1002] Output: Learning plan and progress displayed on the dashboard

[1003] Specific operation: The device analyzes the learning plan received from the server and creates a dashboard so that the user can check their progress.

[1004] Step 12:

[1005] The server periodically monitors the user's learning progress and sends reminders and advice.

[1006] Input: Learning progress data

[1007] Output: Reminders and advice

[1008] Specific operation: The server continuously collects the user's progress data and automatically generates reminders and advice based on the progress and sends them to the device.

[1009] Step 13:

[1010] The device presents users with surveys and self-assessment forms as they progress through the learning process.

[1011] Input: Evaluation request

[1012] Output: User self-assessment data

[1013] Specific operation: The device periodically displays evaluation forms and surveys to the user and accepts input.

[1014] Step 14:

[1015] The user answers the questionnaire and self-evaluation form and sends them to the server from the terminal.

[1016] Input: Self-assessment answers

[1017] Output: Response data sent to the server

[1018] Specific operation: When the user answers the questions in the self-assessment form or survey and presses the send button, the device sends the answer data to the server.

[1019] Step 15:

[1020] The server analyzes the assessment data and evaluates the learning outcomes.

[1021] Input: Self-assessment response data

[1022] Output: Analysis results (learning outcome evaluation)

[1023] Specific operation: The server uses machine learning algorithms to analyze the self-assessment data and generate a report assessing learning outcomes.

[1024] Step 16:

[1025] The server generates feedback and performance reports for the user and sends them to the terminal.

[1026] Input: Analysis results

[1027] Output: Feedback and performance reports sent to your device

[1028] Specific operation: The server sends the generated feedback and performance report to the terminal, which then displays it to the user.

[1029] Step 17:

[1030] The device displays feedback and performance reports to the user.

[1031] Input: Feedback and performance reports

[1032] Output: Displayed feedback and performance report

[1033] Specific operation: The device displays the received feedback and performance report on the screen.

[1034] Step 18:

[1035] Based on the user's learning outcomes, the server suggests what to learn next and new learning programs.

[1036] Input: Learning outcome data

[1037] Output: Next learning program suggestion

[1038] Specific operation: The server generates new learning content and programs based on the user's performance data and sends suggestions for the next step to the terminal.

[1039] (Application example 1)

[1040] 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."

[1041] Conventional learning support systems have difficulty providing optimal learning programs tailored to individual users' learning styles and life schedules, and do not adequately manage learning progress or provide appropriate evaluations and feedback. Furthermore, they lacked reminder and advice functions that utilize smart devices, making it difficult to effectively support users' continuity of learning.

[1042] 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.

[1043] In this invention, the server includes means for acquiring basic information about the user, means for performing an aptitude test using a generation AI to identify the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for sending reminders and advice to the user using a smart device and managing and displaying learning progress, means for periodically monitoring the user's learning progress and evaluating and providing feedback on results, and means for using a content distribution service to push notifications of optimal learning content to the user. This enables the provision of optimal learning programs tailored to the user's individual learning style, as well as learning progress management and effective feedback.

[1044] "Basic information" refers to basic data about a person, such as the user's name, age, occupation, and desired learning.

[1045] "Generative AI" is a technology that uses artificial intelligence to analyze data and diagnose users' characteristics and aptitudes.

[1046] "Aptitude test" is a diagnostic process that evaluates a user's learning style and aptitude based on their basic information and response data.

[1047] "Educational platform" refers to a platform or system that provides online or offline learning programs.

[1048] A "program of study" is a series of educational courses or materials designed to develop specific skills or knowledge.

[1049] A "study plan" is a specific study schedule or progress plan that is set to fit the user's daily schedule.

[1050] A "smart device" is a portable electronic device that is connected to a network, such as a smartphone, tablet, or smartwatch.

[1051] "Reminders" is a feature that sends users notifications about specific dates and times or tasks.

[1052] "Advice" refers to providing users with advice and information about their learning progress and areas for improvement.

[1053] "Learning Progress" is the status or data that indicates how far a user has progressed in a learning program.

[1054] "Monitoring" means the act of regularly and continuously observing and recording a user's learning progress.

[1055] "Outcome assessment" is the process of evaluating users' learning outcomes and progress and providing feedback.

[1056] "Feedback" is information that communicates to users the results of evaluations of their learning outcomes and progress, as well as areas for improvement.

[1057] A "content distribution service" is a service that provides users with various content such as educational and entertainment content via the Internet.

[1058] "Push notifications" are a feature that sends information and notifications to smart devices in real time.

[1059] This invention relates to a learning support system that allows users to study efficiently according to their daily schedule. This system acquires basic information about the user, performs aptitude tests using a generative AI, selects an appropriate learning program, and creates a learning plan and manages progress.

[1060] Overall overview

[1061] The system mainly consists of a server and a smart device (such as a smartphone). The server uses generative AI to diagnose the user's aptitude and select a program, and sends reminders and advice to the smart device to manage learning progress.

[1062] Hardware and software used

[1063] Hardware:

[1064] Smart devices (e.g. smartphones, tablets)

[1065] server

[1066] software:

[1067] Smart device side: Native application using React Native etc.

[1068] Server side: Python, Flask (web framework), generative AI (e.g., OpenAI's GPT-4)

[1069] Processing flow

[1070] The server first obtains the user's basic information. The user enters their name, age, occupation, and desired learning content through an application on their smart device, which is then sent to the server. The server then uses generative AI to generate the basic information and a list of questions for aptitude assessment, which are then sent to the smart device. The smart device then presents these questions to the user, collects their answers, and sends them to the server. The server then analyzes the response data to identify the user's learning style.

[1071] Next, the server searches for and selects an appropriate learning program from the educational platform based on the aptitude test results. This information is sent to the smart device, which presents the user with a list of learning programs and detailed information. Once the user selects the desired program, the server generates an automatic learning plan that takes into account the user's daily schedule and displays it on the smart device.

[1072] The server also automatically sends reminders and advice to smart devices, and manages and displays learning progress. As users progress through their learning, the server periodically monitors their progress, evaluates their progress, and provides feedback. Furthermore, the server pushes the most appropriate learning content to users via a content distribution service.

[1073] Specific examples

[1074] For example, if a 40-year-old professional wants to learn digital marketing skills, the system works as follows: The user enters basic information into the smartphone app and sends it to the server. The generation AI processes the information, conducts an aptitude test, and identifies the user's optimal learning style as "online personalized learning." The server then searches for suitable online programs related to digital marketing and presents them to the user. After the user selects an appropriate program, the server generates a learning plan incorporating two online courses per week based on the user's schedule and displays it on the smartphone app.

[1075] In addition, periodic reminders and advice are sent via push notifications to the smartphone to manage learning progress, allowing users to continue their studies efficiently. Furthermore, surveys and self-assessment forms are provided via the smart device as needed, and the server analyzes them to evaluate results and provide feedback.

[1076] Prompt Sentence Examples

[1077] "Please answer the following questions: What is your age, what is your occupation, and what do you want to study?"

[1078] "We'll take a learning style aptitude test. Answer the following questions: Do you prefer online or offline learning?"

[1079] "Which do you find more effective: individual learning or group learning?"

[1080] "Thank you for your response. An online, tutored learning format would be ideal for you."

[1081] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1082] Step 1:

[1083] User registration and basic information acquisition

[1084] The terminal (smart device) prompts the user to enter basic information such as name, age, occupation, desired study content, etc. Once the user has completed the input and pressed the send button, the terminal sends the information to the server.

[1085] Input: Name, age, occupation, desired study content

[1086] Server Output: Save basic information

[1087] Specific operation: The terminal displays a user interface and prompts the user to enter information. After the user enters the information, the terminal sends the information to the server. The server receives the information and stores it in a database.

[1088] Step 2:

[1089] Aptitude test using generative AI

[1090] The server uses a generation AI to generate a list of aptitude test questions based on the user's basic information and sends them to the device. The user answers the questions on the device, and the answers are sent from the device to the server. The generation AI analyzes these to identify the user's learning style.

[1091] Input: Basic information, aptitude test questions

[1092] Server Output: Identifying the user's learning style

[1093] How it works: A generative AI (e.g., GPT-4) generates aptitude test questions based on the user's basic information and sends them to the device. The device displays the questions to the user, who answers them. The answer data is sent from the device to a server, where it is analyzed. As a result, the user's optimal learning style is identified.

[1094] Step 3:

[1095] Searching and selecting a study program

[1096] The server searches for and scores appropriate learning programs from educational platforms based on the aptitude test results and the user's learning preferences. The optimal program is selected and the information is sent to the device.

[1097] Input: Aptitude test results, user learning preferences

[1098] Server output: List of suitable study programs

[1099] Specific operation: The server accesses the educational platform using APIs and searches for learning programs that match the user's aptitude. The search results are scored to select the most suitable program. The selection results are sent to the device and displayed to the user.

[1100] Step 4:

[1101] Presentation and selection of study programs

[1102] The device displays a list of selected learning programs to the user and provides detailed information (content, duration, cost, etc.). The user selects the appropriate program.

[1103] Input: List of study programs

[1104] Output: User's choice of program

[1105] Specific operation: The terminal displays the details of the learning program through the user interface. The user selects the appropriate program and sends the selection result to the server.

[1106] Step 5:

[1107] Generate and customize a learning plan

[1108] The server automatically generates a specific study plan based on the user's selected study program and taking into account the user's daily schedule. The generated study plan is sent to the terminal and displayed to the user.

[1109] Input: User-selected learning program, user's daily schedule

[1110] Server output: Generated lesson plan

[1111] Specific operation: The server automatically generates a learning program schedule based on the user's schedule. The generated learning plan is sent to the device and displayed to the user.

[1112] Step 6:

[1113] Track your progress and receive reminders

[1114] The server manages the user's learning progress and periodically sends reminders and advice to the smart device, which then displays these notifications to the user.

[1115] Input: User's learning progress data

[1116] Server output: reminder and advice notifications

[1117] Specific operation: The server monitors the user's learning progress and periodically generates and sends reminders and advice to the device, which then displays these notifications to the user.

[1118] Step 7:

[1119] Regular assessment and feedback of learning outcomes

[1120] The server periodically evaluates the user's learning progress, generates feedback, and sends it to the device, where the user can view the feedback and progress report.

[1121] Input: User learning progress data, survey data

[1122] Server output: feedback and performance reports

[1123] Specific operation: The server analyzes the learning progress data and evaluates the user's performance. Based on the evaluation results, it generates feedback and performance reports and sends them to the device. The device displays them to the user.

[1124] Step 8:

[1125] Push notifications for the best learning content

[1126] Using the content delivery service, the server pushes the most suitable learning content to the user's smart device, allowing the user to receive new learning content.

[1127] Input: User's learning history, aptitude test results

[1128] Server output: Pushed learning content

[1129] Specific operation: The server selects new learning content based on the user's learning history and aptitude test results through the content distribution service and pushes it to the smart device, which then displays the new learning content to the user.

[1130] 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.

[1131] This invention is a system that provides an optimal learning environment, motivating users to "study" while continuing their daily work and other activities. This system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. In addition, by using an emotion engine that recognizes the user's emotions, the accuracy and efficiency of learning are further improved.

[1132] User registration and aptitude test

[1133] 1. Enter basic information

[1134] Terminal: The user enters basic information such as name, age, occupation, and desired study content.

[1135] Terminal: The entered information is sent to the server.

[1136] 2. Conducting aptitude tests

[1137] Server: Uses generative AI to perform aptitude diagnosis based on the user's basic information.

[1138] Server: Generates questions for the user to diagnose their learning style and sends them to the terminal.

[1139] User: Answers the aptitude test questions and submits the answers.

[1140] Server: Analyzes the responses and identifies the user's learning style (online / offline, individual / group).

[1141] Emotion recognition by emotion engine

[1142] 3. Acquiring Emotion Data

[1143] Device: Using the emotion engine, emotional data such as the user's facial expressions, voice, and body movements is acquired.

[1144] Terminal: Sends the acquired emotion data to the server.

[1145] 4. Emotion Data Analysis

[1146] Server: Analyzes emotional data and understands the user's emotional state in real time.

[1147] Server: Improve the accuracy of aptitude tests based on emotional data.

[1148] Study program recommendation and application

[1149] 5. Searching for and selecting a study program

[1150] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[1151] Server: Scores the search results and selects the best program.

[1152] Server: Sends detailed information about the selected learning program to the terminal.

[1153] 6. Presentation of the study program

[1154] Device: Displays a list of selected learning programs to the user and provides detailed information about each program (content, duration, cost, etc.).

[1155] User: Choose the program that you think is best.

[1156] Planning and managing your study plan

[1157] 7. Generate a learning plan

[1158] Server: Automatically generates a specific study plan based on the study program selected by the user.

[1159] Server: Customizes the plan to fit the user's schedule, for example, scheduling classes so they can be taken during work breaks.

[1160] 8. Managing learning progress

[1161] Device: Displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[1162] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[1163] 9. Use of Emotional Data

[1164] Server: Monitors emotional data during learning in real time and adjusts learning plans based on that data.

[1165] Server: Analyzes the user's emotional data and generates appropriate feedback and advice.

[1166] Assessment and feedback of learning outcomes

[1167] 10. Periodic evaluation

[1168] Device: Presents users with surveys and self-assessment forms as they progress through the learning process.

[1169] Users: Complete and submit surveys and self-assessment forms.

[1170] 11. Analysis of evaluation data and feedback

[1171] Server: Analyzes assessment data and evaluates learning outcomes.

[1172] Server: Generates feedback and performance reports for users and sends them to the terminal.

[1173] Device: Displaying feedback and performance reports to users.

[1174] 12. Suggested next steps for learning

[1175] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[1176] Specific examples

[1177] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[1178] 1. Enter basic information

[1179] Device: Person A enters his / her name, age, occupation, and desired study subject (marketing).

[1180] Terminal: Information is sent to the server.

[1181] 2. Conducting aptitude tests

[1182] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[1183] User: Answers the question and sends it to the server.

[1184] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[1185] 3. Acquiring Emotion Data

[1186] Terminal: Uses the emotion engine to record A's facial expressions and voice, and sends the emotion data to the server.

[1187] 4. Emotion Data Analysis

[1188] Server: Analyzes A's emotional data and improves the accuracy of the learning style diagnosis results.

[1189] 5. Study Program Recommendations

[1190] Server: Searches for marketing-related online programs based on aptitude tests and emotional data, and selects the most suitable program.

[1191] Server: Sends program information to the terminal.

[1192] Terminal: Display a list of programs to Person A.

[1193] 6. Planning your study plan

[1194] Server: Generates a study plan that fits Mr. A's schedule based on the program he selected.

