System

Abstract

A system is provided.SOLUTION: A system, comprising: a terminal for inputting user information; a server for storing the user information transmitted from the terminal; a means for the server to analyze the stored user information and suggest an optimal learning method; a means for transmitting learning contents to the terminal based on the suggested learning method; a means for collecting user's learning progress and feedback and transmitting them to the server; and a means for analyzing the collected data and adjusting the learning method if necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditional educational systems provide a uniform learning method and do not take into account the differences in individual learners' personalities, interests, and levels of understanding, making it difficult to provide effective education to all learners. Furthermore, if a learning method once selected is ineffective, there is no mechanism for reevaluating it and providing an appropriate alternative method. This has resulted in issues such as learners' levels of understanding not improving and effective learning not being achieved. [Means for solving the problem]

[0005] The present invention includes a terminal for inputting user information, a server for storing the user information sent from the terminal, a means for the server to analyze the stored user information and propose an optimal study method, and a means for sending study content to the terminal based on the proposed study method. The system also includes a means for collecting the user's study progress and feedback and sending it to the server, and a means for analyzing the collected data and adjusting the study method as needed, thereby providing an optimized study method for each individual user and improving the user's level of understanding.

[0006] "User Information" refers to personal information such as the user's name, age, areas of interest, and current level of understanding.

[0007] "Terminal" refers to a device that allows a user to input information and view learning content.

[0008] "Server" refers to a centralized computing system for storing and analyzing user information and delivering learning content.

[0009] "Learning methods" refer to the educational techniques and approaches that are optimal based on the user's interests, personality, and level of understanding.

[0010] "Learning content" refers to digital information such as teaching materials, questions, and explanations that are displayed on a device and allow users to study.

[0011] "Study progress" refers to information that indicates how much progress a user has made through a learning activity. Examples include answer time and number of correct answers.

[0012] "Feedback" refers to ratings and comments users provide about their learning experience, including ratings of clarity and enjoyment.

[0013] "Analysis" refers to the process of examining collected data, evaluating trends and effects, and making appropriate decisions.

[0014] "Adjusting the learning experience" refers to changes and improvements made to provide a new, optimized learning experience based on user understanding and feedback. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that includes a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on that. The system also has a means for collecting the user's learning progress and feedback and sending it to the server, and has the function of analyzing the collected data and adjusting the learning method as necessary. A specific embodiment of this "Teacher AI" system is described below.

[0037] System Overview

[0038] Device role:

[0039] The terminal is a device where the user inputs information and displays the learning content. Specifically, it is assumed that a smartphone, tablet, etc. will be used. The terminal transmits the user's input information to the server and displays the learning content received from the server to the user.

[0040] Server Role:

[0041] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses analytical algorithms to select the most appropriate learning method. It also receives user learning progress and feedback, evaluates the effectiveness of the learning method, and makes appropriate adjustments.

[0042] Program processing overview

[0043] Enter your user information:

[0044] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[0045] Learning suggestions:

[0046] The server analyzes the user information stored in the database and suggests the most suitable learning method, taking into account the user's personality, interests, and past learning history. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is sent to the terminal and presented to the user.

[0047] View learning content:

[0048] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[0049] Learning progress and feedback collection:

[0050] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[0051] Analysis of effects and adjustment of methods:

[0052] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[0053] Specific examples

[0054] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0055] 1. Enter your user information

[0056] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[0057] 2. Learning method suggestions

[0058] The server analyzes user information and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the terminal, which then displays them to the user.

[0059] 3. Viewing learning content

[0060] The server retrieves game-style math problems from the database and sends them to the terminal, which displays them to the user, who then begins learning.

[0061] 4. Learning progress and feedback collection

[0062] The device records the user's response time and the number of correct answers. After the learning process is complete, the device displays a feedback screen, prompting the user to rate the ease of understanding and the level of interest, and then sends the data to the server.

[0063] 5. Analysis of effects and adjustment of methods

[0064] The server analyzes the progress data and feedback to evaluate the effectiveness of the learning method. For example, if the answer time is long and the correct answer rate is low, the server will suggest an alternative problem with a video explanation and re-send it to the device.

[0065] In this way, the system of the present invention can continue to provide the most suitable learning method for each individual user and support effective learning.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] A user logs in from a terminal

[0069] The terminal displays a login screen and the user enters their ID and password.

[0070] The terminal sends the user's authentication information to the server.

[0071] Step 2:

[0072] The server authenticates the user

[0073] The server checks the user's authentication information against a database and sends the authentication results to the terminal.

[0074] If authentication is successful, the terminal displays the following screen to the user.

[0075] Step 3:

[0076] Entering user information

[0077] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[0078] The user inputs this information, and the terminal transmits the input information to the server.

[0079] Step 4:

[0080] The server stores user information

[0081] The server stores the received user information in a database.

[0082] Step 5:

[0083] User information analysis

[0084] The server retrieves the user's personality, interests, and past learning history from a database.

[0085] Using the information acquired by the server, a machine learning algorithm is applied to select the most suitable learning method for the user.

[0086] Step 6:

[0087] Learning method suggestions

[0088] The server transmits data including the selected learning method and the reason for the selection to the terminal.

[0089] The terminal presents the received learning method to the user and asks for confirmation of the start.

[0090] The user checks the learning method and starts learning.

[0091] Step 7:

[0092] Delivery of learning content

[0093] The server retrieves learning content based on the optimal learning method from a database and sends it to the terminal.

[0094] The terminal displays the received study content to the user, and the user begins studying.

[0095] Step 8:

[0096] Record your learning progress

[0097] As the user progresses with their studies, the device records progress data such as answer time and accuracy rate in real time.

[0098] Step 9:

[0099] Gathering feedback

[0100] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[0101] The user inputs feedback and the terminal sends the information to the server.

[0102] Step 10:

[0103] Analysis of training data

[0104] The server analyzes the collected learning progress data and feedback to evaluate the effectiveness of the current learning method.

[0105] Step 11:

[0106] Adjusting your learning methods

[0107] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[0108] The server transmits the new learning method and the corresponding learning content to the terminal again, allowing the user to continue learning.

[0109] By repeating this series of steps, it is possible to continue providing the optimal learning method for each individual user and maximize the learning effect.

[0110] Example 1

[0111] 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."

[0112] Current educational systems struggle to provide optimal learning methods based on individual users' interests and personalities, and instead use standardized educational methods to accommodate all users. As a result, users' learning effectiveness is not fully realized, and their motivation to learn can decline. Furthermore, progress management and the collection and analysis of feedback are time-consuming, making it difficult to quickly adjust effective learning methods. A system that can solve these problems and provide optimal learning methods for individual users is needed.

[0113] 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.

[0114] In this invention, the server includes a device for inputting user information, a storage device for saving the user information sent from the device, means for analyzing the user information saved in the storage device and proposing an optimal educational method, means for sending educational resources to the device based on the suggested educational method, means for collecting the user's educational progress and opinions and sending them to the storage device, and means for analyzing the collected data and adjusting the educational method as necessary. This makes it possible to quickly provide the optimal educational method for each user and to adjust the educational method as appropriate based on the learning progress and feedback.

[0115] "Device" refers to an electronic device for inputting and displaying user information, including smartphones, tablets, and personal computers.

[0116] "Storage device" refers to data storage devices for storing user information and educational resources. This includes database systems and cloud storage.

[0117] "Analysis" is a data processing method for deriving optimal teaching methods based on stored data.

[0118] "Educational methods" refer to personalized learning methods tailored to the user's characteristics, including game-style questions and video explanations.

[0119] "Educational resources" refer to specific learning content provided based on educational methods, including workbooks, videos, interactive learning materials, etc.

[0120] "Education progress" is data that indicates how far a user has progressed in their studies, including the time it took to answer questions and the number of correct answers.

[0121] "Opinions" are data on users' evaluations and impressions of learning methods and educational resources. These include evaluations such as "ease of understanding" and "interestingness."

[0122] "Analyzing the data" refers to the process of evaluating the effectiveness of teaching methods based on collected progress data and feedback, and making adjustments as needed.

[0123] The present invention is a system including a device for inputting user information, a storage device for storing user information transmitted from the device, a means for analyzing the stored user information in the storage device and proposing an optimal educational method, a means for transmitting educational resources to the device based on the proposed educational method, a means for collecting the user's educational progress and opinions and transmitting them to the storage device, and a means for analyzing the collected data and adjusting the educational method as necessary.

[0124] System Overview

[0125] Device role:

[0126] The device is a device that inputs user information and displays educational resources. Specifically, a smartphone, tablet, or PC is used. The device transmits the user's input information to a storage device and displays the educational resources received from the storage device to the user.

[0127] Storage device role:

[0128] The storage device is a centralized computer system that stores and analyzes user information and proposes educational methods. The storage device stores user information in a database and, if necessary, uses an analytical algorithm to select the most appropriate educational method. It also receives the user's educational progress and opinions, and based on this, evaluates the effectiveness of the educational method and makes appropriate adjustments.

[0129] Program processing overview

[0130] Enter your user information:

[0131] Through the device, the user inputs basic information such as name, age, areas of interest, current level of understanding, etc. The device sends this information to a storage device, which then stores the information in a database.

[0132] Teaching suggestions:

[0133] The storage device analyzes the user information stored in the database and proposes the most appropriate educational method based on the user's personality, interests, and past learning history. For example, a method that combines game-style questions and video explanations may be selected. The selected educational method is sent to the device, which then presents it to the user.

[0134] View educational resources:

[0135] The storage device retrieves educational resources corresponding to the selected educational method from the database and transmits them to the device, which displays the received educational resources to the user, allowing the user to begin learning.

[0136] Educational progress and feedback collection:

[0137] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter their evaluation, such as "ease of understanding" or "interestingness." This data is sent to a storage device.

[0138] Analysis of effects and adjustments to methods:

[0139] The storage device analyzes the collected progress data and opinions to evaluate the effectiveness of the current teaching method. If it is determined to be ineffective, the storage device re-proposes an alternative teaching method and transmits it to the device again, thereby presenting the new teaching method to the user.

[0140] Specific examples

[0141] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0142] 1. Enter your user information

[0143] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which the device sends to a memory device that stores it in a database.

[0144] 2. Proposal of educational methods

[0145] The storage device analyzes the user information and suggests game-style math problems based on the user's interest, for example, "I like learning through games." The storage device sends the suggestions to the device, which then displays them to the user.

[0146] 3. Display of educational resources

[0147] The storage device retrieves game-style math problems from the database and sends them to the device, which displays them to the user, who then begins learning.

[0148] 4. Educational progress and feedback collection

[0149] The device records the user's response time and the number of correct answers. After the learning is completed, the device displays a feedback screen and asks the user to rate the "ease of understanding" and "interestingness," and sends the data to the storage device.

[0150] 5. Analysis of effects and adjustment of methods

[0151] The memory device analyzes the progress data and opinions to evaluate the effectiveness of the teaching method. For example, if the answer time is long and the correct answer rate is low, the memory device will suggest an alternative problem with video explanation and send it to the device again.

[0152] Example of input prompt for generative AI model

[0153] "Please explain the system that proposes mathematics learning content for elementary school students. The system proposes individual teaching methods based on user information, collects and analyzes educational progress and feedback, and provides the optimal teaching method."

[0154] In this way, the system of the present invention can continue to provide the user with the most suitable educational method and support effective learning.

[0155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0156] Step 1:

[0157] Entering user information

[0158] The user enters basic information such as name, age, areas of interest, and current level of understanding through the device. Specifically, the terminal displays a form containing text boxes and drop-down menus, which the user fills in. The input data is sent from the device to the server, which receives the data and stores it in a database.

[0159] Input: User's name, age, areas of interest, current understanding level

[0160] Output: User information stored in the database

[0161] Step 2:

[0162] Analysis of user information

[0163] The server retrieves user information from the database and uses an analytical algorithm to select the optimal training method. Specifically, the server uses Python's scikit-learn to perform clustering and regression analysis.

[0164] Input: Saved user information

[0165] Output: Selected optimal teaching method

[0166] Step 3:

[0167] Submit your proposal

[0168] The server sends the selected teaching method to the device. Specifically, the server sends it as text or JSON format data, which the device receives. The device then displays the suggested teaching method to the user.

[0169] Input: Selected teaching method

[0170] Output: Teaching method displayed on the terminal

[0171] Step 4:

[0172] Acquiring educational resources

[0173] The server retrieves educational resources corresponding to the selected educational method from the database, specifically, the server uses an SQL query to extract relevant content from the database.

[0174] Input: Selected teaching method

[0175] Output: Retrieved educational resources

[0176] Step 5:

[0177] Submitting educational resources

[0178] The server sends the acquired educational resources to the terminal. Specifically, the server sends the learning content to the terminal using an HTTP POST request. The terminal receives it and displays it to the user.

[0179] Input: Retrieved educational resources

[0180] Output: Educational resource displayed on the device

[0181] Step 6:

[0182] Recording educational progress

[0183] As the user progresses with their studies, the device records their progress in real time, and stores data such as answer times and the number of correct answers in a local database or memory.

[0184] Input: User learning activity

[0185] Output: Recorded progress data (answer time, number of correct answers, etc.)

[0186] Step 7:

[0187] Enter and submit feedback

[0188] After the learning is completed, the device displays a feedback screen and asks the user to enter their evaluation of the content, such as "ease of understanding" and "interestingness." This data is collected, organized, and sent to the server.

[0189] Input: User feedback

[0190] Output: Feedback data sent to the server

[0191] Step 8:

[0192] Receiving and storing data

[0193] The server receives the progress data and feedback sent from the device and stores it in a database. Specifically, it again stores the data using a database such as MySQL.

[0194] Input: Progress data, feedback data

[0195] Output: Progress and feedback data stored in a database

[0196] Step 9:

[0197] Analyzing the effectiveness and adjusting the teaching method

[0198] The server analyzes the progress and feedback data to evaluate the effectiveness of the current teaching method. Specifically, it uses Python's pandas and numpy to analyze the data. If the method is ineffective, the server selects an alternative teaching method and sends it to the device again.

[0199] Input: Progress data, feedback data

[0200] Output: A new and adjusted teaching method

[0201] (Application example 1)

[0202] 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."

[0203] Traditional learning systems lacked the ability to flexibly provide learning methods based on each user's different interests and level of understanding. This led to problems such as reduced learning efficiency and ineffective education. Furthermore, they lacked the ability to analyze learning progress and feedback in real time and quickly adjust learning methods based on that analysis.

[0204] 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.

[0205] In this invention, the server includes a means for analyzing user information and suggesting an optimal study method, a means for transmitting study content based on the suggested study method to the terminal, and a means for collecting and analyzing the user's study progress and feedback. This makes it possible to provide an optimized study method for each individual user, improving the user's study efficiency and enabling real-time adjustment of the study method. Furthermore, by including a means for suggesting that the provided study method be in the form of a game or video explanation, it is possible to effectively provide study content based on the user's interests and characteristics.

[0206] "User information" is information about an individual learner, such as the user's identity, age, areas of interest, and current level of understanding.

[0207] A "terminal" is a device that allows a user to input information and display learning content, and includes smartphones, tablets, etc.

[0208] A "server" is a centralized computer system that stores user information, analyzes it, and suggests learning methods.

[0209] A "learning method" is a particular learning approach or means offered to a user, such as a game or video tutorial.

[0210] "Learning content" refers to information such as educational materials, study questions, and video tutorials provided to users.

[0211] "Study progress" is data that indicates the progress of a user's learning activities. Specifically, it includes answer time, number of correct answers, etc.

[0212] "Feedback" refers to evaluations and opinions about the learning content provided by users after completing the learning process.

[0213] "Analysis" is the process by which the server uses the data collected to select the most appropriate learning method and evaluate the effectiveness of the learning method.

[0214] "Adjustment" is the activity of re-proposing the optimal learning method based on the user's learning progress and feedback.

[0215] "Game format" is a method of providing learning content in the form of a game, which has the effect of attracting users' interest and encouraging them to learn.

[0216] The "video explanation format" is a method of explaining learning content in video format. It aids visual understanding and is effective in conveying complex information in an easy-to-understand manner.

[0217] System Overview

[0218] This invention is a learning support system for optimizing a user's learning experience. The system performs a series of processes: inputting, saving, and analyzing user information, proposing optimal learning methods, collecting and analyzing learning progress and feedback, and adjusting the learning methods as needed.

[0219] Hardware and Software Configuration

[0220] Terminal

[0221] The terminal is a device that allows users to input information and display learning content. Smartphones and tablets are commonly used. The terminal has the function of inputting user information and sending it to a server.

[0222] server

[0223] The server is a centralized computer system that stores user information, suggests optimal learning methods, and collects and analyzes learning progress and feedback. The main software used is Python, Flask, and an SQLite database.

[0224] Program processing overview

[0225] Entering and saving user information

[0226] Users input basic information such as their name, age, areas of interest, and current level of understanding through the device, which then sends this information to the server, which stores it in an SQLite database.

[0227] Learning method suggestions

[0228] The server analyzes the stored user information and suggests the most suitable learning method based on the user's interests and characteristics. For example, a game-style problem or a video explanation may be selected. The selected learning method is sent to the device and presented to the user.

[0229] Providing learning content

[0230] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[0231] Learning progress and feedback collection

[0232] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[0233] Analyzing the effects and adjusting the methodology

[0234] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[0235] Specific examples

[0236] For example, when an elementary school student is studying math, the following specific process takes place: When the user inputs "I want to learn math in a game format" through their device, the server analyzes this and selects the appropriate game-style learning content. As the study progresses, the server records the user's response time and number of correct answers, and after completing the study, it asks for feedback on the "ease of understanding" and "interestingness." Based on this data, the server analyzes the effectiveness of the study method and suggests new video-based questions as necessary.

[0237] Prompt Sentence Examples

[0238] "Use the PTA app to suggest ways users like to learn math. For example, if they say they like game-style problems, provide content in that format."

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] The user inputs their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server as input data. This data includes basic attribute information of the user.

[0242] Step 2:

[0243] The server receives the user information and stores it in a SQLite database, storing the user's name, age, areas of interest, and current understanding level in the corresponding fields.

[0244] Step 3:

[0245] The server analyzes user information stored in a database and proposes optimal learning methods based on the user's interests and characteristics. Specifically, it analyzes keywords related to the user's interests and selects learning methods such as games or video explanations based on those keywords. The proposed learning methods are output.

[0246] Step 4:

[0247] The server retrieves the corresponding learning content from the database based on the proposed learning method. In this process, the learning method acts as a key to search for corresponding learning resources (such as problem sets and video links). The retrieved learning content is sent to the terminal as content data.

