system

Abstract

Provide a system. [Solution] a means for collecting basic information about users and storing it in a database; A means for distributing a generated AI trainer program to a user's device based on the collected basic information; a means for checking the user's study schedule and sending study reminders; A means of analyzing users' learning data and creating reports on their learning progress and motivation; means for sending motivational messages to the user based on the received analysis results; A means of providing information and study benefits related to the user's areas of interest; A system including:

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] Traditional educational methods have problems in that learning requires time, human capital, and money. Furthermore, learning is solitary and boring, and lack of incentives makes it difficult to maintain motivation. Furthermore, incompatibility with teaching materials and teachers can lead to reduced learning efficiency. A new learning support system that can solve these problems is needed. [Means for solving the problem]

[0005] The present invention provides a learning support system that includes a means for collecting basic user information and storing it in a database, a means for distributing a generative AI trainer program to the user's device based on the collected basic information, and a means for checking the user's study schedule and sending study reminders. It also includes a means for analyzing the user's study data to create a report on their study progress and motivation, a means for sending the user motivational messages based on the received analysis results, and a means for providing information on the user's areas of interest and the benefits of studying. This system allows users to study efficiently without feeling isolated.

[0006] "Basic Information" is information about a person such as the user's name, age, learning goals, and areas of interest.

[0007] The "Generative AI Trainer Program" is software developed using artificial intelligence technology to support user learning.

[0008] A "terminal" is an electronic device used by a user, such as a computer, tablet, or smartphone.

[0009] A "database" is a system for storing and managing collected information.

[0010] A "study schedule" is a plan that indicates the time and period of study set by the user.

[0011] "Reminder" is a notification function that notifies the user of a specific action or time.

[0012] "Study data" refers to data generated by a user through their learning activities, and includes study time, progress, learning materials used, and the like.

[0013] "Analysis" is the process of analyzing the collected data and evaluating the user's learning progress and motivation.

[0014] A "report" is a report summarizing the analysis results, showing the user's learning progress and motivation.

[0015] "Motivational messages" are messages of encouragement and support to increase the user's motivation to learn.

[0016] "Information related to areas of interest" is information related to a particular area in which a user is interested.

[0017] The "benefits of studying" are the advantages and benefits that can be gained by studying. [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 illustrating 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 relates to a generative AI trainer system that effectively supports user learning. An embodiment of the present invention will be described below.

[0040] This system operates among three parties: a server, a terminal, and a user. The main functions and their operations are explained in detail below.

[0041] Initial Setup

[0042] Collection of User Information

[0043] When a new user registers, the server collects basic information about the user (such as name, age, learning goals, areas of interest, etc.) This information is stored in a database.

[0044] AI Trainer Program Distribution

[0045] The server distributes a generative AI trainer program to the user's device based on the collected basic information. This program is the primary means of assisting the user in their learning.

[0046] Study reminder function

[0047] Managing your study schedule

[0048] The device manages the study schedule set by the user. For example, if the user sets a schedule to study at 8:00 every morning, the device will save this schedule and prepare to send reminders at the specified time.

[0049] Send a reminder

[0050] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[0051] Analysis of training data

[0052] Collection of training data

[0053] The server periodically collects data from users' learning activities (such as learning time, progress, learning materials used, etc.) and stores this data in a database for analysis.

[0054] Analyzing the data

[0055] The server analyzes the collected learning data and creates reports on the user's learning progress and motivation, for example, by calculating the learning time and progress rate and evaluating motivation indicators.

[0056] Motivation support

[0057] Utilizing analysis results

[0058] Based on the analysis results sent from the server, the device sends messages to motivate the user. For example, if the device determines that the user's motivation is declining based on recent learning data, it sends a message such as "Keep going, you're doing great!"

[0059] Suggesting breaks and rewards

[0060] The device suggests appropriate breaks and rewards to the user to increase motivation. By suggesting the timing of breaks and simple rewards according to the user's situation, the device aims to maintain motivation.

[0061] Demonstration of study benefits and areas of interest

[0062] Acquiring interest information

[0063] The device checks the areas of interest the user has (e.g., programming, artificial intelligence, etc.) This information is obtained from the user's basic information collected.

[0064] Providing related information

[0065] Based on information provided by the server, the device presents the latest information on areas of interest to the user and the benefits of studying. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[0066] Specific examples

[0067] 1. For User A

[0068] User A registers with this system to deepen his / her IT knowledge.

[0069] The server collects basic information about user A and distributes the AI ​​trainer program to the terminal.

[0070] The terminal manages user A's study schedule and sends reminders at appropriate times.

[0071] The server analyzes user A's learning data and generates a report to increase motivation.

[0072] The terminal sends encouraging messages to User A based on the analysis results and provides relevant study benefits.

[0073] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[0074] The processing flow will be explained below.

[0075] Initial Setup

[0076] Step 1:

[0077] When a user registers with the system, he or she enters basic information such as name, age, learning goals, and areas of interest.

[0078] Step 2:

[0079] The server collects basic information provided by the user and stores it in a database.

[0080] Step 3:

[0081] The server distributes a generative AI trainer program to the user's device based on the collected basic information.

[0082] Study reminder function

[0083] Step 4:

[0084] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[0085] Step 5:

[0086] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[0087] Step 6:

[0088] The device will send users study reminders based on their schedule, and at 8 o'clock, it will send a notification saying "It's time to study!"

[0089] Analysis of training data

[0090] Step 7:

[0091] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and periodically transmits it to the server.

[0092] Step 8:

[0093] The server stores the learning data sent from the terminal in a database.

[0094] Step 9:

[0095] The server analyzes the stored learning data and creates reports on the user's learning progress and motivation.

[0096] Motivation support

[0097] Step 10:

[0098] The server transmits a report generated as a result of the analysis to the user's terminal.

[0099] Step 11:

[0100] The device sends a motivational message to the user based on the received report. For example, if the device determines that the user's motivation is declining, it sends the message "Keep going, you're doing great!"

[0101] Step 12:

[0102] The device will suggest appropriate breaks and rewards based on the user's situation, helping them to maintain their motivation to study.

[0103] Demonstration of study benefits and areas of interest

[0104] Step 13:

[0105] The device checks the user's basic information to see what areas they are interested in. For example, if the user expresses an interest in "programming," that information is acquired.

[0106] Step 14:

[0107] Based on information provided by the server, the device presents the benefits of studying the area the user is interested in. For example, it displays the benefit that "studying programming will increase your chances of earning a high income in the future."

[0108] Step 15:

[0109] Users can refer to the information presented to them as they proceed with their studies, which makes it easier for them to clarify the meaning and purpose of their studies.

[0110] Through the above steps, users can study effectively and achieve their learning goals while maintaining their motivation.

[0111] Example 1

[0112] 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."

[0113] Conventional learning support systems have inadequately managed users' learning progress and motivation, and lacked effective support for users to continue learning over the long term. Furthermore, they did not provide learning programs tailored to individual users' needs, send appropriate learning reminders, or conduct detailed analysis of collected learning data, making it difficult to maintain users' learning efficiency and motivation.

[0114] 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.

[0115] In this invention, the server includes means for collecting basic information about the user and storing it in a database, means for distributing a generating AI trainer program to the user's device based on the collected basic information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and creating a report on study progress and motivation, means for sending a message to the user to motivate the user based on the received analysis results, means for collecting study data and sending it to the server, means for storing the collected data in local storage and managing the study schedule, means for using a RESTful API when distributing the generating AI program, and means for sending study reminders via push notification. This makes it possible to provide a study program that meets the individual needs of the user, and by sending study reminders at appropriate times and performing detailed analysis based on the collected data, it is possible to improve the user's study efficiency and maintain long-term motivation.

[0116] "User" means an individual or corporate user of this system.

[0117] "Basic information" refers to personal information such as the user's name, age, learning goals, and areas of interest.

[0118] "Database" means a structured data storage system for storing user information, learning data, etc.

[0119] "Generative AI trainer program" refers to an artificial intelligence program that is generated by a server and distributed to a user's device to assist the user in learning.

[0120] "Terminal" means an electronic device used by a user, such as a computer, tablet, or smartphone.

[0121] "Study schedule" refers to a study plan or schedule set by a user.

[0122] "Study reminders" refer to notifications that encourage users to study based on their set study schedule.

[0123] "Study data" refers to information about a user's learning activities (study time, progress, learning materials used, etc.).

[0124] "Report" means a document or data summarizing the results of an analysis of collected learning data and evaluation of learning progress and motivation.

[0125] "RESTful API" means an application programming interface that performs operations on resources through HTTP requests.

[0126] "Push notification" refers to a message sent instantly from a server to a client application.

[0127] "Local storage" refers to a storage function for saving data within a user's terminal.

[0128] This invention relates to a generative AI trainer system that effectively supports user learning. This system operates among a server, a terminal, and a user, and provides a series of functions to enhance the user's learning progress and motivation.

[0129] User information collection and initial settings

[0130] When a new user registers with the system, the server collects basic information such as name, age, learning goals, and areas of interest. This information is received via an HTTP request and stored in a database (e.g., MySQL (registered trademark) or PostgreSQL). This allows the server to provide services tailored to each user's individual information.

[0131] Specific examples

[0132] When User A accesses the system and enters the necessary information, the server stores the information in a database. This information is used to provide individualized support in subsequent learning support.

[0133] Distribution of the Generative AI Trainer Program

[0134] The server distributes the generative AI trainer program to the user's device based on the collected user information. Distribution is performed using a RESTful API, and the device receives it and installs it locally. The generative AI trainer program is written in JavaScript (registered trademark) or Python.

[0135] Specific examples

[0136] The server packages an AI trainer program written in Python and sends it to the device as an HTTP response, which the device receives and deploys locally.

[0137] Setting and managing your study schedule

[0138] Users set their own study schedule through the application on their device, allowing them to plan when to start studying.

[0139] The device saves the configured study schedule in local storage (e.g., an SQLite database), which is used to send future reminders.

[0140] Specific examples

[0141] User A enters his / her study plan into the device app every morning at 8:00 and presses the save button. The device saves this schedule in an SQLite database.

[0142] Send study reminders

[0143] The device generates and sends study reminders based on the schedule set by the user, and uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the reminder at the specified time.

[0144] Specific examples

[0145] Every morning at 8:00, a push notification saying "It's time to study!" appears on User A's device.

[0146] Collecting and sending learning data

[0147] As the user progresses with their learning activities, they input learning data, such as their progress and the learning materials they have used, into the terminal.

[0148] The device collects this learning data and periodically sends it to the server, which uses HTTPS for secure communication.

[0149] Specific examples

[0150] When User A enters the study time and materials used into the app and presses the send button, the device sends this data to the server.

[0151] Analysis of training data

[0152] The server analyzes the received learning data and creates reports on learning progress and motivation. It aggregates and analyzes the data using Python libraries such as pandas and scikit-learn.

[0153] Specific examples

[0154] The server extracts learning data from the database and generates a report that compiles and visualizes weekly learning time and progress.

[0155] Motivational feedback

[0156] The terminal sends a message to the user to increase motivation based on the analysis results sent from the server.

[0157] Specific examples

[0158] When the server determines that "recent learning data indicates a decline in motivation" and sends this result to the device, the device notifies the user with an encouraging message such as "Keep going, you're doing great!"

[0159] Providing information about areas of interest

[0160] The server collects the latest information and study benefits based on the user's interests and provides them to the terminal.

[0161] The terminal presents the provided information to the user.

[0162] Specific examples

[0163] The server collects information such as "Learning programming will increase your chances of earning a high income in the future" and sends it to the device, which then displays it to the user.

[0164] Prompt Sentence Examples

[0165] Below are some example prompts to input to the generative AI model:

[0166] Example prompt sentence:

[0167] "Generate a report on User A's current learning progress and motivation based on his learning record data from the past month. Please take into account the following information: total study time, progress, types of learning materials used, and trends in motivation. Also, please include specific advice for User A to continue studying."

[0168] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[0169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0170] Step 1: User registration and information collection

[0171] Input: The user enters basic information such as name, age, learning goals, and areas of interest.

[0172] Specific operation: A user accesses the system, enters personal information into a form, and presses the submit button.

[0173] Data processing: The server receives the entered information, converts it into the required format, and stores it in the database.

[0174] Output: The user's basic information is saved in the database.

[0175] Step 2: Distributing the Generative AI Trainer Program

[0176] Input: User basic information stored in the database

[0177] Specific operation: The server generates an individualized generative AI trainer program based on the stored user information and distributes it to the terminal via an HTTP response.

[0178] Data processing: Customize program settings based on collected information and package the program.

[0179] Output: The customized AI trainer program is distributed to the device and installed locally.

[0180] Step 3: Set and manage your study schedule

[0181] Input: Information for users to set their own study schedule (date, time, subjects, etc.)

[0182] Specific operation: The user inputs a schedule through the terminal application and presses the save button.

[0183] Data processing: The terminal converts the input schedule into a format suitable for saving in the SQLite database and stores it.

[0184] Output: The configured learning schedule is saved to local storage.

[0185] Step 4: Send a reminder

[0186] Input: Information saved in your study schedule

[0187] Specific operation: When the specified time arrives, the device will use the push notification system to generate a reminder and display a notification.

[0188] Data Processing: Prepares push notification messages and sends them to the notification system based on a schedule.

[0189] Output: The user is shown the reminder.

[0190] Step 5: Collect and send training data

[0191] Input: Study data entered by the user (study time, progress, materials used, etc.)

[0192] Specific operation: The user enters learning data on the device and presses the send button. The device then sends the data to the server via HTTPS.

[0193] Data processing: Converts the input data into the appropriate format and sends it using the HTTPS protocol.

[0194] Output: The user's learning data is sent to the server and stored in the database.

[0195] Step 6: Analyze the training data

[0196] Input: Training data stored on the server

[0197] Specific operation: The server periodically extracts training data from the database and analyzes and aggregates the data using Python's pandas and scikit-learn.

[0198] Data calculation: Calculate each user's study time, progress, and motivation, and create a report.

[0199] Output: A user-specific report is generated based on the analyzed data.

[0200] Step 7: Motivational feedback

[0201] Input: Report generated as a result of analysis

[0202] Specific operation: The server sends the generated report to the terminal, and the terminal notifies the user of an encouraging message based on the analysis results.

[0203] Data calculation: Generate motivational messages based on the report content.

[0204] Output: A feedback message is sent to the user.

[0205] Step 8: Provide information about your interests

[0206] Input: Information about the user's interests

[0207] Specific operation: The server collects the information to be provided, organizes it, and sends it to the terminal, which then displays the received information.

[0208] Data processing: Converting collected information into a format that meets the user's interests and providing it to them.

[0209] Output: Information about the area of ​​interest is presented to the user.

[0210] This allows users to receive personalized learning support, enabling them to continue learning while maintaining their motivation.

[0211] (Application example 1)

[0212] 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."

[0213] In brick-and-mortar stores, salespeople and staff need to constantly improve their product knowledge and customer service skills. However, they are often overwhelmed with their daily work and neglect self-study. For this reason, an effective system is needed that allows staff to learn efficiently, manage progress, and maintain motivation. It is also necessary to have a system that provides timely reminders and rewards to support continued learning.

[0214] 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.

[0215] In this invention, the server includes: means for collecting basic user information and storing it in a database; means for distributing a generated AI trainer program to the user's device based on the collected basic information; means for checking the user's study schedule and sending study reminders; means for analyzing the user's study data and creating a report on their study progress and motivation; means for sending motivational messages to the user based on the received analysis results; means for providing information on the user's areas of interest and the benefits of studying; means for supporting store salespeople and staff in improving their product knowledge and customer service skills; means for displaying visual reminders on the smart glasses based on the staff's registered schedule; means for collecting staff study data and analyzing their progress and test results; means for displaying motivational messages and reward suggestions to staff based on the analysis results; and means for presenting information on new products and skills. This allows store staff to study systematically and continuously, ensuring they always have the latest knowledge and skills. Furthermore, by visualizing study progress and providing timely reminders and motivational messages, the system effectively supports continued study and motivation.

[0216] "User basic information" is personal information related to a user, such as name, role, learning goals, etc.

[0217] "Database" refers to a storage device and related software for storing and managing users' basic information and learning data.

[0218] The "Generative AI Trainer Program" is software designed to assist users in their learning using AI technology.

[0219] "User device" refers to a device that can display and operate learning reminders and educational content, including smartphones and smart glasses.

[0220] A "study schedule" is a study timetable set by the user.

[0221] A "study reminder" is a notification or message set to encourage the user to start studying.

[0222] "Study data" is a record of the user's learning activities, including the amount of time spent studying and progress.

[0223] "Analysis results" are reports of the results and trends obtained by analyzing the collected learning data.

[0224] "Motivational messages" are messages of encouragement and encouragement to increase the user's motivation to learn.

[0225] "Information related to fields of interest" refers to the latest information and knowledge related to fields in which the user is interested.

[0226] The "benefits of studying" are the future benefits and improvements that come from learning.

[0227] "Store salespeople and staff" refers to employees working in physical stores who sell products and serve customers.

[0228] "Product knowledge" is detailed information and understanding about the product being sold.

[0229] "Customer service techniques" refer to the skills and know-how required for dealing with customers.

[0230] "Visual reminders" are notifications or messages displayed through devices such as smart glasses.

[0231] "Progress" indicates the current degree of achievement of the learning goal set by the user.

[0232] "Test results" are the results of assessment tests and quizzes taken by users.

[0233] A "reward offer" is an offer of an incentive to be offered to a user depending on the outcome of their learning.

[0234] "Information about new products and skills" refers to information about the latest product knowledge and new skills that staff need to acquire.

[0235] The present invention relates to a generative AI trainer system that effectively supports user learning. This system is intended to improve the product knowledge and customer service skills of salespeople and staff in brick-and-mortar stores. Specific embodiments for implementing the present invention and their processing details are described below.

[0236] Hardware and Software

[0237] This system operates between three parties: a server, a terminal (a device such as smart glasses), and a user. The main hardware and software components are as follows:

[0238] 1. Hardware

[0239] Smart glasses (e.g., Google® Glass®)

[0240] server

[0241] communication network

[0242] 2. Software

[0243] Client app (in smart glasses)

[0244] Server-side database (e.g. MySQL)

[0245] Backend framework (e.g. Django)

[0246] Data analysis libraries (e.g., scikit-learn)

[0247] Generative AI Models

[0248] Processing Details

[0249] 1. Initial Setup

[0250] Users: New users register by entering basic information such as their name, role, and learning goals.

[0251] Server: Stores basic information in a database, generates an AI trainer program based on the collected information, and distributes it to the user's device.

[0252] 2. Study Reminders

[0253] Device: Manages the learning schedule set by the user. For example, if the user sets product knowledge learning at 2 p.m.

[0254] Device: Display a visual notification at the set time saying "It's study time now!"

[0255] 3. Collection and analysis of learning data

[0256] Server: Collects user learning activity data (study time, progress, test results, etc.).

[0257] Server: Analyzes the collected data and creates reports on learning progress and motivation.

[0258] 4. Motivation support

[0259] Device: Display motivational messages to staff based on the analysis. Keep staff motivated with messages like "Continuous learning pays off!"

[0260] Device: We also offer rewards such as visual badges and points systems.

[0261] 5. Providing information about new products and skills

[0262] Terminal: Displays the latest information and knowledge related to the user's areas of interest.

[0263] Device: For example, when a new product is released, detailed information about the product is displayed and users can deepen their understanding through tests and quizzes.

[0264] Specific examples

[0265] For example, when a new product is released, the process proceeds as follows:

[0266] 1. Example prompt

[0267] New product learning prompts (examples): "Answer the following questions. List three features of the new product.", "Describe the specialized features of the new product."

[0268] Progress prompts (examples): "How many minutes did you spend studying today?", "Describe one advanced sales technique you learned."

[0269] In this way, by utilizing the system of the present invention, store staff can plan and continue their studies, enabling them to always acquire the latest knowledge and skills. In addition, by visualizing their learning progress and providing timely reminders and motivational messages, the system can effectively support continuation of learning and maintenance of motivation.

[0270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0271] Step 1:

[0272] Initial Setup - Collecting User Information

[0273] User: A new user registers with the service, entering basic information such as their name, role, and learning goals.

[0274] Input: Basic information such as the user's name, role, and learning goal.

[0275] Server: Saves the entered basic information in a database.

[0276] Output: Basic information of the user stored in the database.

[0277] Specific operation: The user enters information into the form through the terminal and presses the submit button. The server receives the form data and records it in the database.

[0278] Step 2:

[0279] Distribution of the Generative AI Trainer Program

[0280] Server: Customizes the generative AI trainer program based on basic information stored in the database.

[0281] Input: User basic information stored in the database.

[0282] Server: Distributes customized generative AI trainer programs to users' devices.

[0283] Output: The generated AI trainer program installed on the user's device.

[0284] How it works: The server analyzes the user information, generates a customized program based on that information, and sends it to the device, where it is automatically installed.