[1195] Device: Provides a dashboard where you can view your learning plan.

[1196] 7. Learning progress management

[1197] Device: Study according to your study plan and track your progress.

[1198] Server: Monitors emotional data in real time and sends reminders to adjust learning plans.

[1199] 8. Performance evaluation and feedback

[1200] Terminal: Periodically present self-assessment forms and collect responses.

[1201] Server: Analyzes the rating and sentiment data and generates feedback.

[1202] Server: Sends a result report to the terminal.

[1203] This system allows A to efficiently acquire marketing skills. Real-time feedback and progress adjustments from the emotion engine also eliminate mismatches between what A wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[1204] The processing flow will be explained below.

[1205] Step 1:

[1206] Terminal: The user enters basic information (name, age, occupation, desired study content, etc.).

[1207] Step 2:

[1208] Terminal: Sends the entered basic information to the server.

[1209] Step 3:

[1210] Server: Based on the received basic information, the server uses a generative AI to generate aptitude test questions appropriate for the user.

[1211] Step 4:

[1212] Server: Sends the generated aptitude test questions to the terminal.

[1213] Step 5:

[1214] Terminal: Presents the user with aptitude test questions.

[1215] Step 6:

[1216] User: Answers the aptitude test questions.

[1217] Step 7:

[1218] Terminal: Sends the user's answer to the server.

[1219] Step 8:

[1220] Server: Analyzes the aptitude test answers and identifies the user's learning style (online / offline, individual / group).

[1221] Step 9:

[1222] Terminal: Uses an emotion engine to acquire user emotion data (e.g., facial expressions, voice, and movements).

[1223] Step 10:

[1224] Terminal: Sends the acquired emotion data to the server.

[1225] Step 11:

[1226] Server: Analyzes emotional data and understands the user's emotional state.

[1227] Step 12:

[1228] Server: Considers emotional state to improve the accuracy of aptitude test results.

[1229] Step 13:

[1230] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[1231] Step 14:

[1232] Server: Scores the learning programs from the search results and selects the most suitable program, taking into account sentiment data.

[1233] Step 15:

[1234] Server: Sends information about the selected learning program (content, duration, cost, etc.) to the terminal.

[1235] Step 16:

[1236] Terminal: Shows the user a list of learning programs.

[1237] Step 17:

[1238] User: Select the most suitable learning program from the displayed list.

[1239] Step 18:

[1240] Terminal: Sends the user's selection to the server.

[1241] Step 19:

[1242] Server: Automatically generates a personalized learning plan based on user selections.

[1243] Step 20:

[1244] Server: Customize plans to fit your schedule.

[1245] Step 21:

[1246] Server: Sends the generated learning plan to the device.

[1247] Step 22:

[1248] Device: Shows users their learning plan and provides a dashboard where they can track their progress.

[1249] Step 23:

[1250] User: Proceed with learning based on the generated learning plan.

[1251] Step 24:

[1252] Device: Sends learning progress to the server.

[1253] Step 25:

[1254] Server: Regularly monitors learning progress and generates reminders and advice as needed.

[1255] Step 26:

[1256] Server: Sends reminders and advice to the device.

[1257] Step 27:

[1258] Device: Display reminders and advice to the user.

[1259] Step 28:

[1260] Server: Monitors emotional data in real time and adjusts learning plans as needed.

[1261] Step 29:

[1262] Terminal: Periodically present users with surveys and self-assessment forms.

[1263] Step 30:

[1264] Users: Complete surveys and self-assessment forms.

[1265] Step 31:

[1266] Terminal: Sends the user's answer to the server.

[1267] Step 32:

[1268] Server: Analyzes the assessment data and evaluates the user's learning outcomes.

[1269] Step 33:

[1270] Server: Sends generated feedback and performance reports to the device.

[1271] Step 34:

[1272] Device: Shows feedback and performance reports to users.

[1273] Step 35:

[1274] Server: Based on the user's learning outcomes, suggests the next learning step or a new learning program.

[1275] Step 36:

[1276] Server: Sends proposed programs and feedback to the device.

[1277] Step 37:

[1278] Device: Shows the user next learning steps and feedback.

[1279] Example 2

[1280] 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."

[1281] Conventional learning systems provide a uniform learning plan without fully considering the individual learning style or emotional state of the user, which makes learning ineffective. In addition, the lack of real-time feedback based on the user's emotional state or progress can easily lead to a decline in learning motivation and stagnation of progress.

[1282] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring basic information about the user, means for performing an aptitude test using a generation AI based on the basic information and identifying the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan tailored to the user's daily schedule, means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results, means for acquiring and analyzing the user's emotional data in real time, and means for adjusting the learning plan based on the emotional data. This provides an optimal learning environment tailored to the user's individual learning style and emotional state, enabling effective learning and maintaining motivation.

[1283] "Basic user information" refers to information that indicates the individual characteristics of a user, such as name, age, occupation, and desired study content.

[1284] "Generative AI" is an artificial intelligence algorithm that performs aptitude tests, generates questions, analyzes data, and more based on user input.

[1285] Aptitude testing is the process of identifying the best learning style for a user based on basic information about them.

[1286] "Learning style" refers to the method and environment in which a user learns most effectively (online / offline, individual / group, etc.).

[1287] "Educational platform" is a general term for websites and applications that provide online learning content.

[1288] A "program of study" is a series of courses or lectures designed to teach a particular skill or knowledge.

[1289] A "life schedule" is a user's daily schedule and activity plan.

[1290] A "study plan" is a plan that indicates the specific timing and method of study based on the study program selected by the user.

[1291] "Monitoring" refers to the act of regularly observing a user's learning progress and collecting necessary data.

[1292] "Outcomes" refers to the progress or goals a user achieves through a learning program.

[1293] "Feedback" means evaluations and advice provided to users based on their learning progress and achievements.

[1294] "Emotional data" is data that indicates the emotional state of a user, obtained from facial expressions, voice, body movements, etc.

[1295] "Real-time analysis" refers to the process of analyzing emotion data as soon as it is acquired and reflecting the results.

[1296] "Adjusting a study plan" refers to the act of changing an existing study schedule or content depending on the user's emotional state and progress.

[1297] This invention is a system that provides an optimal learning environment while a user continues their daily work or other activities. The system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. It also uses an emotion engine that recognizes the user's emotions to improve the accuracy and efficiency of learning.

[1298] User registration and aptitude test

[1299] 1. Enter basic information

[1300] Device: The user enters basic information such as name, age, occupation, and desired study topic. This is done through a web form or a mobile app interface.

[1301] Terminal: The entered information is encoded and sent to the server via HTTPS. The software used here is the web server Apache or NGINX, and the backend is Node.js or Python frameworks (Django, Flask).

[1302] 2. Conducting aptitude tests

[1303] Server: The server receives the user's basic information and generates aptitude test questions using a generative AI model, which uses a general-purpose AI algorithm.

[1304] Server: The generated question is sent to the terminal in JSON format.

[1305] User: The user answers questions for the aptitude test and sends the answers from the device to the server.

[1306] Server: Analyzes the answers and uses machine learning models (e.g., using scikit-learn or TensorFlow) to identify the user’s learning style.

[1307] Emotion recognition by emotion engine

[1308] 3. Acquiring Emotion Data

[1309] Device: The device uses an emotion engine (e.g., OpenCV for facial recognition and Google Speech-to-Text API for voice analysis) to obtain emotional data such as the user's facial expressions, voice, and body movements.

[1310] Terminal: Encodes the acquired emotion data and sends it to the server.

[1311] 4. Emotion Data Analysis

[1312] Server: Analyzes emotional data and understands the user's emotional state in real time. For example, if the user is determined to be tired, the AI ​​model will adjust its learning pace accordingly.

[1313] Server: Stores the analysis results in a database (e.g., MySQL or PostgreSQL).

[1314] Study program recommendation and application

[1315] 5. Searching for and selecting a study program

[1316] Server: Based on the aptitude test results and learning preferences, the server searches for appropriate learning programs from the educational platform via API. It sends API requests using Python's Requests library, etc.

[1317] Server: Evaluates search results using a scoring algorithm and selects the most suitable program.

[1318] Server: Sends detailed information about the selected learning program to the terminal in JSON format.

[1319] 6. Presentation of the study program

[1320] Terminal: Displays a list of learning programs selected by the user. This screen is built using HTML / CSS / JavaScript.

[1321] User: Choose the program that you think is best.

[1322] Planning and managing your study plan

[1323] 7. Generate a learning plan

[1324] Server: Automatically generates a specific learning plan based on the user's chosen learning program. The plan is created using an AI algorithm.

[1325] Server: Customizes the plan to fit the user's schedule. For example, schedules the course so that it can be taken during work breaks.

[1326] 8. Managing learning progress

[1327] Terminal: Displays the generated learning plan to the user and provides a dashboard where the user can check their progress. Utilizing front-end frameworks such as React.

[1328] Server: Regularly monitors learning progress and sends reminders and motivational messages as needed. Uses Twilio or Firebase Cloud Messaging.

[1329] Assessment and feedback of learning outcomes

[1330] 9. Use of Emotional Data

[1331] Server: Monitors emotional data in real time during learning and adjusts learning plans based on that data, for example, if it determines that a break is needed after a long period of study.

[1332] Server: Analyzes user emotional data and generates appropriate feedback and advice. The AI ​​model suggests appropriate actions.

[1333] 10. Periodic evaluation

[1334] Device: Present surveys and self-assessment forms to users during the learning process. Use Google Forms or Typeform.

[1335] Users: Complete and submit surveys and self-assessment forms.

[1336] 11. Analysis of evaluation data and feedback

[1337] Server: Analyzes assessment data and evaluates learning outcomes. Machine learning algorithms are used.

[1338] Server: Generates feedback and performance reports for users and sends them to the device in JSON format.

[1339] Device: Displaying feedback and performance reports to users.

[1340] 12. Suggested next steps for learning

[1341] Server: Based on the user's learning outcomes, suggests what to study next and new learning programs. This is done using AI models.

[1342] Specific examples

[1343] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[1344] 1. Enter basic information

[1345] Device: Person A enters his / her name, age, occupation, and desired study content (marketing). For example, "Taro Tanaka, 30 years old, company employee, wants to study marketing."

[1346] Terminal: Information is sent to the server.

[1347] 2. Conducting aptitude tests

[1348] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[1349] User: Answers the question, for example, "I only have 30 minutes a day to study."

[1350] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[1351] 3. Acquiring Emotion Data

[1352] Device: Uses the emotion engine to record A's facial expressions and voice, and sends the emotion data to the server. For example, "A's facial expression looks tired."

[1353] 4. Emotion Data Analysis

[1354] Server: Analyzes A's emotional data and improves the accuracy of the learning style diagnosis results. For example, it determines that "A feels fatigued during the learning process."

[1355] 5. Study Program Recommendations

[1356] Server: Based on aptitude tests and emotional data, the server searches for online marketing programs and selects the most suitable one. For example, it selects the "Basic Marketing Course" as the most suitable one.

[1357] Server: Sends program information to the terminal. For example, "Duration: 3 months, Cost: Free."

[1358] Terminal: Display a list of programs to Person A.

[1359] 6. Planning your study plan

[1360] Server: Based on the program selected by Mr. A, a study plan is generated that fits his / her daily schedule, for example, "attend lectures from 8:00 PM to 9:00 PM every day."

[1361] Device: Provides a dashboard where you can view your learning plan.

[1362] 7. Learning progress management

[1363] Device: Study based on the study plan and record your progress. For example, it might record "I progressed three pages today."

[1364] Server: Monitors emotional data in real time, for example, "sends a reminder to take a break if you feel tired while studying."

[1365] 8. Performance evaluation and feedback

[1366] Device: Periodically present students with a self-evaluation form and ask them to answer questions such as, "Are you satisfied with what you have learned?"

[1367] Server: Analyzes the evaluation data and emotion data and generates feedback such as, "Person A is satisfied with the learning content, but there is a possibility that he or she could increase the study time a little more."

[1368] Server: Sends a performance report to the terminal. For example, the report may say, "Mr. A is very promising."

[1369] This system allows A to efficiently acquire marketing skills. Real-time feedback and progress adjustments from the emotion engine also eliminate mismatches between what A wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[1370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1371] The flow of this system's program processing

[1372] Step 1: Enter basic information

[1373] Device: The user enters basic information such as name, age, occupation, and desired study content. This is done through a web form or a mobile app interface.

[1374] Input: User information such as name, age, occupation, desired study content, etc.

[1375] Output: User basic information data in JSON format.

[1376] What happens: When a user enters information into a form and presses the Submit button, the data is encoded and sent to the server via the HTTPS protocol.

[1377] Step 2: Submit basic information and prepare for the aptitude test

[1378] Server: Receives the encoded basic information and uses the generative AI model to analyze the user's basic information and generate questions for aptitude tests.

[1379] Input: Basic user information data sent from the device.

[1380] Output: Generated aptitude test question data.

[1381] Specific operation: The server uses the generative AI model to analyze the user's basic information, generate questions for aptitude testing, and send the questions to the terminal in JSON format.

[1382] Step 3: Answer the aptitude test questions

[1383] Terminal: Displays aptitude test questions to the user, who then answers the questions.

[1384] Input: Generated aptitude test question data.

[1385] Output: User's answers to the aptitude test questions.

[1386] Specific operation: When the user enters an answer to the displayed question and presses the "Submit" button, the answer data is encoded and sent back to the server.

[1387] Step 4: Assess your aptitude and identify your learning style

[1388] Server: Analyzes user response data and uses machine learning models to identify the best learning style (online / offline, individual / group).

[1389] Input: User's answers to the aptitude test questions.

[1390] Output: Aptitude test results and identified learning style data.

[1391] How it works: The server analyzes the response data using machine learning algorithms to identify the learning style that best suits the user and stores the results in a database.

[1392] Step 5: Obtaining emotion data

[1393] Device: Using the emotion engine, emotional data such as the user's facial expressions, voice, and body movements is acquired.

[1394] Input: Real-time facial expressions, voice, and body movements of the user.

[1395] Output: Encoded emotion data.

[1396] Specific operation: Using the device's camera and microphone, emotion data is acquired using an emotion engine (e.g., OpenCV, Google Speech-to-Text API) and sent to the server.

[1397] Step 6: Analyze the sentiment data

[1398] Server: Analyzes emotional data and understands the user's emotional state in real time.

[1399] Input: Emotion data sent from the device.

[1400] Output: Parsed emotional state data.

[1401] Specific operation: Analyzes emotional data, determines whether the user is in an emotional state such as "tired," and stores the results in a database.