[0248] Step 5:

[0249] The device then displays the received learning content to the user, who then begins learning based on the displayed content. In this step, a learning interface is provided, allowing the user to solve problems and watch videos.

[0250] Step 6:

[0251] The device records the user's learning progress data (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This feedback data is sent to the server as progress data and evaluation data.

[0252] Step 7:

[0253] The server analyzes the collected progress and feedback data. At this stage, a data analysis algorithm is used to evaluate the effectiveness of the current learning method. Specifically, a statistical analysis is performed based on the answer time and the number of correct answers, and an evaluation result is generated.

[0254] Step 8:

[0255] If the server determines that the learning method is ineffective, it re-proposes an alternative learning method. This re-proposition returns to the analysis process in step 3, and the optimal learning method is selected again. New learning content based on the re-proposed learning method is then sent to the device and presented to the user.

[0256] 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.

[0257] The present invention provides a system that includes a terminal for inputting user information and a server that stores the user information transmitted from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on the proposed method. The system also includes a means for collecting the user's learning progress and feedback and transmitting the collected data to the server, and also has the function of analyzing the collected data and adjusting the learning method as necessary. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data. A specific embodiment of this system is described below.

[0258] System Overview

[0259] Device role:

[0260] The terminal is a device where the user inputs information and displays learning content. Specifically, it is assumed that a smartphone, tablet, or similar device will be used. The terminal sends the user's input information to the server, and displays the learning content received from the server and feedback from the emotion engine to the user. The terminal is also equipped with sensors such as a camera and microphone, allowing the emotion engine to analyze the user's facial expressions and tone of voice.

[0261] Server Role:

[0262] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses an analytical algorithm to select the optimal learning method. It also analyzes the user's learning progress and emotional data collected by the emotion engine to evaluate the effectiveness of the learning method and make appropriate adjustments.

[0263] The role of the Emotion Engine:

[0264] The emotion engine is a system that collects a user's facial expressions, tone of voice, and other biometric data in real time to determine the user's emotional state. For example, it analyzes the user's facial expressions through a camera and the tone of voice through a microphone. This allows it to recognize emotions such as excitement, confusion, and optimism that the user feels while studying.

[0265] Program processing overview

[0266] Enter your user information:

[0267] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[0268] Learning suggestions:

[0269] The server analyzes the user information stored in the database and proposes the optimal learning method, taking into account the user's personality, interests, past learning history, and even emotional data obtained from the emotion engine. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is then sent to the device and presented to the user.

[0270] View learning content:

[0271] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[0272] Learning progress and emotional data collection:

[0273] The device records the user's learning progress (e.g., answer time, correct answer rate) in real time. In addition, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice and collect emotional data. This emotional data is sent to the server along with the learning progress data.

[0274] Analysis of effects and adjustment of methods:

[0275] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. For example, if the user is confused or their concentration is declining, the server can select and re-suggest an alternative learning method. This allows the user to continuously receive the optimal learning method.

[0276] Specific examples

[0277] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0278] 1. Enter your user information:

[0279] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[0280] 2. Learning Suggestions:

[0281] The server analyzes the user's information, past learning history, and data obtained from the emotion engine, and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the device, which then displays them to the user.

[0282] 3. View learning content:

[0283] The server acquires game-style math problems and sends them to the device, which displays them to the user, who then begins learning.

[0284] 4. Learning progress and emotion data collection:

[0285] The device records the user's response time and the number of correct answers. Furthermore, the device analyzes the user's facial expressions and voice using an emotion engine to determine their emotional state and collect data. This data is then sent to the server.

[0286] 5. Analysis of effects and adjustment of methods:

[0287] The server analyzes the collected progress data and emotional data to evaluate the effectiveness of the learning method. For example, if facial expression analysis indicates that the user is confused, the server will suggest an alternative problem with a video explanation and resend it to the device.

[0288] In this way, the system of the present invention is able to analyze the user's emotions in real time while maximizing their comprehension and learning experience.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] A user logs in from a terminal

[0292] The terminal displays a login screen and the user enters their ID and password.

[0293] The device sends the authentication information to the server.

[0294] Step 2:

[0295] The server authenticates the user

[0296] The server checks the user's authentication information against a database and sends the authentication result to the terminal.

[0297] If authentication is successful, the terminal displays the following screen to the user.

[0298] Step 3:

[0299] Entering user information

[0300] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[0301] The user inputs this information, and the terminal transmits the input information to the server.

[0302] Step 4:

[0303] The server stores user information

[0304] The server stores the received user information in a database.

[0305] Step 5:

[0306] User information analysis

[0307] The server retrieves the user's personality, interests, and past learning history from a database.

[0308] The server applies machine learning algorithms to analyze user information and select the learning method that is best suited for the user.

[0309] Step 6:

[0310] Learning method suggestions

[0311] The server sends data including the selected learning method and the reason for it to the terminal.

[0312] The terminal displays the received learning method to the user and asks for confirmation of the start of learning.

[0313] The user checks the learning method and starts learning.

[0314] Step 7:

[0315] Delivery of learning content

[0316] The server retrieves learning content based on the selected learning method from the database and transmits it to the terminal.

[0317] The terminal displays the received study content to the user, allowing the user to proceed with the study.

[0318] Step 8:

[0319] Record your learning progress

[0320] The device records data such as answer time and accuracy rate in real time as the user progresses with their studies.

[0321] Step 9:

[0322] Collecting Emotional Data

[0323] The device uses a camera and microphone to analyze the user's facial expressions and voice using an emotion engine to collect emotional data.

[0324] The collected emotion data is sent to a server.

[0325] Step 10:

[0326] Gathering feedback

[0327] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[0328] The user inputs feedback and the terminal sends the information to the server.

[0329] Step 11:

[0330] Analysis of training data

[0331] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method.

[0332] Step 12:

[0333] Adjusting your learning methods

[0334] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[0335] The server transmits the new learning method and corresponding learning content to the terminal, allowing the user to continue learning.

[0336] By repeating this series of steps, it is possible to continue providing the optimal learning method while taking into account the user's emotional state.

[0337] Example 2

[0338] 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."

[0339] While conventional online learning systems can track a user's learning progress, they struggle to optimize learning methods by taking into account the user's emotional state. As a result, they are unable to adequately alleviate the stress and confusion users feel while learning, resulting in reduced learning efficiency. This invention aims to solve this problem by analyzing users' emotional data in real time and optimizing the learning experience based on that data.

[0340] 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.

[0341] In this invention, the server includes means for storing user information, means for analyzing the stored user information to suggest an optimal study method, means for transmitting study content based on the suggested study method, means for collecting the user's study progress and feedback and transmitting it to the server, means for analyzing the collected data and adjusting the study method, means for equipping the device with sensors including a camera and microphone to collect the user's facial expressions and tone of voice in real time, means for analyzing emotional data obtained from the sensors to determine the user's emotional state, and means for selecting study content based on the emotional data and study progress data and retransmitting it to the device. This makes it possible to provide an optimal study method that takes the user's emotional state into consideration.

[0342] A "terminal" is a device that allows users to input information and display learning content, specifically a smartphone or tablet.

[0343] A "server" is a centralized computer system that stores and analyzes user information and suggests optimal learning methods.

[0344] "User Information" refers to basic information about a user, such as the user's name, age, areas of interest, and current level of understanding.

[0345] "Analysis means" refers to the function by which the server analyzes stored user information and suggests the optimal learning method.

[0346] "Learning content" refers to the teaching materials such as questions, explanations, and videos that are presented to users when they study.

[0347] "Emotion data" refers to data that indicates the user's emotional state determined by analyzing the user's facial expression and tone of voice.

[0348] "Sensors" refer to devices such as cameras that capture the user's facial expressions and microphones that capture the tone of voice, and are used to collect emotional data.

[0349] "Study progress data" is data that indicates the progress of a user's study, such as the time it takes to answer questions and the percentage of correct answers.

[0350] "Feedback" refers to information about impressions and reactions provided by users during the learning process.

[0351] A "generative AI model" is an artificial intelligence model used by the server, and refers to an algorithm for analyzing user information and generating the optimal learning method.

[0352] "Proposal means" refers to the function in which the server uses the generated AI model to select the optimal learning method and send it to the terminal.

[0353] "Adjustment means" refers to a function that analyzes collected learning progress data and emotional data and changes the learning method as necessary.

[0354] The present invention is a system comprising a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes the optimal learning method for each user, and provides learning content to the terminal based on the proposal. The system also has a means for collecting the user's learning progress and feedback and sending it to the server. It also has a function for analyzing the collected data and adjusting the learning method as needed. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data.

[0355] Specifically, the device is a smartphone, tablet, or other device that users use to input information and display learning content. The device is equipped with sensors such as a camera and microphone, which allow the emotion engine to analyze the user's facial expressions and tone of voice.

[0356] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and selects the optimal learning method using a generative AI model. The generative AI model uses input parameters such as the user's age, areas of interest, and current level of understanding. The selected learning method is sent to the terminal and presented to the user.

[0357] As an example, consider the case where an elementary school user is studying math. The user enters their name, age, areas of interest, and current level of math comprehension through their device. This information is sent from the device to the server and stored in the server's database. The server then uses a generative AI model to suggest a study method based on the user's information, past learning history, and data obtained from the emotion engine. For example, a game-style math problem may be suggested based on the user's interest in "game-style problems." The server sends this suggestion to the device, which then displays it to the user.

[0358] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the device. The user receives this and begins learning. During learning, the device records progress data such as the user's response time and number of correct answers, and collects emotional data from the user via the camera and microphone. This data is then sent from the device to the server.

[0359] The server analyzes the collected progress data and emotion data to evaluate the effectiveness of the learning method. For example, if the server determines that the user is confused, it selects an alternative learning method (e.g., questions with video explanations) and sends it to the device again. This operation ensures that the user is always provided with the optimal learning method.

[0360] The following are examples of prompt sentences:

[0361] "Please suggest the optimal learning method based on the user's learning data. The user is an elementary school student who is not good at math. Based on their past learning history, we know that game-style problems would be effective. Also, please take into account their current emotional state based on their emotional data."

[0362] In this way, the system of the present invention incorporates the user's emotional data while maximizing their comprehension and learning experience.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1:

[0365] The user uses the device to enter basic information such as name, age, areas of interest, and current level of understanding. Specifically, the user enters data such as "Tanaka Hanako, 10 years old, likes math, and has intermediate level of understanding of arithmetic" into a form displayed on the device's application screen. Once the input is complete, the device sends this information to the server via an HTTP request. The input is received as form data, and the output is sent as an HTTP request.

[0366] Step 2:

[0367] The server stores the received user information in a database. Specifically, the server extracts data from the HTTP request and inserts it into the database using SQL commands. The input is the data from the HTTP request, and the output is saved as a record in the SQL database. For example, the SQL command used is INSERT INTO user_data (name, age, interest, comprehension_level) VALUES ('Hanako Tanaka', 10, 'Mathematics', 'Intermediate');

[0368] Step 3:

[0369] The server analyzes the user information stored in the database and uses a generative AI model to suggest the optimal learning method. Specifically, the server retrieves the user information from the database and inputs the data into the generative AI model for analysis. The input is the user information, and the output is a suggestion of the optimal learning method. The generative AI model returns the results in JSON format. For example, analysis is performed with the prompt generate_study_plan({"age": 10, "interest": "Mathematics", "comprehension_level": "Intermediate"}).

[0370] Step 4:

[0371] The server proposes an appropriate learning method based on the analysis results of the generative AI model and sends the results to the device. Specifically, the server analyzes the obtained JSON data, reformats it as a proposal result, and sends it to the device. The input is the result of the generative AI model, and the output is the proposal content sent to the device. For example, JSON data with the format {"study_method": "game format", "content": "related math problem"} is sent.

[0372] Step 5:

[0373] The device displays the received study method suggestions to the user. Specifically, it displays the message "The best study method for you is game-style math problems" on the device screen. The input is the suggestion data received from the server, and the output is the screen display to the user.

[0374] Step 6:

[0375] The server retrieves the learning content corresponding to the proposed learning method from the database and sends it to the device. Specifically, the server selects the appropriate record from the learning content database and sends it to the device. The input is the proposed learning method, and the output is the learning content data. For example, the data is retrieved using the SQL command SELECT FROM study_content WHERE method = 'game format' AND subject = 'mathematics'; and sent in JSON format.

[0376] Step 7:

[0377] The device displays the received learning content to the user and allows them to begin learning. The user enters answers to the questions displayed on the screen. The input is the learning content, and the output is the user input and the answer. Specifically, the question "What is 2 + 3?" is displayed, and the user enters "5."

[0378] Step 8:

[0379] The device records the user's learning progress (e.g., answer time, accuracy rate) in real time and collects emotional data using sensors such as a camera and microphone. The device acquires this data from the sensors and analyzes it. The input is the user's answers and sensor data, and the output is learning progress data and emotional data. For example, the camera captures the user's facial expressions and the microphone analyzes the tone of voice.

[0380] Step 9:

[0381] The device sends the collected learning progress data and emotion data to the server. Specifically, the data obtained in real time is compiled and sent to the server as an HTTP request. The input is the learning progress data and emotion data, and the output is an HTTP request. For example, the following JSON data is sent: {"progress": {"answer_time": 15, "correct_rate": 0.8}, "emotions": {"happiness": 0.2, "confusion": 0.7}}.

[0382] Step 10:

[0383] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. If necessary, it proposes a new learning method and resends it to the device. The input is learning progress data and emotion data, and the output is a proposal for a new learning method. For example, if it determines that the user is confused, it proposes a problem with a video explanation and resends it to the device. Specifically, the JSON data {"study_method": "video explanation", "content": "related math video"} is resent.

[0384] In this way, the system of the present invention can maximize the user's learning experience by analyzing the user's emotional data and learning progress in real time and providing optimal learning methods and learning contents.

[0385] (Application example 2)

[0386] 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."

[0387] Conventional factory operator training systems generally provide a uniform training plan to all operators, without taking into account the skills and understanding of each individual operator. Furthermore, there was a lack of a way to grasp the learning progress and emotions of operators in real time and adjust the training content based on this. This made it difficult to provide effective training to operators.

[0388] 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 storing user information, means for analyzing the user information and suggesting an optimal learning method, means for transmitting learning content based on the optimal learning method to the terminal, means for collecting the user's learning progress and feedback and transmitting them to the server, means for analyzing the collected data and adjusting the learning method as needed, means including an emotion engine for collecting and analyzing user emotion data in real time, and means for further optimizing the learning experience based on the emotion data. This makes it possible to provide factory operators with an optimal learning experience tailored to their individual skill levels and emotional states, enabling effective and efficient training.

[0389] "User information" refers to data that the educational system acquires and stores about individual users, including, for example, their name, age, areas of interest, and current level of understanding.

[0390] A "terminal" is a device that allows a user to input information and display learning content, such as a smartphone or tablet.

[0391] A "server" is a centralized computer system that stores and analyzes user information, suggests learning methods, and transmits learning content to terminals.

[0392] "Suggesting a learning method" refers to the act of the server analyzing user information and providing the user with the most suitable learning method based on the results.

[0393] "Learning content" refers to teaching materials and training materials that users use to actually study based on suggested learning methods.

[0394] "Study progress" is data that indicates the results and progress achieved by the user as they progress through their studies.

[0395] "Feedback" refers to the evaluation and reaction a user receives after learning, and is information that can be used to measure and improve the effectiveness of learning.

[0396] The "emotion engine" is a system that analyzes the user's facial expressions, tone of voice, etc., and determines the user's emotional state in real time.

[0397] "Emotion data" is information that indicates the user's emotional state collected by the emotion engine.

[0398] This invention is specifically implemented as a training system for factory operators. The system is comprised of terminals, a server, and an emotion engine, and performs advanced data analysis and emotion analysis to optimize the operator's learning experience.

[0399] Device role:

[0400] The terminal is a device used by factory operators to input information and display learning content. Smartphones, tablets, etc. are used. The terminal is equipped with sensors such as a camera and microphone, and has the ability to collect the user's facial expressions and tone of voice.

[0401] Server Role:

[0402] The server is a centralized computer system that stores and analyzes user information and proposes optimal learning methods. Specifically, it stores user information in a database and uses analytical algorithms to select appropriate learning methods. It also collects and analyzes learning progress data and emotional data to adjust learning methods.

[0403] The role of the Emotion Engine:

[0404] The emotion engine is a system that analyzes the user's facial expressions and tone of voice in real time to determine the user's emotional state. The camera analyzes the user's facial expressions and the microphone analyzes the tone of voice. This allows the system to collect user emotional data and use it to optimize learning methods.

[0405] Examples:

[0406] When a new operator learns how to operate a factory machine, the process goes as follows: First, the operator uses a smartphone to enter information such as their name, age, and past experience, and sends it to a server. The server saves this information and suggests appropriate learning methods. These suggestions are displayed on the smartphone, such as video tutorials, and the operator learns by watching the videos.

[0407] During training, the device uses a camera and microphone to analyze the operator's facial expressions and voice in real time to collect emotional data. This emotional data and training progress data are sent to the server, which then adjusts the training method based on this information. For example, if the operator is determined to be confused, the server will suggest additional explanatory videos or a different type of problem.

[0408] Example prompt for a generative AI model:

[0409] "I'm a new factory operator and I'm learning how to operate factory machines for the first time. I've recently become interested in video-based learning. I'm 25 years old and have no prior experience. Based on this information, please suggest the best learning method and content."

[0410] Based on these prompts, the AI ​​model is expected to suggest appropriate learning methods and content.

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] The user uses the device to input user information such as name, age, areas of interest, and current level of understanding. This input data is sent from the device to the server. The device converts the input data into JSON format and sends it to the server via an HTTP request.

[0414] Step 2:

[0415] The server receives the submitted user information and stores it in a database. The server receives the HTTP request, parses the user information, and stores it as a record in a database (e.g., SQLite). If the data is successfully saved, the server returns a confirmation message to the user.

[0416] Step 3:

[0417] The server analyzes the user information stored in the database and proposes the optimal learning method. This analysis uses a machine learning algorithm (for example, Scikit-learn's MLPClassifier). The proposed learning method is sent to the device as a result of the analysis. The server determines the optimal learning method using an ML model based on past data and new user information, and returns it to the device in JSON format.

[0418] Step 4:

[0419] The device presents appropriate learning content to the user based on the optimal learning method received from the server. For example, it displays content such as video tutorials or quiz questions. The device parses the received data and displays the learning content through a user interface.