[0285] Step 3:

[0286] Managing your study schedule

[0287] User: Set a study schedule, for example, to study product knowledge at 2 PM.

[0288] Input: User's study schedule (date, time, content).

[0289] Device: Receives and stores the user's study schedule.

[0290] Output: Saved study schedule.

[0291] Specific operation: The user sets up a study schedule on the device app and presses the save button. The device then saves the information in its internal memory.

[0292] Step 4:

[0293] Send study reminders

[0294] On your device: Generate reminders based on your study schedule.

[0295] Input: Saved study schedule.

[0296] Device: At the set time, a visual reminder such as "It's time to study now!" will be displayed on the smart glasses.

[0297] Output: Reminder notification.

[0298] Specific operations: Check the set time on the internal clock, generate reminders, and display notifications on the smart glasses.

[0299] Step 5:

[0300] Collection and analysis of learning data

[0301] Device: Collects user data during learning activities (study time, progress, quiz results, etc.).

[0302] Input: User learning activity data.

[0303] Terminal: Sends collected data to the server.

[0304] Server: Receives collected data and stores it in a database.

[0305] Output: Saved user training data.

[0306] Specific operation: The device monitors learning activities and collects data, which is then sent to a server at specific times and stored in a database.

[0307] Step 6:

[0308] Data analysis and reporting

[0309] Server: Analyzes the stored learning data and performs data processing and calculations to generate reports on progress and motivation.

[0310] Input: Training data stored in a database.

[0311] Server: Analyzes the data using data analysis libraries (e.g., scikit-learn) to evaluate progress and motivation indicators.

[0312] Output: The generated report.

[0313] Specific operation: The server retrieves the user's learning data from the database, performs calculations using analytical functions, and summarizes the results in report format.

[0314] Step 7:

[0315] Sending motivational messages

[0316] Server: Based on the generated report, generate messages to improve user motivation.

[0317] Input: The generated report.

[0318] Server: Generate a message such as "Continuous learning helps!"

[0319] Terminal: Receives messages and displays them visually on the smart glasses.

[0320] Output: Motivation message notification.

[0321] Specific operation: The server extracts information from the report, generates an encouraging message, and sends it to the device, which then displays the received message on the smart glasses.

[0322] Step 8:

[0323] Providing information about new products and skills

[0324] Device: Periodically collect and display information related to your areas of interest.

[0325] Input: Information about the user's areas of interest.

[0326] Terminal: Presents quizzes and tests to the user in the form of prompts, if necessary.

[0327] For example, "Please list three features of your new product.", "Please explain the specialized functions of your new product."

[0328] Output: Presentation of information about the new product or skill and related test results.

[0329] Specific operation: The device periodically receives new information from the server and presents it to the user. It also displays quizzes to check whether the user has understood the content and collects the results.

[0330] 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.

[0331] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[0332] This system operates among three parties: a server, a terminal, and a user, and incorporates an emotion engine that recognizes the user's emotions, thereby providing learning support that corresponds to the user's emotional state.

[0333] Initial Setup

[0334] Collecting user information and emotion data

[0335] When a user registers with the system, they enter basic information such as their name, age, learning goals, and areas of interest.

[0336] The server collects basic information provided by the user and stores it in a database. In addition, it collects the user's emotional state using an emotion engine and stores it in the database.

[0337] AI Trainer Program Distribution

[0338] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional state data. This program has the necessary functions to support the user's learning.

[0339] Study reminder function

[0340] Managing your study schedule

[0341] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[0342] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[0343] Send a reminder

[0344] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[0345] Analysis of training data and sentiment data

[0346] Collection of training data

[0347] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and emotion data from the emotion engine, and periodically transmits them to the server.

[0348] Analyzing the data

[0349] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[0350] Motivation support

[0351] Utilizing analysis results

[0352] The server transmits a report generated as a result of the analysis to the user's terminal.

[0353] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[0354] Suggesting breaks and rewards

[0355] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[0356] Demonstration of study benefits and areas of interest

[0357] Acquiring interest information

[0358] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," that information is acquired.

[0359] Providing related information

[0360] Based on information provided by the server, the device presents the benefits of studying the field the user is interested in. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[0361] Presentation of information based on emotion information

[0362] The device takes into account the user's emotional state obtained from the emotion engine and presents relaxing content and information at the appropriate time.

[0363] Specific examples

[0364] 1. For User A

[0365] User A registers with this system to deepen his / her IT knowledge.

[0366] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[0367] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[0368] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[0369] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[0370] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[0371] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[0372] The processing flow will be explained below.

[0373] Initial Setup

[0374] Step 1:

[0375] To register with the system, users enter basic information such as their name, age, learning goals, and areas of interest.

[0376] Step 2:

[0377] The server receives the basic information provided by the user and stores it in a database.

[0378] Step 3:

[0379] The server activates the emotion engine and begins monitoring the user's emotional state based on the basic information provided by the user.

[0380] Step 4:

[0381] The server distributes the generated AI trainer program to the user's device and prompts the user to install the program.

[0382] Study reminder function

[0383] Step 5:

[0384] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[0385] Step 6:

[0386] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[0387] Step 7:

[0388] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[0389] Analysis of training data and sentiment data

[0390] Step 8:

[0391] The terminal periodically records the user's learning activity data (study time, progress, learning materials used, etc.) and emotional state and transmits them to the server.

[0392] Step 9:

[0393] The server stores the learning data and emotion data sent from the terminal in a database.

[0394] Step 10:

[0395] The server analyzes the stored learning data and emotional data to create a report on the user's learning progress and motivation. By taking the emotional data into account, detailed analysis can be performed according to the user's emotional state.

[0396] Motivation support

[0397] Step 11:

[0398] The server transmits a report generated as a result of the analysis to the user's terminal.

[0399] Step 12:

[0400] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[0401] Step 13:

[0402] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[0403] Demonstration of study benefits and areas of interest

[0404] Step 14:

[0405] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," it uses that information to obtain related information.

[0406] Step 15:

[0407] Based on information provided by the server, the device will present the benefits of studying the area of ​​interest to the user at the appropriate time. For example, it will display specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[0408] Step 16:

[0409] The device provides relaxing content and information at the appropriate time based on the user's emotional state, obtained from the emotion engine, thereby reducing stress and improving learning efficiency.

[0410] Specific examples

[0411] 1. For User A

[0412] User A registers with this system to deepen his / her IT knowledge.

[0413] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[0414] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[0415] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[0416] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[0417] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[0418] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[0419] Example 2

[0420] 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."

[0421] In today's learning environment, it is difficult for users to maintain sustained motivation to study effectively. Furthermore, there are no systems that can properly grasp each user's learning progress and emotional state and provide support accordingly. Therefore, to maximize users' learning effectiveness, a personalized learning support system that takes into account the user's basic information and emotional state is needed.

[0422] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0423] In this invention, the server includes means for collecting basic information and emotional data of the user and storing it in a database, means for distributing a generated AI trainer program to the user's device based on the collected basic information and emotional data, means for checking the user's study schedule and sending study reminders, means for collecting and analyzing the user's study activity data and emotional data, means for suggesting motivational messages, breaks, and rewards to the user based on the analysis results, means for acquiring information about the user's interests and providing information related to the interests and benefits of studying, and means for presenting appropriate relaxation content based on the emotional data. This makes it possible to comprehensively grasp the user's study progress and emotional state and provide optimized study support for each user.

[0424] "User" refers to an individual who uses the system to learn.

[0425] "Basic Information" refers to initial registration data about a user, such as the user's name, age, learning goals, and areas of interest.

[0426] "Emotional data" refers to information that indicates a user's emotional state and is obtained through text analysis or facial recognition technology.

[0427] "Database" refers to an information system for storing and managing collected basic information, emotional data, and learning activity data.

[0428] "Generative AI trainer program" refers to a program that provides learning support to users, generated based on the user's basic information and emotional data.

[0429] "Terminal" refers to a device used by a user to access the system, such as a computer, smartphone, or tablet.

[0430] "Study schedule" refers to a study plan and timetable set by a user.

[0431] "Study reminders" refer to messages and alerts that notify users of study time and encourage them to study.

[0432] "Learning activity data" refers to data related to learning, such as a user's study time, progress, and learning materials used.

[0433] "Analysis results" refers to information generated based on collected learning activity data and emotional data for evaluating a user's learning progress and motivation.

[0434] "Messages" refer to words of encouragement or advice sent to increase the user's motivation to study.

[0435] "Break" refers to information and notifications suggesting that the user take adequate rest.

[0436] "Rewards" refer to incentives given to recognize a user's efforts and maintain motivation.

[0437] "Interest information" refers to information relating to areas in which a user is interested.

[0438] "Relaxation content" refers to content such as music and videos that stabilize the user's emotional state and promote relaxation.

[0439] MODE FOR CARRYING OUT THE INVENTION

[0440] The present invention provides a system for effectively supporting a user's learning, and provides personalized learning support by taking into account the user's emotional state. Hereinafter, an embodiment of the present invention will be described in detail.

[0441] Collecting user information and emotion data

[0442] When users register with the system, they enter basic information such as their name, age, learning goals, and areas of interest via a web form or mobile application.

[0443] The server collects basic information provided by the user and stores it in a database, and also analyzes the user's emotional state using an emotion engine (e.g., IBM Watson® Tone Analyzer) and stores this information in the database as well.

[0444] AI Trainer Program Distribution

[0445] The server generates prompt sentences for the generative AI model (e.g., GPT-4 (registered trademark)) based on the collected basic information and emotional data, and creates a customized generative AI trainer program.

[0446] The server distributes the generated trainer program to the user's terminal, which includes a study reminder function and a study data tracking function.

[0447] Managing your study schedule

[0448] Users set their own study schedules on their own devices. Specifically, they can use the device app to input plans such as "start studying at 8am every morning."

[0449] The device saves the set schedule information in local storage and prepares to send a reminder at the specified time.

[0450] Send study reminders

[0451] When the set study time arrives, the device will send a notification to the user saying, "It's time to study!" This notification is realized using a push notification service.

[0452] Collecting and analyzing training data and sentiment data

[0453] The device transmits the user's learning activity data (study time, progress, learning materials used, etc.) and emotional data collected in real time to the server. Specific emotional data is acquired through the device's camera and microphone.

[0454] The server stores the received learning data and emotion data in a database and analyzes them using R, Python, etc. As a result of the analysis, a report on the user's learning progress and motivation is generated.

[0455] Motivation support

[0456] The server generates a report based on the analysis results and sends it to the user's terminal.

[0457] Based on the report, the device sends the user motivational messages, such as "Keep going, you're doing great!" if their motivation is low.

[0458] The device considers the user's learning progress and emotional state and suggests appropriate breaks and rewards, for example, displaying a notification that says, "Taking a 15-minute break will improve your efficiency."

[0459] Demonstration of study benefits and areas of interest

[0460] The device will check the user's areas of interest from their basic information and then present relevant benefits based on that information, such as "Learning programming will increase your chances of earning a high income in the future."

[0461] The device will then provide appropriate relaxation content based on emotional data, such as relaxing music and guided meditations through the Calm app.

[0462] Specific examples

[0463] When user A uses this system to deepen his / her IT knowledge, the system operates as follows.

[0464] User A enters basic information into the system and registers as a new user.

[0465] The server generates a customized AI trainer program based on basic information and daily collected emotional data and distributes it to User A's device.

[0466] The device manages user A's study schedule and sends a reminder saying "It's time to study!" at the scheduled time of 8:00.

[0467] The server analyzes the learning data and emotional data of User A and creates a report. For example, the report may indicate that motivation is declining.

[0468] Based on the report, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[0469] The device provides User A with benefits such as "Learning programming will increase your chances of earning a high income in the future" and presents relaxing content according to his or her emotional state.

[0470] This allows the system to effectively support users' learning and provide personalized learning assistance that takes into account their emotional state.

[0471] Example prompt sentence:

[0472] "Consider user sentiment data and provide advice on maintaining appropriate learning motivation."

[0473] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0474] Step 1:

[0475] To register with the system, a user enters basic information such as name, age, learning goals, and areas of interest. This information is entered through a web form or a mobile application. The inputs include name, age, learning goals, and areas of interest. The output is sent to the server.

[0476] Step 2:

[0477] The server stores the received basic information in a database. It then uses an emotion engine (e.g., a natural language processing tool) to analyze the user's emotional state. Specifically, it detects emotions from the user's input text. The inputs include the user's basic information and the analysis results from the emotion engine. The output is the basic information and emotional data stored in the database.

[0478] Step 3:

[0479] The server creates a customized generative AI trainer program based on the collected basic information and emotional data. A generative AI model (e.g., a generative AI engine) is used to generate a learning program optimized for the user. The input includes basic information and emotional data from the database and prompt sentences for the generative AI model. The output is a customized generative AI trainer program.

[0480] Step 4:

[0481] The server distributes the generated AI trainer program to the user's device. Specifically, it sends the program in digital format so that the user can install it on their device. The input is the generated AI trainer program. The output is the program sent to the user's device.

[0482] Step 5:

[0483] A user sets a study schedule on their device. Specifically, they use a device application to input a schedule such as "I will start studying at 8:00 every morning." The input includes details of the schedule set by the user. The output is the schedule saved on the device.

[0484] Step 6:

[0485] The device saves the user's study schedule in local storage and prepares to send a reminder at the specified time. The input includes the saved study schedule. The output is the reminder notification.

[0486] Step 7:

[0487] When the scheduled study time arrives, the device sends the user a notification saying "It's time to study!". Specifically, it uses a push notification service to display a reminder on the user's device. The input includes schedule data for sending the reminder. The output is a reminder notification that is displayed on the user's device.

[0488] Step 8:

[0489] The device collects the user's learning activity data (study time, progress, learning materials used, etc.). In addition, emotional data is collected in real time. Specifically, the device's camera and microphone are used to acquire the emotional data. The input includes the user's learning activity and data for emotion detection. As output, these data are sent to the server.

[0490] Step 9:

[0491] The server stores the received learning data and emotion data in a database and analyzes it. Specifically, it analyzes the data using programs such as R and Python and generates reports on the user's learning progress and motivation. The inputs are the learning data and emotion data from the database. The output is a detailed analysis report.

[0492] Step 10:

[0493] The server sends the generated report to the user's terminal, specifically in digital format using a secure protocol (e.g. HTTPS). The input is the generated analysis report. The output is the report that arrives at the user's terminal.

[0494] Step 11:

[0495] The terminal sends a motivational message to the user based on the received report. Specifically, the terminal displays a message such as "Keep going, you're doing great!". The input includes the analysis report and a message template. The output is the motivational message displayed to the user.

[0496] Step 12:

[0497] The device suggests appropriate breaks and rewards based on the user's learning progress and emotional state. Specifically, it sends a notification such as "Taking a 15-minute break will improve your efficiency." The input includes the user's learning data and emotional data. As an output, the break and reward suggestion notification is displayed on the user's device.

[0498] Step 13:

[0499] The device acquires the user's interest information and provides information related to that interest and the benefits of studying. Specifically, it displays benefits such as "Learning programming will increase your chances of earning a high income in the future." The input includes the user's basic information and interest data. The output presents related information and benefits.

[0500] Step 14:

[0501] The device presents relaxing content based on the emotion data. Specifically, it displays content such as "relaxing music" and "meditation guide." The input includes the emotion engine analysis results and a template for the relaxing content. As an output, the relaxing content is displayed on the user's device.

[0502] (Application example 2)

[0503] 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."

[0504] Conventional learning support systems proceed with learning without taking the user's emotional state into consideration, which can lead to a decline in motivation and a decline in learning efficiency. Furthermore, there are problems with users being fatigued and stressed due to inappropriate timing for breaks and the provision of rewards. To address these issues, a system that provides support according to the user's emotional state is needed.

[0505] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information and emotional information about the user and storing it in a database, means for distributing a generative AI trainer program to the user's terminal based on the collected basic information and emotional information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and emotional data and creating a report on study progress and motivation, means for sending a motivating message to the user based on the received analysis results, means for suggesting appropriate breaks and rewards based on the user's emotional state, and means for providing information on the user's areas of interest and the benefits of study. This makes it possible to maximize the effectiveness of study while taking the user's emotional state into consideration in real time.

[0506] "Basic User Information" refers to personal data such as the user's name, age, learning goals, and areas of interest.

[0507] "Emotion information" is data that indicates the user's emotional state, and includes tension, fatigue level, stress state, and the like.

[0508] "Database" refers to an information system for storing and managing collected basic information and emotional information.

[0509] The "generative AI trainer program" is learning support software that is generated based on collected basic information and emotional information and distributed to the user's device.

[0510] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[0511] A "study schedule" is a plan of study time and study content set by the user.

[0512] "Study Reminder" is a function that notifies users to start studying based on the study schedule they set.

[0513] "Study data" refers to data related to a user's learning activities, including study time, progress, learning materials used, and the like.

[0514] "Emotion data" is data about a user's emotional state collected by an emotion engine.

[0515] A "report" is a report that analyzes learning data and emotional data and summarizes information about learning progress and motivation.

[0516] A "motivation message" is a message sent to a user to increase their motivation to study.

[0517] "Break and reward suggestions" is a function that suggests breaks at appropriate times and rewards according to the user's learning progress based on the user's emotional state.

[0518] "Areas of interest" refers to areas of study or topics that interest the user.

[0519] "Learning benefits" refers to the benefits or advantages a user will gain if they continue to learn.

[0520] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[0521] Initial Setup

[0522] Collecting user information and emotion data

[0523] When a user registers with the system, they must enter basic information such as their name, age, learning goals, and areas of interest. The server collects the basic information provided by the user and stores it in a database. The server also periodically collects the user's emotional state (e.g., tension, fatigue level) using an emotion engine and stores this in the database.

[0524] Distribution of the Generative AI Trainer Program

[0525] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional information. This program has the necessary functions (reminders, data collection, analysis, etc.) to support the user's learning.

[0526] Study reminder function

[0527] Managing your study schedule

[0528] Users can set a learning schedule on their device. For example, they can set an appointment to start the driving simulation every morning at 8:00. The device saves the schedule and prepares to send a reminder at the specified time.

[0529] Send a reminder

[0530] The device sends learning reminders to the user based on their learning schedule. For example, at 8:00 a.m., a notification will appear saying, "It's time to start the driving simulation."

[0531] Analysis of training data and sentiment data

[0532] Collection of training data

[0533] The device collects the user's learning activity data and emotion data from the emotion engine, and periodically transmits them to the server. The learning activity data includes the user's learning time, progress, learning materials used, etc.

[0534] Analyzing the data

[0535] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[0536] Motivation support

[0537] Utilizing analysis results

[0538] The server then sends the generated analysis results to the user's device. Based on the received report, the device sends the user a motivating message. For example, if the device determines that the user's motivation is low, it sends a message such as, "You seem tired. Take a short break."

[0539] Suggesting breaks and rewards

[0540] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[0541] Other support

[0542] The device checks the user's areas of interest from basic information and, based on information provided by the server, presents the benefits of studying the user's areas of interest. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future." It also takes emotional information into account and presents relaxing content at appropriate times.

[0543] Examples of concrete examples and prompts

[0544] Specific examples

[0545] 1. When the user starts the simulation, the system checks their emotional state for the day and sends a message saying, "You seem a little tired. Let's take a break."

[0546] 2. When the learning time is over, the system analyzes the progress of the learning data and emotional data, and notifies the driver, "Today's driving simulation took 35 minutes. Let's do our best next time!"

[0547] Prompt Sentence Examples

[0548] "Generate motivational messages based on the user's learning progress and emotional state. For example, generate a message to send when the user is in a 'tired' state."

[0549] This embodiment allows for maximizing learning effectiveness while taking into account the user's emotional state in real time.

[0550] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0551] Step 1:

[0552] A user registers with the system and inputs basic information, learning goals, and areas of interest. The server collects the basic information and emotional state received from the user and stores them in a database.

[0553] Specific behavior:

[0554] (Input) User information (name, age, learning goals, etc.)

[0555] (Data processing) Convert the entered information into a database format

[0556] (Output) Saved user information

[0557] Step 2:

[0558] The server uses an emotion engine to periodically collect the user's emotional state (e.g., tension, fatigue level) and store it in a database.

[0559] Specific behavior:

[0560] (Input) Real-time emotion data

[0561] (Data processing) Emotional data is measured, organized, and saved in a database

[0562] (Output) Updated emotion information

[0563] Step 3:

[0564] The server generates a generative AI trainer program based on the basic information and emotion data, and distributes the program to the user's device.

[0565] Specific behavior:

[0566] (Input) Basic information, emotion data

[0567] (Data calculation) Generation of trainer programs using generative AI models

[0568] (Output) The distributed generative AI trainer program

[0569] Step 4:

[0570] The user sets up a study schedule on the device, which stores this schedule and prepares to send reminders at the specified times.