[1402] Step 7: Search and select a study program

[1403] Server: Based on the aptitude test results and learning preferences, searches for appropriate learning programs from educational platforms and selects the most suitable program.

[1404] Input: Aptitude test results and desired learning content.

[1405] Output: A list of scored learning programs.

[1406] Specific operation: The server sends an API request to the educational platform to obtain appropriate learning programs, evaluates them using a scoring algorithm, and selects the most suitable program.

[1407] Step 8: Present your learning program

[1408] Terminal: A list of selected learning programs is displayed to the user, and the user selects a program.

[1409] Input: Details of the selected study program.

[1410] Output: Data on the study program selected by the user.

[1411] Specific operation: Display a list of programs and detailed information on the terminal screen, and provide an interface for users to select the desired program.

[1412] Step 9: Generate and customize your lesson plan

[1413] Server: Automatically generates a specific study plan based on the study program selected by the user.

[1414] Input: Data of the study program selected by the user.

[1415] Output: A customized study plan.

[1416] How it works: The server uses AI algorithms to generate a learning plan based on the learning program, customizing it to fit the user's schedule and available time.

[1417] Step 10: Managing your learning progress

[1418] Device: Shows the generated learning plan to the user and allows them to see their progress on a dashboard.

[1419] Input: Customized study plan.

[1420] Output: A dashboard display of your learning progress.

[1421] How it works: Users can check their learning plans and progress in real time through the dashboard. The device records their learning progress and sends the data to the server as needed.

[1422] Step 11: Monitor learning progress and send reminders

[1423] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[1424] Input: Learning progress data.

[1425] Output: Reminder and advice messages.

[1426] Specific operation: The server analyzes the user's learning progress and sends reminder messages and advice when progress falls behind or when goals are achieved.

[1427] Step 12: Assessment and feedback of learning outcomes

[1428] Server: Presents questionnaires and self-assessment forms to users during the learning process and collects evaluation data.

[1429] Input: Survey responses and self-assessment data about the user's learning status.

[1430] Output: Assessed learning outcomes and feedback report.

[1431] Specific operation: Based on the collected evaluation data, the server evaluates the learning outcomes, generates an appropriate feedback report, and sends it to the terminal.

[1432] Step 13: Use emotional data to adjust your learning plan

[1433] Server: Monitors emotional data during learning in real time and adjusts learning plans based on that data.

[1434] Input: Real-time emotion data.

[1435] Output: A tailored learning plan.

[1436] Specific operation: The server analyzes the emotional data and makes appropriate adjustments to the learning plan, for example, if the user is tired.

[1437] Step 14: Suggest next learning steps

[1438] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[1439] Input: Learning outcome data.

[1440] Output: Suggested next learning steps.

[1441] Specific operation: The server analyzes the learning outcome data, suggests the next learning course or program to the user, and sends this information to the terminal.

[1442] These steps enable the system to monitor users' individual learning needs and progress in real time and provide an optimal learning environment.

[1443] (Application example 2)

[1444] 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."

[1445] Conventional learning support systems select and customize learning programs based on the user's basic information and aptitude, but this alone has the problem of making it difficult to maximize the learning effect of the user. Also, the learning plan does not adequately take into account the user's emotional state and concentration level, and there is a need to optimize the learning environment, especially for security personnel who are prone to stress.

[1446] 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.

[1447] In this invention, the server includes a means for acquiring basic information about the user, a means for performing an aptitude test using a generative AI model to identify the user's learning style, a means for searching and selecting learning programs from an educational platform, an emotion recognition means for acquiring and analyzing emotion data in real time, and a means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results. This makes it possible to customize a learning plan to suit the user's emotional state and daily schedule, thereby maximizing the effectiveness of learning.

[1448] "Means of obtaining basic information about users" refers to means of collecting information such as the user's name, age, occupation, and desired learning content.

[1449] A "generative AI model" is an artificial intelligence model that analyzes a user's basic information and aptitude test data to generate optimal questions and learning programs.

[1450] "Means for conducting aptitude tests and identifying a user's learning style" refers to a means for conducting a diagnosis based on the user's basic information and finding the optimal learning method for the user (online, offline, individual, group).

[1451] "Means for searching and selecting learning programs from educational platforms" refers to means for finding and selecting appropriate educational programs based on the user's learning preferences and aptitude test results.

[1452] "Emotion recognition means for acquiring and analyzing emotional data in real time" refers to a means for collecting a user's facial expressions, voice, body movements, etc., and analyzing that data to understand the user's emotional state.

[1453] "Means for regularly monitoring users' learning progress and evaluating and providing feedback on results" refers to means for tracking users' learning status, evaluating their progress, and providing appropriate advice and reminders.

[1454] "Means for adjusting the study plan" refers to means for changing the study schedule and content based on the user's emotional data and study progress, thereby providing an optimal study environment.

[1455] This invention is a learning support system that helps users maintain their motivation to learn while continuing their daily work and life. This system acquires basic information about the user, performs aptitude diagnosis using a generative AI model, and provides an optimal learning program. It also maximizes the user's learning effect by using emotion recognition means.

[1456] Hardware and software used

[1457] Hardware:

[1458] Smart glasses (e.g., general smart glasses devices)

[1459] software:

[1460] Emotion recognition engine (e.g. Affectiva SDK)

[1461] Generative AI models (e.g., OpenAI GPT-4)

[1462] Cloud services (e.g. AWS)

[1463] Data analysis tools (e.g. TensorFlow)

[1464] DETAILED DESCRIPTION OF THE EMBODIMENTS

[1465] 1. Obtaining basic information

[1466] The user puts on the smart glasses and enters basic information such as name, age, occupation, and desired study subject, which is then sent to a cloud server via the smart glasses.

[1467] 2. Conducting aptitude tests

[1468] The cloud server uses a generative AI model to generate aptitude test questions based on the received basic information and sends them to the smart glasses. The user answers the questions, and the results are sent back to the server for analysis.

[1469] 3. Acquisition and Analysis of Emotion Data

[1470] The smart glasses' emotion recognition engine captures the user's facial expressions and voice in real time, and the captured emotion data is sent to a cloud server for analysis.

[1471] 4. Study Program Recommendations

[1472] The cloud server searches for the most suitable learning program from the educational platform based on the aptitude test results and emotional data, scores the search results, and presents the most suitable program to the user through the smart glasses.

[1473] 5. Create and customize your study plan

[1474] Based on the user's selected learning program, the cloud server generates an optimal learning plan, which is customized to fit the user's daily schedule and displayed on the smart glasses dashboard.

[1475] 6. Progress monitoring and feedback

[1476] The cloud server periodically monitors the user's learning progress and sends reminders and advice to the smart glasses as needed. It also adjusts learning plans and provides feedback based on emotional data.

[1477] Specific examples

[1478] A 30-year-old security professional wants to learn about a new intrusion detection system.

[1479] 1. Enter basic information:

[1480] Users enter their name, age, occupation, and desired learning content into the smart glasses.

[1481] "Name: Mr. A, Age: 30, Occupation: Security Personnel, Learning Content: Latest Intrusion Detection Systems"

[1482] 2. Conducting aptitude tests:

[1483] The server uses a generative AI model to generate aptitude test questions.

[1484] "User information: {name: "Mr. A", age: 30, role: "Security Personnel", trainingRequest: "Latest Intrusion Detection System"}"

[1485] 3. Acquiring and analyzing emotion data:

[1486] The smart glasses collect the user's facial expressions and voice in real time and send them to a server.

[1487] 4. Study Program Recommendation:

[1488] The server selects the optimal learning program based on the aptitude test results and emotional data.

[1489] "User aptitude test results: {answers: [...]}"

[1490] 5. Create and customize your learning plan:

[1491] The server creates a study plan tailored to the user's schedule and displays it on the smart glasses.

[1492] 6. Progress monitoring and feedback:

[1493] The server adjusts the learning plan and provides feedback based on progress and emotional data.

[1494] These details allow security personnel to effectively learn the latest technologies while continuing to work.

[1495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1496] Step 1:

[1497] Enter basic information

[1498] Input: The user enters their name, age, occupation, and desired study content through the smart glasses.

[1499] Processing: The smart glasses send the input information to the server.

[1500] Output: The server receives the user's basic information and stores it in a database.

[1501] Step 2:

[1502] Preparation for the aptitude test

[1503] Input: The user's basic information received by the server.

[1504] Processing: The server sends a prompt to the generative AI model to generate questions for the aptitude test.

[1505] Output: The server sends the generated question list to the smart glasses.

[1506] Specific operation: A prompt sentence is generated: "User information: {name: "Mr. A", age: 30, role: "Security Personnel", trainingRequest: "Latest Intrusion Detection System"}", and the AI ​​model generates questions for aptitude assessment.

[1507] Step 3:

[1508] Conducting aptitude tests

[1509] Input: A list of questions displayed on smart glasses.

[1510] Processing: The user answers the questions and sends the answers to the server via the smart glasses.

[1511] Output: The server analyzes the response data and generates the user's aptitude test results.

[1512] Specific operation: The generative AI model analyzes the user's answers and generates the data "User aptitude test results: {answers: [...]}".

[1513] Step 4:

[1514] Acquiring emotion data

[1515] Input: Real-time facial and voice data of the user.

[1516] Processing: The smart glasses' emotion recognition engine analyzes these data and obtains the emotional state in real time.

[1517] Output: The emotion data is sent to the server and stored in a database.

[1518] How it works: The smart glasses capture the user's facial expressions and voice in real time, and the emotion recognition engine analyzes them to generate emotional data.

[1519] Step 5:

[1520] Study program recommendations

[1521] Input: Aptitude test results and user emotion data.

[1522] Processing: The server uses the generative AI model to find and score the optimal learning program.

[1523] Output: The server sends the optimal learning program to the smart glasses.

[1524] Specific operation: The data "User's aptitude test results: {answers: [...]}" is input into the AI ​​model, and the optimal learning program is selected.

[1525] Step 6:

[1526] Create and customize a lesson plan

[1527] Input: The study program selected by the user.

[1528] Processing: The server creates and customizes a study plan based on the user's life schedule.

[1529] Output: The generated learning plan is displayed on the dashboard of the smart glasses.

[1530] What it does: Uses a generative AI model to tailor a learning plan based on the data "User's schedule: {...}".

[1531] Step 7:

[1532] Progress monitoring and feedback

[1533] Input: User's learning progress and emotion data.

[1534] Processing: The server analyzes the progress data and emotion data in an integrated manner and generates reminders and advice.

[1535] Output: Feedback and reminders are sent to the smart glasses.

[1536] How it works: Using TensorFlow, it analyzes progress data and emotion data to generate optimal feedback. "User progress data: {...}, emotion data: {...}" are combined to generate a reminder.

[1537] 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.

[1538] 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.

[1539] 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.

[1540] [Third embodiment]

[1541] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1542] 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.

[1543] 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).

[1544] 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.

[1545] 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.

[1546] 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).

[1547] 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. 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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."

[1553] The present invention is a system that provides an optimal learning environment, motivating users to "study" while continuing their daily work and other activities. This system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. Specific embodiments of this system are described below.

[1554] User registration and aptitude test

[1555] 1. Enter basic information

[1556] Terminal: The user enters basic information such as name, age, occupation, and desired study content.

[1557] Terminal: The entered information is sent to the server.

[1558] 2. Conducting aptitude tests

[1559] Server: Uses generative AI to perform aptitude diagnosis based on the user's basic information.

[1560] Server: Generates questions for the user to diagnose their learning style and sends them to the terminal.

[1561] User: Answers the aptitude test questions and submits the answers.

[1562] Server: Analyzes the responses and identifies the user's learning style (online / offline, individual / group).

[1563] Study program recommendation and application

[1564] 3. Searching for and selecting a study program

[1565] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[1566] Server: Scores the search results and selects the best program.

[1567] Server: Sends detailed information about the selected learning program to the terminal.

[1568] 4. Presentation of the study program

[1569] Device: Displays a list of selected learning programs to the user and provides detailed information about each program (content, duration, cost, etc.).

[1570] User: Choose the program that you think is best.

[1571] Planning and managing your study plan

[1572] 5. Generate a learning plan

[1573] Server: Automatically generates a specific study plan based on the study program selected by the user.

[1574] Server: Customize a plan to fit your schedule, for example, scheduling a six-week online course around your work schedule.

[1575] 6. Managing learning progress

[1576] Device: Displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[1577] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[1578] Assessment and feedback of learning outcomes

[1579] 7. Periodic evaluation

[1580] Device: Presents users with surveys and self-assessment forms as they progress through the learning process.

[1581] Users: Complete and submit surveys and self-assessment forms.

[1582] 8. Analysis of evaluation data and feedback

[1583] Server: Analyzes assessment data and evaluates learning outcomes.

[1584] Server: Generates feedback and performance reports for users and sends them to the terminal.

[1585] Device: Displaying feedback and performance reports to users.

[1586] 9. Suggested next steps

[1587] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[1588] Specific examples

[1589] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[1590] 1. Enter basic information

[1591] Device: Person A enters his / her name, age, occupation, and desired study subject (marketing).

[1592] Terminal: Information is sent to the server.

[1593] 2. Conducting aptitude tests

[1594] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[1595] User: Answers the question and sends it to the server.

[1596] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[1597] 3. Study Program Recommendations

[1598] Server: Based on the results of the aptitude test, search for online marketing programs and select the most suitable program.

[1599] Server: Sends program information to the terminal.

[1600] Terminal: Display a list of programs to Person A.

[1601] 4. Planning your study plan

[1602] Server: Generates a study plan that fits Mr. A's schedule based on the program he selected.

[1603] Device: Provides a dashboard where you can view your learning plan.

[1604] 5. Learning progress management

[1605] Device: Study according to your study plan and track your progress.

[1606] 6. Performance evaluation and feedback

[1607] Terminal: Periodically present self-assessment forms and collect responses.

[1608] Server: Analyzes the evaluation data and generates feedback.

[1609] Server: Sends a result report to the terminal.

[1610] This system allows Mr. A to efficiently acquire marketing skills. It also identifies learning styles based on aptitude tests and creates customized learning plans to eliminate mismatches between what he wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[1611] The processing flow will be explained below.

[1612] Step 1:

[1613] Terminal: The user enters basic information (name, age, occupation, desired study content, etc.).

[1614] Step 2:

[1615] Terminal: Sends the entered basic information to the server.

[1616] Step 3:

[1617] Server: Based on the received basic information, the server uses a generative AI to generate aptitude test questions appropriate for the user.

[1618] Step 4:

[1619] Server: Sends the generated aptitude test questions to the terminal.