[0420] Step 5:

[0421] As the user progresses through the learning process, the device uses a camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state. The device captures the camera and microphone input data frame by frame and analyzes it using the emotion engine (e.g., OpenCV and existing emotion recognition models).

[0422] Step 6:

[0423] The device sends the collected learning progress data (e.g., answer time, accuracy rate) and emotion data to the server. The device converts this data into JSON format and sends it to the server via an HTTP request.

[0424] Step 7:

[0425] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. The server stores this data in a database and evaluates its effectiveness using an analytical algorithm. As a result of the analysis, it proposes new learning methods as needed.

[0426] Step 8:

[0427] The server again sends the proposed learning method to the device, and the device presents the user with new learning content, such as additional instructional videos or questions in a different format. The device parses the received data again and displays it through the user interface.

[0428] In this way, users are continually provided with an optimal learning experience tailored to their individual skill level and emotional state.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Second embodiment]

[0433] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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."

[0445] The present invention is a system that includes a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on that. The system also has a means for collecting the user's learning progress and feedback and sending it to the server, and has the function of analyzing the collected data and adjusting the learning method as necessary. A specific embodiment of this "Teacher AI" system is described below.

[0446] System Overview

[0447] Device role:

[0448] The terminal is a device where the user inputs information and displays the learning content. Specifically, it is assumed that a smartphone, tablet, etc. will be used. The terminal transmits the user's input information to the server and displays the learning content received from the server to the user.

[0449] Server Role:

[0450] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses analytical algorithms to select the most appropriate learning method. It also receives user learning progress and feedback, evaluates the effectiveness of the learning method, and makes appropriate adjustments.

[0451] Program processing overview

[0452] Enter your user information:

[0453] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[0454] Learning suggestions:

[0455] The server analyzes the user information stored in the database and suggests the most suitable learning method, taking into account the user's personality, interests, and past learning history. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is sent to the terminal and presented to the user.

[0456] View learning content:

[0457] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[0458] Learning progress and feedback collection:

[0459] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[0460] Analysis of effects and adjustment of methods:

[0461] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[0462] Specific examples

[0463] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0464] 1. Enter your user information

[0465] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[0466] 2. Learning method suggestions

[0467] The server analyzes user information and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the terminal, which then displays them to the user.

[0468] 3. Viewing learning content

[0469] The server retrieves game-style math problems from the database and sends them to the terminal, which displays them to the user, who then begins learning.

[0470] 4. Learning progress and feedback collection

[0471] The device records the user's response time and the number of correct answers. After the learning process is complete, the device displays a feedback screen, prompting the user to rate the ease of understanding and the level of interest, and then sends the data to the server.

[0472] 5. Analysis of effects and adjustment of methods

[0473] The server analyzes the progress data and feedback to evaluate the effectiveness of the learning method. For example, if the answer time is long and the correct answer rate is low, the server will suggest an alternative problem with a video explanation and re-send it to the device.

[0474] In this way, the system of the present invention can continue to provide the most suitable learning method for each individual user and support effective learning.

[0475] The processing flow will be explained below.

[0476] Step 1:

[0477] A user logs in from a terminal

[0478] The terminal displays a login screen and the user enters their ID and password.

[0479] The terminal sends the user's authentication information to the server.

[0480] Step 2:

[0481] The server authenticates the user

[0482] The server checks the user's authentication information against a database and sends the authentication results to the terminal.

[0483] If authentication is successful, the terminal displays the following screen to the user.

[0484] Step 3:

[0485] Entering user information

[0486] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[0487] The user inputs this information, and the terminal transmits the input information to the server.

[0488] Step 4:

[0489] The server stores user information

[0490] The server stores the received user information in a database.

[0491] Step 5:

[0492] User information analysis

[0493] The server retrieves the user's personality, interests, and past learning history from a database.

[0494] Using the information acquired by the server, a machine learning algorithm is applied to select the most suitable learning method for the user.

[0495] Step 6:

[0496] Learning method suggestions

[0497] The server transmits data including the selected learning method and the reason for the selection to the terminal.

[0498] The terminal presents the received learning method to the user and asks for confirmation of the start.

[0499] The user checks the learning method and starts learning.

[0500] Step 7:

[0501] Delivery of learning content

[0502] The server retrieves learning content based on the optimal learning method from a database and sends it to the terminal.

[0503] The terminal displays the received study content to the user, and the user begins studying.

[0504] Step 8:

[0505] Record your learning progress

[0506] As the user progresses with their studies, the device records progress data such as answer time and accuracy rate in real time.

[0507] Step 9:

[0508] Gathering feedback

[0509] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[0510] The user inputs feedback and the terminal sends the information to the server.

[0511] Step 10:

[0512] Analysis of training data

[0513] The server analyzes the collected learning progress data and feedback to evaluate the effectiveness of the current learning method.

[0514] Step 11:

[0515] Adjusting your learning methods

[0516] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[0517] The server transmits the new learning method and the corresponding learning content to the terminal again, allowing the user to continue learning.

[0518] By repeating this series of steps, it is possible to continue providing the optimal learning method for each individual user and maximize the learning effect.

[0519] Example 1

[0520] 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."

[0521] Current educational systems struggle to provide optimal learning methods based on individual users' interests and personalities, and instead use standardized educational methods to accommodate all users. As a result, users' learning effectiveness is not fully realized, and their motivation to learn can decline. Furthermore, progress management and the collection and analysis of feedback are time-consuming, making it difficult to quickly adjust effective learning methods. A system that can solve these problems and provide optimal learning methods for individual users is needed.

[0522] 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.

[0523] In this invention, the server includes a device for inputting user information, a storage device for saving the user information sent from the device, means for analyzing the user information saved in the storage device and proposing an optimal educational method, means for sending educational resources to the device based on the suggested educational method, means for collecting the user's educational progress and opinions and sending them to the storage device, and means for analyzing the collected data and adjusting the educational method as necessary. This makes it possible to quickly provide the optimal educational method for each user and to adjust the educational method as appropriate based on the learning progress and feedback.

[0524] "Device" refers to an electronic device for inputting and displaying user information, including smartphones, tablets, and personal computers.

[0525] "Storage device" refers to data storage devices for storing user information and educational resources. This includes database systems and cloud storage.

[0526] "Analysis" is a data processing method for deriving optimal teaching methods based on stored data.

[0527] "Educational methods" refer to personalized learning methods tailored to the user's characteristics, including game-style questions and video explanations.

[0528] "Educational resources" refer to specific learning content provided based on educational methods, including workbooks, videos, interactive learning materials, etc.

[0529] "Education progress" is data that indicates how far a user has progressed in their studies, including the time it took to answer questions and the number of correct answers.

[0530] "Opinions" are data on users' evaluations and impressions of learning methods and educational resources. These include evaluations such as "ease of understanding" and "interestingness."

[0531] "Analyzing the data" refers to the process of evaluating the effectiveness of teaching methods based on collected progress data and feedback, and making adjustments as needed.

[0532] The present invention is a system including a device for inputting user information, a storage device for storing user information transmitted from the device, a means for analyzing the stored user information in the storage device and proposing an optimal educational method, a means for transmitting educational resources to the device based on the proposed educational method, a means for collecting the user's educational progress and opinions and transmitting them to the storage device, and a means for analyzing the collected data and adjusting the educational method as necessary.

[0533] System Overview

[0534] Device role:

[0535] The device is a device that inputs user information and displays educational resources. Specifically, a smartphone, tablet, or PC is used. The device transmits the user's input information to a storage device and displays the educational resources received from the storage device to the user.

[0536] Storage device role:

[0537] The storage device is a centralized computer system that stores and analyzes user information and proposes educational methods. The storage device stores user information in a database and, if necessary, uses an analytical algorithm to select the most appropriate educational method. It also receives the user's educational progress and opinions, and based on this, evaluates the effectiveness of the educational method and makes appropriate adjustments.

[0538] Program processing overview

[0539] Enter your user information:

[0540] Through the device, the user inputs basic information such as name, age, areas of interest, current level of understanding, etc. The device sends this information to a storage device, which then stores the information in a database.

[0541] Teaching suggestions:

[0542] The storage device analyzes the user information stored in the database and proposes the most appropriate educational method based on the user's personality, interests, and past learning history. For example, a method that combines game-style questions and video explanations may be selected. The selected educational method is sent to the device, which then presents it to the user.

[0543] View educational resources:

[0544] The storage device retrieves educational resources corresponding to the selected educational method from the database and transmits them to the device, which displays the received educational resources to the user, allowing the user to begin learning.

[0545] Educational progress and feedback collection:

[0546] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter their evaluation, such as "ease of understanding" or "interestingness." This data is sent to a storage device.

[0547] Analysis of effects and adjustments to methods:

[0548] The storage device analyzes the collected progress data and opinions to evaluate the effectiveness of the current teaching method. If it is determined to be ineffective, the storage device re-proposes an alternative teaching method and transmits it to the device again, thereby presenting the new teaching method to the user.

[0549] Specific examples

[0550] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0551] 1. Enter your user information

[0552] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which the device sends to a memory device that stores it in a database.

[0553] 2. Proposal of educational methods

[0554] The storage device analyzes the user information and suggests game-style math problems based on the user's interest, for example, "I like learning through games." The storage device sends the suggestions to the device, which then displays them to the user.

[0555] 3. Display of educational resources

[0556] The storage device retrieves game-style math problems from the database and sends them to the device, which displays them to the user, who then begins learning.

[0557] 4. Educational progress and feedback collection

[0558] The device records the user's response time and the number of correct answers. After the learning is completed, the device displays a feedback screen and asks the user to rate the "ease of understanding" and "interestingness," and sends the data to the storage device.

[0559] 5. Analysis of effects and adjustment of methods

[0560] The memory device analyzes the progress data and opinions to evaluate the effectiveness of the teaching method. For example, if the answer time is long and the correct answer rate is low, the memory device will suggest an alternative problem with video explanation and send it to the device again.

[0561] Example of input prompt for generative AI model

[0562] "Please explain the system that proposes mathematics learning content for elementary school students. The system proposes individual teaching methods based on user information, collects and analyzes educational progress and feedback, and provides the optimal teaching method."

[0563] In this way, the system of the present invention can continue to provide the user with the most suitable educational method and support effective learning.

[0564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0565] Step 1:

[0566] Entering user information

[0567] The user enters basic information such as name, age, areas of interest, and current level of understanding through the device. Specifically, the terminal displays a form containing text boxes and drop-down menus, which the user fills in. The input data is sent from the device to the server, which receives the data and stores it in a database.

[0568] Input: User's name, age, areas of interest, current understanding level

[0569] Output: User information stored in the database

[0570] Step 2:

[0571] Analysis of user information

[0572] The server retrieves user information from the database and uses an analytical algorithm to select the optimal training method. Specifically, the server uses Python's scikit-learn to perform clustering and regression analysis.

[0573] Input: Saved user information

[0574] Output: Selected optimal teaching method

[0575] Step 3:

[0576] Submit your proposal

[0577] The server sends the selected teaching method to the device. Specifically, the server sends it as text or JSON format data, which the device receives. The device then displays the suggested teaching method to the user.

[0578] Input: Selected teaching method

[0579] Output: Teaching method displayed on the terminal

[0580] Step 4:

[0581] Acquiring educational resources

[0582] The server retrieves educational resources corresponding to the selected educational method from the database, specifically, the server uses an SQL query to extract relevant content from the database.

[0583] Input: Selected teaching method

[0584] Output: Retrieved educational resources

[0585] Step 5:

[0586] Submitting educational resources

[0587] The server sends the acquired educational resources to the terminal. Specifically, the server sends the learning content to the terminal using an HTTP POST request. The terminal receives it and displays it to the user.

[0588] Input: Retrieved educational resources

[0589] Output: Educational resource displayed on the device

[0590] Step 6:

[0591] Recording educational progress

[0592] As the user progresses with their studies, the device records their progress in real time, and stores data such as answer times and the number of correct answers in a local database or memory.

[0593] Input: User learning activity

[0594] Output: Recorded progress data (answer time, number of correct answers, etc.)

[0595] Step 7:

[0596] Enter and submit feedback

[0597] After the learning is completed, the device displays a feedback screen and asks the user to enter their evaluation of the content, such as "ease of understanding" and "interestingness." This data is collected, organized, and sent to the server.

[0598] Input: User feedback

[0599] Output: Feedback data sent to the server

[0600] Step 8:

[0601] Receiving and storing data

[0602] The server receives the progress data and feedback sent from the device and stores it in a database. Specifically, it again stores the data using a database such as MySQL.

[0603] Input: Progress data, feedback data

[0604] Output: Progress and feedback data stored in a database

[0605] Step 9:

[0606] Analyzing the effectiveness and adjusting the teaching method

[0607] The server analyzes the progress and feedback data to evaluate the effectiveness of the current teaching method. Specifically, it uses Python's pandas and numpy to analyze the data. If the method is ineffective, the server selects an alternative teaching method and sends it to the device again.

[0608] Input: Progress data, feedback data

[0609] Output: A new and adjusted teaching method

[0610] (Application example 1)

[0611] 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."

[0612] Traditional learning systems lacked the ability to flexibly provide learning methods based on each user's different interests and level of understanding. This led to problems such as reduced learning efficiency and ineffective education. Furthermore, they lacked the ability to analyze learning progress and feedback in real time and quickly adjust learning methods based on that analysis.

[0613] 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.

[0614] In this invention, the server includes a means for analyzing user information and suggesting an optimal study method, a means for transmitting study content based on the suggested study method to the terminal, and a means for collecting and analyzing the user's study progress and feedback. This makes it possible to provide an optimized study method for each individual user, improving the user's study efficiency and enabling real-time adjustment of the study method. Furthermore, by including a means for suggesting that the provided study method be in the form of a game or video explanation, it is possible to effectively provide study content based on the user's interests and characteristics.

[0615] "User information" is information about an individual learner, such as the user's identity, age, areas of interest, and current level of understanding.

[0616] A "terminal" is a device that allows a user to input information and display learning content, and includes smartphones, tablets, etc.

[0617] A "server" is a centralized computer system that stores user information, analyzes it, and suggests learning methods.

[0618] A "learning method" is a particular learning approach or means offered to a user, such as a game or video tutorial.

[0619] "Learning content" refers to information such as educational materials, study questions, and video tutorials provided to users.

[0620] "Study progress" is data that indicates the progress of a user's learning activities. Specifically, it includes answer time, number of correct answers, etc.

[0621] "Feedback" refers to evaluations and opinions about the learning content provided by users after completing the learning process.

[0622] "Analysis" is the process by which the server uses the data collected to select the most appropriate learning method and evaluate the effectiveness of the learning method.

[0623] "Adjustment" is the activity of re-proposing the optimal learning method based on the user's learning progress and feedback.

[0624] "Game format" is a method of providing learning content in the form of a game, which has the effect of attracting users' interest and encouraging them to learn.

[0625] The "video explanation format" is a method of explaining learning content in video format. It aids visual understanding and is effective in conveying complex information in an easy-to-understand manner.

[0626] System Overview

[0627] This invention is a learning support system for optimizing a user's learning experience. The system performs a series of processes: inputting, saving, and analyzing user information, proposing optimal learning methods, collecting and analyzing learning progress and feedback, and adjusting the learning methods as needed.

[0628] Hardware and Software Configuration

[0629] Terminal

[0630] The terminal is a device that allows users to input information and display learning content. Smartphones and tablets are commonly used. The terminal has the function of inputting user information and sending it to a server.

[0631] server

[0632] The server is a centralized computer system that stores user information, suggests optimal learning methods, and collects and analyzes learning progress and feedback. The main software used is Python, Flask, and an SQLite database.

[0633] Program processing overview

[0634] Entering and saving user information

[0635] Users input basic information such as their name, age, areas of interest, and current level of understanding through the device, which then sends this information to the server, which stores it in an SQLite database.

[0636] Learning method suggestions

[0637] The server analyzes the stored user information and suggests the most suitable learning method based on the user's interests and characteristics. For example, a game-style problem or a video explanation may be selected. The selected learning method is sent to the device and presented to the user.

[0638] Providing learning content

[0639] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[0640] Learning progress and feedback collection

[0641] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[0642] Analyzing the effects and adjusting the methodology

[0643] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[0644] Specific examples

[0645] For example, when an elementary school student is studying math, the following specific process takes place: When the user inputs "I want to learn math in a game format" through their device, the server analyzes this and selects the appropriate game-style learning content. As the study progresses, the server records the user's response time and number of correct answers, and after completing the study, it asks for feedback on the "ease of understanding" and "interestingness." Based on this data, the server analyzes the effectiveness of the study method and suggests new video-based questions as necessary.

[0646] Prompt Sentence Examples

[0647] "Use the PTA app to suggest ways users like to learn math. For example, if they say they like game-style problems, provide content in that format."

[0648] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0649] Step 1:

[0650] The user inputs their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server as input data. This data includes basic attribute information of the user.

[0651] Step 2:

[0652] The server receives the user information and stores it in a SQLite database, storing the user's name, age, areas of interest, and current understanding level in the corresponding fields.

[0653] Step 3:

[0654] The server analyzes user information stored in a database and proposes optimal learning methods based on the user's interests and characteristics. Specifically, it analyzes keywords related to the user's interests and selects learning methods such as games or video explanations based on those keywords. The proposed learning methods are output.

[0655] Step 4:

[0656] The server retrieves the corresponding learning content from the database based on the proposed learning method. In this process, the learning method acts as a key to search for corresponding learning resources (such as problem sets and video links). The retrieved learning content is sent to the terminal as content data.

[0657] Step 5:

[0658] The device then displays the received learning content to the user, who then begins learning based on the displayed content. In this step, a learning interface is provided, allowing the user to solve problems and watch videos.

[0659] Step 6:

[0660] The device records the user's learning progress data (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This feedback data is sent to the server as progress data and evaluation data.

[0661] Step 7:

[0662] The server analyzes the collected progress and feedback data. At this stage, a data analysis algorithm is used to evaluate the effectiveness of the current learning method. Specifically, a statistical analysis is performed based on the answer time and the number of correct answers, and an evaluation result is generated.

[0663] Step 8:

[0664] If the server determines that the learning method is ineffective, it re-proposes an alternative learning method. This re-proposition returns to the analysis process in step 3, and the optimal learning method is selected again. New learning content based on the re-proposed learning method is then sent to the device and presented to the user.

[0665] 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.