[0571] Specific behavior:

[0572] (Input) Study Schedule

[0573] (Data processing) Saving schedule information

[0574] (Output) Set reminders

[0575] Step 5:

[0576] The device sends learning reminders to the user based on the learning schedule. For example, it displays a notification every morning at 8:00 that says, "It's time to start the driving simulation."

[0577] Specific behavior:

[0578] (Input) Saved schedule information

[0579] (Data calculation) Generate reminders according to schedule

[0580] (Output) Reminders sent

[0581] Step 6:

[0582] The device collects the user's learning activity data (study time, progress, etc.) and emotion data from the emotion engine, and periodically sends them to the server.

[0583] Specific behavior:

[0584] (Input) Learning activity data, real-time emotion data

[0585] (Data calculation) Data collection and organization

[0586] (Output) Data sent to the server

[0587] Step 7:

[0588] The server analyzes the learning and emotional data and generates reports on the user's learning progress and motivation.

[0589] Specific behavior:

[0590] (Input) Learning data, emotion data

[0591] (Data calculation) Report generation by data analysis

[0592] (Output) Generated report

[0593] Step 8:

[0594] The server then sends the generated analysis results to the user's device, which then sends them motivational messages and suggests appropriate breaks and rewards based on their emotional state.

[0595] Specific behavior:

[0596] (Input) Analysis results (report)

[0597] (Data Calculation) Motivation Message Generation

[0598] (Output) Messages sent and break / reward proposals

[0599] Step 9:

[0600] The device provides learning benefits related to the user's areas of interest and presents relaxing content at the appropriate time depending on the user's emotional state.

[0601] Specific behavior:

[0602] (Input) Basic information, area of ​​interest data, emotion data

[0603] (Data calculation)Generation of merit information and relaxation content

[0604] (Output) Presented benefit information and content

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Second embodiment]

[0609] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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."

[0621] The present invention relates to a generative AI trainer system that effectively supports user learning. An embodiment of the present invention will be described below.

[0622] This system operates among three parties: a server, a terminal, and a user. The main functions and their operations are explained in detail below.

[0623] Initial Setup

[0624] Collection of User Information

[0625] When a new user registers, the server collects basic information about the user (such as name, age, learning goals, areas of interest, etc.) This information is stored in a database.

[0626] AI Trainer Program Distribution

[0627] The server distributes a generative AI trainer program to the user's device based on the collected basic information. This program is the primary means of assisting the user in their learning.

[0628] Study reminder function

[0629] Managing your study schedule

[0630] The device manages the study schedule set by the user. For example, if the user sets a schedule to study at 8:00 every morning, the device will save this schedule and prepare to send reminders at the specified time.

[0631] Send a reminder

[0632] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[0633] Analysis of training data

[0634] Collection of training data

[0635] The server periodically collects data from users' learning activities (such as learning time, progress, learning materials used, etc.) and stores this data in a database for analysis.

[0636] Analyzing the data

[0637] The server analyzes the collected learning data and creates reports on the user's learning progress and motivation, for example, by calculating the learning time and progress rate and evaluating motivation indicators.

[0638] Motivation support

[0639] Utilizing analysis results

[0640] Based on the analysis results sent from the server, the device sends messages to motivate the user. For example, if the device determines that the user's motivation is declining based on recent learning data, it sends a message such as "Keep going, you're doing great!"

[0641] Suggesting breaks and rewards

[0642] The device suggests appropriate breaks and rewards to the user to increase motivation. By suggesting the timing of breaks and simple rewards according to the user's situation, the device aims to maintain motivation.

[0643] Demonstration of study benefits and areas of interest

[0644] Acquiring interest information

[0645] The device checks the areas of interest the user has (e.g., programming, artificial intelligence, etc.) This information is obtained from the user's basic information collected.

[0646] Providing related information

[0647] Based on information provided by the server, the device presents the latest information on areas of interest to the user and the benefits of studying. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[0648] Specific examples

[0649] 1. For User A

[0650] User A registers with this system to deepen his / her IT knowledge.

[0651] The server collects basic information about user A and distributes the AI ​​trainer program to the terminal.

[0652] The terminal manages user A's study schedule and sends reminders at appropriate times.

[0653] The server analyzes user A's learning data and generates a report to increase motivation.

[0654] The terminal sends encouraging messages to User A based on the analysis results and provides relevant study benefits.

[0655] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[0656] The processing flow will be explained below.

[0657] Initial Setup

[0658] Step 1:

[0659] When a user registers with the system, he or she enters basic information such as name, age, learning goals, and areas of interest.

[0660] Step 2:

[0661] The server collects basic information provided by the user and stores it in a database.

[0662] Step 3:

[0663] The server distributes a generative AI trainer program to the user's device based on the collected basic information.

[0664] Study reminder function

[0665] Step 4:

[0666] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[0667] Step 5:

[0668] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[0669] Step 6:

[0670] The device will send users study reminders based on their schedule, and at 8 o'clock, it will send a notification saying "It's time to study!"

[0671] Analysis of training data

[0672] Step 7:

[0673] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and periodically transmits it to the server.

[0674] Step 8:

[0675] The server stores the learning data sent from the terminal in a database.

[0676] Step 9:

[0677] The server analyzes the stored learning data and creates reports on the user's learning progress and motivation.

[0678] Motivation support

[0679] Step 10:

[0680] The server transmits a report generated as a result of the analysis to the user's terminal.

[0681] Step 11:

[0682] The device sends a motivational message to the user based on the received report. For example, if the device determines that the user's motivation is declining, it sends the message "Keep going, you're doing great!"

[0683] Step 12:

[0684] The device will suggest appropriate breaks and rewards based on the user's situation, helping them to maintain their motivation to study.

[0685] Demonstration of study benefits and areas of interest

[0686] Step 13:

[0687] The device checks the user's basic information to see what areas they are interested in. For example, if the user expresses an interest in "programming," that information is acquired.

[0688] Step 14:

[0689] Based on information provided by the server, the device presents the benefits of studying the area the user is interested in. For example, it displays the benefit that "studying programming will increase your chances of earning a high income in the future."

[0690] Step 15:

[0691] Users can refer to the information presented to them as they proceed with their studies, which makes it easier for them to clarify the meaning and purpose of their studies.

[0692] Through the above steps, users can study effectively and achieve their learning goals while maintaining their motivation.

[0693] Example 1

[0694] 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."

[0695] Conventional learning support systems have inadequately managed users' learning progress and motivation, and lacked effective support for users to continue learning over the long term. Furthermore, they did not provide learning programs tailored to individual users' needs, send appropriate learning reminders, or conduct detailed analysis of collected learning data, making it difficult to maintain users' learning efficiency and motivation.

[0696] 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.

[0697] In this invention, the server includes means for collecting basic information about the user and storing it in a database, means for distributing a generating AI trainer program to the user's device based on the collected basic information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and creating a report on study progress and motivation, means for sending a message to the user to motivate the user based on the received analysis results, means for collecting study data and sending it to the server, means for storing the collected data in local storage and managing the study schedule, means for using a RESTful API when distributing the generating AI program, and means for sending study reminders via push notification. This makes it possible to provide a study program that meets the individual needs of the user, and by sending study reminders at appropriate times and performing detailed analysis based on the collected data, it is possible to improve the user's study efficiency and maintain long-term motivation.

[0698] "User" means an individual or corporate user of this system.

[0699] "Basic information" refers to personal information such as the user's name, age, learning goals, and areas of interest.

[0700] "Database" means a structured data storage system for storing user information, learning data, etc.

[0701] "Generative AI trainer program" refers to an artificial intelligence program that is generated by a server and distributed to a user's device to assist the user in learning.

[0702] "Terminal" means an electronic device used by a user, such as a computer, tablet, or smartphone.

[0703] "Study schedule" refers to a study plan or schedule set by a user.

[0704] "Study reminders" refer to notifications that encourage users to study based on their set study schedule.

[0705] "Study data" refers to information about a user's learning activities (study time, progress, learning materials used, etc.).

[0706] "Report" means a document or data summarizing the results of an analysis of collected learning data and evaluation of learning progress and motivation.

[0707] "RESTful API" means an application programming interface that performs operations on resources through HTTP requests.

[0708] "Push notification" refers to a message sent instantly from a server to a client application.

[0709] "Local storage" refers to a storage function for saving data within a user's terminal.

[0710] This invention relates to a generative AI trainer system that effectively supports user learning. This system operates among a server, a terminal, and a user, and provides a series of functions to enhance the user's learning progress and motivation.

[0711] User information collection and initial settings

[0712] When a new user registers with the system, the server collects basic information such as name, age, learning goals, and areas of interest. This information is received through an HTTP request and stored in a database (e.g., MySQL or PostgreSQL), allowing the server to provide personalized services for each user.

[0713] Specific examples

[0714] When User A accesses the system and enters the necessary information, the server stores the information in a database. This information is used to provide individualized support in subsequent learning support.

[0715] Distribution of the Generative AI Trainer Program

[0716] The server distributes the generative AI trainer program to the user's device based on the collected user information. Distribution is performed using a RESTful API, and the device receives it and installs it locally. The generative AI trainer program is written in JavaScript or Python.

[0717] Specific examples

[0718] The server packages an AI trainer program written in Python and sends it to the device as an HTTP response, which the device receives and deploys locally.

[0719] Setting and managing your study schedule

[0720] Users set their own study schedule through the application on their device, allowing them to plan when to start studying.

[0721] The device saves the configured study schedule in local storage (e.g., an SQLite database), which is used to send future reminders.

[0722] Specific examples

[0723] User A enters his / her study plan into the device app every morning at 8:00 and presses the save button. The device saves this schedule in an SQLite database.

[0724] Send study reminders

[0725] The device generates and sends study reminders based on the schedule set by the user, and uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the reminder at the specified time.

[0726] Specific examples

[0727] Every morning at 8:00, a push notification saying "It's time to study!" appears on User A's device.

[0728] Collecting and sending learning data

[0729] As the user progresses with their learning activities, they input learning data, such as their progress and the learning materials they have used, into the terminal.

[0730] The device collects this learning data and periodically sends it to the server, which uses HTTPS for secure communication.

[0731] Specific examples

[0732] When User A enters the study time and materials used into the app and presses the send button, the device sends this data to the server.

[0733] Analysis of training data

[0734] The server analyzes the received learning data and creates reports on learning progress and motivation. It aggregates and analyzes the data using Python libraries such as pandas and scikit-learn.

[0735] Specific examples

[0736] The server extracts learning data from the database and generates a report that compiles and visualizes weekly learning time and progress.

[0737] Motivational feedback

[0738] The terminal sends a message to the user to increase motivation based on the analysis results sent from the server.

[0739] Specific examples

[0740] When the server determines that "recent learning data indicates a decline in motivation" and sends this result to the device, the device notifies the user with an encouraging message such as "Keep going, you're doing great!"

[0741] Providing information about areas of interest

[0742] The server collects the latest information and study benefits based on the user's interests and provides them to the terminal.

[0743] The terminal presents the provided information to the user.

[0744] Specific examples

[0745] The server collects information such as "Learning programming will increase your chances of earning a high income in the future" and sends it to the device, which then displays it to the user.

[0746] Prompt Sentence Examples

[0747] Below are some example prompts to input to the generative AI model:

[0748] Example prompt sentence:

[0749] "Generate a report on User A's current learning progress and motivation based on his learning record data from the past month. Please take into account the following information: total study time, progress, types of learning materials used, and trends in motivation. Also, please include specific advice for User A to continue studying."

[0750] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[0751] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0752] Step 1: User registration and information collection

[0753] Input: The user enters basic information such as name, age, learning goals, and areas of interest.

[0754] Specific operation: A user accesses the system, enters personal information into a form, and presses the submit button.

[0755] Data processing: The server receives the entered information, converts it into the required format, and stores it in the database.

[0756] Output: The user's basic information is saved in the database.

[0757] Step 2: Distributing the Generative AI Trainer Program

[0758] Input: User basic information stored in the database

[0759] Specific operation: The server generates an individualized generative AI trainer program based on the stored user information and distributes it to the terminal via an HTTP response.

[0760] Data processing: Customize program settings based on collected information and package the program.

[0761] Output: The customized AI trainer program is distributed to the device and installed locally.

[0762] Step 3: Set and manage your study schedule

[0763] Input: Information for users to set their own study schedule (date, time, subjects, etc.)

[0764] Specific operation: The user inputs a schedule through the terminal application and presses the save button.

[0765] Data processing: The terminal converts the input schedule into a format suitable for saving in the SQLite database and stores it.

[0766] Output: The configured learning schedule is saved to local storage.

[0767] Step 4: Send a reminder

[0768] Input: Information saved in your study schedule

[0769] Specific operation: When the specified time arrives, the device will use the push notification system to generate a reminder and display a notification.

[0770] Data Processing: Prepares push notification messages and sends them to the notification system based on a schedule.

[0771] Output: The user is shown the reminder.

[0772] Step 5: Collect and send training data

[0773] Input: Study data entered by the user (study time, progress, materials used, etc.)

[0774] Specific operation: The user enters learning data on the device and presses the send button. The device then sends the data to the server via HTTPS.

[0775] Data processing: Converts the input data into the appropriate format and sends it using the HTTPS protocol.

[0776] Output: The user's learning data is sent to the server and stored in the database.

[0777] Step 6: Analyze the training data

[0778] Input: Training data stored on the server

[0779] Specific operation: The server periodically extracts training data from the database and analyzes and aggregates the data using Python's pandas and scikit-learn.

[0780] Data calculation: Calculate each user's study time, progress, and motivation, and create a report.

[0781] Output: A user-specific report is generated based on the analyzed data.

[0782] Step 7: Motivational feedback

[0783] Input: Report generated as a result of analysis

[0784] Specific operation: The server sends the generated report to the terminal, and the terminal notifies the user of an encouraging message based on the analysis results.

[0785] Data calculation: Generate motivational messages based on the report content.

[0786] Output: A feedback message is sent to the user.

[0787] Step 8: Provide information about your interests

[0788] Input: Information about the user's interests

[0789] Specific operation: The server collects the information to be provided, organizes it, and sends it to the terminal, which then displays the received information.

[0790] Data processing: Converting collected information into a format that meets the user's interests and providing it to them.

[0791] Output: Information about the area of ​​interest is presented to the user.

[0792] This allows users to receive personalized learning support, enabling them to continue learning while maintaining their motivation.

[0793] (Application example 1)

[0794] 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."

[0795] In brick-and-mortar stores, salespeople and staff need to constantly improve their product knowledge and customer service skills. However, they are often overwhelmed with their daily work and neglect self-study. For this reason, an effective system is needed that allows staff to learn efficiently, manage progress, and maintain motivation. It is also necessary to have a system that provides timely reminders and rewards to support continued learning.

[0796] 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.

[0797] In this invention, the server includes: means for collecting basic user information and storing it in a database; means for distributing a generated AI trainer program to the user's device based on the collected basic information; means for checking the user's study schedule and sending study reminders; means for analyzing the user's study data and creating a report on their study progress and motivation; means for sending motivational messages to the user based on the received analysis results; means for providing information on the user's areas of interest and the benefits of studying; means for supporting store salespeople and staff in improving their product knowledge and customer service skills; means for displaying visual reminders on the smart glasses based on the staff's registered schedule; means for collecting staff study data and analyzing their progress and test results; means for displaying motivational messages and reward suggestions to staff based on the analysis results; and means for presenting information on new products and skills. This allows store staff to study systematically and continuously, ensuring they always have the latest knowledge and skills. Furthermore, by visualizing study progress and providing timely reminders and motivational messages, the system effectively supports continued study and motivation.

[0798] "User basic information" is personal information related to a user, such as name, role, learning goals, etc.

[0799] "Database" refers to a storage device and related software for storing and managing users' basic information and learning data.

[0800] The "Generative AI Trainer Program" is software designed to assist users in their learning using AI technology.

[0801] "User device" refers to a device that can display and operate learning reminders and educational content, including smartphones and smart glasses.

[0802] A "study schedule" is a study timetable set by the user.

[0803] A "study reminder" is a notification or message set to encourage the user to start studying.

[0804] "Study data" is a record of the user's learning activities, including the amount of time spent studying and progress.

[0805] "Analysis results" are reports of the results and trends obtained by analyzing the collected learning data.

[0806] "Motivational messages" are messages of encouragement and encouragement to increase the user's motivation to learn.

[0807] "Information related to fields of interest" refers to the latest information and knowledge related to fields in which the user is interested.

[0808] The "benefits of studying" are the future benefits and improvements that come from learning.

[0809] "Store salespeople and staff" refers to employees working in physical stores who sell products and serve customers.

[0810] "Product knowledge" is detailed information and understanding about the product being sold.

[0811] "Customer service techniques" refer to the skills and know-how required for dealing with customers.

[0812] "Visual reminders" are notifications or messages displayed through devices such as smart glasses.

[0813] "Progress" indicates the current degree of achievement of the learning goal set by the user.

[0814] "Test results" are the results of assessment tests and quizzes taken by users.

[0815] A "reward offer" is an offer of an incentive to be offered to a user depending on the outcome of their learning.

[0816] "Information about new products and skills" refers to information about the latest product knowledge and new skills that staff need to acquire.

[0817] The present invention relates to a generative AI trainer system that effectively supports user learning. This system is intended to improve the product knowledge and customer service skills of salespeople and staff in brick-and-mortar stores. Specific embodiments for implementing the present invention and their processing details are described below.

[0818] Hardware and Software

[0819] This system operates between three parties: a server, a terminal (a device such as smart glasses), and a user. The main hardware and software components are as follows:

[0820] 1. Hardware

[0821] Smart glasses (e.g. Google Glass)

[0822] server

[0823] communication network

[0824] 2. Software

[0825] Client app (in smart glasses)

[0826] Server-side database (e.g. MySQL)

[0827] Backend framework (e.g. Django)

[0828] Data analysis libraries (e.g., scikit-learn)

[0829] Generative AI Models

[0830] Processing Details

[0831] 1. Initial Setup

[0832] Users: New users register by entering basic information such as their name, role, and learning goals.

[0833] Server: Stores basic information in a database, generates an AI trainer program based on the collected information, and distributes it to the user's device.

[0834] 2. Study Reminders

[0835] Device: Manages the learning schedule set by the user. For example, if the user sets product knowledge learning at 2 p.m.

[0836] Device: Display a visual notification at the set time saying "It's study time now!"

[0837] 3. Collection and analysis of learning data

[0838] Server: Collects user learning activity data (study time, progress, test results, etc.).

[0839] Server: Analyzes the collected data and creates reports on learning progress and motivation.

[0840] 4. Motivation support

[0841] Device: Display motivational messages to staff based on the analysis. Keep staff motivated with messages like "Continuous learning pays off!"

[0842] Device: We also offer rewards such as visual badges and points systems.

[0843] 5. Providing information about new products and skills

[0844] Terminal: Displays the latest information and knowledge related to the user's areas of interest.

[0845] Device: For example, when a new product is released, detailed information about the product is displayed and users can deepen their understanding through tests and quizzes.

[0846] Specific examples

[0847] For example, when a new product is released, the process proceeds as follows:

[0848] 1. Example prompt

[0849] New product learning prompts (examples): "Answer the following questions. List three features of the new product.", "Describe the specialized features of the new product."

[0850] Progress prompts (examples): "How many minutes did you spend studying today?", "Describe one advanced sales technique you learned."

[0851] In this way, by utilizing the system of the present invention, store staff can plan and continue their studies, enabling them to always acquire the latest knowledge and skills. In addition, by visualizing their learning progress and providing timely reminders and motivational messages, the system can effectively support continuation of learning and maintenance of motivation.

[0852] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0853] Step 1:

[0854] Initial Setup - Collecting User Information

[0855] User: A new user registers with the service, entering basic information such as their name, role, and learning goals.

[0856] Input: Basic information such as the user's name, role, and learning goal.

[0857] Server: Saves the entered basic information in a database.

[0858] Output: Basic information of the user stored in the database.

[0859] Specific operation: The user enters information into the form through the terminal and presses the submit button. The server receives the form data and records it in the database.

[0860] Step 2:

[0861] Distribution of the Generative AI Trainer Program

[0862] Server: Customizes the generative AI trainer program based on basic information stored in the database.

[0863] Input: User basic information stored in the database.

[0864] Server: Distributes customized generative AI trainer programs to users' devices.

[0865] Output: The generated AI trainer program installed on the user's device.

[0866] How it works: The server analyzes the user information, generates a customized program based on that information, and sends it to the device, where it is automatically installed.

[0867] Step 3:

[0868] Managing your study schedule

[0869] User: Set a study schedule, for example, to study product knowledge at 2 PM.

[0870] Input: User's study schedule (date, time, content).

[0871] Device: Receives and stores the user's study schedule.

[0872] Output: Saved study schedule.

[0873] Specific operation: The user sets up a study schedule on the device app and presses the save button. The device then saves the information in its internal memory.

[0874] Step 4:

[0875] Send study reminders

[0876] On your device: Generate reminders based on your study schedule.