[1620] Step 5:

[1621] Terminal: Presents the user with aptitude test questions.

[1622] Step 6:

[1623] User: Answers the aptitude test questions.

[1624] Step 7:

[1625] Terminal: Sends the user's answer to the server.

[1626] Step 8:

[1627] Server: Analyzes the aptitude test answers and identifies the user's learning style (online / offline, individual / group).

[1628] Step 9:

[1629] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[1630] Step 10:

[1631] Server: Scores the learning programs in the search results and selects the most suitable program.

[1632] Step 11:

[1633] Server: Sends information about the selected learning program (content, duration, cost, etc.) to the terminal.

[1634] Step 12:

[1635] Terminal: Shows the user a list of learning programs.

[1636] Step 13:

[1637] User: Select the most suitable learning program from the displayed list.

[1638] Step 14:

[1639] Terminal: Sends the user's selection to the server.

[1640] Step 15:

[1641] Server: Automatically generates a personalized learning plan based on user selections.

[1642] Step 16:

[1643] Server: Customizes learning plans to fit the user's schedule.

[1644] Step 17:

[1645] Server: Sends the generated learning plan to the device.

[1646] Step 18:

[1647] Device: Shows users their learning plan and provides a dashboard where they can track their progress.

[1648] Step 19:

[1649] User: Proceed with learning based on the generated learning plan.

[1650] Step 20:

[1651] Device: Sends learning progress to the server.

[1652] Step 21:

[1653] Server: Regularly monitors learning progress and generates reminders and advice as needed.

[1654] Step 22:

[1655] Server: Sends reminders and advice to the device.

[1656] Step 23:

[1657] Device: Display reminders and advice to the user.

[1658] Step 24:

[1659] Terminal: Periodically present users with surveys and self-assessment forms.

[1660] Step 25:

[1661] Users: Complete surveys and self-assessment forms.

[1662] Step 26:

[1663] Terminal: Sends the user's answer to the server.

[1664] Step 27:

[1665] Server: Analyzes the assessment data and evaluates the user's learning outcomes.

[1666] Step 28:

[1667] Server: Sends generated feedback and performance reports to the device.

[1668] Step 29:

[1669] Device: Shows feedback and performance reports to users.

[1670] Step 30:

[1671] Server: Based on the user's learning outcomes, suggests the next learning step or a new learning program.

[1672] Example 1

[1673] 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."

[1674] In today's world, it is difficult for users to find and continue learning the optimal learning program effectively and efficiently. Customization to fit the user's schedule and learning style is particularly important, but no system provides such a service. Therefore, a system is needed that can select the optimal learning program based on the results of a unique aptitude test and the user's individual needs, generate a specific learning plan, and track learning progress.

[1675] 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.

[1676] In this invention, the server includes means for acquiring basic information about the user, means for conducting an aptitude test using a generation AI based on the basic information and identifying the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the results of the aptitude test and the user's learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results, and means for suggesting the next content to be learned and new learning programs based on the user's basic information, learning style, and progress. This makes it possible to provide optimal learning programs that meet the individual needs of each user and provide continuous learning support.

[1677] "Basic user information" refers to basic data about the user, such as name, age, occupation, and desired learning content.

[1678] "Generative AI" is an artificial intelligence technology that uses natural language generation models to generate text and analyze data.

[1679] "Aptitude testing" is the process of using generative AI based on a user's basic information to identify the learning style and method that best suits the user.

[1680] "Learning style" refers to the learning format or method that a user finds most suitable (e.g., online learning, face-to-face classes, individual learning, group learning).

[1681] "Educational platform" is a general term for online services that provide educational services and learning programs available to users.

[1682] "Program of Study" means a series of lectures or courses designed to impart specific skills or knowledge.

[1683] A "study plan" is a daily study schedule created based on a study program selected by the user.

[1684] "Study progress" is a status that indicates how far a user has progressed according to their study plan.

[1685] "Result evaluation and feedback" is the process of analyzing the user's learning effectiveness and providing advice on areas for improvement and future learning based on the results.

[1686] "Suggesting next learning content or new learning programs" means recommending the next learning content or program that is best suited to further development based on the user's current learning outcomes.

[1687] The present invention is a system that provides users with an optimal learning environment. The system acquires basic information about the user, uses generative AI to conduct an aptitude test and identify their learning style. It then searches for and selects an appropriate learning program from an educational platform based on the user's learning preferences, and then generates and provides a customized learning plan for the user. It monitors learning progress, evaluates and provides feedback on results, and suggests next steps in learning.

[1688] Specifically, the system is constructed using the following hardware and software.

[1689] Hardware:

[1690] 1. Terminal: A device that accepts and displays user input (e.g., PC, smartphone, tablet)

[1691] 2. Server: A computer system that stores and processes data

[1692] software:

[1693] 1. Use a generative AI model (e.g., OpenAI's GPT-4)

[1694] 2. Program search function using educational platform APIs

[1695] 3. Aptitude test and learning progress evaluation function using data analysis algorithms

[1696] When a user first uses the system, they enter basic information through their terminal, such as their name, age, occupation, and desired course of study, which is then sent to the server via an HTTP POST request.

[1697] The server uses a generative AI to generate aptitude test questions and sends them to the device. The user answers the questions displayed on the device and sends the answers back to the server. The server then identifies the user's learning style based on the aptitude test results. For example, it determines whether online learning or face-to-face classes are more suitable.

[1698] Next, the server uses the API of educational platforms (e.g., Coursera, Udemy) to search for learning programs based on the user's learning preferences. The search results are scored and the most suitable program is selected. Information about the selected learning program is sent to the device and presented to the user.

[1699] After the user selects the most suitable learning program, the server generates a learning plan based on that program. This plan is customized to fit the user's schedule. The generated learning plan is displayed on a dashboard on the device, allowing the user to check their learning progress.

[1700] The server periodically monitors the user's learning progress and sends reminders and advice. It also periodically generates feedback and achievement reports and sends them to the device. The user can refer to this feedback to progress with their learning.

[1701] Finally, the system will suggest the next learning content or new learning program based on the user's learning outcomes, allowing users to efficiently acquire skills in a learning environment that is always optimal for them.

[1702] Example prompt sentence:

[1703] "I'm a 30-year-old office worker who wants to learn marketing skills. I'd like an aptitude test and a study plan created."

[1704] This system allows users to find the best learning program for them and progress through their studies effectively and efficiently.

[1705] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1706] Step 1:

[1707] Users enter basic information such as their name, age, occupation, and desired study content into the terminal.

[1708] Input: User's basic information (name, age, occupation, desired study content)

[1709] Output: Basic information sent to the server (e.g., JSON format)

[1710] Specific operation: When the user enters the required information into the form and clicks the submit button, the device sends this data to the server as an HTTP POST request.

[1711] Step 2:

[1712] The server uses a generative AI model to generate aptitude questions based on the user's basic information.

[1713] Input: User basic information

[1714] Output: Aptitude test questions (question data output by the generative AI model)

[1715] Specific operation: The server inputs basic information as a prompt to the generative AI model and sends a request saying, "Please generate aptitude test questions based on the user's basic information." The generative AI model then generates aptitude test questions.

[1716] Step 3:

[1717] The server transmits the generated aptitude test questions to the terminal.

[1718] Input: Aptitude test question

[1719] Output: Aptitude test questions displayed on the device (JSON format)

[1720] Specific operation: The server converts the question received from the generative AI model into an appropriate format and sends it to the device, which then displays the question on the screen.

[1721] Step 4:

[1722] The user answers questions for the aptitude test displayed on the terminal and sends the answers from the terminal to the server.

[1723] Input: User's answer

[1724] Output: Response data (JSON format)

[1725] Specific operation: When the user enters an answer to a question on the device screen and presses the send answer button, the device sends this data to the server as an HTTP POST request.

[1726] Step 5:

[1727] The server analyzes the user's responses and identifies the user's learning style.

[1728] Input: User response data

[1729] Output: Learning style (analysis results)

[1730] How it works: The server uses machine learning algorithms to analyze the response data and identify learning styles, such as online learning formats or individualized learning formats.

[1731] Step 6:

[1732] The server searches and selects relevant learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[1733] Input: Aptitude test results, user learning preferences

[1734] Output: List of study programs (with scores)

[1735] Specific operation: The server uses the educational platform's API to search for learning programs that meet the criteria, evaluates the results using a scoring algorithm, and selects the most suitable program.

[1736] Step 7:

[1737] The server sends detailed information about the selected learning program to the terminal.

[1738] Input: List of study programs

[1739] Output: A list of learning programs displayed on the device (JSON format)

[1740] Specific operation: The server obtains detailed information about the selected learning program and sends it to the terminal, which has a display interface and presents it to the user in list form.

[1741] Step 8:

[1742] The user selects the program they think is most suitable from a list of study programs displayed on the terminal.

[1743] Input: Select a study program

[1744] Output: Data for the selected study program

[1745] Specific operation: The user selects the most suitable learning program from the list and presses the selection button, and information about the selected program is sent to the server.

[1746] Step 9:

[1747] The server automatically generates a specific study plan based on the study program selected by the user.

[1748] Input: Data for the selected study program

[1749] Output: Learning plan data

[1750] Specific operation: The server executes a script to generate a learning plan that fits the user's schedule based on the content of the selected learning program.

[1751] Step 10:

[1752] The server transmits the generated study plan to the terminal.

[1753] Input: Learning plan data

[1754] Output: The lesson plan displayed on the device

[1755] Specific operation: The server sends the generated learning plan to the device, which displays it on the device's dashboard.

[1756] Step 11:

[1757] The device displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[1758] Input: Learning plan data

[1759] Output: Learning plan and progress displayed on the dashboard

[1760] Specific operation: The device analyzes the learning plan received from the server and creates a dashboard so that the user can check their progress.

[1761] Step 12:

[1762] The server periodically monitors the user's learning progress and sends reminders and advice.

[1763] Input: Learning progress data

[1764] Output: Reminders and advice

[1765] Specific operation: The server continuously collects the user's progress data and automatically generates reminders and advice based on the progress and sends them to the device.

[1766] Step 13:

[1767] The device presents users with surveys and self-assessment forms as they progress through the learning process.

[1768] Input: Evaluation request

[1769] Output: User self-assessment data

[1770] Specific operation: The device periodically displays evaluation forms and surveys to the user and accepts input.

[1771] Step 14:

[1772] The user answers the questionnaire and self-evaluation form and sends them to the server from the terminal.

[1773] Input: Self-assessment answers

[1774] Output: Response data sent to the server

[1775] Specific operation: When the user answers the questions in the self-assessment form or survey and presses the send button, the device sends the answer data to the server.

[1776] Step 15:

[1777] The server analyzes the assessment data and evaluates the learning outcomes.

[1778] Input: Self-assessment response data

[1779] Output: Analysis results (learning outcome evaluation)

[1780] Specific operation: The server uses machine learning algorithms to analyze the self-assessment data and generate a report assessing learning outcomes.

[1781] Step 16:

[1782] The server generates feedback and performance reports for the user and sends them to the terminal.

[1783] Input: Analysis results

[1784] Output: Feedback and performance reports sent to your device

[1785] Specific operation: The server sends the generated feedback and performance report to the terminal, which then displays it to the user.

[1786] Step 17:

[1787] The device displays feedback and performance reports to the user.

[1788] Input: Feedback and performance reports

[1789] Output: Displayed feedback and performance report

[1790] Specific operation: The device displays the received feedback and performance report on the screen.

[1791] Step 18:

[1792] Based on the user's learning outcomes, the server suggests what to learn next and new learning programs.

[1793] Input: Learning outcome data

[1794] Output: Next learning program suggestion

[1795] Specific operation: The server generates new learning content and programs based on the user's performance data and sends suggestions for the next step to the terminal.

[1796] (Application example 1)

[1797] 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."

[1798] Conventional learning support systems have difficulty providing optimal learning programs tailored to individual users' learning styles and life schedules, and do not adequately manage learning progress or provide appropriate evaluations and feedback. Furthermore, they lacked reminder and advice functions that utilize smart devices, making it difficult to effectively support users' continuity of learning.

[1799] 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.

[1800] In this invention, the server includes means for acquiring basic information about the user, means for performing an aptitude test using a generation AI to identify the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for sending reminders and advice to the user using a smart device and managing and displaying learning progress, means for periodically monitoring the user's learning progress and evaluating and providing feedback on results, and means for using a content distribution service to push notifications of optimal learning content to the user. This enables the provision of optimal learning programs tailored to the user's individual learning style, as well as learning progress management and effective feedback.

[1801] "Basic information" refers to basic data about a person, such as the user's name, age, occupation, and desired learning.

[1802] "Generative AI" is a technology that uses artificial intelligence to analyze data and diagnose users' characteristics and aptitudes.

[1803] "Aptitude test" is a diagnostic process that evaluates a user's learning style and aptitude based on their basic information and response data.

[1804] "Educational platform" refers to a platform or system that provides online or offline learning programs.

[1805] A "program of study" is a series of educational courses or materials designed to develop specific skills or knowledge.

[1806] A "study plan" is a specific study schedule or progress plan that is set to fit the user's daily schedule.

[1807] A "smart device" is a portable electronic device that is connected to a network, such as a smartphone, tablet, or smartwatch.

[1808] "Reminders" is a feature that sends users notifications about specific dates and times or tasks.

[1809] "Advice" refers to providing users with advice and information about their learning progress and areas for improvement.

[1810] "Learning Progress" is the status or data that indicates how far a user has progressed in a learning program.

[1811] "Monitoring" means the act of regularly and continuously observing and recording a user's learning progress.

[1812] "Outcome assessment" is the process of evaluating users' learning outcomes and progress and providing feedback.

[1813] "Feedback" is information that communicates to users the results of evaluations of their learning outcomes and progress, as well as areas for improvement.

[1814] A "content distribution service" is a service that provides users with various content such as educational and entertainment content via the Internet.

[1815] "Push notifications" are a feature that sends information and notifications to smart devices in real time.

[1816] This invention relates to a learning support system that allows users to study efficiently according to their daily schedule. This system acquires basic information about the user, performs aptitude tests using a generative AI, selects an appropriate learning program, and creates a learning plan and manages progress.

[1817] Overall overview

[1818] The system mainly consists of a server and a smart device (such as a smartphone). The server uses generative AI to diagnose the user's aptitude and select a program, and sends reminders and advice to the smart device to manage learning progress.

[1819] Hardware and software used

[1820] Hardware:

[1821] Smart devices (e.g. smartphones, tablets)

[1822] server

[1823] software:

[1824] Smart device side: Native application using React Native etc.