[0666] The present invention provides a system that includes a terminal for inputting user information and a server that stores the user information transmitted from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on the proposed method. The system also includes a means for collecting the user's learning progress and feedback and transmitting the collected data to the server, and also has the function of analyzing the collected data and adjusting the learning method as necessary. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data. A specific embodiment of this system is described below.

[0667] System Overview

[0668] Device role:

[0669] The terminal is a device where the user inputs information and displays learning content. Specifically, it is assumed that a smartphone, tablet, or similar device will be used. The terminal sends the user's input information to the server, and displays the learning content received from the server and feedback from the emotion engine to the user. The terminal is also equipped with sensors such as a camera and microphone, allowing the emotion engine to analyze the user's facial expressions and tone of voice.

[0670] Server Role:

[0671] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses an analytical algorithm to select the optimal learning method. It also analyzes the user's learning progress and emotional data collected by the emotion engine to evaluate the effectiveness of the learning method and make appropriate adjustments.

[0672] The role of the Emotion Engine:

[0673] The emotion engine is a system that collects a user's facial expressions, tone of voice, and other biometric data in real time to determine the user's emotional state. For example, it analyzes the user's facial expressions through a camera and the tone of voice through a microphone. This allows it to recognize emotions such as excitement, confusion, and optimism that the user feels while studying.

[0674] Program processing overview

[0675] Enter your user information:

[0676] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[0677] Learning suggestions:

[0678] The server analyzes the user information stored in the database and proposes the optimal learning method, taking into account the user's personality, interests, past learning history, and even emotional data obtained from the emotion engine. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is then sent to the device and presented to the user.

[0679] View learning content:

[0680] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[0681] Learning progress and emotional data collection:

[0682] The device records the user's learning progress (e.g., answer time, correct answer rate) in real time. In addition, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice and collect emotional data. This emotional data is sent to the server along with the learning progress data.

[0683] Analysis of effects and adjustment of methods:

[0684] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. For example, if the user is confused or their concentration is declining, the server can select and re-suggest an alternative learning method. This allows the user to continuously receive the optimal learning method.

[0685] Specific examples

[0686] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0687] 1. Enter your user information:

[0688] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[0689] 2. Learning Suggestions:

[0690] The server analyzes the user's information, past learning history, and data obtained from the emotion engine, and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the device, which then displays them to the user.

[0691] 3. View learning content:

[0692] The server acquires game-style math problems and sends them to the device, which displays them to the user, who then begins learning.

[0693] 4. Learning progress and emotion data collection:

[0694] The device records the user's response time and the number of correct answers. Furthermore, the device analyzes the user's facial expressions and voice using an emotion engine to determine their emotional state and collect data. This data is then sent to the server.

[0695] 5. Analysis of effects and adjustment of methods:

[0696] The server analyzes the collected progress data and emotional data to evaluate the effectiveness of the learning method. For example, if facial expression analysis indicates that the user is confused, the server will suggest an alternative problem with a video explanation and resend it to the device.

[0697] In this way, the system of the present invention is able to analyze the user's emotions in real time while maximizing their comprehension and learning experience.

[0698] The processing flow will be explained below.

[0699] Step 1:

[0700] A user logs in from a terminal

[0701] The terminal displays a login screen and the user enters their ID and password.

[0702] The device sends the authentication information to the server.

[0703] Step 2:

[0704] The server authenticates the user

[0705] The server checks the user's authentication information against a database and sends the authentication result to the terminal.

[0706] If authentication is successful, the terminal displays the following screen to the user.

[0707] Step 3:

[0708] Entering user information

[0709] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[0710] The user inputs this information, and the terminal transmits the input information to the server.

[0711] Step 4:

[0712] The server stores user information

[0713] The server stores the received user information in a database.

[0714] Step 5:

[0715] User information analysis

[0716] The server retrieves the user's personality, interests, and past learning history from a database.

[0717] The server applies machine learning algorithms to analyze user information and select the learning method that is best suited for the user.

[0718] Step 6:

[0719] Learning method suggestions

[0720] The server sends data including the selected learning method and the reason for it to the terminal.

[0721] The terminal displays the received learning method to the user and asks for confirmation of the start of learning.

[0722] The user checks the learning method and starts learning.

[0723] Step 7:

[0724] Delivery of learning content

[0725] The server retrieves learning content based on the selected learning method from the database and transmits it to the terminal.

[0726] The terminal displays the received study content to the user, allowing the user to proceed with the study.

[0727] Step 8:

[0728] Record your learning progress

[0729] The device records data such as answer time and accuracy rate in real time as the user progresses with their studies.

[0730] Step 9:

[0731] Collecting Emotional Data

[0732] The device uses a camera and microphone to analyze the user's facial expressions and voice using an emotion engine to collect emotional data.

[0733] The collected emotion data is sent to a server.

[0734] Step 10:

[0735] Gathering feedback

[0736] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[0737] The user inputs feedback and the terminal sends the information to the server.

[0738] Step 11:

[0739] Analysis of training data

[0740] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method.

[0741] Step 12:

[0742] Adjusting your learning methods

[0743] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[0744] The server transmits the new learning method and corresponding learning content to the terminal, allowing the user to continue learning.

[0745] By repeating this series of steps, it is possible to continue providing the optimal learning method while taking into account the user's emotional state.

[0746] Example 2

[0747] 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."

[0748] While conventional online learning systems can track a user's learning progress, they struggle to optimize learning methods by taking into account the user's emotional state. As a result, they are unable to adequately alleviate the stress and confusion users feel while learning, resulting in reduced learning efficiency. This invention aims to solve this problem by analyzing users' emotional data in real time and optimizing the learning experience based on that data.

[0749] 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.

[0750] In this invention, the server includes means for storing user information, means for analyzing the stored user information to suggest an optimal study method, means for transmitting study content based on the suggested study method, means for collecting the user's study progress and feedback and transmitting it to the server, means for analyzing the collected data and adjusting the study method, means for equipping the device with sensors including a camera and microphone to collect the user's facial expressions and tone of voice in real time, means for analyzing emotional data obtained from the sensors to determine the user's emotional state, and means for selecting study content based on the emotional data and study progress data and retransmitting it to the device. This makes it possible to provide an optimal study method that takes the user's emotional state into consideration.

[0751] A "terminal" is a device that allows users to input information and display learning content, specifically a smartphone or tablet.

[0752] A "server" is a centralized computer system that stores and analyzes user information and suggests optimal learning methods.

[0753] "User Information" refers to basic information about a user, such as the user's name, age, areas of interest, and current level of understanding.

[0754] "Analysis means" refers to the function by which the server analyzes stored user information and suggests the optimal learning method.

[0755] "Learning content" refers to the teaching materials such as questions, explanations, and videos that are presented to users when they study.

[0756] "Emotion data" refers to data that indicates the user's emotional state determined by analyzing the user's facial expression and tone of voice.

[0757] "Sensors" refer to devices such as cameras that capture the user's facial expressions and microphones that capture the tone of voice, and are used to collect emotional data.

[0758] "Study progress data" is data that indicates the progress of a user's study, such as the time it takes to answer questions and the percentage of correct answers.

[0759] "Feedback" refers to information about impressions and reactions provided by users during the learning process.

[0760] A "generative AI model" is an artificial intelligence model used by the server, and refers to an algorithm for analyzing user information and generating the optimal learning method.

[0761] "Proposal means" refers to the function in which the server uses the generated AI model to select the optimal learning method and send it to the terminal.

[0762] "Adjustment means" refers to a function that analyzes collected learning progress data and emotional data and changes the learning method as necessary.

[0763] The present invention is a system comprising a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes the optimal learning method for each user, and provides learning content to the terminal based on the proposal. The system also has a means for collecting the user's learning progress and feedback and sending it to the server. It also has a function for analyzing the collected data and adjusting the learning method as needed. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data.

[0764] Specifically, the device is a smartphone, tablet, or other device that users use to input information and display learning content. The device is equipped with sensors such as a camera and microphone, which allow the emotion engine to analyze the user's facial expressions and tone of voice.

[0765] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and selects the optimal learning method using a generative AI model. The generative AI model uses input parameters such as the user's age, areas of interest, and current level of understanding. The selected learning method is sent to the terminal and presented to the user.

[0766] As an example, consider the case where an elementary school user is studying math. The user enters their name, age, areas of interest, and current level of math comprehension through their device. This information is sent from the device to the server and stored in the server's database. The server then uses a generative AI model to suggest a study method based on the user's information, past learning history, and data obtained from the emotion engine. For example, a game-style math problem may be suggested based on the user's interest in "game-style problems." The server sends this suggestion to the device, which then displays it to the user.

[0767] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the device. The user receives this and begins learning. During learning, the device records progress data such as the user's response time and number of correct answers, and collects emotional data from the user via the camera and microphone. This data is then sent from the device to the server.

[0768] The server analyzes the collected progress data and emotion data to evaluate the effectiveness of the learning method. For example, if the server determines that the user is confused, it selects an alternative learning method (e.g., questions with video explanations) and sends it to the device again. This operation ensures that the user is always provided with the optimal learning method.

[0769] The following are examples of prompt sentences:

[0770] "Please suggest the optimal learning method based on the user's learning data. The user is an elementary school student who is not good at math. Based on their past learning history, we know that game-style problems would be effective. Also, please take into account their current emotional state based on their emotional data."

[0771] In this way, the system of the present invention incorporates the user's emotional data while maximizing their comprehension and learning experience.

[0772] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0773] Step 1:

[0774] The user uses the device to enter basic information such as name, age, areas of interest, and current level of understanding. Specifically, the user enters data such as "Tanaka Hanako, 10 years old, likes math, and has intermediate level of understanding of arithmetic" into a form displayed on the device's application screen. Once the input is complete, the device sends this information to the server via an HTTP request. The input is received as form data, and the output is sent as an HTTP request.

[0775] Step 2:

[0776] The server stores the received user information in a database. Specifically, the server extracts data from the HTTP request and inserts it into the database using SQL commands. The input is the data from the HTTP request, and the output is saved as a record in the SQL database. For example, the SQL command used is INSERT INTO user_data (name, age, interest, comprehension_level) VALUES ('Hanako Tanaka', 10, 'Mathematics', 'Intermediate');

[0777] Step 3:

[0778] The server analyzes the user information stored in the database and uses a generative AI model to suggest the optimal learning method. Specifically, the server retrieves the user information from the database and inputs the data into the generative AI model for analysis. The input is the user information, and the output is a suggestion of the optimal learning method. The generative AI model returns the results in JSON format. For example, analysis is performed with the prompt generate_study_plan({"age": 10, "interest": "Mathematics", "comprehension_level": "Intermediate"}).

[0779] Step 4:

[0780] The server proposes an appropriate learning method based on the analysis results of the generative AI model and sends the results to the device. Specifically, the server analyzes the obtained JSON data, reformats it as a proposal result, and sends it to the device. The input is the result of the generative AI model, and the output is the proposal content sent to the device. For example, JSON data with the format {"study_method": "game format", "content": "related math problem"} is sent.

[0781] Step 5:

[0782] The device displays the received study method suggestions to the user. Specifically, it displays the message "The best study method for you is game-style math problems" on the device screen. The input is the suggestion data received from the server, and the output is the screen display to the user.

[0783] Step 6:

[0784] The server retrieves the learning content corresponding to the proposed learning method from the database and sends it to the device. Specifically, the server selects the appropriate record from the learning content database and sends it to the device. The input is the proposed learning method, and the output is the learning content data. For example, the data is retrieved using the SQL command SELECT FROM study_content WHERE method = 'game format' AND subject = 'mathematics'; and sent in JSON format.

[0785] Step 7:

[0786] The device displays the received learning content to the user and allows them to begin learning. The user enters answers to the questions displayed on the screen. The input is the learning content, and the output is the user input and the answer. Specifically, the question "What is 2 + 3?" is displayed, and the user enters "5."

[0787] Step 8:

[0788] The device records the user's learning progress (e.g., answer time, accuracy rate) in real time and collects emotional data using sensors such as a camera and microphone. The device acquires this data from the sensors and analyzes it. The input is the user's answers and sensor data, and the output is learning progress data and emotional data. For example, the camera captures the user's facial expressions and the microphone analyzes the tone of voice.

[0789] Step 9:

[0790] The device sends the collected learning progress data and emotion data to the server. Specifically, the data obtained in real time is compiled and sent to the server as an HTTP request. The input is the learning progress data and emotion data, and the output is an HTTP request. For example, the following JSON data is sent: {"progress": {"answer_time": 15, "correct_rate": 0.8}, "emotions": {"happiness": 0.2, "confusion": 0.7}}.

[0791] Step 10:

[0792] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. If necessary, it proposes a new learning method and resends it to the device. The input is learning progress data and emotion data, and the output is a proposal for a new learning method. For example, if it determines that the user is confused, it proposes a problem with a video explanation and resends it to the device. Specifically, the JSON data {"study_method": "video explanation", "content": "related math video"} is resent.

[0793] In this way, the system of the present invention can maximize the user's learning experience by analyzing the user's emotional data and learning progress in real time and providing optimal learning methods and learning contents.

[0794] (Application example 2)

[0795] 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."

[0796] Conventional factory operator training systems generally provide a uniform training plan to all operators, without taking into account the skills and understanding of each individual operator. Furthermore, there was a lack of a way to grasp the learning progress and emotions of operators in real time and adjust the training content based on this. This made it difficult to provide effective training to operators.

[0797] 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 storing user information, means for analyzing the user information and suggesting an optimal learning method, means for transmitting learning content based on the optimal learning method to the terminal, means for collecting the user's learning progress and feedback and transmitting them to the server, means for analyzing the collected data and adjusting the learning method as needed, means including an emotion engine for collecting and analyzing user emotion data in real time, and means for further optimizing the learning experience based on the emotion data. This makes it possible to provide factory operators with an optimal learning experience tailored to their individual skill levels and emotional states, enabling effective and efficient training.

[0798] "User information" refers to data that the educational system acquires and stores about individual users, including, for example, their name, age, areas of interest, and current level of understanding.

[0799] A "terminal" is a device that allows a user to input information and display learning content, such as a smartphone or tablet.

[0800] A "server" is a centralized computer system that stores and analyzes user information, suggests learning methods, and transmits learning content to terminals.

[0801] "Suggesting a learning method" refers to the act of the server analyzing user information and providing the user with the most suitable learning method based on the results.

[0802] "Learning content" refers to teaching materials and training materials that users use to actually study based on suggested learning methods.

[0803] "Study progress" is data that indicates the results and progress achieved by the user as they progress through their studies.

[0804] "Feedback" refers to the evaluation and reaction a user receives after learning, and is information that can be used to measure and improve the effectiveness of learning.

[0805] The "emotion engine" is a system that analyzes the user's facial expressions, tone of voice, etc., and determines the user's emotional state in real time.

[0806] "Emotion data" is information that indicates the user's emotional state collected by the emotion engine.

[0807] This invention is specifically implemented as a training system for factory operators. The system is comprised of terminals, a server, and an emotion engine, and performs advanced data analysis and emotion analysis to optimize the operator's learning experience.

[0808] Device role:

[0809] The terminal is a device used by factory operators to input information and display learning content. Smartphones, tablets, etc. are used. The terminal is equipped with sensors such as a camera and microphone, and has the ability to collect the user's facial expressions and tone of voice.

[0810] Server Role:

[0811] The server is a centralized computer system that stores and analyzes user information and proposes optimal learning methods. Specifically, it stores user information in a database and uses analytical algorithms to select appropriate learning methods. It also collects and analyzes learning progress data and emotional data to adjust learning methods.

[0812] The role of the Emotion Engine:

[0813] The emotion engine is a system that analyzes the user's facial expressions and tone of voice in real time to determine the user's emotional state. The camera analyzes the user's facial expressions and the microphone analyzes the tone of voice. This allows the system to collect user emotional data and use it to optimize learning methods.

[0814] Examples:

[0815] When a new operator learns how to operate a factory machine, the process goes as follows: First, the operator uses a smartphone to enter information such as their name, age, and past experience, and sends it to a server. The server saves this information and suggests appropriate learning methods. These suggestions are displayed on the smartphone, such as video tutorials, and the operator learns by watching the videos.

[0816] During training, the device uses a camera and microphone to analyze the operator's facial expressions and voice in real time to collect emotional data. This emotional data and training progress data are sent to the server, which then adjusts the training method based on this information. For example, if the operator is determined to be confused, the server will suggest additional explanatory videos or a different type of problem.

[0817] Example prompt for a generative AI model:

[0818] "I'm a new factory operator and I'm learning how to operate factory machines for the first time. I've recently become interested in video-based learning. I'm 25 years old and have no prior experience. Based on this information, please suggest the best learning method and content."

[0819] Based on these prompts, the AI ​​model is expected to suggest appropriate learning methods and content.

[0820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0821] Step 1:

[0822] The user uses the device to input user information such as name, age, areas of interest, and current level of understanding. This input data is sent from the device to the server. The device converts the input data into JSON format and sends it to the server via an HTTP request.

[0823] Step 2:

[0824] The server receives the submitted user information and stores it in a database. The server receives the HTTP request, parses the user information, and stores it as a record in a database (e.g., SQLite). If the data is successfully saved, the server returns a confirmation message to the user.

[0825] Step 3:

[0826] The server analyzes the user information stored in the database and proposes the optimal learning method. This analysis uses a machine learning algorithm (for example, Scikit-learn's MLPClassifier). The proposed learning method is sent to the device as a result of the analysis. The server determines the optimal learning method using an ML model based on past data and new user information, and returns it to the device in JSON format.

[0827] Step 4:

[0828] The device presents appropriate learning content to the user based on the optimal learning method received from the server. For example, it displays content such as video tutorials or quiz questions. The device parses the received data and displays the learning content through a user interface.

[0829] Step 5:

[0830] As the user progresses through the learning process, the device uses a camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state. The device captures the camera and microphone input data frame by frame and analyzes it using the emotion engine (e.g., OpenCV and existing emotion recognition models).

[0831] Step 6:

[0832] The device sends the collected learning progress data (e.g., answer time, accuracy rate) and emotion data to the server. The device converts this data into JSON format and sends it to the server via an HTTP request.

[0833] Step 7:

[0834] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. The server stores this data in a database and evaluates its effectiveness using an analytical algorithm. As a result of the analysis, it proposes new learning methods as needed.

[0835] Step 8:

[0836] The server again sends the proposed learning method to the device, and the device presents the user with new learning content, such as additional instructional videos or questions in a different format. The device parses the received data again and displays it through the user interface.