[0877] Input: Saved study schedule.

[0878] Device: At the set time, a visual reminder such as "It's time to study now!" will be displayed on the smart glasses.

[0879] Output: Reminder notification.

[0880] Specific operations: Check the set time on the internal clock, generate reminders, and display notifications on the smart glasses.

[0881] Step 5:

[0882] Collection and analysis of learning data

[0883] Device: Collects user data during learning activities (study time, progress, quiz results, etc.).

[0884] Input: User learning activity data.

[0885] Terminal: Sends collected data to the server.

[0886] Server: Receives collected data and stores it in a database.

[0887] Output: Saved user training data.

[0888] Specific operation: The device monitors learning activities and collects data, which is then sent to a server at specific times and stored in a database.

[0889] Step 6:

[0890] Data analysis and reporting

[0891] Server: Analyzes the stored learning data and performs data processing and calculations to generate reports on progress and motivation.

[0892] Input: Training data stored in a database.

[0893] Server: Analyzes the data using data analysis libraries (e.g., scikit-learn) to evaluate progress and motivation indicators.

[0894] Output: The generated report.

[0895] Specific operation: The server retrieves the user's learning data from the database, performs calculations using analytical functions, and summarizes the results in report format.

[0896] Step 7:

[0897] Sending motivational messages

[0898] Server: Based on the generated report, generate messages to improve user motivation.

[0899] Input: The generated report.

[0900] Server: Generate a message such as "Continuous learning helps!"

[0901] Terminal: Receives messages and displays them visually on the smart glasses.

[0902] Output: Motivation message notification.

[0903] Specific operation: The server extracts information from the report, generates an encouraging message, and sends it to the device, which then displays the received message on the smart glasses.

[0904] Step 8:

[0905] Providing information about new products and skills

[0906] Device: Periodically collect and display information related to your areas of interest.

[0907] Input: Information about the user's areas of interest.

[0908] Terminal: Presents quizzes and tests to the user in the form of prompts, if necessary.

[0909] For example, "Please list three features of your new product.", "Please explain the specialized functions of your new product."

[0910] Output: Presentation of information about the new product or skill and related test results.

[0911] Specific operation: The device periodically receives new information from the server and presents it to the user. It also displays quizzes to check whether the user has understood the content and collects the results.

[0912] 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.

[0913] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[0914] This system operates among three parties: a server, a terminal, and a user, and incorporates an emotion engine that recognizes the user's emotions, thereby providing learning support that corresponds to the user's emotional state.

[0915] Initial Setup

[0916] Collecting user information and emotion data

[0917] When a user registers with the system, they enter basic information such as their name, age, learning goals, and areas of interest.

[0918] The server collects basic information provided by the user and stores it in a database. In addition, it collects the user's emotional state using an emotion engine and stores it in the database.

[0919] AI Trainer Program Distribution

[0920] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional state data. This program has the necessary functions to support the user's learning.

[0921] Study reminder function

[0922] Managing your study schedule

[0923] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[0924] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[0925] Send a reminder

[0926] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[0927] Analysis of training data and sentiment data

[0928] Collection of training data

[0929] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and emotion data from the emotion engine, and periodically transmits them to the server.

[0930] Analyzing the data

[0931] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[0932] Motivation support

[0933] Utilizing analysis results

[0934] The server transmits a report generated as a result of the analysis to the user's terminal.

[0935] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[0936] Suggesting breaks and rewards

[0937] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[0938] Demonstration of study benefits and areas of interest

[0939] Acquiring interest information

[0940] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," that information is acquired.

[0941] Providing related information

[0942] Based on information provided by the server, the device presents the benefits of studying the field the user is interested in. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[0943] Presentation of information based on emotion information

[0944] The device takes into account the user's emotional state obtained from the emotion engine and presents relaxing content and information at the appropriate time.

[0945] Specific examples

[0946] 1. For User A

[0947] User A registers with this system to deepen his / her IT knowledge.

[0948] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[0949] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[0950] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[0951] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[0952] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[0953] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[0954] The processing flow will be explained below.

[0955] Initial Setup

[0956] Step 1:

[0957] To register with the system, users enter basic information such as their name, age, learning goals, and areas of interest.

[0958] Step 2:

[0959] The server receives the basic information provided by the user and stores it in a database.

[0960] Step 3:

[0961] The server activates the emotion engine and begins monitoring the user's emotional state based on the basic information provided by the user.

[0962] Step 4:

[0963] The server distributes the generated AI trainer program to the user's device and prompts the user to install the program.

[0964] Study reminder function

[0965] Step 5:

[0966] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[0967] Step 6:

[0968] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[0969] Step 7:

[0970] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[0971] Analysis of training data and sentiment data

[0972] Step 8:

[0973] The terminal periodically records the user's learning activity data (study time, progress, learning materials used, etc.) and emotional state and transmits them to the server.

[0974] Step 9:

[0975] The server stores the learning data and emotion data sent from the terminal in a database.

[0976] Step 10:

[0977] The server analyzes the stored learning data and emotional data to create a report on the user's learning progress and motivation. By taking the emotional data into account, detailed analysis can be performed according to the user's emotional state.

[0978] Motivation support

[0979] Step 11:

[0980] The server transmits a report generated as a result of the analysis to the user's terminal.

[0981] Step 12:

[0982] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[0983] Step 13:

[0984] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[0985] Demonstration of study benefits and areas of interest

[0986] Step 14:

[0987] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," it uses that information to obtain related information.

[0988] Step 15:

[0989] Based on information provided by the server, the device will present the benefits of studying the area of ​​interest to the user at the appropriate time. For example, it will display specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[0990] Step 16:

[0991] The device provides relaxing content and information at the appropriate time based on the user's emotional state, obtained from the emotion engine, thereby reducing stress and improving learning efficiency.

[0992] Specific examples

[0993] 1. For User A

[0994] User A registers with this system to deepen his / her IT knowledge.

[0995] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[0996] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[0997] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[0998] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[0999] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[1000] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[1001] Example 2

[1002] 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."

[1003] In today's learning environment, it is difficult for users to maintain sustained motivation to study effectively. Furthermore, there are no systems that can properly grasp each user's learning progress and emotional state and provide support accordingly. Therefore, to maximize users' learning effectiveness, a personalized learning support system that takes into account the user's basic information and emotional state is needed.

[1004] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1005] In this invention, the server includes means for collecting basic information and emotional data of the user and storing it in a database, means for distributing a generated AI trainer program to the user's device based on the collected basic information and emotional data, means for checking the user's study schedule and sending study reminders, means for collecting and analyzing the user's study activity data and emotional data, means for suggesting motivational messages, breaks, and rewards to the user based on the analysis results, means for acquiring information about the user's interests and providing information related to the interests and benefits of studying, and means for presenting appropriate relaxation content based on the emotional data. This makes it possible to comprehensively grasp the user's study progress and emotional state and provide optimized study support for each user.

[1006] "User" refers to an individual who uses the system to learn.

[1007] "Basic Information" refers to initial registration data about a user, such as the user's name, age, learning goals, and areas of interest.

[1008] "Emotional data" refers to information that indicates a user's emotional state and is obtained through text analysis or facial recognition technology.

[1009] "Database" refers to an information system for storing and managing collected basic information, emotional data, and learning activity data.

[1010] "Generative AI trainer program" refers to a program that provides learning support to users, generated based on the user's basic information and emotional data.

[1011] "Terminal" refers to a device used by a user to access the system, such as a computer, smartphone, or tablet.

[1012] "Study schedule" refers to a study plan and timetable set by a user.

[1013] "Study reminders" refer to messages and alerts that notify users of study time and encourage them to study.

[1014] "Learning activity data" refers to data related to learning, such as a user's study time, progress, and learning materials used.

[1015] "Analysis results" refers to information generated based on collected learning activity data and emotional data for evaluating a user's learning progress and motivation.

[1016] "Messages" refer to words of encouragement or advice sent to increase the user's motivation to study.

[1017] "Break" refers to information and notifications suggesting that the user take adequate rest.

[1018] "Rewards" refer to incentives given to recognize a user's efforts and maintain motivation.

[1019] "Interest information" refers to information relating to areas in which a user is interested.

[1020] "Relaxation content" refers to content such as music and videos that stabilize the user's emotional state and promote relaxation.

[1021] MODE FOR CARRYING OUT THE INVENTION

[1022] The present invention provides a system for effectively supporting a user's learning, and provides personalized learning support by taking into account the user's emotional state. Hereinafter, an embodiment of the present invention will be described in detail.

[1023] Collecting user information and emotion data

[1024] When users register with the system, they enter basic information such as their name, age, learning goals, and areas of interest via a web form or mobile application.

[1025] The server collects basic information provided by the user and stores it in a database, and also analyzes the user's emotional state using an emotion engine (e.g., IBM Watson Tone Analyzer) and stores this information in the database.

[1026] AI Trainer Program Distribution

[1027] The server generates prompt sentences for the generative AI model (e.g., GPT-4) based on the collected basic information and emotional data, and creates a customized generative AI trainer program.

[1028] The server distributes the generated trainer program to the user's terminal, which includes a study reminder function and a study data tracking function.

[1029] Managing your study schedule

[1030] Users set their own study schedules on their own devices. Specifically, they can use the device app to input plans such as "start studying at 8am every morning."

[1031] The device saves the set schedule information in local storage and prepares to send a reminder at the specified time.

[1032] Send study reminders

[1033] When the set study time arrives, the device will send a notification to the user saying, "It's time to study!" This notification is realized using a push notification service.

[1034] Collecting and analyzing training data and sentiment data

[1035] The device transmits the user's learning activity data (study time, progress, learning materials used, etc.) and emotional data collected in real time to the server. Specific emotional data is acquired through the device's camera and microphone.

[1036] The server stores the received learning data and emotion data in a database and analyzes them using R, Python, etc. As a result of the analysis, a report on the user's learning progress and motivation is generated.

[1037] Motivation support

[1038] The server generates a report based on the analysis results and sends it to the user's terminal.

[1039] Based on the report, the device sends the user motivational messages, such as "Keep going, you're doing great!" if their motivation is low.

[1040] The device considers the user's learning progress and emotional state and suggests appropriate breaks and rewards, for example, displaying a notification that says, "Taking a 15-minute break will improve your efficiency."

[1041] Demonstration of study benefits and areas of interest

[1042] The device will check the user's areas of interest from their basic information and then present relevant benefits based on that information, such as "Learning programming will increase your chances of earning a high income in the future."

[1043] The device will then provide appropriate relaxation content based on emotional data, such as relaxing music and guided meditations through the Calm app.

[1044] Specific examples

[1045] When user A uses this system to deepen his / her IT knowledge, the system operates as follows.

[1046] User A enters basic information into the system and registers as a new user.

[1047] The server generates a customized AI trainer program based on basic information and daily collected emotional data and distributes it to User A's device.

[1048] The device manages user A's study schedule and sends a reminder saying "It's time to study!" at the scheduled time of 8:00.

[1049] The server analyzes the learning data and emotional data of User A and creates a report. For example, the report may indicate that motivation is declining.

[1050] Based on the report, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[1051] The device provides User A with benefits such as "Learning programming will increase your chances of earning a high income in the future" and presents relaxing content according to his or her emotional state.

[1052] This allows the system to effectively support users' learning and provide personalized learning assistance that takes into account their emotional state.

[1053] Example prompt sentence:

[1054] "Consider user sentiment data and provide advice on maintaining appropriate learning motivation."

[1055] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1056] Step 1:

[1057] To register with the system, a user enters basic information such as name, age, learning goals, and areas of interest. This information is entered through a web form or a mobile application. The inputs include name, age, learning goals, and areas of interest. The output is sent to the server.

[1058] Step 2:

[1059] The server stores the received basic information in a database. It then uses an emotion engine (e.g., a natural language processing tool) to analyze the user's emotional state. Specifically, it detects emotions from the user's input text. The inputs include the user's basic information and the analysis results from the emotion engine. The output is the basic information and emotional data stored in the database.

[1060] Step 3:

[1061] The server creates a customized generative AI trainer program based on the collected basic information and emotional data. A generative AI model (e.g., a generative AI engine) is used to generate a learning program optimized for the user. The input includes basic information and emotional data from the database and prompt sentences for the generative AI model. The output is a customized generative AI trainer program.

[1062] Step 4:

[1063] The server distributes the generated AI trainer program to the user's device. Specifically, it sends the program in digital format so that the user can install it on their device. The input is the generated AI trainer program. The output is the program sent to the user's device.

[1064] Step 5:

[1065] A user sets a study schedule on their device. Specifically, they use a device application to input a schedule such as "I will start studying at 8:00 every morning." The input includes details of the schedule set by the user. The output is the schedule saved on the device.

[1066] Step 6:

[1067] The device saves the user's study schedule in local storage and prepares to send a reminder at the specified time. The input includes the saved study schedule. The output is the reminder notification.

[1068] Step 7:

[1069] When the scheduled study time arrives, the device sends the user a notification saying "It's time to study!". Specifically, it uses a push notification service to display a reminder on the user's device. The input includes schedule data for sending the reminder. The output is a reminder notification that is displayed on the user's device.

[1070] Step 8:

[1071] The device collects the user's learning activity data (study time, progress, learning materials used, etc.). In addition, emotional data is collected in real time. Specifically, the device's camera and microphone are used to acquire the emotional data. The input includes the user's learning activity and data for emotion detection. As output, these data are sent to the server.

[1072] Step 9:

[1073] The server stores the received learning data and emotion data in a database and analyzes it. Specifically, it analyzes the data using programs such as R and Python and generates reports on the user's learning progress and motivation. The inputs are the learning data and emotion data from the database. The output is a detailed analysis report.

[1074] Step 10:

[1075] The server sends the generated report to the user's terminal, specifically in digital format using a secure protocol (e.g. HTTPS). The input is the generated analysis report. The output is the report that arrives at the user's terminal.

[1076] Step 11:

[1077] The terminal sends a motivational message to the user based on the received report. Specifically, the terminal displays a message such as "Keep going, you're doing great!". The input includes the analysis report and a message template. The output is the motivational message displayed to the user.

[1078] Step 12:

[1079] The device suggests appropriate breaks and rewards based on the user's learning progress and emotional state. Specifically, it sends a notification such as "Taking a 15-minute break will improve your efficiency." The input includes the user's learning data and emotional data. As an output, the break and reward suggestion notification is displayed on the user's device.

[1080] Step 13:

[1081] The device acquires the user's interest information and provides information related to that interest and the benefits of studying. Specifically, it displays benefits such as "Learning programming will increase your chances of earning a high income in the future." The input includes the user's basic information and interest data. The output presents related information and benefits.

[1082] Step 14:

[1083] The device presents relaxing content based on the emotion data. Specifically, it displays content such as "relaxing music" and "meditation guide." The input includes the emotion engine analysis results and a template for the relaxing content. As an output, the relaxing content is displayed on the user's device.

[1084] (Application example 2)

[1085] 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."

[1086] Conventional learning support systems proceed with learning without taking the user's emotional state into consideration, which can lead to a decline in motivation and a decline in learning efficiency. Furthermore, there are problems with users being fatigued and stressed due to inappropriate timing for breaks and the provision of rewards. To address these issues, a system that provides support according to the user's emotional state is needed.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information and emotional information about the user and storing it in a database, means for distributing a generative AI trainer program to the user's terminal based on the collected basic information and emotional information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and emotional data and creating a report on study progress and motivation, means for sending a motivating message to the user based on the received analysis results, means for suggesting appropriate breaks and rewards based on the user's emotional state, and means for providing information on the user's areas of interest and the benefits of study. This makes it possible to maximize the effectiveness of study while taking the user's emotional state into consideration in real time.

[1088] "Basic User Information" refers to personal data such as the user's name, age, learning goals, and areas of interest.

[1089] "Emotion information" is data that indicates the user's emotional state, and includes tension, fatigue level, stress state, and the like.

[1090] "Database" refers to an information system for storing and managing collected basic information and emotional information.

[1091] The "generative AI trainer program" is learning support software that is generated based on collected basic information and emotional information and distributed to the user's device.

[1092] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[1093] A "study schedule" is a plan of study time and study content set by the user.

[1094] "Study Reminder" is a function that notifies users to start studying based on the study schedule they set.

[1095] "Study data" refers to data related to a user's learning activities, including study time, progress, learning materials used, and the like.

[1096] "Emotion data" is data about a user's emotional state collected by an emotion engine.

[1097] A "report" is a report that analyzes learning data and emotional data and summarizes information about learning progress and motivation.

[1098] A "motivation message" is a message sent to a user to increase their motivation to study.

[1099] "Break and reward suggestions" is a function that suggests breaks at appropriate times and rewards according to the user's learning progress based on the user's emotional state.

[1100] "Areas of interest" refers to areas of study or topics that interest the user.

[1101] "Learning benefits" refers to the benefits or advantages a user will gain if they continue to learn.

[1102] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[1103] Initial Setup

[1104] Collecting user information and emotion data

[1105] When a user registers with the system, they must enter basic information such as their name, age, learning goals, and areas of interest. The server collects the basic information provided by the user and stores it in a database. The server also periodically collects the user's emotional state (e.g., tension, fatigue level) using an emotion engine and stores this in the database.

[1106] Distribution of the Generative AI Trainer Program

[1107] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional information. This program has the necessary functions (reminders, data collection, analysis, etc.) to support the user's learning.

[1108] Study reminder function

[1109] Managing your study schedule

[1110] Users can set a learning schedule on their device. For example, they can set an appointment to start the driving simulation every morning at 8:00. The device saves the schedule and prepares to send a reminder at the specified time.

[1111] Send a reminder

[1112] The device sends learning reminders to the user based on their learning schedule. For example, at 8:00 a.m., a notification will appear saying, "It's time to start the driving simulation."

[1113] Analysis of training data and sentiment data

[1114] Collection of training data

[1115] The device collects the user's learning activity data and emotion data from the emotion engine, and periodically transmits them to the server. The learning activity data includes the user's learning time, progress, learning materials used, etc.

[1116] Analyzing the data

[1117] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[1118] Motivation support

[1119] Utilizing analysis results

[1120] The server then sends the generated analysis results to the user's device. Based on the received report, the device sends the user a motivating message. For example, if the device determines that the user's motivation is low, it sends a message such as, "You seem tired. Take a short break."

[1121] Suggesting breaks and rewards

[1122] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[1123] Other support

[1124] The device checks the user's areas of interest from basic information and, based on information provided by the server, presents the benefits of studying the user's areas of interest. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future." It also takes emotional information into account and presents relaxing content at appropriate times.

[1125] Examples of concrete examples and prompts

[1126] Specific examples

[1127] 1. When the user starts the simulation, the system checks their emotional state for the day and sends a message saying, "You seem a little tired. Let's take a break."

[1128] 2. When the learning time is over, the system analyzes the progress of the learning data and emotional data, and notifies the driver, "Today's driving simulation took 35 minutes. Let's do our best next time!"

[1129] Prompt Sentence Examples

[1130] "Generate motivational messages based on the user's learning progress and emotional state. For example, generate a message to send when the user is in a 'tired' state."

[1131] This embodiment allows for maximizing learning effectiveness while taking into account the user's emotional state in real time.

[1132] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1133] Step 1:

[1134] A user registers with the system and inputs basic information, learning goals, and areas of interest. The server collects the basic information and emotional state received from the user and stores them in a database.

[1135] Specific behavior:

[1136] (Input) User information (name, age, learning goals, etc.)

[1137] (Data processing) Convert the entered information into a database format

[1138] (Output) Saved user information

[1139] Step 2:

[1140] The server uses an emotion engine to periodically collect the user's emotional state (e.g., tension, fatigue level) and store it in a database.

[1141] Specific behavior:

[1142] (Input) Real-time emotion data

[1143] (Data processing) Emotional data is measured, organized, and saved in a database

[1144] (Output) Updated emotion information

[1145] Step 3:

[1146] The server generates a generative AI trainer program based on the basic information and emotion data, and distributes the program to the user's device.

[1147] Specific behavior:

[1148] (Input) Basic information, emotion data

[1149] (Data calculation) Generation of trainer programs using generative AI models

[1150] (Output) The distributed generative AI trainer program

[1151] Step 4:

[1152] The user sets up a study schedule on the device, which stores this schedule and prepares to send reminders at the specified times.

[1153] Specific behavior:

[1154] (Input) Study Schedule

[1155] (Data processing) Saving schedule information

[1156] (Output) Set reminders

[1157] Step 5:

[1158] The device sends learning reminders to the user based on the learning schedule. For example, it displays a notification every morning at 8:00 that says, "It's time to start the driving simulation."

[1159] Specific behavior:

[1160] (Input) Saved schedule information

[1161] (Data calculation) Generate reminders according to schedule

[1162] (Output) Reminders sent

[1163] Step 6:

[1164] The device collects the user's learning activity data (study time, progress, etc.) and emotion data from the emotion engine, and periodically sends them to the server.