[1825] Server side: Python, Flask (web framework), generative AI (e.g., OpenAI's GPT-4)

[1826] Processing flow

[1827] The server first obtains the user's basic information. The user enters their name, age, occupation, and desired learning content through an application on their smart device, which is then sent to the server. The server then uses generative AI to generate the basic information and a list of questions for aptitude assessment, which are then sent to the smart device. The smart device then presents these questions to the user, collects their answers, and sends them to the server. The server then analyzes the response data to identify the user's learning style.

[1828] Next, the server searches for and selects an appropriate learning program from the educational platform based on the aptitude test results. This information is sent to the smart device, which presents the user with a list of learning programs and detailed information. Once the user selects the desired program, the server generates an automatic learning plan that takes into account the user's daily schedule and displays it on the smart device.

[1829] The server also automatically sends reminders and advice to smart devices, and manages and displays learning progress. As users progress through their learning, the server periodically monitors their progress, evaluates their progress, and provides feedback. Furthermore, the server pushes the most appropriate learning content to users via a content distribution service.

[1830] Specific examples

[1831] For example, if a 40-year-old professional wants to learn digital marketing skills, the system works as follows: The user enters basic information into the smartphone app and sends it to the server. The generation AI processes the information, conducts an aptitude test, and identifies the user's optimal learning style as "online personalized learning." The server then searches for suitable online programs related to digital marketing and presents them to the user. After the user selects an appropriate program, the server generates a learning plan incorporating two online courses per week based on the user's schedule and displays it on the smartphone app.

[1832] In addition, periodic reminders and advice are sent via push notifications to the smartphone to manage learning progress, allowing users to continue their studies efficiently. Furthermore, surveys and self-assessment forms are provided via the smart device as needed, and the server analyzes them to evaluate results and provide feedback.

[1833] Prompt Sentence Examples

[1834] "Please answer the following questions: What is your age, what is your occupation, and what do you want to study?"

[1835] "We'll take a learning style aptitude test. Answer the following questions: Do you prefer online or offline learning?"

[1836] "Which do you find more effective: individual learning or group learning?"

[1837] "Thank you for your response. An online, tutored learning format would be ideal for you."

[1838] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1839] Step 1:

[1840] User registration and basic information acquisition

[1841] The terminal (smart device) prompts the user to enter basic information such as name, age, occupation, desired study content, etc. Once the user has completed the input and pressed the send button, the terminal sends the information to the server.

[1842] Input: Name, age, occupation, desired study content

[1843] Server Output: Save basic information

[1844] Specific operation: The terminal displays a user interface and prompts the user to enter information. After the user enters the information, the terminal sends the information to the server. The server receives the information and stores it in a database.

[1845] Step 2:

[1846] Aptitude test using generative AI

[1847] The server uses a generation AI to generate a list of aptitude test questions based on the user's basic information and sends them to the device. The user answers the questions on the device, and the answers are sent from the device to the server. The generation AI analyzes these to identify the user's learning style.

[1848] Input: Basic information, aptitude test questions

[1849] Server Output: Identifying the user's learning style

[1850] How it works: A generative AI (e.g., GPT-4) generates aptitude test questions based on the user's basic information and sends them to the device. The device displays the questions to the user, who answers them. The answer data is sent from the device to a server, where it is analyzed. As a result, the user's optimal learning style is identified.

[1851] Step 3:

[1852] Searching and selecting a study program

[1853] The server searches for and scores appropriate learning programs from educational platforms based on the aptitude test results and the user's learning preferences. The optimal program is selected and the information is sent to the device.

[1854] Input: Aptitude test results, user learning preferences

[1855] Server output: List of suitable study programs

[1856] Specific operation: The server accesses the educational platform using APIs and searches for learning programs that match the user's aptitude. The search results are scored to select the most suitable program. The selection results are sent to the device and displayed to the user.

[1857] Step 4:

[1858] Presentation and selection of study programs

[1859] The device displays a list of selected learning programs to the user and provides detailed information (content, duration, cost, etc.). The user selects the appropriate program.

[1860] Input: List of study programs

[1861] Output: User's choice of program

[1862] Specific operation: The terminal displays the details of the learning program through the user interface. The user selects the appropriate program and sends the selection result to the server.

[1863] Step 5:

[1864] Generate and customize a learning plan

[1865] The server automatically generates a specific study plan based on the user's selected study program and taking into account the user's daily schedule. The generated study plan is sent to the terminal and displayed to the user.

[1866] Input: User-selected learning program, user's daily schedule

[1867] Server output: Generated lesson plan

[1868] Specific operation: The server automatically generates a learning program schedule based on the user's schedule. The generated learning plan is sent to the device and displayed to the user.

[1869] Step 6:

[1870] Track your progress and receive reminders

[1871] The server manages the user's learning progress and periodically sends reminders and advice to the smart device, which then displays these notifications to the user.

[1872] Input: User's learning progress data

[1873] Server output: reminder and advice notifications

[1874] Specific operation: The server monitors the user's learning progress and periodically generates and sends reminders and advice to the device, which then displays these notifications to the user.

[1875] Step 7:

[1876] Regular assessment and feedback of learning outcomes

[1877] The server periodically evaluates the user's learning progress, generates feedback, and sends it to the device, where the user can view the feedback and progress report.

[1878] Input: User learning progress data, survey data

[1879] Server output: feedback and performance reports

[1880] Specific operation: The server analyzes the learning progress data and evaluates the user's performance. Based on the evaluation results, it generates feedback and performance reports and sends them to the device. The device displays them to the user.

[1881] Step 8:

[1882] Push notifications for the best learning content

[1883] Using the content delivery service, the server pushes the most suitable learning content to the user's smart device, allowing the user to receive new learning content.

[1884] Input: User's learning history, aptitude test results

[1885] Server output: Pushed learning content

[1886] Specific operation: The server selects new learning content based on the user's learning history and aptitude test results through the content distribution service and pushes it to the smart device, which then displays the new learning content to the user.

[1887] 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.

[1888] This invention is a system that provides an optimal learning environment, motivating users to "study" while continuing their daily work and other activities. This system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. In addition, by using an emotion engine that recognizes the user's emotions, the accuracy and efficiency of learning are further improved.

[1889] User registration and aptitude test

[1890] 1. Enter basic information

[1891] Terminal: The user enters basic information such as name, age, occupation, and desired study content.

[1892] Terminal: The entered information is sent to the server.

[1893] 2. Conducting aptitude tests

[1894] Server: Uses generative AI to perform aptitude diagnosis based on the user's basic information.

[1895] Server: Generates questions for the user to diagnose their learning style and sends them to the terminal.

[1896] User: Answers the aptitude test questions and submits the answers.

[1897] Server: Analyzes the responses and identifies the user's learning style (online / offline, individual / group).

[1898] Emotion recognition by emotion engine

[1899] 3. Acquiring Emotion Data

[1900] Device: Using the emotion engine, emotional data such as the user's facial expressions, voice, and body movements is acquired.

[1901] Terminal: Sends the acquired emotion data to the server.

[1902] 4. Emotion Data Analysis

[1903] Server: Analyzes emotional data and understands the user's emotional state in real time.

[1904] Server: Improve the accuracy of aptitude tests based on emotional data.

[1905] Study program recommendation and application

[1906] 5. Searching for and selecting a study program

[1907] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[1908] Server: Scores the search results and selects the best program.

[1909] Server: Sends detailed information about the selected learning program to the terminal.

[1910] 6. Presentation of the study program

[1911] Device: Displays a list of selected learning programs to the user and provides detailed information about each program (content, duration, cost, etc.).

[1912] User: Choose the program that you think is best.

[1913] Planning and managing your study plan

[1914] 7. Generate a learning plan

[1915] Server: Automatically generates a specific study plan based on the study program selected by the user.

[1916] Server: Customizes the plan to fit the user's schedule, for example, scheduling classes so they can be taken during work breaks.

[1917] 8. Managing learning progress

[1918] Device: Displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[1919] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[1920] 9. Use of Emotional Data

[1921] Server: Monitors emotional data during learning in real time and adjusts learning plans based on that data.

[1922] Server: Analyzes the user's emotional data and generates appropriate feedback and advice.

[1923] Assessment and feedback of learning outcomes

[1924] 10. Periodic evaluation

[1925] Device: Presents users with surveys and self-assessment forms as they progress through the learning process.

[1926] Users: Complete and submit surveys and self-assessment forms.

[1927] 11. Analysis of evaluation data and feedback

[1928] Server: Analyzes assessment data and evaluates learning outcomes.

[1929] Server: Generates feedback and performance reports for users and sends them to the terminal.

[1930] Device: Displaying feedback and performance reports to users.

[1931] 12. Suggested next steps for learning

[1932] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[1933] Specific examples

[1934] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[1935] 1. Enter basic information

[1936] Device: Person A enters his / her name, age, occupation, and desired study subject (marketing).

[1937] Terminal: Information is sent to the server.

[1938] 2. Conducting aptitude tests

[1939] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[1940] User: Answers the question and sends it to the server.

[1941] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[1942] 3. Acquiring Emotion Data

[1943] Terminal: Uses the emotion engine to record A's facial expressions and voice, and sends the emotion data to the server.

[1944] 4. Emotion Data Analysis

[1945] Server: Analyzes A's emotional data and improves the accuracy of the learning style diagnosis results.

[1946] 5. Study Program Recommendations

[1947] Server: Searches for marketing-related online programs based on aptitude tests and emotional data, and selects the most suitable program.

[1948] Server: Sends program information to the terminal.

[1949] Terminal: Display a list of programs to Person A.

[1950] 6. Planning your study plan

[1951] Server: Generates a study plan that fits Mr. A's schedule based on the program he selected.

[1952] Device: Provides a dashboard where you can view your learning plan.

[1953] 7. Learning progress management

[1954] Device: Study according to your study plan and track your progress.

[1955] Server: Monitors emotional data in real time and sends reminders to adjust learning plans.

[1956] 8. Performance evaluation and feedback

[1957] Terminal: Periodically present self-assessment forms and collect responses.

[1958] Server: Analyzes the rating and sentiment data and generates feedback.

[1959] Server: Sends a result report to the terminal.

[1960] This system allows A to efficiently acquire marketing skills. Real-time feedback and progress adjustments from the emotion engine also eliminate mismatches between what A wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[1961] The processing flow will be explained below.

[1962] Step 1:

[1963] Terminal: The user enters basic information (name, age, occupation, desired study content, etc.).

[1964] Step 2:

[1965] Terminal: Sends the entered basic information to the server.

[1966] Step 3:

[1967] Server: Based on the received basic information, the server uses a generative AI to generate aptitude test questions appropriate for the user.

[1968] Step 4:

[1969] Server: Sends the generated aptitude test questions to the terminal.

[1970] Step 5:

[1971] Terminal: Presents the user with aptitude test questions.

[1972] Step 6:

[1973] User: Answers the aptitude test questions.

[1974] Step 7:

[1975] Terminal: Sends the user's answer to the server.

[1976] Step 8:

[1977] Server: Analyzes the aptitude test answers and identifies the user's learning style (online / offline, individual / group).

[1978] Step 9:

[1979] Terminal: Uses an emotion engine to acquire user emotion data (e.g., facial expressions, voice, and movements).

[1980] Step 10:

[1981] Terminal: Sends the acquired emotion data to the server.

[1982] Step 11:

[1983] Server: Analyzes emotional data and understands the user's emotional state.

[1984] Step 12:

[1985] Server: Considers emotional state to improve the accuracy of aptitude test results.

[1986] Step 13:

[1987] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[1988] Step 14:

[1989] Server: Scores the learning programs from the search results and selects the most suitable program, taking into account sentiment data.

[1990] Step 15:

[1991] Server: Sends information about the selected learning program (content, duration, cost, etc.) to the terminal.

[1992] Step 16:

[1993] Terminal: Shows the user a list of learning programs.

[1994] Step 17:

[1995] User: Select the most suitable learning program from the displayed list.

[1996] Step 18:

[1997] Terminal: Sends the user's selection to the server.

[1998] Step 19:

[1999] Server: Automatically generates a personalized learning plan based on user selections.

[2000] Step 20:

[2001] Server: Customize plans to fit your schedule.

[2002] Step 21:

[2003] Server: Sends the generated learning plan to the device.

[2004] Step 22:

[2005] Device: Shows users their learning plan and provides a dashboard where they can track their progress.

[2006] Step 23:

[2007] User: Proceed with learning based on the generated learning plan.

[2008] Step 24:

[2009] Device: Sends learning progress to the server.

[2010] Step 25:

[2011] Server: Regularly monitors learning progress and generates reminders and advice as needed.

[2012] Step 26:

[2013] Server: Sends reminders and advice to the device.

[2014] Step 27:

[2015] Device: Display reminders and advice to the user.

[2016] Step 28:

[2017] Server: Monitors emotional data in real time and adjusts learning plans as needed.

[2018] Step 29:

[2019] Terminal: Periodically present users with surveys and self-assessment forms.

[2020] Step 30:

[2021] Users: Complete surveys and self-assessment forms.

[2022] Step 31:

[2023] Terminal: Sends the user's answer to the server.

[2024] Step 32:

[2025] Server: Analyzes the assessment data and evaluates the user's learning outcomes.

[2026] Step 33:

[2027] Server: Sends generated feedback and performance reports to the device.

[2028] Step 34:

[2029] Device: Shows feedback and performance reports to users.

[2030] Step 35:

[2031] Server: Based on the user's learning outcomes, suggests the next learning step or a new learning program.

[2032] Step 36:

[2033] Server: Sends proposed programs and feedback to the device.

[2034] Step 37:

[2035] Device: Shows the user next learning steps and feedback.

[2036] Example 2

[2037] 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."

[2038] Conventional learning systems provide a uniform learning plan without fully considering the individual learning style or emotional state of the user, which makes learning ineffective. In addition, the lack of real-time feedback based on the user's emotional state or progress can easily lead to a decline in learning motivation and stagnation of progress.

[2039] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring basic information about the user, means for performing an aptitude test using a generation AI based on the basic information and identifying the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan tailored to the user's daily schedule, means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results, means for acquiring and analyzing the user's emotional data in real time, and means for adjusting the learning plan based on the emotional data. This provides an optimal learning environment tailored to the user's individual learning style and emotional state, enabling effective learning and maintaining motivation.

[2040] "Basic user information" refers to information that indicates the individual characteristics of a user, such as name, age, occupation, and desired study content.

[2041] "Generative AI" is an artificial intelligence algorithm that performs aptitude tests, generates questions, analyzes data, and more based on user input.