[0837] In this way, users are continually provided with an optimal learning experience tailored to their individual skill level and emotional state.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] [Third embodiment]

[0842] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0843] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0844] 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).

[0845] 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.

[0846] 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.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] The present invention is a system that includes a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on that. The system also has a means for collecting the user's learning progress and feedback and sending it to the server, and has the function of analyzing the collected data and adjusting the learning method as necessary. A specific embodiment of this "Teacher AI" system is described below.

[0855] System Overview

[0856] Device role:

[0857] The terminal is a device where the user inputs information and displays the learning content. Specifically, it is assumed that a smartphone, tablet, etc. will be used. The terminal transmits the user's input information to the server and displays the learning content received from the server to the user.

[0858] Server Role:

[0859] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses analytical algorithms to select the most appropriate learning method. It also receives user learning progress and feedback, evaluates the effectiveness of the learning method, and makes appropriate adjustments.

[0860] Program processing overview

[0861] Enter your user information:

[0862] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[0863] Learning suggestions:

[0864] The server analyzes the user information stored in the database and suggests the most suitable learning method, taking into account the user's personality, interests, and past learning history. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is sent to the terminal and presented to the user.

[0865] View learning content:

[0866] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[0867] Learning progress and feedback collection:

[0868] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[0869] Analysis of effects and adjustment of methods:

[0870] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[0871] Specific examples

[0872] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0873] 1. Enter your user information

[0874] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[0875] 2. Learning method suggestions

[0876] The server analyzes user information and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the terminal, which then displays them to the user.

[0877] 3. Viewing learning content

[0878] The server retrieves game-style math problems from the database and sends them to the terminal, which displays them to the user, who then begins learning.

[0879] 4. Learning progress and feedback collection

[0880] The device records the user's response time and the number of correct answers. After the learning process is complete, the device displays a feedback screen, prompting the user to rate the ease of understanding and the level of interest, and then sends the data to the server.

[0881] 5. Analysis of effects and adjustment of methods

[0882] The server analyzes the progress data and feedback to evaluate the effectiveness of the learning method. For example, if the answer time is long and the correct answer rate is low, the server will suggest an alternative problem with a video explanation and re-send it to the device.

[0883] In this way, the system of the present invention can continue to provide the most suitable learning method for each individual user and support effective learning.

[0884] The processing flow will be explained below.

[0885] Step 1:

[0886] A user logs in from a terminal

[0887] The terminal displays a login screen and the user enters their ID and password.

[0888] The terminal sends the user's authentication information to the server.

[0889] Step 2:

[0890] The server authenticates the user

[0891] The server checks the user's authentication information against a database and sends the authentication results to the terminal.

[0892] If authentication is successful, the terminal displays the following screen to the user.

[0893] Step 3:

[0894] Entering user information

[0895] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[0896] The user inputs this information, and the terminal transmits the input information to the server.

[0897] Step 4:

[0898] The server stores user information

[0899] The server stores the received user information in a database.

[0900] Step 5:

[0901] User information analysis

[0902] The server retrieves the user's personality, interests, and past learning history from a database.

[0903] Using the information acquired by the server, a machine learning algorithm is applied to select the most suitable learning method for the user.

[0904] Step 6:

[0905] Learning method suggestions

[0906] The server transmits data including the selected learning method and the reason for the selection to the terminal.

[0907] The terminal presents the received learning method to the user and asks for confirmation of the start.

[0908] The user checks the learning method and starts learning.

[0909] Step 7:

[0910] Delivery of learning content

[0911] The server retrieves learning content based on the optimal learning method from a database and sends it to the terminal.

[0912] The terminal displays the received study content to the user, and the user begins studying.

[0913] Step 8:

[0914] Record your learning progress

[0915] As the user progresses with their studies, the device records progress data such as answer time and accuracy rate in real time.

[0916] Step 9:

[0917] Gathering feedback

[0918] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[0919] The user inputs feedback and the terminal sends the information to the server.

[0920] Step 10:

[0921] Analysis of training data

[0922] The server analyzes the collected learning progress data and feedback to evaluate the effectiveness of the current learning method.

[0923] Step 11:

[0924] Adjusting your learning methods

[0925] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[0926] The server transmits the new learning method and the corresponding learning content to the terminal again, allowing the user to continue learning.

[0927] By repeating this series of steps, it is possible to continue providing the optimal learning method for each individual user and maximize the learning effect.

[0928] Example 1

[0929] 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."

[0930] Current educational systems struggle to provide optimal learning methods based on individual users' interests and personalities, and instead use standardized educational methods to accommodate all users. As a result, users' learning effectiveness is not fully realized, and their motivation to learn can decline. Furthermore, progress management and the collection and analysis of feedback are time-consuming, making it difficult to quickly adjust effective learning methods. A system that can solve these problems and provide optimal learning methods for individual users is needed.

[0931] 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.

[0932] In this invention, the server includes a device for inputting user information, a storage device for saving the user information sent from the device, means for analyzing the user information saved in the storage device and proposing an optimal educational method, means for sending educational resources to the device based on the suggested educational method, means for collecting the user's educational progress and opinions and sending them to the storage device, and means for analyzing the collected data and adjusting the educational method as necessary. This makes it possible to quickly provide the optimal educational method for each user and to adjust the educational method as appropriate based on the learning progress and feedback.

[0933] "Device" refers to an electronic device for inputting and displaying user information, including smartphones, tablets, and personal computers.

[0934] "Storage device" refers to data storage devices for storing user information and educational resources. This includes database systems and cloud storage.

[0935] "Analysis" is a data processing method for deriving optimal teaching methods based on stored data.

[0936] "Educational methods" refer to personalized learning methods tailored to the user's characteristics, including game-style questions and video explanations.

[0937] "Educational resources" refer to specific learning content provided based on educational methods, including workbooks, videos, interactive learning materials, etc.

[0938] "Education progress" is data that indicates how far a user has progressed in their studies, including the time it took to answer questions and the number of correct answers.

[0939] "Opinions" are data on users' evaluations and impressions of learning methods and educational resources. These include evaluations such as "ease of understanding" and "interestingness."

[0940] "Analyzing the data" refers to the process of evaluating the effectiveness of teaching methods based on collected progress data and feedback, and making adjustments as needed.

[0941] The present invention is a system including a device for inputting user information, a storage device for storing user information transmitted from the device, a means for analyzing the stored user information in the storage device and proposing an optimal educational method, a means for transmitting educational resources to the device based on the proposed educational method, a means for collecting the user's educational progress and opinions and transmitting them to the storage device, and a means for analyzing the collected data and adjusting the educational method as necessary.

[0942] System Overview

[0943] Device role:

[0944] The device is a device that inputs user information and displays educational resources. Specifically, a smartphone, tablet, or PC is used. The device transmits the user's input information to a storage device and displays the educational resources received from the storage device to the user.

[0945] Storage device role:

[0946] The storage device is a centralized computer system that stores and analyzes user information and proposes educational methods. The storage device stores user information in a database and, if necessary, uses an analytical algorithm to select the most appropriate educational method. It also receives the user's educational progress and opinions, and based on this, evaluates the effectiveness of the educational method and makes appropriate adjustments.

[0947] Program processing overview

[0948] Enter your user information:

[0949] Through the device, the user inputs basic information such as name, age, areas of interest, current level of understanding, etc. The device sends this information to a storage device, which then stores the information in a database.

[0950] Teaching suggestions:

[0951] The storage device analyzes the user information stored in the database and proposes the most appropriate educational method based on the user's personality, interests, and past learning history. For example, a method that combines game-style questions and video explanations may be selected. The selected educational method is sent to the device, which then presents it to the user.

[0952] View educational resources:

[0953] The storage device retrieves educational resources corresponding to the selected educational method from the database and transmits them to the device, which displays the received educational resources to the user, allowing the user to begin learning.

[0954] Educational progress and feedback collection:

[0955] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter their evaluation, such as "ease of understanding" or "interestingness." This data is sent to a storage device.

[0956] Analysis of effects and adjustments to methods:

[0957] The storage device analyzes the collected progress data and opinions to evaluate the effectiveness of the current teaching method. If it is determined to be ineffective, the storage device re-proposes an alternative teaching method and transmits it to the device again, thereby presenting the new teaching method to the user.

[0958] Specific examples

[0959] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[0960] 1. Enter your user information

[0961] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which the device sends to a memory device that stores it in a database.

[0962] 2. Proposal of educational methods

[0963] The storage device analyzes the user information and suggests game-style math problems based on the user's interest, for example, "I like learning through games." The storage device sends the suggestions to the device, which then displays them to the user.

[0964] 3. Display of educational resources

[0965] The storage device retrieves game-style math problems from the database and sends them to the device, which displays them to the user, who then begins learning.

[0966] 4. Educational progress and feedback collection

[0967] The device records the user's response time and the number of correct answers. After the learning is completed, the device displays a feedback screen and asks the user to rate the "ease of understanding" and "interestingness," and sends the data to the storage device.

[0968] 5. Analysis of effects and adjustment of methods

[0969] The memory device analyzes the progress data and opinions to evaluate the effectiveness of the teaching method. For example, if the answer time is long and the correct answer rate is low, the memory device will suggest an alternative problem with video explanation and send it to the device again.

[0970] Example of input prompt for generative AI model

[0971] "Please explain the system that proposes mathematics learning content for elementary school students. The system proposes individual teaching methods based on user information, collects and analyzes educational progress and feedback, and provides the optimal teaching method."

[0972] In this way, the system of the present invention can continue to provide the user with the most suitable educational method and support effective learning.

[0973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0974] Step 1:

[0975] Entering user information

[0976] The user enters basic information such as name, age, areas of interest, and current level of understanding through the device. Specifically, the terminal displays a form containing text boxes and drop-down menus, which the user fills in. The input data is sent from the device to the server, which receives the data and stores it in a database.

[0977] Input: User's name, age, areas of interest, current understanding level

[0978] Output: User information stored in the database

[0979] Step 2:

[0980] Analysis of user information

[0981] The server retrieves user information from the database and uses an analytical algorithm to select the optimal training method. Specifically, the server uses Python's scikit-learn to perform clustering and regression analysis.

[0982] Input: Saved user information

[0983] Output: Selected optimal teaching method

[0984] Step 3:

[0985] Submit your proposal

[0986] The server sends the selected teaching method to the device. Specifically, the server sends it as text or JSON format data, which the device receives. The device then displays the suggested teaching method to the user.

[0987] Input: Selected teaching method

[0988] Output: Teaching method displayed on the terminal

[0989] Step 4:

[0990] Acquiring educational resources

[0991] The server retrieves educational resources corresponding to the selected educational method from the database, specifically, the server uses an SQL query to extract relevant content from the database.

[0992] Input: Selected teaching method

[0993] Output: Retrieved educational resources

[0994] Step 5:

[0995] Submitting educational resources

[0996] The server sends the acquired educational resources to the terminal. Specifically, the server sends the learning content to the terminal using an HTTP POST request. The terminal receives it and displays it to the user.

[0997] Input: Retrieved educational resources

[0998] Output: Educational resource displayed on the device

[0999] Step 6:

[1000] Recording educational progress

[1001] As the user progresses with their studies, the device records their progress in real time, and stores data such as answer times and the number of correct answers in a local database or memory.

[1002] Input: User learning activity

[1003] Output: Recorded progress data (answer time, number of correct answers, etc.)

[1004] Step 7:

[1005] Enter and submit feedback

[1006] After the learning is completed, the device displays a feedback screen and asks the user to enter their evaluation of the content, such as "ease of understanding" and "interestingness." This data is collected, organized, and sent to the server.

[1007] Input: User feedback

[1008] Output: Feedback data sent to the server

[1009] Step 8:

[1010] Receiving and storing data

[1011] The server receives the progress data and feedback sent from the device and stores it in a database. Specifically, it again stores the data using a database such as MySQL.

[1012] Input: Progress data, feedback data

[1013] Output: Progress and feedback data stored in a database

[1014] Step 9:

[1015] Analyzing the effectiveness and adjusting the teaching method

[1016] The server analyzes the progress and feedback data to evaluate the effectiveness of the current teaching method. Specifically, it uses Python's pandas and numpy to analyze the data. If the method is ineffective, the server selects an alternative teaching method and sends it to the device again.

[1017] Input: Progress data, feedback data

[1018] Output: A new and adjusted teaching method

[1019] (Application example 1)

[1020] 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."

[1021] Traditional learning systems lacked the ability to flexibly provide learning methods based on each user's different interests and level of understanding. This led to problems such as reduced learning efficiency and ineffective education. Furthermore, they lacked the ability to analyze learning progress and feedback in real time and quickly adjust learning methods based on that analysis.

[1022] 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.

[1023] In this invention, the server includes a means for analyzing user information and suggesting an optimal study method, a means for transmitting study content based on the suggested study method to the terminal, and a means for collecting and analyzing the user's study progress and feedback. This makes it possible to provide an optimized study method for each individual user, improving the user's study efficiency and enabling real-time adjustment of the study method. Furthermore, by including a means for suggesting that the provided study method be in the form of a game or video explanation, it is possible to effectively provide study content based on the user's interests and characteristics.

[1024] "User information" is information about an individual learner, such as the user's identity, age, areas of interest, and current level of understanding.

[1025] A "terminal" is a device that allows a user to input information and display learning content, and includes smartphones, tablets, etc.

[1026] A "server" is a centralized computer system that stores user information, analyzes it, and suggests learning methods.

[1027] A "learning method" is a particular learning approach or means offered to a user, such as a game or video tutorial.

[1028] "Learning content" refers to information such as educational materials, study questions, and video tutorials provided to users.

[1029] "Study progress" is data that indicates the progress of a user's learning activities. Specifically, it includes answer time, number of correct answers, etc.

[1030] "Feedback" refers to evaluations and opinions about the learning content provided by users after completing the learning process.

[1031] "Analysis" is the process by which the server uses the data collected to select the most appropriate learning method and evaluate the effectiveness of the learning method.

[1032] "Adjustment" is the activity of re-proposing the optimal learning method based on the user's learning progress and feedback.

[1033] "Game format" is a method of providing learning content in the form of a game, which has the effect of attracting users' interest and encouraging them to learn.

[1034] The "video explanation format" is a method of explaining learning content in video format. It aids visual understanding and is effective in conveying complex information in an easy-to-understand manner.

[1035] System Overview

[1036] This invention is a learning support system for optimizing a user's learning experience. The system performs a series of processes: inputting, saving, and analyzing user information, proposing optimal learning methods, collecting and analyzing learning progress and feedback, and adjusting the learning methods as needed.

[1037] Hardware and Software Configuration

[1038] Terminal

[1039] The terminal is a device that allows users to input information and display learning content. Smartphones and tablets are commonly used. The terminal has the function of inputting user information and sending it to a server.

[1040] server

[1041] The server is a centralized computer system that stores user information, suggests optimal learning methods, and collects and analyzes learning progress and feedback. The main software used is Python, Flask, and an SQLite database.

[1042] Program processing overview

[1043] Entering and saving user information

[1044] Users input basic information such as their name, age, areas of interest, and current level of understanding through the device, which then sends this information to the server, which stores it in an SQLite database.

[1045] Learning method suggestions

[1046] The server analyzes the stored user information and suggests the most suitable learning method based on the user's interests and characteristics. For example, a game-style problem or a video explanation may be selected. The selected learning method is sent to the device and presented to the user.

[1047] Providing learning content

[1048] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[1049] Learning progress and feedback collection

[1050] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[1051] Analyzing the effects and adjusting the methodology

[1052] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[1053] Specific examples

[1054] For example, when an elementary school student is studying math, the following specific process takes place: When the user inputs "I want to learn math in a game format" through their device, the server analyzes this and selects the appropriate game-style learning content. As the study progresses, the server records the user's response time and number of correct answers, and after completing the study, it asks for feedback on the "ease of understanding" and "interestingness." Based on this data, the server analyzes the effectiveness of the study method and suggests new video-based questions as necessary.

[1055] Prompt Sentence Examples

[1056] "Use the PTA app to suggest ways users like to learn math. For example, if they say they like game-style problems, provide content in that format."

[1057] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1058] Step 1:

[1059] The user inputs their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server as input data. This data includes basic attribute information of the user.

[1060] Step 2:

[1061] The server receives the user information and stores it in a SQLite database, storing the user's name, age, areas of interest, and current understanding level in the corresponding fields.

[1062] Step 3:

[1063] The server analyzes user information stored in a database and proposes optimal learning methods based on the user's interests and characteristics. Specifically, it analyzes keywords related to the user's interests and selects learning methods such as games or video explanations based on those keywords. The proposed learning methods are output.

[1064] Step 4:

[1065] The server retrieves the corresponding learning content from the database based on the proposed learning method. In this process, the learning method acts as a key to search for corresponding learning resources (such as problem sets and video links). The retrieved learning content is sent to the terminal as content data.

[1066] Step 5:

[1067] The device then displays the received learning content to the user, who then begins learning based on the displayed content. In this step, a learning interface is provided, allowing the user to solve problems and watch videos.

[1068] Step 6:

[1069] The device records the user's learning progress data (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This feedback data is sent to the server as progress data and evaluation data.

[1070] Step 7:

[1071] The server analyzes the collected progress and feedback data. At this stage, a data analysis algorithm is used to evaluate the effectiveness of the current learning method. Specifically, a statistical analysis is performed based on the answer time and the number of correct answers, and an evaluation result is generated.

[1072] Step 8:

[1073] If the server determines that the learning method is ineffective, it re-proposes an alternative learning method. This re-proposition returns to the analysis process in step 3, and the optimal learning method is selected again. New learning content based on the re-proposed learning method is then sent to the device and presented to the user.

[1074] 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.

[1075] The present invention provides a system that includes a terminal for inputting user information and a server that stores the user information transmitted from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on the proposed method. The system also includes a means for collecting the user's learning progress and feedback and transmitting the collected data to the server, and also has the function of analyzing the collected data and adjusting the learning method as necessary. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data. A specific embodiment of this system is described below.

[1076] System Overview

[1077] Device role:

[1078] The terminal is a device where the user inputs information and displays learning content. Specifically, it is assumed that a smartphone, tablet, or similar device will be used. The terminal sends the user's input information to the server, and displays the learning content received from the server and feedback from the emotion engine to the user. The terminal is also equipped with sensors such as a camera and microphone, allowing the emotion engine to analyze the user's facial expressions and tone of voice.

[1079] Server Role:

[1080] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses an analytical algorithm to select the optimal learning method. It also analyzes the user's learning progress and emotional data collected by the emotion engine to evaluate the effectiveness of the learning method and make appropriate adjustments.