[1165] Specific behavior:

[1166] (Input) Learning activity data, real-time emotion data

[1167] (Data calculation) Data collection and organization

[1168] (Output) Data sent to the server

[1169] Step 7:

[1170] The server analyzes the learning and emotional data and generates reports on the user's learning progress and motivation.

[1171] Specific behavior:

[1172] (Input) Learning data, emotion data

[1173] (Data calculation) Report generation by data analysis

[1174] (Output) Generated report

[1175] Step 8:

[1176] The server then sends the generated analysis results to the user's device, which then sends them motivational messages and suggests appropriate breaks and rewards based on their emotional state.

[1177] Specific behavior:

[1178] (Input) Analysis results (report)

[1179] (Data Calculation) Motivation Message Generation

[1180] (Output) Messages sent and break / reward proposals

[1181] Step 9:

[1182] The device provides learning benefits related to the user's areas of interest and presents relaxing content at the appropriate time depending on the user's emotional state.

[1183] Specific behavior:

[1184] (Input) Basic information, area of ​​interest data, emotion data

[1185] (Data calculation)Generation of merit information and relaxation content

[1186] (Output) Presented benefit information and content

[1187] 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.

[1188] 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.

[1189] 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.

[1190] [Third embodiment]

[1191] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1192] 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.

[1193] 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).

[1194] 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.

[1195] 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.

[1196] 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).

[1197] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1198] 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.

[1199] 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.

[1200] 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.

[1201] 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.

[1202] 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."

[1203] The present invention relates to a generative AI trainer system that effectively supports user learning. An embodiment of the present invention will be described below.

[1204] This system operates among three parties: a server, a terminal, and a user. The main functions and their operations are explained in detail below.

[1205] Initial Setup

[1206] Collection of User Information

[1207] When a new user registers, the server collects basic information about the user (such as name, age, learning goals, areas of interest, etc.) This information is stored in a database.

[1208] AI Trainer Program Distribution

[1209] The server distributes a generative AI trainer program to the user's device based on the collected basic information. This program is the primary means of assisting the user in their learning.

[1210] Study reminder function

[1211] Managing your study schedule

[1212] The device manages the study schedule set by the user. For example, if the user sets a schedule to study at 8:00 every morning, the device will save this schedule and prepare to send reminders at the specified time.

[1213] Send a reminder

[1214] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[1215] Analysis of training data

[1216] Collection of training data

[1217] The server periodically collects data from users' learning activities (such as learning time, progress, learning materials used, etc.) and stores this data in a database for analysis.

[1218] Analyzing the data

[1219] The server analyzes the collected learning data and creates reports on the user's learning progress and motivation, for example, by calculating the learning time and progress rate and evaluating motivation indicators.

[1220] Motivation support

[1221] Utilizing analysis results

[1222] Based on the analysis results sent from the server, the device sends messages to motivate the user. For example, if the device determines that the user's motivation is declining based on recent learning data, it sends a message such as "Keep going, you're doing great!"

[1223] Suggesting breaks and rewards

[1224] The device suggests appropriate breaks and rewards to the user to increase motivation. By suggesting the timing of breaks and simple rewards according to the user's situation, the device aims to maintain motivation.

[1225] Demonstration of study benefits and areas of interest

[1226] Acquiring interest information

[1227] The device checks the areas of interest the user has (e.g., programming, artificial intelligence, etc.) This information is obtained from the user's basic information collected.

[1228] Providing related information

[1229] Based on information provided by the server, the device presents the latest information on areas of interest to the user and the benefits of studying. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[1230] Specific examples

[1231] 1. For User A

[1232] User A registers with this system to deepen his / her IT knowledge.

[1233] The server collects basic information about user A and distributes the AI ​​trainer program to the terminal.

[1234] The terminal manages user A's study schedule and sends reminders at appropriate times.

[1235] The server analyzes user A's learning data and generates a report to increase motivation.

[1236] The terminal sends encouraging messages to User A based on the analysis results and provides relevant study benefits.

[1237] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[1238] The processing flow will be explained below.

[1239] Initial Setup

[1240] Step 1:

[1241] When a user registers with the system, he or she enters basic information such as name, age, learning goals, and areas of interest.

[1242] Step 2:

[1243] The server collects basic information provided by the user and stores it in a database.

[1244] Step 3:

[1245] The server distributes a generative AI trainer program to the user's device based on the collected basic information.

[1246] Study reminder function

[1247] Step 4:

[1248] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[1249] Step 5:

[1250] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[1251] Step 6:

[1252] The device will send users study reminders based on their schedule, and at 8 o'clock, it will send a notification saying "It's time to study!"

[1253] Analysis of training data

[1254] Step 7:

[1255] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and periodically transmits it to the server.

[1256] Step 8:

[1257] The server stores the learning data sent from the terminal in a database.

[1258] Step 9:

[1259] The server analyzes the stored learning data and creates reports on the user's learning progress and motivation.

[1260] Motivation support

[1261] Step 10:

[1262] The server transmits a report generated as a result of the analysis to the user's terminal.

[1263] Step 11:

[1264] The device sends a motivational message to the user based on the received report. For example, if the device determines that the user's motivation is declining, it sends the message "Keep going, you're doing great!"

[1265] Step 12:

[1266] The device will suggest appropriate breaks and rewards based on the user's situation, helping them to maintain their motivation to study.

[1267] Demonstration of study benefits and areas of interest

[1268] Step 13:

[1269] The device checks the user's basic information to see what areas they are interested in. For example, if the user expresses an interest in "programming," that information is acquired.

[1270] Step 14:

[1271] Based on information provided by the server, the device presents the benefits of studying the area the user is interested in. For example, it displays the benefit that "studying programming will increase your chances of earning a high income in the future."

[1272] Step 15:

[1273] Users can refer to the information presented to them as they proceed with their studies, which makes it easier for them to clarify the meaning and purpose of their studies.

[1274] Through the above steps, users can study effectively and achieve their learning goals while maintaining their motivation.

[1275] Example 1

[1276] 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."

[1277] Conventional learning support systems have inadequately managed users' learning progress and motivation, and lacked effective support for users to continue learning over the long term. Furthermore, they did not provide learning programs tailored to individual users' needs, send appropriate learning reminders, or conduct detailed analysis of collected learning data, making it difficult to maintain users' learning efficiency and motivation.

[1278] 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.

[1279] In this invention, the server includes means for collecting basic information about the user and storing it in a database, means for distributing a generating AI trainer program to the user's device based on the collected basic information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and creating a report on study progress and motivation, means for sending a message to the user to motivate the user based on the received analysis results, means for collecting study data and sending it to the server, means for storing the collected data in local storage and managing the study schedule, means for using a RESTful API when distributing the generating AI program, and means for sending study reminders via push notification. This makes it possible to provide a study program that meets the individual needs of the user, and by sending study reminders at appropriate times and performing detailed analysis based on the collected data, it is possible to improve the user's study efficiency and maintain long-term motivation.

[1280] "User" means an individual or corporate user of this system.

[1281] "Basic information" refers to personal information such as the user's name, age, learning goals, and areas of interest.

[1282] "Database" means a structured data storage system for storing user information, learning data, etc.

[1283] "Generative AI trainer program" refers to an artificial intelligence program that is generated by a server and distributed to a user's device to assist the user in learning.

[1284] "Terminal" means an electronic device used by a user, such as a computer, tablet, or smartphone.

[1285] "Study schedule" refers to a study plan or schedule set by a user.

[1286] "Study reminders" refer to notifications that encourage users to study based on their set study schedule.

[1287] "Study data" refers to information about a user's learning activities (study time, progress, learning materials used, etc.).

[1288] "Report" means a document or data summarizing the results of an analysis of collected learning data and evaluation of learning progress and motivation.

[1289] "RESTful API" means an application programming interface that performs operations on resources through HTTP requests.

[1290] "Push notification" refers to a message sent instantly from a server to a client application.

[1291] "Local storage" refers to a storage function for saving data within a user's terminal.

[1292] This invention relates to a generative AI trainer system that effectively supports user learning. This system operates among a server, a terminal, and a user, and provides a series of functions to enhance the user's learning progress and motivation.

[1293] User information collection and initial settings

[1294] When a new user registers with the system, the server collects basic information such as name, age, learning goals, and areas of interest. This information is received through an HTTP request and stored in a database (e.g., MySQL or PostgreSQL), allowing the server to provide personalized services for each user.

[1295] Specific examples

[1296] When User A accesses the system and enters the necessary information, the server stores the information in a database. This information is used to provide individualized support in subsequent learning support.

[1297] Distribution of the Generative AI Trainer Program

[1298] The server distributes the generative AI trainer program to the user's device based on the collected user information. Distribution is performed using a RESTful API, and the device receives it and installs it locally. The generative AI trainer program is written in JavaScript or Python.

[1299] Specific examples

[1300] The server packages an AI trainer program written in Python and sends it to the device as an HTTP response, which the device receives and deploys locally.

[1301] Setting and managing your study schedule

[1302] Users set their own study schedule through the application on their device, allowing them to plan when to start studying.

[1303] The device saves the configured study schedule in local storage (e.g., an SQLite database), which is used to send future reminders.

[1304] Specific examples

[1305] User A enters his / her study plan into the device app every morning at 8:00 and presses the save button. The device saves this schedule in an SQLite database.

[1306] Send study reminders

[1307] The device generates and sends study reminders based on the schedule set by the user, and uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the reminder at the specified time.

[1308] Specific examples

[1309] Every morning at 8:00, a push notification saying "It's time to study!" appears on User A's device.

[1310] Collecting and sending learning data

[1311] As the user progresses with their learning activities, they input learning data, such as their progress and the learning materials they have used, into the terminal.

[1312] The device collects this learning data and periodically sends it to the server, which uses HTTPS for secure communication.

[1313] Specific examples

[1314] When User A enters the study time and materials used into the app and presses the send button, the device sends this data to the server.

[1315] Analysis of training data

[1316] The server analyzes the received learning data and creates reports on learning progress and motivation. It aggregates and analyzes the data using Python libraries such as pandas and scikit-learn.

[1317] Specific examples

[1318] The server extracts learning data from the database and generates a report that compiles and visualizes weekly learning time and progress.

[1319] Motivational feedback

[1320] The terminal sends a message to the user to increase motivation based on the analysis results sent from the server.

[1321] Specific examples

[1322] When the server determines that "recent learning data indicates a decline in motivation" and sends this result to the device, the device notifies the user with an encouraging message such as "Keep going, you're doing great!"

[1323] Providing information about areas of interest

[1324] The server collects the latest information and study benefits based on the user's interests and provides them to the terminal.

[1325] The terminal presents the provided information to the user.

[1326] Specific examples

[1327] The server collects information such as "Learning programming will increase your chances of earning a high income in the future" and sends it to the device, which then displays it to the user.

[1328] Prompt Sentence Examples

[1329] Below are some example prompts to input to the generative AI model:

[1330] Example prompt sentence:

[1331] "Generate a report on User A's current learning progress and motivation based on his learning record data from the past month. Please take into account the following information: total study time, progress, types of learning materials used, and trends in motivation. Also, please include specific advice for User A to continue studying."

[1332] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[1333] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1334] Step 1: User registration and information collection

[1335] Input: The user enters basic information such as name, age, learning goals, and areas of interest.

[1336] Specific operation: A user accesses the system, enters personal information into a form, and presses the submit button.

[1337] Data processing: The server receives the entered information, converts it into the required format, and stores it in the database.

[1338] Output: The user's basic information is saved in the database.

[1339] Step 2: Distributing the Generative AI Trainer Program

[1340] Input: User basic information stored in the database

[1341] Specific operation: The server generates an individualized generative AI trainer program based on the stored user information and distributes it to the terminal via an HTTP response.

[1342] Data processing: Customize program settings based on collected information and package the program.

[1343] Output: The customized AI trainer program is distributed to the device and installed locally.

[1344] Step 3: Set and manage your study schedule

[1345] Input: Information for users to set their own study schedule (date, time, subjects, etc.)

[1346] Specific operation: The user inputs a schedule through the terminal application and presses the save button.

[1347] Data processing: The terminal converts the input schedule into a format suitable for saving in the SQLite database and stores it.

[1348] Output: The configured learning schedule is saved to local storage.

[1349] Step 4: Send a reminder

[1350] Input: Information saved in your study schedule

[1351] Specific operation: When the specified time arrives, the device will use the push notification system to generate a reminder and display a notification.

[1352] Data Processing: Prepares push notification messages and sends them to the notification system based on a schedule.

[1353] Output: The user is shown the reminder.

[1354] Step 5: Collect and send training data

[1355] Input: Study data entered by the user (study time, progress, materials used, etc.)

[1356] Specific operation: The user enters learning data on the device and presses the send button. The device then sends the data to the server via HTTPS.

[1357] Data processing: Converts the input data into the appropriate format and sends it using the HTTPS protocol.

[1358] Output: The user's learning data is sent to the server and stored in the database.

[1359] Step 6: Analyze the training data

[1360] Input: Training data stored on the server

[1361] Specific operation: The server periodically extracts training data from the database and analyzes and aggregates the data using Python's pandas and scikit-learn.

[1362] Data calculation: Calculate each user's study time, progress, and motivation, and create a report.

[1363] Output: A user-specific report is generated based on the analyzed data.

[1364] Step 7: Motivational feedback

[1365] Input: Report generated as a result of analysis

[1366] Specific operation: The server sends the generated report to the terminal, and the terminal notifies the user of an encouraging message based on the analysis results.

[1367] Data calculation: Generate motivational messages based on the report content.

[1368] Output: A feedback message is sent to the user.

[1369] Step 8: Provide information about your interests

[1370] Input: Information about the user's interests

[1371] Specific operation: The server collects the information to be provided, organizes it, and sends it to the terminal, which then displays the received information.

[1372] Data processing: Converting collected information into a format that meets the user's interests and providing it to them.

[1373] Output: Information about the area of ​​interest is presented to the user.

[1374] This allows users to receive personalized learning support, enabling them to continue learning while maintaining their motivation.

[1375] (Application example 1)

[1376] 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."

[1377] In brick-and-mortar stores, salespeople and staff need to constantly improve their product knowledge and customer service skills. However, they are often overwhelmed with their daily work and neglect self-study. For this reason, an effective system is needed that allows staff to learn efficiently, manage progress, and maintain motivation. It is also necessary to have a system that provides timely reminders and rewards to support continued learning.

[1378] 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.

[1379] In this invention, the server includes: means for collecting basic user information and storing it in a database; means for distributing a generated AI trainer program to the user's device based on the collected basic information; means for checking the user's study schedule and sending study reminders; means for analyzing the user's study data and creating a report on their study progress and motivation; means for sending motivational messages to the user based on the received analysis results; means for providing information on the user's areas of interest and the benefits of studying; means for supporting store salespeople and staff in improving their product knowledge and customer service skills; means for displaying visual reminders on the smart glasses based on the staff's registered schedule; means for collecting staff study data and analyzing their progress and test results; means for displaying motivational messages and reward suggestions to staff based on the analysis results; and means for presenting information on new products and skills. This allows store staff to study systematically and continuously, ensuring they always have the latest knowledge and skills. Furthermore, by visualizing study progress and providing timely reminders and motivational messages, the system effectively supports continued study and motivation.

[1380] "User basic information" is personal information related to a user, such as name, role, learning goals, etc.

[1381] "Database" refers to a storage device and related software for storing and managing users' basic information and learning data.

[1382] The "Generative AI Trainer Program" is software designed to assist users in their learning using AI technology.

[1383] "User device" refers to a device that can display and operate learning reminders and educational content, including smartphones and smart glasses.

[1384] A "study schedule" is a study timetable set by the user.

[1385] A "study reminder" is a notification or message set to encourage the user to start studying.

[1386] "Study data" is a record of the user's learning activities, including the amount of time spent studying and progress.

[1387] "Analysis results" are reports of the results and trends obtained by analyzing the collected learning data.

[1388] "Motivational messages" are messages of encouragement and encouragement to increase the user's motivation to learn.

[1389] "Information related to fields of interest" refers to the latest information and knowledge related to fields in which the user is interested.

[1390] The "benefits of studying" are the future benefits and improvements that come from learning.

[1391] "Store salespeople and staff" refers to employees working in physical stores who sell products and serve customers.

[1392] "Product knowledge" is detailed information and understanding about the product being sold.

[1393] "Customer service techniques" refer to the skills and know-how required for dealing with customers.

[1394] "Visual reminders" are notifications or messages displayed through devices such as smart glasses.

[1395] "Progress" indicates the current degree of achievement of the learning goal set by the user.

[1396] "Test results" are the results of assessment tests and quizzes taken by users.

[1397] A "reward offer" is an offer of an incentive to be offered to a user depending on the outcome of their learning.

[1398] "Information about new products and skills" refers to information about the latest product knowledge and new skills that staff need to acquire.

[1399] The present invention relates to a generative AI trainer system that effectively supports user learning. This system is intended to improve the product knowledge and customer service skills of salespeople and staff in brick-and-mortar stores. Specific embodiments for implementing the present invention and their processing details are described below.

[1400] Hardware and Software

[1401] This system operates between three parties: a server, a terminal (a device such as smart glasses), and a user. The main hardware and software components are as follows:

[1402] 1. Hardware

[1403] Smart glasses (e.g. Google Glass)

[1404] server

[1405] communication network

[1406] 2. Software

[1407] Client app (in smart glasses)

[1408] Server-side database (e.g. MySQL)

[1409] Backend framework (e.g. Django)

[1410] Data analysis libraries (e.g., scikit-learn)

[1411] Generative AI Models

[1412] Processing Details

[1413] 1. Initial Setup

[1414] Users: New users register by entering basic information such as their name, role, and learning goals.

[1415] Server: Stores basic information in a database, generates an AI trainer program based on the collected information, and distributes it to the user's device.

[1416] 2. Study Reminders

[1417] Device: Manages the learning schedule set by the user. For example, if the user sets product knowledge learning at 2 p.m.

[1418] Device: Display a visual notification at the set time saying "It's study time now!"

[1419] 3. Collection and analysis of learning data

[1420] Server: Collects user learning activity data (study time, progress, test results, etc.).

[1421] Server: Analyzes the collected data and creates reports on learning progress and motivation.

[1422] 4. Motivation support

[1423] Device: Display motivational messages to staff based on the analysis. Keep staff motivated with messages like "Continuous learning pays off!"

[1424] Device: We also offer rewards such as visual badges and points systems.

[1425] 5. Providing information about new products and skills

[1426] Terminal: Displays the latest information and knowledge related to the user's areas of interest.

[1427] Device: For example, when a new product is released, detailed information about the product is displayed and users can deepen their understanding through tests and quizzes.

[1428] Specific examples

[1429] For example, when a new product is released, the process proceeds as follows:

[1430] 1. Example prompt

[1431] New product learning prompts (examples): "Answer the following questions. List three features of the new product.", "Describe the specialized features of the new product."

[1432] Progress prompts (examples): "How many minutes did you spend studying today?", "Describe one advanced sales technique you learned."

[1433] In this way, by utilizing the system of the present invention, store staff can plan and continue their studies, enabling them to always acquire the latest knowledge and skills. In addition, by visualizing their learning progress and providing timely reminders and motivational messages, the system can effectively support continuation of learning and maintenance of motivation.

[1434] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1435] Step 1:

[1436] Initial Setup - Collecting User Information

[1437] User: A new user registers with the service, entering basic information such as their name, role, and learning goals.

[1438] Input: Basic information such as the user's name, role, and learning goal.

[1439] Server: Saves the entered basic information in a database.

[1440] Output: Basic information of the user stored in the database.

[1441] Specific operation: The user enters information into the form through the terminal and presses the submit button. The server receives the form data and records it in the database.

[1442] Step 2:

[1443] Distribution of the Generative AI Trainer Program

[1444] Server: Customizes the generative AI trainer program based on basic information stored in the database.

[1445] Input: User basic information stored in the database.

[1446] Server: Distributes customized generative AI trainer programs to users' devices.

[1447] Output: The generated AI trainer program installed on the user's device.

[1448] How it works: The server analyzes the user information, generates a customized program based on that information, and sends it to the device, where it is automatically installed.

[1449] Step 3:

[1450] Managing your study schedule

[1451] User: Set a study schedule, for example, to study product knowledge at 2 PM.

[1452] Input: User's study schedule (date, time, content).

[1453] Device: Receives and stores the user's study schedule.

[1454] Output: Saved study schedule.

[1455] Specific operation: The user sets up a study schedule on the device app and presses the save button. The device then saves the information in its internal memory.

[1456] Step 4:

[1457] Send study reminders

[1458] On your device: Generate reminders based on your study schedule.

[1459] Input: Saved study schedule.

[1460] Device: At the set time, a visual reminder such as "It's time to study now!" will be displayed on the smart glasses.

[1461] Output: Reminder notification.

[1462] Specific operations: Check the set time on the internal clock, generate reminders, and display notifications on the smart glasses.