[2042] Aptitude testing is the process of identifying the best learning style for a user based on basic information about them.

[2043] "Learning style" refers to the method and environment in which a user learns most effectively (online / offline, individual / group, etc.).

[2044] "Educational platform" is a general term for websites and applications that provide online learning content.

[2045] A "program of study" is a series of courses or lectures designed to teach a particular skill or knowledge.

[2046] A "life schedule" is a user's daily schedule and activity plan.

[2047] A "study plan" is a plan that indicates the specific timing and method of study based on the study program selected by the user.

[2048] "Monitoring" refers to the act of regularly observing a user's learning progress and collecting necessary data.

[2049] "Outcomes" refers to the progress or goals a user achieves through a learning program.

[2050] "Feedback" means evaluations and advice provided to users based on their learning progress and achievements.

[2051] "Emotional data" is data that indicates the emotional state of a user, obtained from facial expressions, voice, body movements, etc.

[2052] "Real-time analysis" refers to the process of analyzing emotion data as soon as it is acquired and reflecting the results.

[2053] "Adjusting a study plan" refers to the act of changing an existing study schedule or content depending on the user's emotional state and progress.

[2054] This invention is a system that provides an optimal learning environment while a user continues their daily work or other activities. The system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. It also uses an emotion engine that recognizes the user's emotions to improve the accuracy and efficiency of learning.

[2055] User registration and aptitude test

[2056] 1. Enter basic information

[2057] Device: The user enters basic information such as name, age, occupation, and desired study topic. This is done through a web form or a mobile app interface.

[2058] Terminal: The entered information is encoded and sent to the server via HTTPS. The software used here is the web server Apache or NGINX, and the backend is Node.js or Python frameworks (Django, Flask).

[2059] 2. Conducting aptitude tests

[2060] Server: The server receives the user's basic information and generates aptitude test questions using a generative AI model, which uses a general-purpose AI algorithm.

[2061] Server: The generated question is sent to the terminal in JSON format.

[2062] User: The user answers questions for the aptitude test and sends the answers from the device to the server.

[2063] Server: Analyzes the answers and uses machine learning models (e.g., using scikit-learn or TensorFlow) to identify the user’s learning style.

[2064] Emotion recognition by emotion engine

[2065] 3. Acquiring Emotion Data

[2066] Device: The device uses an emotion engine (e.g., OpenCV for facial recognition and Google Speech-to-Text API for voice analysis) to obtain emotional data such as the user's facial expressions, voice, and body movements.

[2067] Terminal: Encodes the acquired emotion data and sends it to the server.

[2068] 4. Emotion Data Analysis

[2069] Server: Analyzes emotional data and understands the user's emotional state in real time. For example, if the user is determined to be tired, the AI ​​model will adjust its learning pace accordingly.

[2070] Server: Stores the analysis results in a database (e.g., MySQL or PostgreSQL).

[2071] Study program recommendation and application

[2072] 5. Searching for and selecting a study program

[2073] Server: Based on the aptitude test results and learning preferences, the server searches for appropriate learning programs from the educational platform via API. It sends API requests using Python's Requests library, etc.

[2074] Server: Evaluates search results using a scoring algorithm and selects the most suitable program.

[2075] Server: Sends detailed information about the selected learning program to the terminal in JSON format.

[2076] 6. Presentation of the study program

[2077] Terminal: Displays a list of learning programs selected by the user. This screen is built using HTML / CSS / JavaScript.

[2078] User: Choose the program that you think is best.

[2079] Planning and managing your study plan

[2080] 7. Generate a learning plan

[2081] Server: Automatically generates a specific learning plan based on the user's chosen learning program. The plan is created using an AI algorithm.

[2082] Server: Customizes the plan to fit the user's schedule. For example, schedules the course so that it can be taken during work breaks.

[2083] 8. Managing learning progress

[2084] Terminal: Displays the generated learning plan to the user and provides a dashboard where the user can check their progress. Utilizing front-end frameworks such as React.

[2085] Server: Regularly monitors learning progress and sends reminders and motivational messages as needed. Uses Twilio or Firebase Cloud Messaging.

[2086] Assessment and feedback of learning outcomes

[2087] 9. Use of Emotional Data

[2088] Server: Monitors emotional data in real time during learning and adjusts learning plans based on that data, for example, if it determines that a break is needed after a long period of study.

[2089] Server: Analyzes user emotional data and generates appropriate feedback and advice. The AI ​​model suggests appropriate actions.

[2090] 10. Periodic evaluation

[2091] Device: Present surveys and self-assessment forms to users during the learning process. Use Google Forms or Typeform.

[2092] Users: Complete and submit surveys and self-assessment forms.

[2093] 11. Analysis of evaluation data and feedback

[2094] Server: Analyzes assessment data and evaluates learning outcomes. Machine learning algorithms are used.

[2095] Server: Generates feedback and performance reports for users and sends them to the device in JSON format.

[2096] Device: Displaying feedback and performance reports to users.

[2097] 12. Suggested next steps for learning

[2098] Server: Based on the user's learning outcomes, suggests what to study next and new learning programs. This is done using AI models.

[2099] Specific examples

[2100] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[2101] 1. Enter basic information

[2102] Device: Person A enters his / her name, age, occupation, and desired study content (marketing). For example, "Taro Tanaka, 30 years old, company employee, wants to study marketing."

[2103] Terminal: Information is sent to the server.

[2104] 2. Conducting aptitude tests

[2105] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[2106] User: Answers the question, for example, "I only have 30 minutes a day to study."

[2107] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[2108] 3. Acquiring Emotion Data

[2109] Device: Uses the emotion engine to record A's facial expressions and voice, and sends the emotion data to the server. For example, "A's facial expression looks tired."

[2110] 4. Emotion Data Analysis

[2111] Server: Analyzes A's emotional data and improves the accuracy of the learning style diagnosis results. For example, it determines that "A feels fatigued during the learning process."

[2112] 5. Study Program Recommendations

[2113] Server: Based on aptitude tests and emotional data, the server searches for online marketing programs and selects the most suitable one. For example, it selects the "Basic Marketing Course" as the most suitable one.

[2114] Server: Sends program information to the terminal. For example, "Duration: 3 months, Cost: Free."

[2115] Terminal: Display a list of programs to Person A.

[2116] 6. Planning your study plan

[2117] Server: Based on the program selected by Mr. A, a study plan is generated that fits his / her daily schedule, for example, "attend lectures from 8:00 PM to 9:00 PM every day."

[2118] Device: Provides a dashboard where you can view your learning plan.

[2119] 7. Learning progress management

[2120] Device: Study based on the study plan and record your progress. For example, it might record "I progressed three pages today."

[2121] Server: Monitors emotional data in real time, for example, "sends a reminder to take a break if you feel tired while studying."

[2122] 8. Performance evaluation and feedback

[2123] Device: Periodically present students with a self-evaluation form and ask them to answer questions such as, "Are you satisfied with what you have learned?"

[2124] Server: Analyzes the evaluation data and emotion data and generates feedback such as, "Person A is satisfied with the learning content, but there is a possibility that he or she could increase the study time a little more."

[2125] Server: Sends a performance report to the terminal. For example, the report may say, "Mr. A is very promising."

[2126] This system allows A to efficiently acquire marketing skills. Real-time feedback and progress adjustments from the emotion engine also eliminate mismatches between what A wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[2127] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2128] The flow of this system's program processing

[2129] Step 1: Enter basic information

[2130] Device: The user enters basic information such as name, age, occupation, and desired study content. This is done through a web form or a mobile app interface.

[2131] Input: User information such as name, age, occupation, desired study content, etc.

[2132] Output: User basic information data in JSON format.

[2133] What happens: When a user enters information into a form and presses the Submit button, the data is encoded and sent to the server via the HTTPS protocol.

[2134] Step 2: Submit basic information and prepare for the aptitude test

[2135] Server: Receives the encoded basic information and uses the generative AI model to analyze the user's basic information and generate questions for aptitude tests.

[2136] Input: Basic user information data sent from the device.

[2137] Output: Generated aptitude test question data.

[2138] Specific operation: The server uses the generative AI model to analyze the user's basic information, generate questions for aptitude testing, and send the questions to the terminal in JSON format.

[2139] Step 3: Answer the aptitude test questions

[2140] Terminal: Displays aptitude test questions to the user, who then answers the questions.

[2141] Input: Generated aptitude test question data.

[2142] Output: User's answers to the aptitude test questions.

[2143] Specific operation: When the user enters an answer to the displayed question and presses the "Submit" button, the answer data is encoded and sent back to the server.

[2144] Step 4: Assess your aptitude and identify your learning style

[2145] Server: Analyzes user response data and uses machine learning models to identify the best learning style (online / offline, individual / group).

[2146] Input: User's answers to the aptitude test questions.

[2147] Output: Aptitude test results and identified learning style data.

[2148] How it works: The server analyzes the response data using machine learning algorithms to identify the learning style that best suits the user and stores the results in a database.

[2149] Step 5: Obtaining emotion data

[2150] Device: Using the emotion engine, emotional data such as the user's facial expressions, voice, and body movements is acquired.

[2151] Input: Real-time facial expressions, voice, and body movements of the user.

[2152] Output: Encoded emotion data.

[2153] Specific operation: Using the device's camera and microphone, emotion data is acquired using an emotion engine (e.g., OpenCV, Google Speech-to-Text API) and sent to the server.

[2154] Step 6: Analyze the sentiment data

[2155] Server: Analyzes emotional data and understands the user's emotional state in real time.

[2156] Input: Emotion data sent from the device.

[2157] Output: Parsed emotional state data.

[2158] Specific operation: Analyzes emotional data, determines whether the user is in an emotional state such as "tired," and stores the results in a database.

[2159] Step 7: Search and select a study program

[2160] Server: Based on the aptitude test results and learning preferences, searches for appropriate learning programs from educational platforms and selects the most suitable program.

[2161] Input: Aptitude test results and desired learning content.

[2162] Output: A list of scored learning programs.

[2163] Specific operation: The server sends an API request to the educational platform to obtain appropriate learning programs, evaluates them using a scoring algorithm, and selects the most suitable program.

[2164] Step 8: Present your learning program

[2165] Terminal: A list of selected learning programs is displayed to the user, and the user selects a program.

[2166] Input: Details of the selected study program.

[2167] Output: Data on the study program selected by the user.

[2168] Specific operation: Display a list of programs and detailed information on the terminal screen, and provide an interface for users to select the desired program.

[2169] Step 9: Generate and customize your lesson plan

[2170] Server: Automatically generates a specific study plan based on the study program selected by the user.

[2171] Input: Data of the study program selected by the user.

[2172] Output: A customized study plan.

[2173] How it works: The server uses AI algorithms to generate a learning plan based on the learning program, customizing it to fit the user's schedule and available time.

[2174] Step 10: Managing your learning progress

[2175] Device: Shows the generated learning plan to the user and allows them to see their progress on a dashboard.

[2176] Input: Customized study plan.

[2177] Output: A dashboard display of your learning progress.

[2178] How it works: Users can check their learning plans and progress in real time through the dashboard. The device records their learning progress and sends the data to the server as needed.

[2179] Step 11: Monitor learning progress and send reminders

[2180] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[2181] Input: Learning progress data.

[2182] Output: Reminder and advice messages.

[2183] Specific operation: The server analyzes the user's learning progress and sends reminder messages and advice when progress falls behind or when goals are achieved.

[2184] Step 12: Assessment and feedback of learning outcomes

[2185] Server: Presents questionnaires and self-assessment forms to users during the learning process and collects evaluation data.

[2186] Input: Survey responses and self-assessment data about the user's learning status.

[2187] Output: Assessed learning outcomes and feedback report.

[2188] Specific operation: Based on the collected evaluation data, the server evaluates the learning outcomes, generates an appropriate feedback report, and sends it to the terminal.

[2189] Step 13: Use emotional data to adjust your learning plan

[2190] Server: Monitors emotional data during learning in real time and adjusts learning plans based on that data.

[2191] Input: Real-time emotion data.

[2192] Output: A tailored learning plan.

[2193] Specific operation: The server analyzes the emotional data and makes appropriate adjustments to the learning plan, for example, if the user is tired.

[2194] Step 14: Suggest next learning steps

[2195] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[2196] Input: Learning outcome data.

[2197] Output: Suggested next learning steps.

[2198] Specific operation: The server analyzes the learning outcome data, suggests the next learning course or program to the user, and sends this information to the terminal.

[2199] These steps enable the system to monitor users' individual learning needs and progress in real time and provide an optimal learning environment.

[2200] (Application example 2)

[2201] 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."

[2202] Conventional learning support systems select and customize learning programs based on the user's basic information and aptitude, but this alone has the problem of making it difficult to maximize the learning effect of the user. Also, the learning plan does not adequately take into account the user's emotional state and concentration level, and there is a need to optimize the learning environment, especially for security personnel who are prone to stress.

[2203] 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.

[2204] In this invention, the server includes a means for acquiring basic information about the user, a means for performing an aptitude test using a generative AI model to identify the user's learning style, a means for searching and selecting learning programs from an educational platform, an emotion recognition means for acquiring and analyzing emotion data in real time, and a means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results. This makes it possible to customize a learning plan to suit the user's emotional state and daily schedule, thereby maximizing the effectiveness of learning.

[2205] "Means of obtaining basic information about users" refers to means of collecting information such as the user's name, age, occupation, and desired learning content.

[2206] A "generative AI model" is an artificial intelligence model that analyzes a user's basic information and aptitude test data to generate optimal questions and learning programs.

[2207] "Means for conducting aptitude tests and identifying a user's learning style" refers to a means for conducting a diagnosis based on the user's basic information and finding the optimal learning method for the user (online, offline, individual, group).

[2208] "Means for searching and selecting learning programs from educational platforms" refers to means for finding and selecting appropriate educational programs based on the user's learning preferences and aptitude test results.

[2209] "Emotion recognition means for acquiring and analyzing emotional data in real time" refers to a means for collecting a user's facial expressions, voice, body movements, etc., and analyzing that data to understand the user's emotional state.

[2210] "Means for regularly monitoring users' learning progress and evaluating and providing feedback on results" refers to means for tracking users' learning status, evaluating their progress, and providing appropriate advice and reminders.

[2211] "Means for adjusting the study plan" refers to means for changing the study schedule and content based on the user's emotional data and study progress, thereby providing an optimal study environment.

[2212] This invention is a learning support system that helps users maintain their motivation to learn while continuing their daily work and life. This system acquires basic information about the user, performs aptitude diagnosis using a generative AI model, and provides an optimal learning program. It also maximizes the user's learning effect by using emotion recognition means.