[1081] The role of the Emotion Engine:

[1082] The emotion engine is a system that collects a user's facial expressions, tone of voice, and other biometric data in real time to determine the user's emotional state. For example, it analyzes the user's facial expressions through a camera and the tone of voice through a microphone. This allows it to recognize emotions such as excitement, confusion, and optimism that the user feels while studying.

[1083] Program processing overview

[1084] Enter your user information:

[1085] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[1086] Learning suggestions:

[1087] The server analyzes the user information stored in the database and proposes the optimal learning method, taking into account the user's personality, interests, past learning history, and even emotional data obtained from the emotion engine. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is then sent to the device and presented to the user.

[1088] View learning content:

[1089] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[1090] Learning progress and emotional data collection:

[1091] The device records the user's learning progress (e.g., answer time, correct answer rate) in real time. In addition, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice and collect emotional data. This emotional data is sent to the server along with the learning progress data.

[1092] Analysis of effects and adjustment of methods:

[1093] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. For example, if the user is confused or their concentration is declining, the server can select and re-suggest an alternative learning method. This allows the user to continuously receive the optimal learning method.

[1094] Specific examples

[1095] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[1096] 1. Enter your user information:

[1097] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[1098] 2. Learning Suggestions:

[1099] The server analyzes the user's information, past learning history, and data obtained from the emotion engine, and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the device, which then displays them to the user.

[1100] 3. View learning content:

[1101] The server acquires game-style math problems and sends them to the device, which displays them to the user, who then begins learning.

[1102] 4. Learning progress and emotion data collection:

[1103] The device records the user's response time and the number of correct answers. Furthermore, the device analyzes the user's facial expressions and voice using an emotion engine to determine their emotional state and collect data. This data is then sent to the server.

[1104] 5. Analysis of effects and adjustment of methods:

[1105] The server analyzes the collected progress data and emotional data to evaluate the effectiveness of the learning method. For example, if facial expression analysis indicates that the user is confused, the server will suggest an alternative problem with a video explanation and resend it to the device.

[1106] In this way, the system of the present invention is able to analyze the user's emotions in real time while maximizing their comprehension and learning experience.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] A user logs in from a terminal

[1110] The terminal displays a login screen and the user enters their ID and password.

[1111] The device sends the authentication information to the server.

[1112] Step 2:

[1113] The server authenticates the user

[1114] The server checks the user's authentication information against a database and sends the authentication result to the terminal.

[1115] If authentication is successful, the terminal displays the following screen to the user.

[1116] Step 3:

[1117] Entering user information

[1118] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[1119] The user inputs this information, and the terminal transmits the input information to the server.

[1120] Step 4:

[1121] The server stores user information

[1122] The server stores the received user information in a database.

[1123] Step 5:

[1124] User information analysis

[1125] The server retrieves the user's personality, interests, and past learning history from a database.

[1126] The server applies machine learning algorithms to analyze user information and select the learning method that is best suited for the user.

[1127] Step 6:

[1128] Learning method suggestions

[1129] The server sends data including the selected learning method and the reason for it to the terminal.

[1130] The terminal displays the received learning method to the user and asks for confirmation of the start of learning.

[1131] The user checks the learning method and starts learning.

[1132] Step 7:

[1133] Delivery of learning content

[1134] The server retrieves learning content based on the selected learning method from the database and transmits it to the terminal.

[1135] The terminal displays the received study content to the user, allowing the user to proceed with the study.

[1136] Step 8:

[1137] Record your learning progress

[1138] The device records data such as answer time and accuracy rate in real time as the user progresses with their studies.

[1139] Step 9:

[1140] Collecting Emotional Data

[1141] The device uses a camera and microphone to analyze the user's facial expressions and voice using an emotion engine to collect emotional data.

[1142] The collected emotion data is sent to a server.

[1143] Step 10:

[1144] Gathering feedback

[1145] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[1146] The user inputs feedback and the terminal sends the information to the server.

[1147] Step 11:

[1148] Analysis of training data

[1149] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method.

[1150] Step 12:

[1151] Adjusting your learning methods

[1152] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[1153] The server transmits the new learning method and corresponding learning content to the terminal, allowing the user to continue learning.

[1154] By repeating this series of steps, it is possible to continue providing the optimal learning method while taking into account the user's emotional state.

[1155] Example 2

[1156] 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."

[1157] While conventional online learning systems can track a user's learning progress, they struggle to optimize learning methods by taking into account the user's emotional state. As a result, they are unable to adequately alleviate the stress and confusion users feel while learning, resulting in reduced learning efficiency. This invention aims to solve this problem by analyzing users' emotional data in real time and optimizing the learning experience based on that data.

[1158] 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.

[1159] In this invention, the server includes means for storing user information, means for analyzing the stored user information to suggest an optimal study method, means for transmitting study content based on the suggested study method, means for collecting the user's study progress and feedback and transmitting it to the server, means for analyzing the collected data and adjusting the study method, means for equipping the device with sensors including a camera and microphone to collect the user's facial expressions and tone of voice in real time, means for analyzing emotional data obtained from the sensors to determine the user's emotional state, and means for selecting study content based on the emotional data and study progress data and retransmitting it to the device. This makes it possible to provide an optimal study method that takes the user's emotional state into consideration.

[1160] A "terminal" is a device that allows users to input information and display learning content, specifically a smartphone or tablet.

[1161] A "server" is a centralized computer system that stores and analyzes user information and suggests optimal learning methods.

[1162] "User Information" refers to basic information about a user, such as the user's name, age, areas of interest, and current level of understanding.

[1163] "Analysis means" refers to the function by which the server analyzes stored user information and suggests the optimal learning method.

[1164] "Learning content" refers to the teaching materials such as questions, explanations, and videos that are presented to users when they study.

[1165] "Emotion data" refers to data that indicates the user's emotional state determined by analyzing the user's facial expression and tone of voice.

[1166] "Sensors" refer to devices such as cameras that capture the user's facial expressions and microphones that capture the tone of voice, and are used to collect emotional data.

[1167] "Study progress data" is data that indicates the progress of a user's study, such as the time it takes to answer questions and the percentage of correct answers.

[1168] "Feedback" refers to information about impressions and reactions provided by users during the learning process.

[1169] A "generative AI model" is an artificial intelligence model used by the server, and refers to an algorithm for analyzing user information and generating the optimal learning method.

[1170] "Proposal means" refers to the function in which the server uses the generated AI model to select the optimal learning method and send it to the terminal.

[1171] "Adjustment means" refers to a function that analyzes collected learning progress data and emotional data and changes the learning method as necessary.

[1172] The present invention is a system comprising a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes the optimal learning method for each user, and provides learning content to the terminal based on the proposal. The system also has a means for collecting the user's learning progress and feedback and sending it to the server. It also has a function for analyzing the collected data and adjusting the learning method as needed. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data.

[1173] Specifically, the device is a smartphone, tablet, or other device that users use to input information and display learning content. The device is equipped with sensors such as a camera and microphone, which allow the emotion engine to analyze the user's facial expressions and tone of voice.

[1174] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and selects the optimal learning method using a generative AI model. The generative AI model uses input parameters such as the user's age, areas of interest, and current level of understanding. The selected learning method is sent to the terminal and presented to the user.

[1175] As an example, consider the case where an elementary school user is studying math. The user enters their name, age, areas of interest, and current level of math comprehension through their device. This information is sent from the device to the server and stored in the server's database. The server then uses a generative AI model to suggest a study method based on the user's information, past learning history, and data obtained from the emotion engine. For example, a game-style math problem may be suggested based on the user's interest in "game-style problems." The server sends this suggestion to the device, which then displays it to the user.

[1176] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the device. The user receives this and begins learning. During learning, the device records progress data such as the user's response time and number of correct answers, and collects emotional data from the user via the camera and microphone. This data is then sent from the device to the server.

[1177] The server analyzes the collected progress data and emotion data to evaluate the effectiveness of the learning method. For example, if the server determines that the user is confused, it selects an alternative learning method (e.g., questions with video explanations) and sends it to the device again. This operation ensures that the user is always provided with the optimal learning method.

[1178] The following are examples of prompt sentences:

[1179] "Please suggest the optimal learning method based on the user's learning data. The user is an elementary school student who is not good at math. Based on their past learning history, we know that game-style problems would be effective. Also, please take into account their current emotional state based on their emotional data."

[1180] In this way, the system of the present invention incorporates the user's emotional data while maximizing their comprehension and learning experience.

[1181] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1182] Step 1:

[1183] The user uses the device to enter basic information such as name, age, areas of interest, and current level of understanding. Specifically, the user enters data such as "Tanaka Hanako, 10 years old, likes math, and has intermediate level of understanding of arithmetic" into a form displayed on the device's application screen. Once the input is complete, the device sends this information to the server via an HTTP request. The input is received as form data, and the output is sent as an HTTP request.

[1184] Step 2:

[1185] The server stores the received user information in a database. Specifically, the server extracts data from the HTTP request and inserts it into the database using SQL commands. The input is the data from the HTTP request, and the output is saved as a record in the SQL database. For example, the SQL command used is INSERT INTO user_data (name, age, interest, comprehension_level) VALUES ('Hanako Tanaka', 10, 'Mathematics', 'Intermediate');

[1186] Step 3:

[1187] The server analyzes the user information stored in the database and uses a generative AI model to suggest the optimal learning method. Specifically, the server retrieves the user information from the database and inputs the data into the generative AI model for analysis. The input is the user information, and the output is a suggestion of the optimal learning method. The generative AI model returns the results in JSON format. For example, analysis is performed with the prompt generate_study_plan({"age": 10, "interest": "Mathematics", "comprehension_level": "Intermediate"}).

[1188] Step 4:

[1189] The server proposes an appropriate learning method based on the analysis results of the generative AI model and sends the results to the device. Specifically, the server analyzes the obtained JSON data, reformats it as a proposal result, and sends it to the device. The input is the result of the generative AI model, and the output is the proposal content sent to the device. For example, JSON data with the format {"study_method": "game format", "content": "related math problem"} is sent.

[1190] Step 5:

[1191] The device displays the received study method suggestions to the user. Specifically, it displays the message "The best study method for you is game-style math problems" on the device screen. The input is the suggestion data received from the server, and the output is the screen display to the user.

[1192] Step 6:

[1193] The server retrieves the learning content corresponding to the proposed learning method from the database and sends it to the device. Specifically, the server selects the appropriate record from the learning content database and sends it to the device. The input is the proposed learning method, and the output is the learning content data. For example, the data is retrieved using the SQL command SELECT FROM study_content WHERE method = 'game format' AND subject = 'mathematics'; and sent in JSON format.

[1194] Step 7:

[1195] The device displays the received learning content to the user and allows them to begin learning. The user enters answers to the questions displayed on the screen. The input is the learning content, and the output is the user input and the answer. Specifically, the question "What is 2 + 3?" is displayed, and the user enters "5."

[1196] Step 8:

[1197] The device records the user's learning progress (e.g., answer time, accuracy rate) in real time and collects emotional data using sensors such as a camera and microphone. The device acquires this data from the sensors and analyzes it. The input is the user's answers and sensor data, and the output is learning progress data and emotional data. For example, the camera captures the user's facial expressions and the microphone analyzes the tone of voice.

[1198] Step 9:

[1199] The device sends the collected learning progress data and emotion data to the server. Specifically, the data obtained in real time is compiled and sent to the server as an HTTP request. The input is the learning progress data and emotion data, and the output is an HTTP request. For example, the following JSON data is sent: {"progress": {"answer_time": 15, "correct_rate": 0.8}, "emotions": {"happiness": 0.2, "confusion": 0.7}}.

[1200] Step 10:

[1201] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. If necessary, it proposes a new learning method and resends it to the device. The input is learning progress data and emotion data, and the output is a proposal for a new learning method. For example, if it determines that the user is confused, it proposes a problem with a video explanation and resends it to the device. Specifically, the JSON data {"study_method": "video explanation", "content": "related math video"} is resent.

[1202] In this way, the system of the present invention can maximize the user's learning experience by analyzing the user's emotional data and learning progress in real time and providing optimal learning methods and learning contents.

[1203] (Application example 2)

[1204] 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."

[1205] Conventional factory operator training systems generally provide a uniform training plan to all operators, without taking into account the skills and understanding of each individual operator. Furthermore, there was a lack of a way to grasp the learning progress and emotions of operators in real time and adjust the training content based on this. This made it difficult to provide effective training to operators.

[1206] 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 storing user information, means for analyzing the user information and suggesting an optimal learning method, means for transmitting learning content based on the optimal learning method to the terminal, means for collecting the user's learning progress and feedback and transmitting them to the server, means for analyzing the collected data and adjusting the learning method as needed, means including an emotion engine for collecting and analyzing user emotion data in real time, and means for further optimizing the learning experience based on the emotion data. This makes it possible to provide factory operators with an optimal learning experience tailored to their individual skill levels and emotional states, enabling effective and efficient training.

[1207] "User information" refers to data that the educational system acquires and stores about individual users, including, for example, their name, age, areas of interest, and current level of understanding.

[1208] A "terminal" is a device that allows a user to input information and display learning content, such as a smartphone or tablet.

[1209] A "server" is a centralized computer system that stores and analyzes user information, suggests learning methods, and transmits learning content to terminals.

[1210] "Suggesting a learning method" refers to the act of the server analyzing user information and providing the user with the most suitable learning method based on the results.

[1211] "Learning content" refers to teaching materials and training materials that users use to actually study based on suggested learning methods.

[1212] "Study progress" is data that indicates the results and progress achieved by the user as they progress through their studies.

[1213] "Feedback" refers to the evaluation and reaction a user receives after learning, and is information that can be used to measure and improve the effectiveness of learning.

[1214] The "emotion engine" is a system that analyzes the user's facial expressions, tone of voice, etc., and determines the user's emotional state in real time.

[1215] "Emotion data" is information that indicates the user's emotional state collected by the emotion engine.

[1216] This invention is specifically implemented as a training system for factory operators. The system is comprised of terminals, a server, and an emotion engine, and performs advanced data analysis and emotion analysis to optimize the operator's learning experience.

[1217] Device role:

[1218] The terminal is a device used by factory operators to input information and display learning content. Smartphones, tablets, etc. are used. The terminal is equipped with sensors such as a camera and microphone, and has the ability to collect the user's facial expressions and tone of voice.

[1219] Server Role:

[1220] The server is a centralized computer system that stores and analyzes user information and proposes optimal learning methods. Specifically, it stores user information in a database and uses analytical algorithms to select appropriate learning methods. It also collects and analyzes learning progress data and emotional data to adjust learning methods.

[1221] The role of the Emotion Engine:

[1222] The emotion engine is a system that analyzes the user's facial expressions and tone of voice in real time to determine the user's emotional state. The camera analyzes the user's facial expressions and the microphone analyzes the tone of voice. This allows the system to collect user emotional data and use it to optimize learning methods.

[1223] Examples:

[1224] When a new operator learns how to operate a factory machine, the process goes as follows: First, the operator uses a smartphone to enter information such as their name, age, and past experience, and sends it to a server. The server saves this information and suggests appropriate learning methods. These suggestions are displayed on the smartphone, such as video tutorials, and the operator learns by watching the videos.

[1225] During training, the device uses a camera and microphone to analyze the operator's facial expressions and voice in real time to collect emotional data. This emotional data and training progress data are sent to the server, which then adjusts the training method based on this information. For example, if the operator is determined to be confused, the server will suggest additional explanatory videos or a different type of problem.

[1226] Example prompt for a generative AI model:

[1227] "I'm a new factory operator and I'm learning how to operate factory machines for the first time. I've recently become interested in video-based learning. I'm 25 years old and have no prior experience. Based on this information, please suggest the best learning method and content."

[1228] Based on these prompts, the AI ​​model is expected to suggest appropriate learning methods and content.

[1229] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1230] Step 1:

[1231] The user uses the device to input user information such as name, age, areas of interest, and current level of understanding. This input data is sent from the device to the server. The device converts the input data into JSON format and sends it to the server via an HTTP request.

[1232] Step 2:

[1233] The server receives the submitted user information and stores it in a database. The server receives the HTTP request, parses the user information, and stores it as a record in a database (e.g., SQLite). If the data is successfully saved, the server returns a confirmation message to the user.

[1234] Step 3:

[1235] The server analyzes the user information stored in the database and proposes the optimal learning method. This analysis uses a machine learning algorithm (for example, Scikit-learn's MLPClassifier). The proposed learning method is sent to the device as a result of the analysis. The server determines the optimal learning method using an ML model based on past data and new user information, and returns it to the device in JSON format.

[1236] Step 4:

[1237] The device presents appropriate learning content to the user based on the optimal learning method received from the server. For example, it displays content such as video tutorials or quiz questions. The device parses the received data and displays the learning content through a user interface.

[1238] Step 5:

[1239] As the user progresses through the learning process, the device uses a camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state. The device captures the camera and microphone input data frame by frame and analyzes it using the emotion engine (e.g., OpenCV and existing emotion recognition models).

[1240] Step 6:

[1241] The device sends the collected learning progress data (e.g., answer time, accuracy rate) and emotion data to the server. The device converts this data into JSON format and sends it to the server via an HTTP request.

[1242] Step 7:

[1243] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. The server stores this data in a database and evaluates its effectiveness using an analytical algorithm. As a result of the analysis, it proposes new learning methods as needed.

[1244] Step 8:

[1245] The server again sends the proposed learning method to the device, and the device presents the user with new learning content, such as additional instructional videos or questions in a different format. The device parses the received data again and displays it through the user interface.

[1246] In this way, users are continually provided with an optimal learning experience tailored to their individual skill level and emotional state.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] [Fourth embodiment]

[1251] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1252] 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.

[1253] 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).

[1254] 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.

[1255] 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.

[1256] 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).

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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."

[1264] The present invention is a system that includes a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on that. The system also has a means for collecting the user's learning progress and feedback and sending it to the server, and has the function of analyzing the collected data and adjusting the learning method as necessary. A specific embodiment of this "Teacher AI" system is described below.

[1265] System Overview

[1266] Device role:

[1267] The terminal is a device where the user inputs information and displays the learning content. Specifically, it is assumed that a smartphone, tablet, etc. will be used. The terminal transmits the user's input information to the server and displays the learning content received from the server to the user.

[1268] Server Role:

[1269] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses analytical algorithms to select the most appropriate learning method. It also receives user learning progress and feedback, evaluates the effectiveness of the learning method, and makes appropriate adjustments.