[1463] Step 5:

[1464] Collection and analysis of learning data

[1465] Device: Collects user data during learning activities (study time, progress, quiz results, etc.).

[1466] Input: User learning activity data.

[1467] Terminal: Sends collected data to the server.

[1468] Server: Receives collected data and stores it in a database.

[1469] Output: Saved user training data.

[1470] Specific operation: The device monitors learning activities and collects data, which is then sent to a server at specific times and stored in a database.

[1471] Step 6:

[1472] Data analysis and reporting

[1473] Server: Analyzes the stored learning data and performs data processing and calculations to generate reports on progress and motivation.

[1474] Input: Training data stored in a database.

[1475] Server: Analyzes the data using data analysis libraries (e.g., scikit-learn) to evaluate progress and motivation indicators.

[1476] Output: The generated report.

[1477] Specific operation: The server retrieves the user's learning data from the database, performs calculations using analytical functions, and summarizes the results in report format.

[1478] Step 7:

[1479] Sending motivational messages

[1480] Server: Based on the generated report, generate messages to improve user motivation.

[1481] Input: The generated report.

[1482] Server: Generate a message such as "Continuous learning helps!"

[1483] Terminal: Receives messages and displays them visually on the smart glasses.

[1484] Output: Motivation message notification.

[1485] Specific operation: The server extracts information from the report, generates an encouraging message, and sends it to the device, which then displays the received message on the smart glasses.

[1486] Step 8:

[1487] Providing information about new products and skills

[1488] Device: Periodically collect and display information related to your areas of interest.

[1489] Input: Information about the user's areas of interest.

[1490] Terminal: Presents quizzes and tests to the user in the form of prompts, if necessary.

[1491] For example, "Please list three features of your new product.", "Please explain the specialized functions of your new product."

[1492] Output: Presentation of information about the new product or skill and related test results.

[1493] Specific operation: The device periodically receives new information from the server and presents it to the user. It also displays quizzes to check whether the user has understood the content and collects the results.

[1494] 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.

[1495] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[1496] This system operates among three parties: a server, a terminal, and a user, and incorporates an emotion engine that recognizes the user's emotions, thereby providing learning support that corresponds to the user's emotional state.

[1497] Initial Setup

[1498] Collecting user information and emotion data

[1499] When a user registers with the system, they enter basic information such as their name, age, learning goals, and areas of interest.

[1500] The server collects basic information provided by the user and stores it in a database. In addition, it collects the user's emotional state using an emotion engine and stores it in the database.

[1501] AI Trainer Program Distribution

[1502] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional state data. This program has the necessary functions to support the user's learning.

[1503] Study reminder function

[1504] Managing your study schedule

[1505] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[1506] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[1507] Send a reminder

[1508] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[1509] Analysis of training data and sentiment data

[1510] Collection of training data

[1511] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and emotion data from the emotion engine, and periodically transmits them to the server.

[1512] Analyzing the data

[1513] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[1514] Motivation support

[1515] Utilizing analysis results

[1516] The server transmits a report generated as a result of the analysis to the user's terminal.

[1517] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[1518] Suggesting breaks and rewards

[1519] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[1520] Demonstration of study benefits and areas of interest

[1521] Acquiring interest information

[1522] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," that information is acquired.

[1523] Providing related information

[1524] Based on information provided by the server, the device presents the benefits of studying the field the user is interested in. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[1525] Presentation of information based on emotion information

[1526] The device takes into account the user's emotional state obtained from the emotion engine and presents relaxing content and information at the appropriate time.

[1527] Specific examples

[1528] 1. For User A

[1529] User A registers with this system to deepen his / her IT knowledge.

[1530] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[1531] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[1532] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[1533] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[1534] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[1535] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[1536] The processing flow will be explained below.

[1537] Initial Setup

[1538] Step 1:

[1539] To register with the system, users enter basic information such as their name, age, learning goals, and areas of interest.

[1540] Step 2:

[1541] The server receives the basic information provided by the user and stores it in a database.

[1542] Step 3:

[1543] The server activates the emotion engine and begins monitoring the user's emotional state based on the basic information provided by the user.

[1544] Step 4:

[1545] The server distributes the generated AI trainer program to the user's device and prompts the user to install the program.

[1546] Study reminder function

[1547] Step 5:

[1548] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[1549] Step 6:

[1550] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[1551] Step 7:

[1552] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[1553] Analysis of training data and sentiment data

[1554] Step 8:

[1555] The terminal periodically records the user's learning activity data (study time, progress, learning materials used, etc.) and emotional state and transmits them to the server.

[1556] Step 9:

[1557] The server stores the learning data and emotion data sent from the terminal in a database.

[1558] Step 10:

[1559] The server analyzes the stored learning data and emotional data to create a report on the user's learning progress and motivation. By taking the emotional data into account, detailed analysis can be performed according to the user's emotional state.

[1560] Motivation support

[1561] Step 11:

[1562] The server transmits a report generated as a result of the analysis to the user's terminal.

[1563] Step 12:

[1564] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[1565] Step 13:

[1566] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[1567] Demonstration of study benefits and areas of interest

[1568] Step 14:

[1569] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," it uses that information to obtain related information.

[1570] Step 15:

[1571] Based on information provided by the server, the device will present the benefits of studying the area of ​​interest to the user at the appropriate time. For example, it will display specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[1572] Step 16:

[1573] The device provides relaxing content and information at the appropriate time based on the user's emotional state, obtained from the emotion engine, thereby reducing stress and improving learning efficiency.

[1574] Specific examples

[1575] 1. For User A

[1576] User A registers with this system to deepen his / her IT knowledge.

[1577] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[1578] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[1579] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[1580] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[1581] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[1582] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[1583] Example 2

[1584] 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."

[1585] In today's learning environment, it is difficult for users to maintain sustained motivation to study effectively. Furthermore, there are no systems that can properly grasp each user's learning progress and emotional state and provide support accordingly. Therefore, to maximize users' learning effectiveness, a personalized learning support system that takes into account the user's basic information and emotional state is needed.

[1586] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1587] In this invention, the server includes means for collecting basic information and emotional data of the user and storing it in a database, means for distributing a generated AI trainer program to the user's device based on the collected basic information and emotional data, means for checking the user's study schedule and sending study reminders, means for collecting and analyzing the user's study activity data and emotional data, means for suggesting motivational messages, breaks, and rewards to the user based on the analysis results, means for acquiring information about the user's interests and providing information related to the interests and benefits of studying, and means for presenting appropriate relaxation content based on the emotional data. This makes it possible to comprehensively grasp the user's study progress and emotional state and provide optimized study support for each user.

[1588] "User" refers to an individual who uses the system to learn.

[1589] "Basic Information" refers to initial registration data about a user, such as the user's name, age, learning goals, and areas of interest.

[1590] "Emotional data" refers to information that indicates a user's emotional state and is obtained through text analysis or facial recognition technology.

[1591] "Database" refers to an information system for storing and managing collected basic information, emotional data, and learning activity data.

[1592] "Generative AI trainer program" refers to a program that provides learning support to users, generated based on the user's basic information and emotional data.

[1593] "Terminal" refers to a device used by a user to access the system, such as a computer, smartphone, or tablet.

[1594] "Study schedule" refers to a study plan and timetable set by a user.

[1595] "Study reminders" refer to messages and alerts that notify users of study time and encourage them to study.

[1596] "Learning activity data" refers to data related to learning, such as a user's study time, progress, and learning materials used.

[1597] "Analysis results" refers to information generated based on collected learning activity data and emotional data for evaluating a user's learning progress and motivation.

[1598] "Messages" refer to words of encouragement or advice sent to increase the user's motivation to study.

[1599] "Break" refers to information and notifications suggesting that the user take adequate rest.

[1600] "Rewards" refer to incentives given to recognize a user's efforts and maintain motivation.

[1601] "Interest information" refers to information relating to areas in which a user is interested.

[1602] "Relaxation content" refers to content such as music and videos that stabilize the user's emotional state and promote relaxation.

[1603] MODE FOR CARRYING OUT THE INVENTION

[1604] The present invention provides a system for effectively supporting a user's learning, and provides personalized learning support by taking into account the user's emotional state. Hereinafter, an embodiment of the present invention will be described in detail.

[1605] Collecting user information and emotion data

[1606] When users register with the system, they enter basic information such as their name, age, learning goals, and areas of interest via a web form or mobile application.

[1607] The server collects basic information provided by the user and stores it in a database, and also analyzes the user's emotional state using an emotion engine (e.g., IBM Watson Tone Analyzer) and stores this information in the database.

[1608] AI Trainer Program Distribution

[1609] The server generates prompt sentences for the generative AI model (e.g., GPT-4) based on the collected basic information and emotional data, and creates a customized generative AI trainer program.

[1610] The server distributes the generated trainer program to the user's terminal, which includes a study reminder function and a study data tracking function.

[1611] Managing your study schedule

[1612] Users set their own study schedules on their own devices. Specifically, they can use the device app to input plans such as "start studying at 8am every morning."

[1613] The device saves the set schedule information in local storage and prepares to send a reminder at the specified time.

[1614] Send study reminders

[1615] When the set study time arrives, the device will send a notification to the user saying, "It's time to study!" This notification is realized using a push notification service.

[1616] Collecting and analyzing training data and sentiment data

[1617] The device transmits the user's learning activity data (study time, progress, learning materials used, etc.) and emotional data collected in real time to the server. Specific emotional data is acquired through the device's camera and microphone.

[1618] The server stores the received learning data and emotion data in a database and analyzes them using R, Python, etc. As a result of the analysis, a report on the user's learning progress and motivation is generated.

[1619] Motivation support

[1620] The server generates a report based on the analysis results and sends it to the user's terminal.

[1621] Based on the report, the device sends the user motivational messages, such as "Keep going, you're doing great!" if their motivation is low.

[1622] The device considers the user's learning progress and emotional state and suggests appropriate breaks and rewards, for example, displaying a notification that says, "Taking a 15-minute break will improve your efficiency."

[1623] Demonstration of study benefits and areas of interest

[1624] The device will check the user's areas of interest from their basic information and then present relevant benefits based on that information, such as "Learning programming will increase your chances of earning a high income in the future."

[1625] The device will then provide appropriate relaxation content based on emotional data, such as relaxing music and guided meditations through the Calm app.

[1626] Specific examples

[1627] When user A uses this system to deepen his / her IT knowledge, the system operates as follows.

[1628] User A enters basic information into the system and registers as a new user.

[1629] The server generates a customized AI trainer program based on basic information and daily collected emotional data and distributes it to User A's device.

[1630] The device manages user A's study schedule and sends a reminder saying "It's time to study!" at the scheduled time of 8:00.

[1631] The server analyzes the learning data and emotional data of User A and creates a report. For example, the report may indicate that motivation is declining.

[1632] Based on the report, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[1633] The device provides User A with benefits such as "Learning programming will increase your chances of earning a high income in the future" and presents relaxing content according to his or her emotional state.

[1634] This allows the system to effectively support users' learning and provide personalized learning assistance that takes into account their emotional state.

[1635] Example prompt sentence:

[1636] "Consider user sentiment data and provide advice on maintaining appropriate learning motivation."

[1637] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1638] Step 1:

[1639] To register with the system, a user enters basic information such as name, age, learning goals, and areas of interest. This information is entered through a web form or a mobile application. The inputs include name, age, learning goals, and areas of interest. The output is sent to the server.

[1640] Step 2:

[1641] The server stores the received basic information in a database. It then uses an emotion engine (e.g., a natural language processing tool) to analyze the user's emotional state. Specifically, it detects emotions from the user's input text. The inputs include the user's basic information and the analysis results from the emotion engine. The output is the basic information and emotional data stored in the database.

[1642] Step 3:

[1643] The server creates a customized generative AI trainer program based on the collected basic information and emotional data. A generative AI model (e.g., a generative AI engine) is used to generate a learning program optimized for the user. The input includes basic information and emotional data from the database and prompt sentences for the generative AI model. The output is a customized generative AI trainer program.

[1644] Step 4:

[1645] The server distributes the generated AI trainer program to the user's device. Specifically, it sends the program in digital format so that the user can install it on their device. The input is the generated AI trainer program. The output is the program sent to the user's device.

[1646] Step 5:

[1647] A user sets a study schedule on their device. Specifically, they use a device application to input a schedule such as "I will start studying at 8:00 every morning." The input includes details of the schedule set by the user. The output is the schedule saved on the device.

[1648] Step 6:

[1649] The device saves the user's study schedule in local storage and prepares to send a reminder at the specified time. The input includes the saved study schedule. The output is the reminder notification.

[1650] Step 7:

[1651] When the scheduled study time arrives, the device sends the user a notification saying "It's time to study!". Specifically, it uses a push notification service to display a reminder on the user's device. The input includes schedule data for sending the reminder. The output is a reminder notification that is displayed on the user's device.

[1652] Step 8:

[1653] The device collects the user's learning activity data (study time, progress, learning materials used, etc.). In addition, emotional data is collected in real time. Specifically, the device's camera and microphone are used to acquire the emotional data. The input includes the user's learning activity and data for emotion detection. As output, these data are sent to the server.

[1654] Step 9:

[1655] The server stores the received learning data and emotion data in a database and analyzes it. Specifically, it analyzes the data using programs such as R and Python and generates reports on the user's learning progress and motivation. The inputs are the learning data and emotion data from the database. The output is a detailed analysis report.

[1656] Step 10:

[1657] The server sends the generated report to the user's terminal, specifically in digital format using a secure protocol (e.g. HTTPS). The input is the generated analysis report. The output is the report that arrives at the user's terminal.

[1658] Step 11:

[1659] The terminal sends a motivational message to the user based on the received report. Specifically, the terminal displays a message such as "Keep going, you're doing great!". The input includes the analysis report and a message template. The output is the motivational message displayed to the user.

[1660] Step 12:

[1661] The device suggests appropriate breaks and rewards based on the user's learning progress and emotional state. Specifically, it sends a notification such as "Taking a 15-minute break will improve your efficiency." The input includes the user's learning data and emotional data. As an output, the break and reward suggestion notification is displayed on the user's device.

[1662] Step 13:

[1663] The device acquires the user's interest information and provides information related to that interest and the benefits of studying. Specifically, it displays benefits such as "Learning programming will increase your chances of earning a high income in the future." The input includes the user's basic information and interest data. The output presents related information and benefits.

[1664] Step 14:

[1665] The device presents relaxing content based on the emotion data. Specifically, it displays content such as "relaxing music" and "meditation guide." The input includes the emotion engine analysis results and a template for the relaxing content. As an output, the relaxing content is displayed on the user's device.

[1666] (Application example 2)

[1667] 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."

[1668] Conventional learning support systems proceed with learning without taking the user's emotional state into consideration, which can lead to a decline in motivation and a decline in learning efficiency. Furthermore, there are problems with users being fatigued and stressed due to inappropriate timing for breaks and the provision of rewards. To address these issues, a system that provides support according to the user's emotional state is needed.

[1669] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information and emotional information about the user and storing it in a database, means for distributing a generative AI trainer program to the user's terminal based on the collected basic information and emotional information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and emotional data and creating a report on study progress and motivation, means for sending a motivating message to the user based on the received analysis results, means for suggesting appropriate breaks and rewards based on the user's emotional state, and means for providing information on the user's areas of interest and the benefits of study. This makes it possible to maximize the effectiveness of study while taking the user's emotional state into consideration in real time.

[1670] "Basic User Information" refers to personal data such as the user's name, age, learning goals, and areas of interest.

[1671] "Emotion information" is data that indicates the user's emotional state, and includes tension, fatigue level, stress state, and the like.

[1672] "Database" refers to an information system for storing and managing collected basic information and emotional information.

[1673] The "generative AI trainer program" is learning support software that is generated based on collected basic information and emotional information and distributed to the user's device.

[1674] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[1675] A "study schedule" is a plan of study time and study content set by the user.

[1676] "Study Reminder" is a function that notifies users to start studying based on the study schedule they set.

[1677] "Study data" refers to data related to a user's learning activities, including study time, progress, learning materials used, and the like.

[1678] "Emotion data" is data about a user's emotional state collected by an emotion engine.

[1679] A "report" is a report that analyzes learning data and emotional data and summarizes information about learning progress and motivation.

[1680] A "motivation message" is a message sent to a user to increase their motivation to study.

[1681] "Break and reward suggestions" is a function that suggests breaks at appropriate times and rewards according to the user's learning progress based on the user's emotional state.

[1682] "Areas of interest" refers to areas of study or topics that interest the user.

[1683] "Learning benefits" refers to the benefits or advantages a user will gain if they continue to learn.

[1684] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[1685] Initial Setup

[1686] Collecting user information and emotion data

[1687] When a user registers with the system, they must enter basic information such as their name, age, learning goals, and areas of interest. The server collects the basic information provided by the user and stores it in a database. The server also periodically collects the user's emotional state (e.g., tension, fatigue level) using an emotion engine and stores this in the database.

[1688] Distribution of the Generative AI Trainer Program

[1689] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional information. This program has the necessary functions (reminders, data collection, analysis, etc.) to support the user's learning.

[1690] Study reminder function

[1691] Managing your study schedule

[1692] Users can set a learning schedule on their device. For example, they can set an appointment to start the driving simulation every morning at 8:00. The device saves the schedule and prepares to send a reminder at the specified time.

[1693] Send a reminder

[1694] The device sends learning reminders to the user based on their learning schedule. For example, at 8:00 a.m., a notification will appear saying, "It's time to start the driving simulation."

[1695] Analysis of training data and sentiment data

[1696] Collection of training data

[1697] The device collects the user's learning activity data and emotion data from the emotion engine, and periodically transmits them to the server. The learning activity data includes the user's learning time, progress, learning materials used, etc.

[1698] Analyzing the data

[1699] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[1700] Motivation support

[1701] Utilizing analysis results

[1702] The server then sends the generated analysis results to the user's device. Based on the received report, the device sends the user a motivating message. For example, if the device determines that the user's motivation is low, it sends a message such as, "You seem tired. Take a short break."

[1703] Suggesting breaks and rewards

[1704] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[1705] Other support

[1706] The device checks the user's areas of interest from basic information and, based on information provided by the server, presents the benefits of studying the user's areas of interest. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future." It also takes emotional information into account and presents relaxing content at appropriate times.

[1707] Examples of concrete examples and prompts

[1708] Specific examples

[1709] 1. When the user starts the simulation, the system checks their emotional state for the day and sends a message saying, "You seem a little tired. Let's take a break."

[1710] 2. When the learning time is over, the system analyzes the progress of the learning data and emotional data, and notifies the driver, "Today's driving simulation took 35 minutes. Let's do our best next time!"

[1711] Prompt Sentence Examples

[1712] "Generate motivational messages based on the user's learning progress and emotional state. For example, generate a message to send when the user is in a 'tired' state."

[1713] This embodiment allows for maximizing learning effectiveness while taking into account the user's emotional state in real time.

[1714] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1715] Step 1:

[1716] A user registers with the system and inputs basic information, learning goals, and areas of interest. The server collects the basic information and emotional state received from the user and stores them in a database.

[1717] Specific behavior:

[1718] (Input) User information (name, age, learning goals, etc.)

[1719] (Data processing) Convert the entered information into a database format

[1720] (Output) Saved user information

[1721] Step 2:

[1722] The server uses an emotion engine to periodically collect the user's emotional state (e.g., tension, fatigue level) and store it in a database.

[1723] Specific behavior:

[1724] (Input) Real-time emotion data

[1725] (Data processing) Emotional data is measured, organized, and saved in a database

[1726] (Output) Updated emotion information

[1727] Step 3:

[1728] The server generates a generative AI trainer program based on the basic information and emotion data, and distributes the program to the user's device.

[1729] Specific behavior:

[1730] (Input) Basic information, emotion data

[1731] (Data calculation) Generation of trainer programs using generative AI models

[1732] (Output) The distributed generative AI trainer program

[1733] Step 4:

[1734] The user sets up a study schedule on the device, which stores this schedule and prepares to send reminders at the specified times.

[1735] Specific behavior:

[1736] (Input) Study Schedule

[1737] (Data processing) Saving schedule information

[1738] (Output) Set reminders

[1739] Step 5:

[1740] The device sends learning reminders to the user based on the learning schedule. For example, it displays a notification every morning at 8:00 that says, "It's time to start the driving simulation."

[1741] Specific behavior:

[1742] (Input) Saved schedule information

[1743] (Data calculation) Generate reminders according to schedule

[1744] (Output) Reminders sent

[1745] Step 6:

[1746] The device collects the user's learning activity data (study time, progress, etc.) and emotion data from the emotion engine, and periodically sends them to the server.

[1747] Specific behavior:

[1748] (Input) Learning activity data, real-time emotion data

[1749] (Data calculation) Data collection and organization

[1750] (Output) Data sent to the server

[1751] Step 7:

[1752] The server analyzes the learning and emotional data and generates reports on the user's learning progress and motivation.