[2213] Hardware and software used

[2214] Hardware:

[2215] Smart glasses (e.g., general smart glasses devices)

[2216] software:

[2217] Emotion recognition engine (e.g. Affectiva SDK)

[2218] Generative AI models (e.g., OpenAI GPT-4)

[2219] Cloud services (e.g. AWS)

[2220] Data analysis tools (e.g. TensorFlow)

[2221] DETAILED DESCRIPTION OF THE EMBODIMENTS

[2222] 1. Obtaining basic information

[2223] The user puts on the smart glasses and enters basic information such as name, age, occupation, and desired study subject, which is then sent to a cloud server via the smart glasses.

[2224] 2. Conducting aptitude tests

[2225] The cloud server uses a generative AI model to generate aptitude test questions based on the received basic information and sends them to the smart glasses. The user answers the questions, and the results are sent back to the server for analysis.

[2226] 3. Acquisition and Analysis of Emotion Data

[2227] The smart glasses' emotion recognition engine captures the user's facial expressions and voice in real time, and the captured emotion data is sent to a cloud server for analysis.

[2228] 4. Study Program Recommendations

[2229] The cloud server searches for the most suitable learning program from the educational platform based on the aptitude test results and emotional data, scores the search results, and presents the most suitable program to the user through the smart glasses.

[2230] 5. Create and customize your study plan

[2231] Based on the user's selected learning program, the cloud server generates an optimal learning plan, which is customized to fit the user's daily schedule and displayed on the smart glasses dashboard.

[2232] 6. Progress monitoring and feedback

[2233] The cloud server periodically monitors the user's learning progress and sends reminders and advice to the smart glasses as needed. It also adjusts learning plans and provides feedback based on emotional data.

[2234] Specific examples

[2235] A 30-year-old security professional wants to learn about a new intrusion detection system.

[2236] 1. Enter basic information:

[2237] Users enter their name, age, occupation, and desired learning content into the smart glasses.

[2238] "Name: Mr. A, Age: 30, Occupation: Security Personnel, Learning Content: Latest Intrusion Detection Systems"

[2239] 2. Conducting aptitude tests:

[2240] The server uses a generative AI model to generate aptitude test questions.

[2241] "User information: {name: "Mr. A", age: 30, role: "Security Personnel", trainingRequest: "Latest Intrusion Detection System"}"

[2242] 3. Acquiring and analyzing emotion data:

[2243] The smart glasses collect the user's facial expressions and voice in real time and send them to a server.

[2244] 4. Study Program Recommendation:

[2245] The server selects the optimal learning program based on the aptitude test results and emotional data.

[2246] "User aptitude test results: {answers: [...]}"

[2247] 5. Create and customize your learning plan:

[2248] The server creates a study plan tailored to the user's schedule and displays it on the smart glasses.

[2249] 6. Progress monitoring and feedback:

[2250] The server adjusts the learning plan and provides feedback based on progress and emotional data.

[2251] These details allow security personnel to effectively learn the latest technologies while continuing to work.

[2252] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2253] Step 1:

[2254] Enter basic information

[2255] Input: The user enters their name, age, occupation, and desired study content through the smart glasses.

[2256] Processing: The smart glasses send the input information to the server.

[2257] Output: The server receives the user's basic information and stores it in a database.

[2258] Step 2:

[2259] Preparation for the aptitude test

[2260] Input: The user's basic information received by the server.

[2261] Processing: The server sends a prompt to the generative AI model to generate questions for the aptitude test.

[2262] Output: The server sends the generated question list to the smart glasses.

[2263] Specific operation: A prompt sentence is generated: "User information: {name: "Mr. A", age: 30, role: "Security Personnel", trainingRequest: "Latest Intrusion Detection System"}", and the AI ​​model generates questions for aptitude assessment.

[2264] Step 3:

[2265] Conducting aptitude tests

[2266] Input: A list of questions displayed on smart glasses.

[2267] Processing: The user answers the questions and sends the answers to the server via the smart glasses.

[2268] Output: The server analyzes the response data and generates the user's aptitude test results.

[2269] Specific operation: The generative AI model analyzes the user's answers and generates the data "User aptitude test results: {answers: [...]}".

[2270] Step 4:

[2271] Acquiring emotion data

[2272] Input: Real-time facial and voice data of the user.

[2273] Processing: The smart glasses' emotion recognition engine analyzes these data and obtains the emotional state in real time.

[2274] Output: The emotion data is sent to the server and stored in a database.

[2275] How it works: The smart glasses capture the user's facial expressions and voice in real time, and the emotion recognition engine analyzes them to generate emotional data.

[2276] Step 5:

[2277] Study program recommendations

[2278] Input: Aptitude test results and user emotion data.

[2279] Processing: The server uses the generative AI model to find and score the optimal learning program.

[2280] Output: The server sends the optimal learning program to the smart glasses.

[2281] Specific operation: The data "User's aptitude test results: {answers: [...]}" is input into the AI ​​model, and the optimal learning program is selected.

[2282] Step 6:

[2283] Create and customize a lesson plan

[2284] Input: The study program selected by the user.

[2285] Processing: The server creates and customizes a study plan based on the user's life schedule.

[2286] Output: The generated learning plan is displayed on the dashboard of the smart glasses.

[2287] What it does: Uses a generative AI model to tailor a learning plan based on the data "User's schedule: {...}".

[2288] Step 7:

[2289] Progress monitoring and feedback

[2290] Input: User's learning progress and emotion data.

[2291] Processing: The server analyzes the progress data and emotion data in an integrated manner and generates reminders and advice.

[2292] Output: Feedback and reminders are sent to the smart glasses.

[2293] How it works: Using TensorFlow, it analyzes progress data and emotion data to generate optimal feedback. "User progress data: {...}, emotion data: {...}" are combined to generate a reminder.

[2294] 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.

[2295] 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.

[2296] 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.

[2297] [Fourth embodiment]

[2298] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2299] 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.

[2300] 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).

[2301] 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.

[2302] 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.

[2303] 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).

[2304] 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. 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.

[2305] 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.

[2306] 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.

[2307] 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.

[2308] 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.

[2309] 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.

[2310] 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."

[2311] The present invention is a system that provides an optimal learning environment, motivating users to "study" while continuing their daily work and other activities. This system acquires basic information about the user, selects the optimal learning program through aptitude tests, and provides an efficient learning plan. Specific embodiments of this system are described below.

[2312] User registration and aptitude test

[2313] 1. Enter basic information

[2314] Terminal: The user enters basic information such as name, age, occupation, and desired study content.

[2315] Terminal: The entered information is sent to the server.

[2316] 2. Conducting aptitude tests

[2317] Server: Uses generative AI to perform aptitude diagnosis based on the user's basic information.

[2318] Server: Generates questions for the user to diagnose their learning style and sends them to the terminal.

[2319] User: Answers the aptitude test questions and submits the answers.

[2320] Server: Analyzes the responses and identifies the user's learning style (online / offline, individual / group).

[2321] Study program recommendation and application

[2322] 3. Searching for and selecting a study program

[2323] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[2324] Server: Scores the search results and selects the best program.

[2325] Server: Sends detailed information about the selected learning program to the terminal.

[2326] 4. Presentation of the study program

[2327] Device: Displays a list of selected learning programs to the user and provides detailed information about each program (content, duration, cost, etc.).

[2328] User: Choose the program that you think is best.

[2329] Planning and managing your study plan

[2330] 5. Generate a learning plan

[2331] Server: Automatically generates a specific study plan based on the study program selected by the user.

[2332] Server: Customize a plan to fit your schedule, for example, scheduling a six-week online course around your work schedule.

[2333] 6. Managing learning progress

[2334] Device: Displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[2335] Server: Regularly monitors learning progress and sends reminders and advice as needed.

[2336] Assessment and feedback of learning outcomes

[2337] 7. Periodic evaluation

[2338] Device: Presents users with surveys and self-assessment forms as they progress through the learning process.

[2339] Users: Complete and submit surveys and self-assessment forms.

[2340] 8. Analysis of evaluation data and feedback

[2341] Server: Analyzes assessment data and evaluates learning outcomes.

[2342] Server: Generates feedback and performance reports for users and sends them to the terminal.

[2343] Device: Displaying feedback and performance reports to users.

[2344] 9. Suggested next steps

[2345] Server: Based on the user's learning outcomes, suggests what to learn next and new learning programs.

[2346] Specific examples

[2347] For example, if a 30-year-old office worker named A wants to learn new marketing skills, the system works as follows:

[2348] 1. Enter basic information

[2349] Device: Person A enters his / her name, age, occupation, and desired study subject (marketing).

[2350] Terminal: Information is sent to the server.

[2351] 2. Conducting aptitude tests

[2352] Server: Generates aptitude test questions based on Mr. A's information and sends them to the terminal.

[2353] User: Answers the question and sends it to the server.

[2354] Server: Analyzes the responses and determines that an online lecture format would be best for A.

[2355] 3. Study Program Recommendations

[2356] Server: Based on the results of the aptitude test, search for online marketing programs and select the most suitable program.

[2357] Server: Sends program information to the terminal.

[2358] Terminal: Display a list of programs to Person A.

[2359] 4. Planning your study plan

[2360] Server: Generates a study plan that fits Mr. A's schedule based on the program he selected.

[2361] Device: Provides a dashboard where you can view your learning plan.

[2362] 5. Learning progress management

[2363] Device: Study according to your study plan and track your progress.

[2364] 6. Performance evaluation and feedback

[2365] Terminal: Periodically present self-assessment forms and collect responses.

[2366] Server: Analyzes the evaluation data and generates feedback.

[2367] Server: Sends a result report to the terminal.

[2368] This system allows Mr. A to efficiently acquire marketing skills. It also identifies learning styles based on aptitude tests and creates customized learning plans to eliminate mismatches between what he wants to learn and what he can learn. This provides an optimal learning environment and reduces wasted effort and time.

[2369] The processing flow will be explained below.

[2370] Step 1:

[2371] Terminal: The user enters basic information (name, age, occupation, desired study content, etc.).

[2372] Step 2:

[2373] Terminal: Sends the entered basic information to the server.

[2374] Step 3:

[2375] Server: Based on the received basic information, the server uses a generative AI to generate aptitude test questions appropriate for the user.

[2376] Step 4:

[2377] Server: Sends the generated aptitude test questions to the terminal.

[2378] Step 5:

[2379] Terminal: Presents the user with aptitude test questions.

[2380] Step 6:

[2381] User: Answers the aptitude test questions.

[2382] Step 7:

[2383] Terminal: Sends the user's answer to the server.

[2384] Step 8:

[2385] Server: Analyzes the aptitude test answers and identifies the user's learning style (online / offline, individual / group).

[2386] Step 9:

[2387] Server: Searches for appropriate learning programs from educational platforms based on the aptitude test results and learning preferences.

[2388] Step 10:

[2389] Server: Scores the learning programs in the search results and selects the most suitable program.

[2390] Step 11:

[2391] Server: Sends information about the selected learning program (content, duration, cost, etc.) to the terminal.

[2392] Step 12:

[2393] Terminal: Shows the user a list of learning programs.

[2394] Step 13:

[2395] User: Select the most suitable learning program from the displayed list.

[2396] Step 14:

[2397] Terminal: Sends the user's selection to the server.

[2398] Step 15:

[2399] Server: Automatically generates a personalized learning plan based on user selections.

[2400] Step 16:

[2401] Server: Customizes learning plans to fit the user's schedule.

[2402] Step 17:

[2403] Server: Sends the generated learning plan to the device.

[2404] Step 18:

[2405] Device: Shows users their learning plan and provides a dashboard where they can track their progress.

[2406] Step 19:

[2407] User: Proceed with learning based on the generated learning plan.

[2408] Step 20:

[2409] Device: Sends learning progress to the server.

[2410] Step 21:

[2411] Server: Regularly monitors learning progress and generates reminders and advice as needed.

[2412] Step 22:

[2413] Server: Sends reminders and advice to the device.

[2414] Step 23:

[2415] Device: Display reminders and advice to the user.

[2416] Step 24:

[2417] Terminal: Periodically present users with surveys and self-assessment forms.

[2418] Step 25:

[2419] Users: Complete surveys and self-assessment forms.

[2420] Step 26:

[2421] Terminal: Sends the user's answer to the server.

[2422] Step 27:

[2423] Server: Analyzes the assessment data and evaluates the user's learning outcomes.

[2424] Step 28:

[2425] Server: Sends generated feedback and performance reports to the device.

[2426] Step 29:

[2427] Device: Shows feedback and performance reports to users.

[2428] Step 30:

[2429] Server: Based on the user's learning outcomes, suggests the next learning step or a new learning program.

[2430] Example 1

[2431] 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."

[2432] In today's world, it is difficult for users to find and continue learning the optimal learning program effectively and efficiently. Customization to fit the user's schedule and learning style is particularly important, but no system provides such a service. Therefore, a system is needed that can select the optimal learning program based on the results of a unique aptitude test and the user's individual needs, generate a specific learning plan, and track learning progress.

[2433] 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.

[2434] In this invention, the server includes means for acquiring basic information about the user, means for conducting an aptitude test using a generation AI based on the basic information and identifying the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the results of the aptitude test and the user's learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for periodically monitoring the user's learning progress and evaluating and providing feedback on the results, and means for suggesting the next content to be learned and new learning programs based on the user's basic information, learning style, and progress. This makes it possible to provide optimal learning programs that meet the individual needs of each user and provide continuous learning support.

[2435] "Basic user information" refers to basic data about the user, such as name, age, occupation, and desired learning content.

[2436] "Generative AI" is an artificial intelligence technology that uses natural language generation models to generate text and analyze data.

[2437] "Aptitude testing" is the process of using generative AI based on a user's basic information to identify the learning style and method that best suits the user.

[2438] "Learning style" refers to the learning format or method that a user finds most suitable (e.g., online learning, face-to-face classes, individual learning, group learning).

[2439] "Educational platform" is a general term for online services that provide educational services and learning programs available to users.

[2440] "Program of Study" means a series of lectures or courses designed to impart specific skills or knowledge.

[2441] A "study plan" is a daily study schedule created based on a study program selected by the user.

[2442] "Study progress" is a status that indicates how far a user has progressed according to their study plan.

[2443] "Result evaluation and feedback" is the process of analyzing the user's learning effectiveness and providing advice on areas for improvement and future learning based on the results.

[2444] "Suggesting next learning content or new learning programs" means recommending the next learning content or program that is best suited to further development based on the user's current learning outcomes.