[1270] Program processing overview

[1271] Enter your user information:

[1272] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[1273] Learning suggestions:

[1274] The server analyzes the user information stored in the database and suggests the most suitable learning method, taking into account the user's personality, interests, and past learning history. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is sent to the terminal and presented to the user.

[1275] View learning content:

[1276] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[1277] Learning progress and feedback collection:

[1278] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[1279] Analysis of effects and adjustment of methods:

[1280] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[1281] Specific examples

[1282] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[1283] 1. Enter your user information

[1284] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[1285] 2. Learning method suggestions

[1286] The server analyzes user information and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the terminal, which then displays them to the user.

[1287] 3. Viewing learning content

[1288] The server retrieves game-style math problems from the database and sends them to the terminal, which displays them to the user, who then begins learning.

[1289] 4. Learning progress and feedback collection

[1290] The device records the user's response time and the number of correct answers. After the learning process is complete, the device displays a feedback screen, prompting the user to rate the ease of understanding and the level of interest, and then sends the data to the server.

[1291] 5. Analysis of effects and adjustment of methods

[1292] The server analyzes the progress data and feedback to evaluate the effectiveness of the learning method. For example, if the answer time is long and the correct answer rate is low, the server will suggest an alternative problem with a video explanation and re-send it to the device.

[1293] In this way, the system of the present invention can continue to provide the most suitable learning method for each individual user and support effective learning.

[1294] The processing flow will be explained below.

[1295] Step 1:

[1296] A user logs in from a terminal

[1297] The terminal displays a login screen and the user enters their ID and password.

[1298] The terminal sends the user's authentication information to the server.

[1299] Step 2:

[1300] The server authenticates the user

[1301] The server checks the user's authentication information against a database and sends the authentication results to the terminal.

[1302] If authentication is successful, the terminal displays the following screen to the user.

[1303] Step 3:

[1304] Entering user information

[1305] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[1306] The user inputs this information, and the terminal transmits the input information to the server.

[1307] Step 4:

[1308] The server stores user information

[1309] The server stores the received user information in a database.

[1310] Step 5:

[1311] User information analysis

[1312] The server retrieves the user's personality, interests, and past learning history from a database.

[1313] Using the information acquired by the server, a machine learning algorithm is applied to select the most suitable learning method for the user.

[1314] Step 6:

[1315] Learning method suggestions

[1316] The server transmits data including the selected learning method and the reason for the selection to the terminal.

[1317] The terminal presents the received learning method to the user and asks for confirmation of the start.

[1318] The user checks the learning method and starts learning.

[1319] Step 7:

[1320] Delivery of learning content

[1321] The server retrieves learning content based on the optimal learning method from a database and sends it to the terminal.

[1322] The terminal displays the received study content to the user, and the user begins studying.

[1323] Step 8:

[1324] Record your learning progress

[1325] As the user progresses with their studies, the device records progress data such as answer time and accuracy rate in real time.

[1326] Step 9:

[1327] Gathering feedback

[1328] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[1329] The user inputs feedback and the terminal sends the information to the server.

[1330] Step 10:

[1331] Analysis of training data

[1332] The server analyzes the collected learning progress data and feedback to evaluate the effectiveness of the current learning method.

[1333] Step 11:

[1334] Adjusting your learning methods

[1335] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[1336] The server transmits the new learning method and the corresponding learning content to the terminal again, allowing the user to continue learning.

[1337] By repeating this series of steps, it is possible to continue providing the optimal learning method for each individual user and maximize the learning effect.

[1338] Example 1

[1339] 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."

[1340] Current educational systems struggle to provide optimal learning methods based on individual users' interests and personalities, and instead use standardized educational methods to accommodate all users. As a result, users' learning effectiveness is not fully realized, and their motivation to learn can decline. Furthermore, progress management and the collection and analysis of feedback are time-consuming, making it difficult to quickly adjust effective learning methods. A system that can solve these problems and provide optimal learning methods for individual users is needed.

[1341] 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.

[1342] In this invention, the server includes a device for inputting user information, a storage device for saving the user information sent from the device, means for analyzing the user information saved in the storage device and proposing an optimal educational method, means for sending educational resources to the device based on the suggested educational method, means for collecting the user's educational progress and opinions and sending them to the storage device, and means for analyzing the collected data and adjusting the educational method as necessary. This makes it possible to quickly provide the optimal educational method for each user and to adjust the educational method as appropriate based on the learning progress and feedback.

[1343] "Device" refers to an electronic device for inputting and displaying user information, including smartphones, tablets, and personal computers.

[1344] "Storage device" refers to data storage devices for storing user information and educational resources. This includes database systems and cloud storage.

[1345] "Analysis" is a data processing method for deriving optimal teaching methods based on stored data.

[1346] "Educational methods" refer to personalized learning methods tailored to the user's characteristics, including game-style questions and video explanations.

[1347] "Educational resources" refer to specific learning content provided based on educational methods, including workbooks, videos, interactive learning materials, etc.

[1348] "Education progress" is data that indicates how far a user has progressed in their studies, including the time it took to answer questions and the number of correct answers.

[1349] "Opinions" are data on users' evaluations and impressions of learning methods and educational resources. These include evaluations such as "ease of understanding" and "interestingness."

[1350] "Analyzing the data" refers to the process of evaluating the effectiveness of teaching methods based on collected progress data and feedback, and making adjustments as needed.

[1351] The present invention is a system including a device for inputting user information, a storage device for storing user information transmitted from the device, a means for analyzing the stored user information in the storage device and proposing an optimal educational method, a means for transmitting educational resources to the device based on the proposed educational method, a means for collecting the user's educational progress and opinions and transmitting them to the storage device, and a means for analyzing the collected data and adjusting the educational method as necessary.

[1352] System Overview

[1353] Device role:

[1354] The device is a device that inputs user information and displays educational resources. Specifically, a smartphone, tablet, or PC is used. The device transmits the user's input information to a storage device and displays the educational resources received from the storage device to the user.

[1355] Storage device role:

[1356] The storage device is a centralized computer system that stores and analyzes user information and proposes educational methods. The storage device stores user information in a database and, if necessary, uses an analytical algorithm to select the most appropriate educational method. It also receives the user's educational progress and opinions, and based on this, evaluates the effectiveness of the educational method and makes appropriate adjustments.

[1357] Program processing overview

[1358] Enter your user information:

[1359] Through the device, the user inputs basic information such as name, age, areas of interest, current level of understanding, etc. The device sends this information to a storage device, which then stores the information in a database.

[1360] Teaching suggestions:

[1361] The storage device analyzes the user information stored in the database and proposes the most appropriate educational method based on the user's personality, interests, and past learning history. For example, a method that combines game-style questions and video explanations may be selected. The selected educational method is sent to the device, which then presents it to the user.

[1362] View educational resources:

[1363] The storage device retrieves educational resources corresponding to the selected educational method from the database and transmits them to the device, which displays the received educational resources to the user, allowing the user to begin learning.

[1364] Educational progress and feedback collection:

[1365] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter their evaluation, such as "ease of understanding" or "interestingness." This data is sent to a storage device.

[1366] Analysis of effects and adjustments to methods:

[1367] The storage device analyzes the collected progress data and opinions to evaluate the effectiveness of the current teaching method. If it is determined to be ineffective, the storage device re-proposes an alternative teaching method and transmits it to the device again, thereby presenting the new teaching method to the user.

[1368] Specific examples

[1369] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[1370] 1. Enter your user information

[1371] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which the device sends to a memory device that stores it in a database.

[1372] 2. Proposal of educational methods

[1373] The storage device analyzes the user information and suggests game-style math problems based on the user's interest, for example, "I like learning through games." The storage device sends the suggestions to the device, which then displays them to the user.

[1374] 3. Display of educational resources

[1375] The storage device retrieves game-style math problems from the database and sends them to the device, which displays them to the user, who then begins learning.

[1376] 4. Educational progress and feedback collection

[1377] The device records the user's response time and the number of correct answers. After the learning is completed, the device displays a feedback screen and asks the user to rate the "ease of understanding" and "interestingness," and sends the data to the storage device.

[1378] 5. Analysis of effects and adjustment of methods

[1379] The memory device analyzes the progress data and opinions to evaluate the effectiveness of the teaching method. For example, if the answer time is long and the correct answer rate is low, the memory device will suggest an alternative problem with video explanation and send it to the device again.

[1380] Example of input prompt for generative AI model

[1381] "Please explain the system that proposes mathematics learning content for elementary school students. The system proposes individual teaching methods based on user information, collects and analyzes educational progress and feedback, and provides the optimal teaching method."

[1382] In this way, the system of the present invention can continue to provide the user with the most suitable educational method and support effective learning.

[1383] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1384] Step 1:

[1385] Entering user information

[1386] The user enters basic information such as name, age, areas of interest, and current level of understanding through the device. Specifically, the terminal displays a form containing text boxes and drop-down menus, which the user fills in. The input data is sent from the device to the server, which receives the data and stores it in a database.

[1387] Input: User's name, age, areas of interest, current understanding level

[1388] Output: User information stored in the database

[1389] Step 2:

[1390] Analysis of user information

[1391] The server retrieves user information from the database and uses an analytical algorithm to select the optimal training method. Specifically, the server uses Python's scikit-learn to perform clustering and regression analysis.

[1392] Input: Saved user information

[1393] Output: Selected optimal teaching method

[1394] Step 3:

[1395] Submit your proposal

[1396] The server sends the selected teaching method to the device. Specifically, the server sends it as text or JSON format data, which the device receives. The device then displays the suggested teaching method to the user.

[1397] Input: Selected teaching method

[1398] Output: Teaching method displayed on the terminal

[1399] Step 4:

[1400] Acquiring educational resources

[1401] The server retrieves educational resources corresponding to the selected educational method from the database, specifically, the server uses an SQL query to extract relevant content from the database.

[1402] Input: Selected teaching method

[1403] Output: Retrieved educational resources

[1404] Step 5:

[1405] Submitting educational resources

[1406] The server sends the acquired educational resources to the terminal. Specifically, the server sends the learning content to the terminal using an HTTP POST request. The terminal receives it and displays it to the user.

[1407] Input: Retrieved educational resources

[1408] Output: Educational resource displayed on the device

[1409] Step 6:

[1410] Recording educational progress

[1411] As the user progresses with their studies, the device records their progress in real time, and stores data such as answer times and the number of correct answers in a local database or memory.

[1412] Input: User learning activity

[1413] Output: Recorded progress data (answer time, number of correct answers, etc.)

[1414] Step 7:

[1415] Enter and submit feedback

[1416] After the learning is completed, the device displays a feedback screen and asks the user to enter their evaluation of the content, such as "ease of understanding" and "interestingness." This data is collected, organized, and sent to the server.

[1417] Input: User feedback

[1418] Output: Feedback data sent to the server

[1419] Step 8:

[1420] Receiving and storing data

[1421] The server receives the progress data and feedback sent from the device and stores it in a database. Specifically, it again stores the data using a database such as MySQL.

[1422] Input: Progress data, feedback data

[1423] Output: Progress and feedback data stored in a database

[1424] Step 9:

[1425] Analyzing the effectiveness and adjusting the teaching method

[1426] The server analyzes the progress and feedback data to evaluate the effectiveness of the current teaching method. Specifically, it uses Python's pandas and numpy to analyze the data. If the method is ineffective, the server selects an alternative teaching method and sends it to the device again.

[1427] Input: Progress data, feedback data

[1428] Output: A new and adjusted teaching method

[1429] (Application example 1)

[1430] 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."

[1431] Traditional learning systems lacked the ability to flexibly provide learning methods based on each user's different interests and level of understanding. This led to problems such as reduced learning efficiency and ineffective education. Furthermore, they lacked the ability to analyze learning progress and feedback in real time and quickly adjust learning methods based on that analysis.

[1432] 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.

[1433] In this invention, the server includes a means for analyzing user information and suggesting an optimal study method, a means for transmitting study content based on the suggested study method to the terminal, and a means for collecting and analyzing the user's study progress and feedback. This makes it possible to provide an optimized study method for each individual user, improving the user's study efficiency and enabling real-time adjustment of the study method. Furthermore, by including a means for suggesting that the provided study method be in the form of a game or video explanation, it is possible to effectively provide study content based on the user's interests and characteristics.

[1434] "User information" is information about an individual learner, such as the user's identity, age, areas of interest, and current level of understanding.

[1435] A "terminal" is a device that allows a user to input information and display learning content, and includes smartphones, tablets, etc.

[1436] A "server" is a centralized computer system that stores user information, analyzes it, and suggests learning methods.

[1437] A "learning method" is a particular learning approach or means offered to a user, such as a game or video tutorial.

[1438] "Learning content" refers to information such as educational materials, study questions, and video tutorials provided to users.

[1439] "Study progress" is data that indicates the progress of a user's learning activities. Specifically, it includes answer time, number of correct answers, etc.

[1440] "Feedback" refers to evaluations and opinions about the learning content provided by users after completing the learning process.

[1441] "Analysis" is the process by which the server uses the data collected to select the most appropriate learning method and evaluate the effectiveness of the learning method.

[1442] "Adjustment" is the activity of re-proposing the optimal learning method based on the user's learning progress and feedback.

[1443] "Game format" is a method of providing learning content in the form of a game, which has the effect of attracting users' interest and encouraging them to learn.

[1444] The "video explanation format" is a method of explaining learning content in video format. It aids visual understanding and is effective in conveying complex information in an easy-to-understand manner.

[1445] System Overview

[1446] This invention is a learning support system for optimizing a user's learning experience. The system performs a series of processes: inputting, saving, and analyzing user information, proposing optimal learning methods, collecting and analyzing learning progress and feedback, and adjusting the learning methods as needed.

[1447] Hardware and Software Configuration

[1448] Terminal

[1449] The terminal is a device that allows users to input information and display learning content. Smartphones and tablets are commonly used. The terminal has the function of inputting user information and sending it to a server.

[1450] server

[1451] The server is a centralized computer system that stores user information, suggests optimal learning methods, and collects and analyzes learning progress and feedback. The main software used is Python, Flask, and an SQLite database.

[1452] Program processing overview

[1453] Entering and saving user information

[1454] Users input basic information such as their name, age, areas of interest, and current level of understanding through the device, which then sends this information to the server, which stores it in an SQLite database.

[1455] Learning method suggestions

[1456] The server analyzes the stored user information and suggests the most suitable learning method based on the user's interests and characteristics. For example, a game-style problem or a video explanation may be selected. The selected learning method is sent to the device and presented to the user.

[1457] Providing learning content

[1458] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[1459] Learning progress and feedback collection

[1460] The device records the user's learning progress (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This data is sent to the server.

[1461] Analyzing the effects and adjusting the methodology

[1462] The server analyzes the collected progress data and feedback to evaluate the effectiveness of the current learning method. If it is determined to be ineffective, the server re-proposes an alternative learning method and sends it to the device again, presenting the new learning method to the user.

[1463] Specific examples

[1464] For example, when an elementary school student is studying math, the following specific process takes place: When the user inputs "I want to learn math in a game format" through their device, the server analyzes this and selects the appropriate game-style learning content. As the study progresses, the server records the user's response time and number of correct answers, and after completing the study, it asks for feedback on the "ease of understanding" and "interestingness." Based on this data, the server analyzes the effectiveness of the study method and suggests new video-based questions as necessary.

[1465] Prompt Sentence Examples

[1466] "Use the PTA app to suggest ways users like to learn math. For example, if they say they like game-style problems, provide content in that format."

[1467] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1468] Step 1:

[1469] The user inputs their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server as input data. This data includes basic attribute information of the user.

[1470] Step 2:

[1471] The server receives the user information and stores it in a SQLite database, storing the user's name, age, areas of interest, and current understanding level in the corresponding fields.

[1472] Step 3:

[1473] The server analyzes user information stored in a database and proposes optimal learning methods based on the user's interests and characteristics. Specifically, it analyzes keywords related to the user's interests and selects learning methods such as games or video explanations based on those keywords. The proposed learning methods are output.

[1474] Step 4:

[1475] The server retrieves the corresponding learning content from the database based on the proposed learning method. In this process, the learning method acts as a key to search for corresponding learning resources (such as problem sets and video links). The retrieved learning content is sent to the terminal as content data.

[1476] Step 5:

[1477] The device then displays the received learning content to the user, who then begins learning based on the displayed content. In this step, a learning interface is provided, allowing the user to solve problems and watch videos.

[1478] Step 6:

[1479] The device records the user's learning progress data (e.g., answer time, number of correct answers) in real time. After the learning is completed, a feedback screen is displayed, prompting the user to enter an evaluation such as "ease of understanding" or "interestingness." This feedback data is sent to the server as progress data and evaluation data.

[1480] Step 7:

[1481] The server analyzes the collected progress and feedback data. At this stage, a data analysis algorithm is used to evaluate the effectiveness of the current learning method. Specifically, a statistical analysis is performed based on the answer time and the number of correct answers, and an evaluation result is generated.

[1482] Step 8:

[1483] If the server determines that the learning method is ineffective, it re-proposes an alternative learning method. This re-proposition returns to the analysis process in step 3, and the optimal learning method is selected again. New learning content based on the re-proposed learning method is then sent to the device and presented to the user.

[1484] 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.

[1485] The present invention provides a system that includes a terminal for inputting user information and a server that stores the user information transmitted from the terminal. The server analyzes the stored user information, proposes an optimal learning method for each user, and provides learning content based on the proposed method. The system also includes a means for collecting the user's learning progress and feedback and transmitting the collected data to the server, and also has the function of analyzing the collected data and adjusting the learning method as necessary. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data. A specific embodiment of this system is described below.

[1486] System Overview

[1487] Device role:

[1488] The terminal is a device where the user inputs information and displays learning content. Specifically, it is assumed that a smartphone, tablet, or similar device will be used. The terminal sends the user's input information to the server, and displays the learning content received from the server and feedback from the emotion engine to the user. The terminal is also equipped with sensors such as a camera and microphone, allowing the emotion engine to analyze the user's facial expressions and tone of voice.

[1489] Server Role:

[1490] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and, if necessary, uses an analytical algorithm to select the optimal learning method. It also analyzes the user's learning progress and emotional data collected by the emotion engine to evaluate the effectiveness of the learning method and make appropriate adjustments.

[1491] The role of the Emotion Engine:

[1492] The emotion engine is a system that collects a user's facial expressions, tone of voice, and other biometric data in real time to determine the user's emotional state. For example, it analyzes the user's facial expressions through a camera and the tone of voice through a microphone. This allows it to recognize emotions such as excitement, confusion, and optimism that the user feels while studying.