[1753] Specific behavior:

[1754] (Input) Learning data, emotion data

[1755] (Data calculation) Report generation by data analysis

[1756] (Output) Generated report

[1757] Step 8:

[1758] The server then sends the generated analysis results to the user's device, which then sends them motivational messages and suggests appropriate breaks and rewards based on their emotional state.

[1759] Specific behavior:

[1760] (Input) Analysis results (report)

[1761] (Data Calculation) Motivation Message Generation

[1762] (Output) Messages sent and break / reward proposals

[1763] Step 9:

[1764] The device provides learning benefits related to the user's areas of interest and presents relaxing content at the appropriate time depending on the user's emotional state.

[1765] Specific behavior:

[1766] (Input) Basic information, area of ​​interest data, emotion data

[1767] (Data calculation)Generation of merit information and relaxation content

[1768] (Output) Presented benefit information and content

[1769] 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.

[1770] 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.

[1771] 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.

[1772] [Fourth embodiment]

[1773] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1774] 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.

[1775] 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).

[1776] 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.

[1777] 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.

[1778] 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).

[1779] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1780] 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.

[1781] 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.

[1782] 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.

[1783] 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.

[1784] 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.

[1785] 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."

[1786] The present invention relates to a generative AI trainer system that effectively supports user learning. An embodiment of the present invention will be described below.

[1787] This system operates among three parties: a server, a terminal, and a user. The main functions and their operations are explained in detail below.

[1788] Initial Setup

[1789] Collection of User Information

[1790] When a new user registers, the server collects basic information about the user (such as name, age, learning goals, areas of interest, etc.) This information is stored in a database.

[1791] AI Trainer Program Distribution

[1792] The server distributes a generative AI trainer program to the user's device based on the collected basic information. This program is the primary means of assisting the user in their learning.

[1793] Study reminder function

[1794] Managing your study schedule

[1795] The device manages the study schedule set by the user. For example, if the user sets a schedule to study at 8:00 every morning, the device will save this schedule and prepare to send reminders at the specified time.

[1796] Send a reminder

[1797] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[1798] Analysis of training data

[1799] Collection of training data

[1800] The server periodically collects data from users' learning activities (such as learning time, progress, learning materials used, etc.) and stores this data in a database for analysis.

[1801] Analyzing the data

[1802] The server analyzes the collected learning data and creates reports on the user's learning progress and motivation, for example, by calculating the learning time and progress rate and evaluating motivation indicators.

[1803] Motivation support

[1804] Utilizing analysis results

[1805] Based on the analysis results sent from the server, the device sends messages to motivate the user. For example, if the device determines that the user's motivation is declining based on recent learning data, it sends a message such as "Keep going, you're doing great!"

[1806] Suggesting breaks and rewards

[1807] The device suggests appropriate breaks and rewards to the user to increase motivation. By suggesting the timing of breaks and simple rewards according to the user's situation, the device aims to maintain motivation.

[1808] Demonstration of study benefits and areas of interest

[1809] Acquiring interest information

[1810] The device checks the areas of interest the user has (e.g., programming, artificial intelligence, etc.) This information is obtained from the user's basic information collected.

[1811] Providing related information

[1812] Based on information provided by the server, the device presents the latest information on areas of interest to the user and the benefits of studying. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[1813] Specific examples

[1814] 1. For User A

[1815] User A registers with this system to deepen his / her IT knowledge.

[1816] The server collects basic information about user A and distributes the AI ​​trainer program to the terminal.

[1817] The terminal manages user A's study schedule and sends reminders at appropriate times.

[1818] The server analyzes user A's learning data and generates a report to increase motivation.

[1819] The terminal sends encouraging messages to User A based on the analysis results and provides relevant study benefits.

[1820] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[1821] The processing flow will be explained below.

[1822] Initial Setup

[1823] Step 1:

[1824] When a user registers with the system, he or she enters basic information such as name, age, learning goals, and areas of interest.

[1825] Step 2:

[1826] The server collects basic information provided by the user and stores it in a database.

[1827] Step 3:

[1828] The server distributes a generative AI trainer program to the user's device based on the collected basic information.

[1829] Study reminder function

[1830] Step 4:

[1831] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[1832] Step 5:

[1833] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[1834] Step 6:

[1835] The device will send users study reminders based on their schedule, and at 8 o'clock, it will send a notification saying "It's time to study!"

[1836] Analysis of training data

[1837] Step 7:

[1838] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and periodically transmits it to the server.

[1839] Step 8:

[1840] The server stores the learning data sent from the terminal in a database.

[1841] Step 9:

[1842] The server analyzes the stored learning data and creates reports on the user's learning progress and motivation.

[1843] Motivation support

[1844] Step 10:

[1845] The server transmits a report generated as a result of the analysis to the user's terminal.

[1846] Step 11:

[1847] The device sends a motivational message to the user based on the received report. For example, if the device determines that the user's motivation is declining, it sends the message "Keep going, you're doing great!"

[1848] Step 12:

[1849] The device will suggest appropriate breaks and rewards based on the user's situation, helping them to maintain their motivation to study.

[1850] Demonstration of study benefits and areas of interest

[1851] Step 13:

[1852] The device checks the user's basic information to see what areas they are interested in. For example, if the user expresses an interest in "programming," that information is acquired.

[1853] Step 14:

[1854] Based on information provided by the server, the device presents the benefits of studying the area the user is interested in. For example, it displays the benefit that "studying programming will increase your chances of earning a high income in the future."

[1855] Step 15:

[1856] Users can refer to the information presented to them as they proceed with their studies, which makes it easier for them to clarify the meaning and purpose of their studies.

[1857] Through the above steps, users can study effectively and achieve their learning goals while maintaining their motivation.

[1858] Example 1

[1859] 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."

[1860] Conventional learning support systems have inadequately managed users' learning progress and motivation, and lacked effective support for users to continue learning over the long term. Furthermore, they did not provide learning programs tailored to individual users' needs, send appropriate learning reminders, or conduct detailed analysis of collected learning data, making it difficult to maintain users' learning efficiency and motivation.

[1861] 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.

[1862] In this invention, the server includes means for collecting basic information about the user and storing it in a database, means for distributing a generating AI trainer program to the user's device based on the collected basic information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and creating a report on study progress and motivation, means for sending a message to the user to motivate the user based on the received analysis results, means for collecting study data and sending it to the server, means for storing the collected data in local storage and managing the study schedule, means for using a RESTful API when distributing the generating AI program, and means for sending study reminders via push notification. This makes it possible to provide a study program that meets the individual needs of the user, and by sending study reminders at appropriate times and performing detailed analysis based on the collected data, it is possible to improve the user's study efficiency and maintain long-term motivation.

[1863] "User" means an individual or corporate user of this system.

[1864] "Basic information" refers to personal information such as the user's name, age, learning goals, and areas of interest.

[1865] "Database" means a structured data storage system for storing user information, learning data, etc.

[1866] "Generative AI trainer program" refers to an artificial intelligence program that is generated by a server and distributed to a user's device to assist the user in learning.

[1867] "Terminal" means an electronic device used by a user, such as a computer, tablet, or smartphone.

[1868] "Study schedule" refers to a study plan or schedule set by a user.

[1869] "Study reminders" refer to notifications that encourage users to study based on their set study schedule.

[1870] "Study data" refers to information about a user's learning activities (study time, progress, learning materials used, etc.).

[1871] "Report" means a document or data summarizing the results of an analysis of collected learning data and evaluation of learning progress and motivation.

[1872] "RESTful API" means an application programming interface that performs operations on resources through HTTP requests.

[1873] "Push notification" refers to a message sent instantly from a server to a client application.

[1874] "Local storage" refers to a storage function for saving data within a user's terminal.

[1875] This invention relates to a generative AI trainer system that effectively supports user learning. This system operates among a server, a terminal, and a user, and provides a series of functions to enhance the user's learning progress and motivation.

[1876] User information collection and initial settings

[1877] When a new user registers with the system, the server collects basic information such as name, age, learning goals, and areas of interest. This information is received through an HTTP request and stored in a database (e.g., MySQL or PostgreSQL), allowing the server to provide personalized services for each user.

[1878] Specific examples

[1879] When User A accesses the system and enters the necessary information, the server stores the information in a database. This information is used to provide individualized support in subsequent learning support.

[1880] Distribution of the Generative AI Trainer Program

[1881] The server distributes the generative AI trainer program to the user's device based on the collected user information. Distribution is performed using a RESTful API, and the device receives it and installs it locally. The generative AI trainer program is written in JavaScript or Python.

[1882] Specific examples

[1883] The server packages an AI trainer program written in Python and sends it to the device as an HTTP response, which the device receives and deploys locally.

[1884] Setting and managing your study schedule

[1885] Users set their own study schedule through the application on their device, allowing them to plan when to start studying.

[1886] The device saves the configured study schedule in local storage (e.g., an SQLite database), which is used to send future reminders.

[1887] Specific examples

[1888] User A enters his / her study plan into the device app every morning at 8:00 and presses the save button. The device saves this schedule in an SQLite database.

[1889] Send study reminders

[1890] The device generates and sends study reminders based on the schedule set by the user, and uses a push notification system (e.g., Firebase Cloud Messaging) to notify the user of the reminder at the specified time.

[1891] Specific examples

[1892] Every morning at 8:00, a push notification saying "It's time to study!" appears on User A's device.

[1893] Collecting and sending learning data

[1894] As the user progresses with their learning activities, they input learning data, such as their progress and the learning materials they have used, into the terminal.

[1895] The device collects this learning data and periodically sends it to the server, which uses HTTPS for secure communication.

[1896] Specific examples

[1897] When User A enters the study time and materials used into the app and presses the send button, the device sends this data to the server.

[1898] Analysis of training data

[1899] The server analyzes the received learning data and creates reports on learning progress and motivation. It aggregates and analyzes the data using Python libraries such as pandas and scikit-learn.

[1900] Specific examples

[1901] The server extracts learning data from the database and generates a report that compiles and visualizes weekly learning time and progress.

[1902] Motivational feedback

[1903] The terminal sends a message to the user to increase motivation based on the analysis results sent from the server.

[1904] Specific examples

[1905] When the server determines that "recent learning data indicates a decline in motivation" and sends this result to the device, the device notifies the user with an encouraging message such as "Keep going, you're doing great!"

[1906] Providing information about areas of interest

[1907] The server collects the latest information and study benefits based on the user's interests and provides them to the terminal.

[1908] The terminal presents the provided information to the user.

[1909] Specific examples

[1910] The server collects information such as "Learning programming will increase your chances of earning a high income in the future" and sends it to the device, which then displays it to the user.

[1911] Prompt Sentence Examples

[1912] Below are some example prompts to input to the generative AI model:

[1913] Example prompt sentence:

[1914] "Generate a report on User A's current learning progress and motivation based on his learning record data from the past month. Please take into account the following information: total study time, progress, types of learning materials used, and trends in motivation. Also, please include specific advice for User A to continue studying."

[1915] In this way, the system of the present invention can effectively support the user's learning and improve learning outcomes while maintaining motivation.

[1916] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1917] Step 1: User registration and information collection

[1918] Input: The user enters basic information such as name, age, learning goals, and areas of interest.

[1919] Specific operation: A user accesses the system, enters personal information into a form, and presses the submit button.

[1920] Data processing: The server receives the entered information, converts it into the required format, and stores it in the database.

[1921] Output: The user's basic information is saved in the database.

[1922] Step 2: Distributing the Generative AI Trainer Program

[1923] Input: User basic information stored in the database

[1924] Specific operation: The server generates an individualized generative AI trainer program based on the stored user information and distributes it to the terminal via an HTTP response.

[1925] Data processing: Customize program settings based on collected information and package the program.

[1926] Output: The customized AI trainer program is distributed to the device and installed locally.

[1927] Step 3: Set and manage your study schedule

[1928] Input: Information for users to set their own study schedule (date, time, subjects, etc.)

[1929] Specific operation: The user inputs a schedule through the terminal application and presses the save button.

[1930] Data processing: The terminal converts the input schedule into a format suitable for saving in the SQLite database and stores it.

[1931] Output: The configured learning schedule is saved to local storage.

[1932] Step 4: Send a reminder

[1933] Input: Information saved in your study schedule

[1934] Specific operation: When the specified time arrives, the device will use the push notification system to generate a reminder and display a notification.

[1935] Data Processing: Prepares push notification messages and sends them to the notification system based on a schedule.

[1936] Output: The user is shown the reminder.

[1937] Step 5: Collect and send training data

[1938] Input: Study data entered by the user (study time, progress, materials used, etc.)

[1939] Specific operation: The user enters learning data on the device and presses the send button. The device then sends the data to the server via HTTPS.

[1940] Data processing: Converts the input data into the appropriate format and sends it using the HTTPS protocol.

[1941] Output: The user's learning data is sent to the server and stored in the database.

[1942] Step 6: Analyze the training data

[1943] Input: Training data stored on the server

[1944] Specific operation: The server periodically extracts training data from the database and analyzes and aggregates the data using Python's pandas and scikit-learn.

[1945] Data calculation: Calculate each user's study time, progress, and motivation, and create a report.

[1946] Output: A user-specific report is generated based on the analyzed data.

[1947] Step 7: Motivational feedback

[1948] Input: Report generated as a result of analysis

[1949] Specific operation: The server sends the generated report to the terminal, and the terminal notifies the user of an encouraging message based on the analysis results.

[1950] Data calculation: Generate motivational messages based on the report content.

[1951] Output: A feedback message is sent to the user.

[1952] Step 8: Provide information about your interests

[1953] Input: Information about the user's interests

[1954] Specific operation: The server collects the information to be provided, organizes it, and sends it to the terminal, which then displays the received information.

[1955] Data processing: Converting collected information into a format that meets the user's interests and providing it to them.

[1956] Output: Information about the area of ​​interest is presented to the user.

[1957] This allows users to receive personalized learning support, enabling them to continue learning while maintaining their motivation.

[1958] (Application example 1)

[1959] 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."

[1960] In brick-and-mortar stores, salespeople and staff need to constantly improve their product knowledge and customer service skills. However, they are often overwhelmed with their daily work and neglect self-study. For this reason, an effective system is needed that allows staff to learn efficiently, manage progress, and maintain motivation. It is also necessary to have a system that provides timely reminders and rewards to support continued learning.

[1961] 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.

[1962] In this invention, the server includes: means for collecting basic user information and storing it in a database; means for distributing a generated AI trainer program to the user's device based on the collected basic information; means for checking the user's study schedule and sending study reminders; means for analyzing the user's study data and creating a report on their study progress and motivation; means for sending motivational messages to the user based on the received analysis results; means for providing information on the user's areas of interest and the benefits of studying; means for supporting store salespeople and staff in improving their product knowledge and customer service skills; means for displaying visual reminders on the smart glasses based on the staff's registered schedule; means for collecting staff study data and analyzing their progress and test results; means for displaying motivational messages and reward suggestions to staff based on the analysis results; and means for presenting information on new products and skills. This allows store staff to study systematically and continuously, ensuring they always have the latest knowledge and skills. Furthermore, by visualizing study progress and providing timely reminders and motivational messages, the system effectively supports continued study and motivation.

[1963] "User basic information" is personal information related to a user, such as name, role, learning goals, etc.

[1964] "Database" refers to a storage device and related software for storing and managing users' basic information and learning data.

[1965] The "Generative AI Trainer Program" is software designed to assist users in their learning using AI technology.

[1966] "User device" refers to a device that can display and operate learning reminders and educational content, including smartphones and smart glasses.

[1967] A "study schedule" is a study timetable set by the user.

[1968] A "study reminder" is a notification or message set to encourage the user to start studying.

[1969] "Study data" is a record of the user's learning activities, including the amount of time spent studying and progress.

[1970] "Analysis results" are reports of the results and trends obtained by analyzing the collected learning data.

[1971] "Motivational messages" are messages of encouragement and encouragement to increase the user's motivation to learn.

[1972] "Information related to fields of interest" refers to the latest information and knowledge related to fields in which the user is interested.

[1973] The "benefits of studying" are the future benefits and improvements that come from learning.

[1974] "Store salespeople and staff" refers to employees working in physical stores who sell products and serve customers.

[1975] "Product knowledge" is detailed information and understanding about the product being sold.

[1976] "Customer service techniques" refer to the skills and know-how required for dealing with customers.

[1977] "Visual reminders" are notifications or messages displayed through devices such as smart glasses.

[1978] "Progress" indicates the current degree of achievement of the learning goal set by the user.

[1979] "Test results" are the results of assessment tests and quizzes taken by users.

[1980] A "reward offer" is an offer of an incentive to be offered to a user depending on the outcome of their learning.

[1981] "Information about new products and skills" refers to information about the latest product knowledge and new skills that staff need to acquire.

[1982] The present invention relates to a generative AI trainer system that effectively supports user learning. This system is intended to improve the product knowledge and customer service skills of salespeople and staff in brick-and-mortar stores. Specific embodiments for implementing the present invention and their processing details are described below.

[1983] Hardware and Software

[1984] This system operates between three parties: a server, a terminal (a device such as smart glasses), and a user. The main hardware and software components are as follows:

[1985] 1. Hardware

[1986] Smart glasses (e.g. Google Glass)

[1987] server

[1988] communication network

[1989] 2. Software

[1990] Client app (in smart glasses)

[1991] Server-side database (e.g. MySQL)

[1992] Backend framework (e.g. Django)

[1993] Data analysis libraries (e.g., scikit-learn)

[1994] Generative AI Models

[1995] Processing Details

[1996] 1. Initial Setup

[1997] Users: New users register by entering basic information such as their name, role, and learning goals.

[1998] Server: Stores basic information in a database, generates an AI trainer program based on the collected information, and distributes it to the user's device.

[1999] 2. Study Reminders

[2000] Device: Manages the learning schedule set by the user. For example, if the user sets product knowledge learning at 2 p.m.

[2001] Device: Display a visual notification at the set time saying "It's study time now!"

[2002] 3. Collection and analysis of learning data

[2003] Server: Collects user learning activity data (study time, progress, test results, etc.).

[2004] Server: Analyzes the collected data and creates reports on learning progress and motivation.

[2005] 4. Motivation support

[2006] Device: Display motivational messages to staff based on the analysis. Keep staff motivated with messages like "Continuous learning pays off!"

[2007] Device: We also offer rewards such as visual badges and points systems.

[2008] 5. Providing information about new products and skills

[2009] Terminal: Displays the latest information and knowledge related to the user's areas of interest.

[2010] Device: For example, when a new product is released, detailed information about the product is displayed and users can deepen their understanding through tests and quizzes.

[2011] Specific examples

[2012] For example, when a new product is released, the process proceeds as follows:

[2013] 1. Example prompt

[2014] New product learning prompts (examples): "Answer the following questions. List three features of the new product.", "Describe the specialized features of the new product."

[2015] Progress prompts (examples): "How many minutes did you spend studying today?", "Describe one advanced sales technique you learned."

[2016] In this way, by utilizing the system of the present invention, store staff can plan and continue their studies, enabling them to always acquire the latest knowledge and skills. In addition, by visualizing their learning progress and providing timely reminders and motivational messages, the system can effectively support continuation of learning and maintenance of motivation.

[2017] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2018] Step 1:

[2019] Initial Setup - Collecting User Information

[2020] User: A new user registers with the service, entering basic information such as their name, role, and learning goals.

[2021] Input: Basic information such as the user's name, role, and learning goal.

[2022] Server: Saves the entered basic information in a database.

[2023] Output: Basic information of the user stored in the database.

[2024] Specific operation: The user enters information into the form through the terminal and presses the submit button. The server receives the form data and records it in the database.

[2025] Step 2:

[2026] Distribution of the Generative AI Trainer Program

[2027] Server: Customizes the generative AI trainer program based on basic information stored in the database.

[2028] Input: User basic information stored in the database.

[2029] Server: Distributes customized generative AI trainer programs to users' devices.

[2030] Output: The generated AI trainer program installed on the user's device.

[2031] How it works: The server analyzes the user information, generates a customized program based on that information, and sends it to the device, where it is automatically installed.

[2032] Step 3:

[2033] Managing your study schedule

[2034] User: Set a study schedule, for example, to study product knowledge at 2 PM.

[2035] Input: User's study schedule (date, time, content).

[2036] Device: Receives and stores the user's study schedule.

[2037] Output: Saved study schedule.

[2038] Specific operation: The user sets up a study schedule on the device app and presses the save button. The device then saves the information in its internal memory.

[2039] Step 4:

[2040] Send study reminders

[2041] On your device: Generate reminders based on your study schedule.

[2042] Input: Saved study schedule.

[2043] Device: At the set time, a visual reminder such as "It's time to study now!" will be displayed on the smart glasses.

[2044] Output: Reminder notification.

[2045] Specific operations: Check the set time on the internal clock, generate reminders, and display notifications on the smart glasses.

[2046] Step 5:

[2047] Collection and analysis of learning data

[2048] Device: Collects user data during learning activities (study time, progress, quiz results, etc.).