[2445] The present invention is a system that provides users with an optimal learning environment. The system acquires basic information about the user, uses generative AI to conduct an aptitude test and identify their learning style. It then searches for and selects an appropriate learning program from an educational platform based on the user's learning preferences, and then generates and provides a customized learning plan for the user. It monitors learning progress, evaluates and provides feedback on results, and suggests next steps in learning.

[2446] Specifically, the system is constructed using the following hardware and software.

[2447] Hardware:

[2448] 1. Terminal: A device that accepts and displays user input (e.g., PC, smartphone, tablet)

[2449] 2. Server: A computer system that stores and processes data

[2450] software:

[2451] 1. Use a generative AI model (e.g., OpenAI's GPT-4)

[2452] 2. Program search function using educational platform APIs

[2453] 3. Aptitude test and learning progress evaluation function using data analysis algorithms

[2454] When a user first uses the system, they enter basic information through their terminal, such as their name, age, occupation, and desired course of study, which is then sent to the server via an HTTP POST request.

[2455] The server uses a generative AI to generate aptitude test questions and sends them to the device. The user answers the questions displayed on the device and sends the answers back to the server. The server then identifies the user's learning style based on the aptitude test results. For example, it determines whether online learning or face-to-face classes are more suitable.

[2456] Next, the server uses the API of educational platforms (e.g., Coursera, Udemy) to search for learning programs based on the user's learning preferences. The search results are scored and the most suitable program is selected. Information about the selected learning program is sent to the device and presented to the user.

[2457] After the user selects the most suitable learning program, the server generates a learning plan based on that program. This plan is customized to fit the user's schedule. The generated learning plan is displayed on a dashboard on the device, allowing the user to check their learning progress.

[2458] The server periodically monitors the user's learning progress and sends reminders and advice. It also periodically generates feedback and achievement reports and sends them to the device. The user can refer to this feedback to progress with their learning.

[2459] Finally, the system will suggest the next learning content or new learning program based on the user's learning outcomes, allowing users to efficiently acquire skills in a learning environment that is always optimal for them.

[2460] Example prompt sentence:

[2461] "I'm a 30-year-old office worker who wants to learn marketing skills. I'd like an aptitude test and a study plan created."

[2462] This system allows users to find the best learning program for them and progress through their studies effectively and efficiently.

[2463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2464] Step 1:

[2465] Users enter basic information such as their name, age, occupation, and desired study content into the terminal.

[2466] Input: User's basic information (name, age, occupation, desired study content)

[2467] Output: Basic information sent to the server (e.g., JSON format)

[2468] Specific operation: When the user enters the required information into the form and clicks the submit button, the device sends this data to the server as an HTTP POST request.

[2469] Step 2:

[2470] The server uses a generative AI model to generate aptitude questions based on the user's basic information.

[2471] Input: User basic information

[2472] Output: Aptitude test questions (question data output by the generative AI model)

[2473] Specific operation: The server inputs basic information as a prompt to the generative AI model and sends a request saying, "Please generate aptitude test questions based on the user's basic information." The generative AI model then generates aptitude test questions.

[2474] Step 3:

[2475] The server transmits the generated aptitude test questions to the terminal.

[2476] Input: Aptitude test question

[2477] Output: Aptitude test questions displayed on the device (JSON format)

[2478] Specific operation: The server converts the question received from the generative AI model into an appropriate format and sends it to the device, which then displays the question on the screen.

[2479] Step 4:

[2480] The user answers questions for the aptitude test displayed on the terminal and sends the answers from the terminal to the server.

[2481] Input: User's answer

[2482] Output: Response data (JSON format)

[2483] Specific operation: When the user enters an answer to a question on the device screen and presses the send answer button, the device sends this data to the server as an HTTP POST request.

[2484] Step 5:

[2485] The server analyzes the user's responses and identifies the user's learning style.

[2486] Input: User response data

[2487] Output: Learning style (analysis results)

[2488] How it works: The server uses machine learning algorithms to analyze the response data and identify learning styles, such as online learning formats or individualized learning formats.

[2489] Step 6:

[2490] The server searches and selects relevant learning programs from educational platforms based on the aptitude test results and the user's learning preferences.

[2491] Input: Aptitude test results, user learning preferences

[2492] Output: List of study programs (with scores)

[2493] Specific operation: The server uses the educational platform's API to search for learning programs that meet the criteria, evaluates the results using a scoring algorithm, and selects the most suitable program.

[2494] Step 7:

[2495] The server sends detailed information about the selected learning program to the terminal.

[2496] Input: List of study programs

[2497] Output: A list of learning programs displayed on the device (JSON format)

[2498] Specific operation: The server obtains detailed information about the selected learning program and sends it to the terminal, which has a display interface and presents it to the user in list form.

[2499] Step 8:

[2500] The user selects the program they think is most suitable from a list of study programs displayed on the terminal.

[2501] Input: Select a study program

[2502] Output: Data for the selected study program

[2503] Specific operation: The user selects the most suitable learning program from the list and presses the selection button, and information about the selected program is sent to the server.

[2504] Step 9:

[2505] The server automatically generates a specific study plan based on the study program selected by the user.

[2506] Input: Data for the selected study program

[2507] Output: Learning plan data

[2508] Specific operation: The server executes a script to generate a learning plan that fits the user's schedule based on the content of the selected learning program.

[2509] Step 10:

[2510] The server transmits the generated study plan to the terminal.

[2511] Input: Learning plan data

[2512] Output: The lesson plan displayed on the device

[2513] Specific operation: The server sends the generated learning plan to the device, which displays it on the device's dashboard.

[2514] Step 11:

[2515] The device displays the generated learning plan to the user and provides a dashboard where they can check their progress.

[2516] Input: Learning plan data

[2517] Output: Learning plan and progress displayed on the dashboard

[2518] Specific operation: The device analyzes the learning plan received from the server and creates a dashboard so that the user can check their progress.

[2519] Step 12:

[2520] The server periodically monitors the user's learning progress and sends reminders and advice.

[2521] Input: Learning progress data

[2522] Output: Reminders and advice

[2523] Specific operation: The server continuously collects the user's progress data and automatically generates reminders and advice based on the progress and sends them to the device.

[2524] Step 13:

[2525] The device presents users with surveys and self-assessment forms as they progress through the learning process.

[2526] Input: Evaluation request

[2527] Output: User self-assessment data

[2528] Specific operation: The device periodically displays evaluation forms and surveys to the user and accepts input.

[2529] Step 14:

[2530] The user answers the questionnaire and self-evaluation form and sends them to the server from the terminal.

[2531] Input: Self-assessment answers

[2532] Output: Response data sent to the server

[2533] Specific operation: When the user answers the questions in the self-assessment form or survey and presses the send button, the device sends the answer data to the server.

[2534] Step 15:

[2535] The server analyzes the assessment data and evaluates the learning outcomes.

[2536] Input: Self-assessment response data

[2537] Output: Analysis results (learning outcome evaluation)

[2538] Specific operation: The server uses machine learning algorithms to analyze the self-assessment data and generate a report assessing learning outcomes.

[2539] Step 16:

[2540] The server generates feedback and performance reports for the user and sends them to the terminal.

[2541] Input: Analysis results

[2542] Output: Feedback and performance reports sent to your device

[2543] Specific operation: The server sends the generated feedback and performance report to the terminal, which then displays it to the user.

[2544] Step 17:

[2545] The device displays feedback and performance reports to the user.

[2546] Input: Feedback and performance reports

[2547] Output: Displayed feedback and performance report

[2548] Specific operation: The device displays the received feedback and performance report on the screen.

[2549] Step 18:

[2550] Based on the user's learning outcomes, the server suggests what to learn next and new learning programs.

[2551] Input: Learning outcome data

[2552] Output: Next learning program suggestion

[2553] Specific operation: The server generates new learning content and programs based on the user's performance data and sends suggestions for the next step to the terminal.

[2554] (Application example 1)

[2555] 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."

[2556] Conventional learning support systems have difficulty providing optimal learning programs tailored to individual users' learning styles and life schedules, and do not adequately manage learning progress or provide appropriate evaluations and feedback. Furthermore, they lacked reminder and advice functions that utilize smart devices, making it difficult to effectively support users' continuity of learning.

[2557] 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.

[2558] In this invention, the server includes means for acquiring basic information about the user, means for performing an aptitude test using a generation AI to identify the user's learning style, means for searching for and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences, means for presenting the selected learning program to the user, means for generating and customizing a learning plan that fits the user's schedule, means for sending reminders and advice to the user using a smart device and managing and displaying learning progress, means for periodically monitoring the user's learning progress and evaluating and providing feedback on results, and means for using a content distribution service to push notifications of optimal learning content to the user. This enables the provision of optimal learning programs tailored to the user's individual learning style, as well as learning progress management and effective feedback.

[2559] "Basic information" refers to basic data about a person, such as the user's name, age, occupation, and desired learning.

[2560] "Generative AI" is a technology that uses artificial intelligence to analyze data and diagnose users' characteristics and aptitudes.

[2561] "Aptitude test" is a diagnostic process that evaluates a user's learning style and aptitude based on their basic information and response data.

[2562] "Educational platform" refers to a platform or system that provides online or offline learning programs.

[2563] A "program of study" is a series of educational courses or materials designed to develop specific skills or knowledge.

[2564] A "study plan" is a specific study schedule or progress plan that is set to fit the user's daily schedule.

[2565] A "smart device" is a portable electronic device that is connected to a network, such as a smartphone, tablet, or smartwatch.

[2566] "Reminders" is a feature that sends users notifications about specific dates and times or tasks.

[2567] "Advice" refers to providing users with advice and information about their learning progress and areas for improvement.

[2568] "Learning Progress" is the status or data that indicates how far a user has progressed in a learning program.

[2569] "Monitoring" means the act of regularly and continuously observing and recording a user's learning progress.

[2570] "Outcome assessment" is the process of evaluating users' learning outcomes and progress and providing feedback.

[2571] "Feedback" is information that communicates to users the results of evaluations of their learning outcomes and progress, as well as areas for improvement.

[2572] A "content distribution service" is a service that provides users with various content such as educational and entertainment content via the Internet.

[2573] "Push notifications" are a feature that sends information and notifications to smart devices in real time.

[2574] This invention relates to a learning support system that allows users to study efficiently according to their daily schedule. This system acquires basic information about the user, performs aptitude tests using a generative AI, selects an appropriate learning program, and creates a learning plan and manages progress.

[2575] Overall overview

[2576] The system mainly consists of a server and a smart device (such as a smartphone). The server uses generative AI to diagnose the user's aptitude and select a program, and sends reminders and advice to the smart device to manage learning progress.

[2577] Hardware and software used

[2578] Hardware:

[2579] Smart devices (e.g. smartphones, tablets)

[2580] server

[2581] software:

[2582] Smart device side: Native application using React Native etc.

[2583] Server side: Python, Flask (web framework), generative AI (e.g., OpenAI's GPT-4)

[2584] Processing flow

[2585] The server first obtains the user's basic information. The user enters their name, age, occupation, and desired learning content through an application on their smart device, which is then sent to the server. The server then uses generative AI to generate the basic information and a list of questions for aptitude assessment, which are then sent to the smart device. The smart device then presents these questions to the user, collects their answers, and sends them to the server. The server then analyzes the response data to identify the user's learning style.

[2586] Next, the server searches for and selects an appropriate learning program from the educational platform based on the aptitude test results. This information is sent to the smart device, which presents the user with a list of learning programs and detailed information. Once the user selects the desired program, the server generates an automatic learning plan that takes into account the user's daily schedule and displays it on the smart device.

[2587] The server also automatically sends reminders and advice to smart devices, and manages and displays learning progress. As users progress through their learning, the server periodically monitors their progress, evaluates their progress, and provides feedback. Furthermore, the server pushes the most appropriate learning content to users via a content distribution service.

[2588] Specific examples

[2589] For example, if a 40-year-old professional wants to learn digital marketing skills, the system works as follows: The user enters basic information into the smartphone app and sends it to the server. The generation AI processes the information, conducts an aptitude test, and identifies the user's optimal learning style as "online personalized learning." The server then searches for suitable online programs related to digital marketing and presents them to the user. After the user selects an appropriate program, the server generates a learning plan incorporating two online courses per week based on the user's schedule and displays it on the smartphone app.

[2590] In addition, periodic reminders and advice are sent via push notifications to the smartphone to manage learning progress, allowing users to continue their studies efficiently. Furthermore, surveys and self-assessment forms are provided via the smart device as needed, and the server analyzes them to evaluate results and provide feedback.

[2591] Prompt Sentence Examples

[2592] "Please answer the following questions: What is your age, what is your occupation, and what do you want to study?"

[2593] "We'll take a learning style aptitude test. Answer the following questions: Do you prefer online or offline learning?"

[2594] "Which do you find more effective: individual learning or group learning?"

[2595] "Thank you for your response. An online, tutored learning format would be ideal for you."

[2596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2597] Step 1:

[2598] User registration and basic information acquisition

[2599] The terminal (smart device) prompts the user to enter basic information such as name, age, occupation, desired study content, etc. Once the user has completed the input and pressed the send button, the terminal sends the information to the server.

[2600] Input: Name, age, occupation, desired study content

[2601] Server Output: Save basic information

[2602] Specific operation: The terminal displays a user interface and prompts the user to enter information. After the user enters the information, the terminal sends the information to the server. The server receives the information and stores it in a database.

[2603] Step 2:

[2604] Aptitude test using generative AI

[2605] The server uses a generation AI to generate a list of aptitude test questions based on the user's basic information and sends them to the device. The user answers the questions on the device, and the answers are sent from the device to the server. The generation AI analyzes these to identify the user's learning style.

[2606] Input: Basic information, aptitude test questions

[2607] Server Output: Identifying the user's learning style

[2608] How it works: A generative AI (e.g., GPT-4) generates aptitude test questions based on the user's basic information and sends them to the device. The device displays the questions to the user, who answers them. The answer data is sent from the device to a server, where it is analyzed. As a result, the user's optimal learning style is identified.

[2609] Step 3:

[2610] Searching and selecting a study program

[2611] The server searches for and...

Claims

1. A means of obtaining basic information about the user; A means for performing an aptitude diagnosis using a generating AI based on the basic information and identifying the user's learning style; A means for searching and selecting an appropriate learning program from an educational platform based on the aptitude test results and learning preferences; means for presenting the selected learning program to a user; A means to generate and customize a learning plan that fits the user's life schedule; A means of regularly monitoring users' learning progress and providing evaluation and feedback on their achievements; A system including:

2. The system of claim 1 further comprising means for performing an aptitude test including audio and image data using the generating AI.

3. The system of claim 1 , further comprising means for sending reminders and advice based on the user's learning progress.

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