[2049] Input: User learning activity data.

[2050] Terminal: Sends collected data to the server.

[2051] Server: Receives collected data and stores it in a database.

[2052] Output: Saved user training data.

[2053] Specific operation: The device monitors learning activities and collects data, which is then sent to a server at specific times and stored in a database.

[2054] Step 6:

[2055] Data analysis and reporting

[2056] Server: Analyzes the stored learning data and performs data processing and calculations to generate reports on progress and motivation.

[2057] Input: Training data stored in a database.

[2058] Server: Analyzes the data using data analysis libraries (e.g., scikit-learn) to evaluate progress and motivation indicators.

[2059] Output: The generated report.

[2060] Specific operation: The server retrieves the user's learning data from the database, performs calculations using analytical functions, and summarizes the results in report format.

[2061] Step 7:

[2062] Sending motivational messages

[2063] Server: Based on the generated report, generate messages to improve user motivation.

[2064] Input: The generated report.

[2065] Server: Generate a message such as "Continuous learning helps!"

[2066] Terminal: Receives messages and displays them visually on the smart glasses.

[2067] Output: Motivation message notification.

[2068] Specific operation: The server extracts information from the report, generates an encouraging message, and sends it to the device, which then displays the received message on the smart glasses.

[2069] Step 8:

[2070] Providing information about new products and skills

[2071] Device: Periodically collect and display information related to your areas of interest.

[2072] Input: Information about the user's areas of interest.

[2073] Terminal: Presents quizzes and tests to the user in the form of prompts, if necessary.

[2074] For example, "Please list three features of your new product.", "Please explain the specialized functions of your new product."

[2075] Output: Presentation of information about the new product or skill and related test results.

[2076] Specific operation: The device periodically receives new information from the server and presents it to the user. It also displays quizzes to check whether the user has understood the content and collects the results.

[2077] 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.

[2078] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[2079] This system operates among three parties: a server, a terminal, and a user, and incorporates an emotion engine that recognizes the user's emotions, thereby providing learning support that corresponds to the user's emotional state.

[2080] Initial Setup

[2081] Collecting user information and emotion data

[2082] When a user registers with the system, they enter basic information such as their name, age, learning goals, and areas of interest.

[2083] The server collects basic information provided by the user and stores it in a database. In addition, it collects the user's emotional state using an emotion engine and stores it in the database.

[2084] AI Trainer Program Distribution

[2085] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional state data. This program has the necessary functions to support the user's learning.

[2086] Study reminder function

[2087] Managing your study schedule

[2088] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[2089] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[2090] Send a reminder

[2091] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[2092] Analysis of training data and sentiment data

[2093] Collection of training data

[2094] The terminal collects the user's learning activity data (study time, progress, learning materials used, etc.) and emotion data from the emotion engine, and periodically transmits them to the server.

[2095] Analyzing the data

[2096] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[2097] Motivation support

[2098] Utilizing analysis results

[2099] The server transmits a report generated as a result of the analysis to the user's terminal.

[2100] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[2101] Suggesting breaks and rewards

[2102] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[2103] Demonstration of study benefits and areas of interest

[2104] Acquiring interest information

[2105] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," that information is acquired.

[2106] Providing related information

[2107] Based on information provided by the server, the device presents the benefits of studying the field the user is interested in. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[2108] Presentation of information based on emotion information

[2109] The device takes into account the user's emotional state obtained from the emotion engine and presents relaxing content and information at the appropriate time.

[2110] Specific examples

[2111] 1. For User A

[2112] User A registers with this system to deepen his / her IT knowledge.

[2113] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[2114] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[2115] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[2116] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[2117] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[2118] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[2119] The processing flow will be explained below.

[2120] Initial Setup

[2121] Step 1:

[2122] To register with the system, users enter basic information such as their name, age, learning goals, and areas of interest.

[2123] Step 2:

[2124] The server receives the basic information provided by the user and stores it in a database.

[2125] Step 3:

[2126] The server activates the emotion engine and begins monitoring the user's emotional state based on the basic information provided by the user.

[2127] Step 4:

[2128] The server distributes the generated AI trainer program to the user's device and prompts the user to install the program.

[2129] Study reminder function

[2130] Step 5:

[2131] The user sets a study schedule on their device. For example, they set a schedule to start studying at 8:00 every morning.

[2132] Step 6:

[2133] The device saves the study schedule set by the user and prepares to send reminders at the specified times.

[2134] Step 7:

[2135] The device will send users study reminders based on their schedule, such as an "It's time to study!" notification at 8 a.m.

[2136] Analysis of training data and sentiment data

[2137] Step 8:

[2138] The terminal periodically records the user's learning activity data (study time, progress, learning materials used, etc.) and emotional state and transmits them to the server.

[2139] Step 9:

[2140] The server stores the learning data and emotion data sent from the terminal in a database.

[2141] Step 10:

[2142] The server analyzes the stored learning data and emotional data to create a report on the user's learning progress and motivation. By taking the emotional data into account, detailed analysis can be performed according to the user's emotional state.

[2143] Motivation support

[2144] Step 11:

[2145] The server transmits a report generated as a result of the analysis to the user's terminal.

[2146] Step 12:

[2147] Based on the received report, the device sends a motivational message to the user. For example, if it determines that motivation is declining, it will send the message "Keep going, you're doing great!". The appropriate message is selected taking into account the user's emotional state.

[2148] Step 13:

[2149] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[2150] Demonstration of study benefits and areas of interest

[2151] Step 14:

[2152] The device checks the user's basic information to see what areas they are interested in. For example, if the user is interested in "programming," it uses that information to obtain related information.

[2153] Step 15:

[2154] Based on information provided by the server, the device will present the benefits of studying the area of ​​interest to the user at the appropriate time. For example, it will display specific benefits such as "Learning programming will increase your chances of earning a high income in the future."

[2155] Step 16:

[2156] The device provides relaxing content and information at the appropriate time based on the user's emotional state, obtained from the emotion engine, thereby reducing stress and improving learning efficiency.

[2157] Specific examples

[2158] 1. For User A

[2159] User A registers with this system to deepen his / her IT knowledge.

[2160] The server collects basic information about user A and his daily emotional state, and distributes the AI ​​trainer program to the device.

[2161] The device manages user A's study schedule and sends a reminder at 8 o'clock saying, "It's time to study!"

[2162] The server analyzes user A's learning data and emotional data and compiles a report showing that motivation is declining.

[2163] Based on the analysis results, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[2164] The device informs user A of the benefits of studying, such as "learning programming will increase your chances of earning a high income in the future," and presents relaxing content according to the user's emotional state.

[2165] In this way, the system of the present invention effectively supports the user's learning and, by utilizing the emotion engine, can improve learning outcomes while maintaining motivation.

[2166] Example 2

[2167] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2168] In today's learning environment, it is difficult for users to maintain sustained motivation to study effectively. Furthermore, there are no systems that can properly grasp each user's learning progress and emotional state and provide support accordingly. Therefore, to maximize users' learning effectiveness, a personalized learning support system that takes into account the user's basic information and emotional state is needed.

[2169] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2170] In this invention, the server includes means for collecting basic information and emotional data of the user and storing it in a database, means for distributing a generated AI trainer program to the user's device based on the collected basic information and emotional data, means for checking the user's study schedule and sending study reminders, means for collecting and analyzing the user's study activity data and emotional data, means for suggesting motivational messages, breaks, and rewards to the user based on the analysis results, means for acquiring information about the user's interests and providing information related to the interests and benefits of studying, and means for presenting appropriate relaxation content based on the emotional data. This makes it possible to comprehensively grasp the user's study progress and emotional state and provide optimized study support for each user.

[2171] "User" refers to an individual who uses the system to learn.

[2172] "Basic Information" refers to initial registration data about a user, such as the user's name, age, learning goals, and areas of interest.

[2173] "Emotional data" refers to information that indicates a user's emotional state and is obtained through text analysis or facial recognition technology.

[2174] "Database" refers to an information system for storing and managing collected basic information, emotional data, and learning activity data.

[2175] "Generative AI trainer program" refers to a program that provides learning support to users, generated based on the user's basic information and emotional data.

[2176] "Terminal" refers to a device used by a user to access the system, such as a computer, smartphone, or tablet.

[2177] "Study schedule" refers to a study plan and timetable set by a user.

[2178] "Study reminders" refer to messages and alerts that notify users of study time and encourage them to study.

[2179] "Learning activity data" refers to data related to learning, such as a user's study time, progress, and learning materials used.

[2180] "Analysis results" refers to information generated based on collected learning activity data and emotional data for evaluating a user's learning progress and motivation.

[2181] "Messages" refer to words of encouragement or advice sent to increase the user's motivation to study.

[2182] "Break" refers to information and notifications suggesting that the user take adequate rest.

[2183] "Rewards" refer to incentives given to recognize a user's efforts and maintain motivation.

[2184] "Interest information" refers to information relating to areas in which a user is interested.

[2185] "Relaxation content" refers to content such as music and videos that stabilize the user's emotional state and promote relaxation.

[2186] MODE FOR CARRYING OUT THE INVENTION

[2187] The present invention provides a system for effectively supporting a user's learning, and provides personalized learning support by taking into account the user's emotional state. Hereinafter, an embodiment of the present invention will be described in detail.

[2188] Collecting user information and emotion data

[2189] When users register with the system, they enter basic information such as their name, age, learning goals, and areas of interest via a web form or mobile application.

[2190] The server collects basic information provided by the user and stores it in a database, and also analyzes the user's emotional state using an emotion engine (e.g., IBM Watson Tone Analyzer) and stores this information in the database.

[2191] AI Trainer Program Distribution

[2192] The server generates prompt sentences for the generative AI model (e.g., GPT-4) based on the collected basic information and emotional data, and creates a customized generative AI trainer program.

[2193] The server distributes the generated trainer program to the user's terminal, which includes a study reminder function and a study data tracking function.

[2194] Managing your study schedule

[2195] Users set their own study schedules on their own devices. Specifically, they can use the device app to input plans such as "start studying at 8am every morning."

[2196] The device saves the set schedule information in local storage and prepares to send a reminder at the specified time.

[2197] Send study reminders

[2198] When the set study time arrives, the device will send a notification to the user saying, "It's time to study!" This notification is realized using a push notification service.

[2199] Collecting and analyzing training data and sentiment data

[2200] The device transmits the user's learning activity data (study time, progress, learning materials used, etc.) and emotional data collected in real time to the server. Specific emotional data is acquired through the device's camera and microphone.

[2201] The server stores the received learning data and emotion data in a database and analyzes them using R, Python, etc. As a result of the analysis, a report on the user's learning progress and motivation is generated.

[2202] Motivation support

[2203] The server generates a report based on the analysis results and sends it to the user's terminal.

[2204] Based on the report, the device sends the user motivational messages, such as "Keep going, you're doing great!" if their motivation is low.

[2205] The device considers the user's learning progress and emotional state and suggests appropriate breaks and rewards, for example, displaying a notification that says, "Taking a 15-minute break will improve your efficiency."

[2206] Demonstration of study benefits and areas of interest

[2207] The device will check the user's areas of interest from their basic information and then present relevant benefits based on that information, such as "Learning programming will increase your chances of earning a high income in the future."

[2208] The device will then provide appropriate relaxation content based on emotional data, such as relaxing music and guided meditations through the Calm app.

[2209] Specific examples

[2210] When user A uses this system to deepen his / her IT knowledge, the system operates as follows.

[2211] User A enters basic information into the system and registers as a new user.

[2212] The server generates a customized AI trainer program based on basic information and daily collected emotional data and distributes it to User A's device.

[2213] The device manages user A's study schedule and sends a reminder saying "It's time to study!" at the scheduled time of 8:00.

[2214] The server analyzes the learning data and emotional data of User A and creates a report. For example, the report may indicate that motivation is declining.

[2215] Based on the report, the device will send encouraging messages such as "Keep going, you're doing great!" and suggest appropriate breaks and rewards.

[2216] The device provides User A with benefits such as "Learning programming will increase your chances of earning a high income in the future" and presents relaxing content according to his or her emotional state.

[2217] This allows the system to effectively support users' learning and provide personalized learning assistance that takes into account their emotional state.

[2218] Example prompt sentence:

[2219] "Consider user sentiment data and provide advice on maintaining appropriate learning motivation."

[2220] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2221] Step 1:

[2222] To register with the system, a user enters basic information such as name, age, learning goals, and areas of interest. This information is entered through a web form or a mobile application. The inputs include name, age, learning goals, and areas of interest. The output is sent to the server.

[2223] Step 2:

[2224] The server stores the received basic information in a database. It then uses an emotion engine (e.g., a natural language processing tool) to analyze the user's emotional state. Specifically, it detects emotions from the user's input text. The inputs include the user's basic information and the analysis results from the emotion engine. The output is the basic information and emotional data stored in the database.

[2225] Step 3:

[2226] The server creates a customized generative AI trainer program based on the collected basic information and emotional data. A generative AI model (e.g., a generative AI engine) is used to generate a learning program optimized for the user. The input includes basic information and emotional data from the database and prompt sentences for the generative AI model. The output is a customized generative AI trainer program.

[2227] Step 4:

[2228] The server distributes the generated AI trainer program to the user's device. Specifically, it sends the program in digital format so that the user can install it on their device. The input is the generated AI trainer program. The output is the program sent to the user's device.

[2229] Step 5:

[2230] A user sets a study schedule on their device. Specifically, they use a device application to input a schedule such as "I will start studying at 8:00 every morning." The input includes details of the schedule set by the user. The output is the schedule saved on the device.

[2231] Step 6:

[2232] The device saves the user's study schedule in local storage and prepares to send a reminder at the specified time. The input includes the saved study schedule. The output is the reminder notification.

[2233] Step 7:

[2234] When the scheduled study time arrives, the device sends the user a notification saying "It's time to study!". Specifically, it uses a push notification service to display a reminder on the user's device. The input includes schedule data for sending the reminder. The output is a reminder notification that is displayed on the user's device.

[2235] Step 8:

[2236] The device collects the user's learning activity data (study time, progress, learning materials used, etc.). In addition, emotional data is collected in real time. Specifically, the device's camera and microphone are used to acquire the emotional data. The input includes the user's learning activity and data for emotion detection. As output, these data are sent to the server.

[2237] Step 9:

[2238] The server stores the received learning data and emotion data in a database and analyzes it. Specifically, it analyzes the data using programs such as R and Python and generates reports on the user's learning progress and motivation. The inputs are the learning data and emotion data from the database. The output is a detailed analysis report.

[2239] Step 10:

[2240] The server sends the generated report to the user's terminal, specifically in digital format using a secure protocol (e.g. HTTPS). The input is the generated analysis report. The output is the report that arrives at the user's terminal.

[2241] Step 11:

[2242] The terminal sends a motivational message to the user based on the received report. Specifically, the terminal displays a message such as "Keep going, you're doing great!". The input includes the analysis report and a message template. The output is the motivational message displayed to the user.

[2243] Step 12:

[2244] The device suggests appropriate breaks and rewards based on the user's learning progress and emotional state. Specifically, it sends a notification such as "Taking a 15-minute break will improve your efficiency." The input includes the user's learning data and emotional data. As an output, the break and reward suggestion notification is displayed on the user's device.

[2245] Step 13:

[2246] The device acquires the user's interest information and provides information related to that interest and the benefits of studying. Specifically, it displays benefits such as "Learning programming will increase your chances of earning a high income in the future." The input includes the user's basic information and interest data. The output presents related information and benefits.

[2247] Step 14:

[2248] The device presents relaxing content based on the emotion data. Specifically, it displays content such as "relaxing music" and "meditation guide." The input includes the emotion engine analysis results and a template for the relaxing content. As an output, the relaxing content is displayed on the user's device.

[2249] (Application example 2)

[2250] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2251] Conventional learning support systems proceed with learning without taking the user's emotional state into consideration, which can lead to a decline in motivation and a decline in learning efficiency. Furthermore, there are problems with users being fatigued and stressed due to inappropriate timing for breaks and the provision of rewards. To address these issues, a system that provides support according to the user's emotional state is needed.

[2252] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information and emotional information about the user and storing it in a database, means for distributing a generative AI trainer program to the user's terminal based on the collected basic information and emotional information, means for checking the user's study schedule and sending study reminders, means for analyzing the user's study data and emotional data and creating a report on study progress and motivation, means for sending a motivating message to the user based on the received analysis results, means for suggesting appropriate breaks and rewards based on the user's emotional state, and means for providing information on the user's areas of interest and the benefits of study. This makes it possible to maximize the effectiveness of study while taking the user's emotional state into consideration in real time.

[2253] "Basic User Information" refers to personal data such as the user's name, age, learning goals, and areas of interest.

[2254] "Emotion information" is data that indicates the user's emotional state, and includes tension, fatigue level, stress state, and the like.

[2255] "Database" refers to an information system for storing and managing collected basic information and emotional information.

[2256] The "generative AI trainer program" is learning support software that is generated based on collected basic information and emotional information and distributed to the user's device.

[2257] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[2258] A "study schedule" is a plan of study time and study content set by the user.

[2259] "Study Reminder" is a function that notifies users to start studying based on the study schedule they set.

[2260] "Study data" refers to data related to a user's learning activities, including study time, progress, learning materials used, and the like.

[2261] "Emotion data" is data about a user's emotional state collected by an emotion engine.

[2262] A "report" is a report that analyzes learning data and emotional data and summarizes information about learning progress and motivation.

[2263] A "motivation message" is a message sent to a user to increase their motivation to study.

[2264] "Break and reward suggestions" is a function that suggests breaks at appropriate times and rewards according to the user's learning progress based on the user's emotional state.

[2265] "Areas of interest" refers to areas of study or topics that interest the user.

[2266] "Learning benefits" refers to the benefits or advantages a user will gain if they continue to learn.

[2267] The present invention combines an emotion engine with a generative AI trainer system that effectively supports user learning. One embodiment of the present invention will be described below.

[2268] Initial Setup

[2269] Collecting user information and emotion data

[2270] When a user registers with the system, they must enter basic information such as their name, age, learning goals, and areas of interest. The server collects the basic information provided by the user and stores it in a database. The server also periodically collects the user's emotional state (e.g., tension, fatigue level) using an emotion engine and stores this in the database.

[2271] Distribution of the Generative AI Trainer Program

[2272] The server distributes a generative AI trainer program to the user's device based on the collected basic information and emotional information. This program has the necessary functions (reminders, data collection, analysis, etc.) to support the user's learning.

[2273] Study reminder function

[2274] Managing your study schedule

[2275] Users can set a learning schedule on their device. For example, they can set an appointment to start the driving simulation every morning at 8:00. The device saves the schedule and prepares to send a reminder at the specified time.

[2276] Send a reminder

[2277] The device sends learning reminders to the user based on their learning schedule. For example, at 8:00 a.m., a notification will appear saying, "It's time to start the driving simulation."

[2278] Analysis of training data and sentiment data

[2279] Collection of training data

[2280] The device collects the user's learning activity data and emotion data from the emotion engine, and periodically transmits them to the server. The learning activity data includes the user's learning time, progress, learning materials used, etc.

[2281] Analyzing the data

[2282] The server stores learning data and emotional data in a database, analyzes them, and creates reports on the user's learning progress and motivation. By taking emotional data into account, detailed analysis according to the user's emotional state is possible.

[2283] Motivation support

[2284] Utilizing analysis results

[2285] The server then sends the generated analysis results to the user's device. Based on the received report, the device sends the user a motivating message. For example, if the device determines that the user's motivation is low, it sends a message such as, "You seem tired. Take a short break."

[2286] Suggesting breaks and rewards

[2287] The device suggests appropriate breaks and rewards based on the user's situation and emotional state, helping them to maintain their motivation to study.

[2288] Other support

[2289] The device checks the user's areas of interest from basic information and, based on information provided by the server, presents the benefits of studying the user's areas of interest. For example, it displays specific benefits such as "Learning programming will increase your chances of earning a high income in the future." It also takes emotional information into account and presents relaxing content at appropriate times.

[2290] Examples of concrete examples and prompts

[2291] Specific examples

[2292] 1. When the user starts the simulation, the system checks their emotional state for the day and sends a message saying, "You seem a little tired. Let's take a break."

[2293] 2. When the learning time is ...

Claims

1. a means for collecting basic information about users and storing it in a database; A means for distributing a generated AI trainer program to a user's device based on the collected basic information; a means for checking the user's study schedule and sending study reminders; A means of analyzing users' learning data and creating reports on their learning progress and motivation; means for sending motivational messages to the user based on the received analysis results; A means of providing information and study benefits related to the user's areas of interest; A system including:

2. 10. The system of claim 1, wherein the means for sending study reminders sends the reminders based on a schedule set by the user.

3. The system according to claim 1, wherein the motivational message sent to the user is based on the results of an analysis of the user's learning data, and suggests breaks and rewards as necessary.

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