[1493] Program processing overview

[1494] Enter your user information:

[1495] Users input basic information such as their name, age, areas of interest, and current level of understanding through the terminal. The terminal sends this information to the server, which then stores it in a database.

[1496] Learning suggestions:

[1497] The server analyzes the user information stored in the database and proposes the optimal learning method, taking into account the user's personality, interests, past learning history, and even emotional data obtained from the emotion engine. For example, a learning method that combines game-style questions and video explanations may be selected. The selected learning method is then sent to the device and presented to the user.

[1498] View learning content:

[1499] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the terminal, which then displays the received learning content to the user, allowing the user to begin learning.

[1500] Learning progress and emotional data collection:

[1501] The device records the user's learning progress (e.g., answer time, correct answer rate) in real time. In addition, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice and collect emotional data. This emotional data is sent to the server along with the learning progress data.

[1502] Analysis of effects and adjustment of methods:

[1503] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. For example, if the user is confused or their concentration is declining, the server can select and re-suggest an alternative learning method. This allows the user to continuously receive the optimal learning method.

[1504] Specific examples

[1505] For example, when an elementary school student user is studying arithmetic, the following specific processing is carried out.

[1506] 1. Enter your user information:

[1507] The device prompts the user to enter their name, age, areas of interest, and current level of math comprehension, which is then sent to the server, which stores it in a database.

[1508] 2. Learning Suggestions:

[1509] The server analyzes the user's information, past learning history, and data obtained from the emotion engine, and suggests game-style math problems based on the user's interests, such as "I like learning through games." The server sends these suggestions to the device, which then displays them to the user.

[1510] 3. View learning content:

[1511] The server acquires game-style math problems and sends them to the device, which displays them to the user, who then begins learning.

[1512] 4. Learning progress and emotion data collection:

[1513] The device records the user's response time and the number of correct answers. Furthermore, the device analyzes the user's facial expressions and voice using an emotion engine to determine their emotional state and collect data. This data is then sent to the server.

[1514] 5. Analysis of effects and adjustment of methods:

[1515] The server analyzes the collected progress data and emotional data to evaluate the effectiveness of the learning method. For example, if facial expression analysis indicates that the user is confused, the server will suggest an alternative problem with a video explanation and resend it to the device.

[1516] In this way, the system of the present invention is able to analyze the user's emotions in real time while maximizing their comprehension and learning experience.

[1517] The processing flow will be explained below.

[1518] Step 1:

[1519] A user logs in from a terminal

[1520] The terminal displays a login screen and the user enters their ID and password.

[1521] The device sends the authentication information to the server.

[1522] Step 2:

[1523] The server authenticates the user

[1524] The server checks the user's authentication information against a database and sends the authentication result to the terminal.

[1525] If authentication is successful, the terminal displays the following screen to the user.

[1526] Step 3:

[1527] Entering user information

[1528] The device displays a screen that asks the user to enter information such as "name," "age," "areas of interest," and "current level of understanding."

[1529] The user inputs this information, and the terminal transmits the input information to the server.

[1530] Step 4:

[1531] The server stores user information

[1532] The server stores the received user information in a database.

[1533] Step 5:

[1534] User information analysis

[1535] The server retrieves the user's personality, interests, and past learning history from a database.

[1536] The server applies machine learning algorithms to analyze user information and select the learning method that is best suited for the user.

[1537] Step 6:

[1538] Learning method suggestions

[1539] The server sends data including the selected learning method and the reason for it to the terminal.

[1540] The terminal displays the received learning method to the user and asks for confirmation of the start of learning.

[1541] The user checks the learning method and starts learning.

[1542] Step 7:

[1543] Delivery of learning content

[1544] The server retrieves learning content based on the selected learning method from the database and transmits it to the terminal.

[1545] The terminal displays the received study content to the user, allowing the user to proceed with the study.

[1546] Step 8:

[1547] Record your learning progress

[1548] The device records data such as answer time and accuracy rate in real time as the user progresses with their studies.

[1549] Step 9:

[1550] Collecting Emotional Data

[1551] The device uses a camera and microphone to analyze the user's facial expressions and voice using an emotion engine to collect emotional data.

[1552] The collected emotion data is sent to a server.

[1553] Step 10:

[1554] Gathering feedback

[1555] After the learning is complete, the device displays a feedback screen and asks the user to enter evaluations such as "ease of understanding" and "interestingness."

[1556] The user inputs feedback and the terminal sends the information to the server.

[1557] Step 11:

[1558] Analysis of training data

[1559] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method.

[1560] Step 12:

[1561] Adjusting your learning methods

[1562] If the server determines that a learning method is ineffective, it selects an alternative learning method.

[1563] The server transmits the new learning method and corresponding learning content to the terminal, allowing the user to continue learning.

[1564] By repeating this series of steps, it is possible to continue providing the optimal learning method while taking into account the user's emotional state.

[1565] Example 2

[1566] 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."

[1567] While conventional online learning systems can track a user's learning progress, they struggle to optimize learning methods by taking into account the user's emotional state. As a result, they are unable to adequately alleviate the stress and confusion users feel while learning, resulting in reduced learning efficiency. This invention aims to solve this problem by analyzing users' emotional data in real time and optimizing the learning experience based on that data.

[1568] 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.

[1569] In this invention, the server includes means for storing user information, means for analyzing the stored user information to suggest an optimal study method, means for transmitting study content based on the suggested study method, means for collecting the user's study progress and feedback and transmitting it to the server, means for analyzing the collected data and adjusting the study method, means for equipping the device with sensors including a camera and microphone to collect the user's facial expressions and tone of voice in real time, means for analyzing emotional data obtained from the sensors to determine the user's emotional state, and means for selecting study content based on the emotional data and study progress data and retransmitting it to the device. This makes it possible to provide an optimal study method that takes the user's emotional state into consideration.

[1570] A "terminal" is a device that allows users to input information and display learning content, specifically a smartphone or tablet.

[1571] A "server" is a centralized computer system that stores and analyzes user information and suggests optimal learning methods.

[1572] "User Information" refers to basic information about a user, such as the user's name, age, areas of interest, and current level of understanding.

[1573] "Analysis means" refers to the function by which the server analyzes stored user information and suggests the optimal learning method.

[1574] "Learning content" refers to the teaching materials such as questions, explanations, and videos that are presented to users when they study.

[1575] "Emotion data" refers to data that indicates the user's emotional state determined by analyzing the user's facial expression and tone of voice.

[1576] "Sensors" refer to devices such as cameras that capture the user's facial expressions and microphones that capture the tone of voice, and are used to collect emotional data.

[1577] "Study progress data" is data that indicates the progress of a user's study, such as the time it takes to answer questions and the percentage of correct answers.

[1578] "Feedback" refers to information about impressions and reactions provided by users during the learning process.

[1579] A "generative AI model" is an artificial intelligence model used by the server, and refers to an algorithm for analyzing user information and generating the optimal learning method.

[1580] "Proposal means" refers to the function in which the server uses the generated AI model to select the optimal learning method and send it to the terminal.

[1581] "Adjustment means" refers to a function that analyzes collected learning progress data and emotional data and changes the learning method as necessary.

[1582] The present invention is a system comprising a terminal for inputting user information and a server that stores the user information sent from the terminal. The server analyzes the stored user information, proposes the optimal learning method for each user, and provides learning content to the terminal based on the proposal. The system also has a means for collecting the user's learning progress and feedback and sending it to the server. It also has a function for analyzing the collected data and adjusting the learning method as needed. Furthermore, the present invention aims to incorporate an emotion engine to further optimize the learning experience based on the user's emotion data.

[1583] Specifically, the device is a smartphone, tablet, or other device that users use to input information and display learning content. The device is equipped with sensors such as a camera and microphone, which allow the emotion engine to analyze the user's facial expressions and tone of voice.

[1584] The server is a centralized computer system that stores and analyzes user information and suggests learning methods. The server stores user information in a database and selects the optimal learning method using a generative AI model. The generative AI model uses input parameters such as the user's age, areas of interest, and current level of understanding. The selected learning method is sent to the terminal and presented to the user.

[1585] As an example, consider the case where an elementary school user is studying math. The user enters their name, age, areas of interest, and current level of math comprehension through their device. This information is sent from the device to the server and stored in the server's database. The server then uses a generative AI model to suggest a study method based on the user's information, past learning history, and data obtained from the emotion engine. For example, a game-style math problem may be suggested based on the user's interest in "game-style problems." The server sends this suggestion to the device, which then displays it to the user.

[1586] The server retrieves learning content corresponding to the selected learning method from the database and sends it to the device. The user receives this and begins learning. During learning, the device records progress data such as the user's response time and number of correct answers, and collects emotional data from the user via the camera and microphone. This data is then sent from the device to the server.

[1587] The server analyzes the collected progress data and emotion data to evaluate the effectiveness of the learning method. For example, if the server determines that the user is confused, it selects an alternative learning method (e.g., questions with video explanations) and sends it to the device again. This operation ensures that the user is always provided with the optimal learning method.

[1588] The following are examples of prompt sentences:

[1589] "Please suggest the optimal learning method based on the user's learning data. The user is an elementary school student who is not good at math. Based on their past learning history, we know that game-style problems would be effective. Also, please take into account their current emotional state based on their emotional data."

[1590] In this way, the system of the present invention incorporates the user's emotional data while maximizing their comprehension and learning experience.

[1591] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1592] Step 1:

[1593] The user uses the device to enter basic information such as name, age, areas of interest, and current level of understanding. Specifically, the user enters data such as "Tanaka Hanako, 10 years old, likes math, and has intermediate level of understanding of arithmetic" into a form displayed on the device's application screen. Once the input is complete, the device sends this information to the server via an HTTP request. The input is received as form data, and the output is sent as an HTTP request.

[1594] Step 2:

[1595] The server stores the received user information in a database. Specifically, the server extracts data from the HTTP request and inserts it into the database using SQL commands. The input is the data from the HTTP request, and the output is saved as a record in the SQL database. For example, the SQL command used is INSERT INTO user_data (name, age, interest, comprehension_level) VALUES ('Hanako Tanaka', 10, 'Mathematics', 'Intermediate');

[1596] Step 3:

[1597] The server analyzes the user information stored in the database and uses a generative AI model to suggest the optimal learning method. Specifically, the server retrieves the user information from the database and inputs the data into the generative AI model for analysis. The input is the user information, and the output is a suggestion of the optimal learning method. The generative AI model returns the results in JSON format. For example, analysis is performed with the prompt generate_study_plan({"age": 10, "interest": "Mathematics", "comprehension_level": "Intermediate"}).

[1598] Step 4:

[1599] The server proposes an appropriate learning method based on the analysis results of the generative AI model and sends the results to the device. Specifically, the server analyzes the obtained JSON data, reformats it as a proposal result, and sends it to the device. The input is the result of the generative AI model, and the output is the proposal content sent to the device. For example, JSON data with the format {"study_method": "game format", "content": "related math problem"} is sent.

[1600] Step 5:

[1601] The device displays the received study method suggestions to the user. Specifically, it displays the message "The best study method for you is game-style math problems" on the device screen. The input is the suggestion data received from the server, and the output is the screen display to the user.

[1602] Step 6:

[1603] The server retrieves the learning content corresponding to the proposed learning method from the database and sends it to the device. Specifically, the server selects the appropriate record from the learning content database and sends it to the device. The input is the proposed learning method, and the output is the learning content data. For example, the data is retrieved using the SQL command SELECT FROM study_content WHERE method = 'game format' AND subject = 'mathematics'; and sent in JSON format.

[1604] Step 7:

[1605] The device displays the received learning content to the user and allows them to begin learning. The user enters answers to the questions displayed on the screen. The input is the learning content, and the output is the user input and the answer. Specifically, the question "What is 2 + 3?" is displayed, and the user enters "5."

[1606] Step 8:

[1607] The device records the user's learning progress (e.g., answer time, accuracy rate) in real time and collects emotional data using sensors such as a camera and microphone. The device acquires this data from the sensors and analyzes it. The input is the user's answers and sensor data, and the output is learning progress data and emotional data. For example, the camera captures the user's facial expressions and the microphone analyzes the tone of voice.

[1608] Step 9:

[1609] The device sends the collected learning progress data and emotion data to the server. Specifically, the data obtained in real time is compiled and sent to the server as an HTTP request. The input is the learning progress data and emotion data, and the output is an HTTP request. For example, the following JSON data is sent: {"progress": {"answer_time": 15, "correct_rate": 0.8}, "emotions": {"happiness": 0.2, "confusion": 0.7}}.

[1610] Step 10:

[1611] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. If necessary, it proposes a new learning method and resends it to the device. The input is learning progress data and emotion data, and the output is a proposal for a new learning method. For example, if it determines that the user is confused, it proposes a problem with a video explanation and resends it to the device. Specifically, the JSON data {"study_method": "video explanation", "content": "related math video"} is resent.

[1612] In this way, the system of the present invention can maximize the user's learning experience by analyzing the user's emotional data and learning progress in real time and providing optimal learning methods and learning contents.

[1613] (Application example 2)

[1614] 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."

[1615] Conventional factory operator training systems generally provide a uniform training plan to all operators, without taking into account the skills and understanding of each individual operator. Furthermore, there was a lack of a way to grasp the learning progress and emotions of operators in real time and adjust the training content based on this. This made it difficult to provide effective training to operators.

[1616] 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 storing user information, means for analyzing the user information and suggesting an optimal learning method, means for transmitting learning content based on the optimal learning method to the terminal, means for collecting the user's learning progress and feedback and transmitting them to the server, means for analyzing the collected data and adjusting the learning method as needed, means including an emotion engine for collecting and analyzing user emotion data in real time, and means for further optimizing the learning experience based on the emotion data. This makes it possible to provide factory operators with an optimal learning experience tailored to their individual skill levels and emotional states, enabling effective and efficient training.

[1617] "User information" refers to data that the educational system acquires and stores about individual users, including, for example, their name, age, areas of interest, and current level of understanding.

[1618] A "terminal" is a device that allows a user to input information and display learning content, such as a smartphone or tablet.

[1619] A "server" is a centralized computer system that stores and analyzes user information, suggests learning methods, and transmits learning content to terminals.

[1620] "Suggesting a learning method" refers to the act of the server analyzing user information and providing the user with the most suitable learning method based on the results.

[1621] "Learning content" refers to teaching materials and training materials that users use to actually study based on suggested learning methods.

[1622] "Study progress" is data that indicates the results and progress achieved by the user as they progress through their studies.

[1623] "Feedback" refers to the evaluation and reaction a user receives after learning, and is information that can be used to measure and improve the effectiveness of learning.

[1624] The "emotion engine" is a system that analyzes the user's facial expressions, tone of voice, etc., and determines the user's emotional state in real time.

[1625] "Emotion data" is information that indicates the user's emotional state collected by the emotion engine.

[1626] This invention is specifically implemented as a training system for factory operators. The system is comprised of terminals, a server, and an emotion engine, and performs advanced data analysis and emotion analysis to optimize the operator's learning experience.

[1627] Device role:

[1628] The terminal is a device used by factory operators to input information and display learning content. Smartphones, tablets, etc. are used. The terminal is equipped with sensors such as a camera and microphone, and has the ability to collect the user's facial expressions and tone of voice.

[1629] Server Role:

[1630] The server is a centralized computer system that stores and analyzes user information and proposes optimal learning methods. Specifically, it stores user information in a database and uses analytical algorithms to select appropriate learning methods. It also collects and analyzes learning progress data and emotional data to adjust learning methods.

[1631] The role of the Emotion Engine:

[1632] The emotion engine is a system that analyzes the user's facial expressions and tone of voice in real time to determine the user's emotional state. The camera analyzes the user's facial expressions and the microphone analyzes the tone of voice. This allows the system to collect user emotional data and use it to optimize learning methods.

[1633] Examples:

[1634] When a new operator learns how to operate a factory machine, the process goes as follows: First, the operator uses a smartphone to enter information such as their name, age, and past experience, and sends it to a server. The server saves this information and suggests appropriate learning methods. These suggestions are displayed on the smartphone, such as video tutorials, and the operator learns by watching the videos.

[1635] During training, the device uses a camera and microphone to analyze the operator's facial expressions and voice in real time to collect emotional data. This emotional data and training progress data are sent to the server, which then adjusts the training method based on this information. For example, if the operator is determined to be confused, the server will suggest additional explanatory videos or a different type of problem.

[1636] Example prompt for a generative AI model:

[1637] "I'm a new factory operator and I'm learning how to operate factory machines for the first time. I've recently become interested in video-based learning. I'm 25 years old and have no prior experience. Based on this information, please suggest the best learning method and content."

[1638] Based on these prompts, the AI ​​model is expected to suggest appropriate learning methods and content.

[1639] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1640] Step 1:

[1641] The user uses the device to input user information such as name, age, areas of interest, and current level of understanding. This input data is sent from the device to the server. The device converts the input data into JSON format and sends it to the server via an HTTP request.

[1642] Step 2:

[1643] The server receives the submitted user information and stores it in a database. The server receives the HTTP request, parses the user information, and stores it as a record in a database (e.g., SQLite). If the data is successfully saved, the server returns a confirmation message to the user.

[1644] Step 3:

[1645] The server analyzes the user information stored in the database and proposes the optimal learning method. This analysis uses a machine learning algorithm (for example, Scikit-learn's MLPClassifier). The proposed learning method is sent to the device as a result of the analysis. The server determines the optimal learning method using an ML model based on past data and new user information, and returns it to the device in JSON format.

[1646] Step 4:

[1647] The device presents appropriate learning content to the user based on the optimal learning method received from the server. For example, it displays content such as video tutorials or quiz questions. The device parses the received data and displays the learning content through a user interface.

[1648] Step 5:

[1649] As the user progresses through the learning process, the device uses a camera and microphone to collect the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state. The device captures the camera and microphone input data frame by frame and analyzes it using the emotion engine (e.g., OpenCV and existing emotion recognition models).

[1650] Step 6:

[1651] The device sends the collected learning progress data (e.g., answer time, accuracy rate) and emotion data to the server. The device converts this data into JSON format and sends it to the server via an HTTP request.

[1652] Step 7:

[1653] The server analyzes the collected learning progress data and emotion data to evaluate the effectiveness of the current learning method. The server stores this data in a database and evaluates its effectiveness using an analytical algorithm. As a result of the analysis, it proposes new learning methods as needed.

[1654] Step 8:

[1655] The server again sends the proposed learning method to the device, and the device presents the user with new learning content, such as additional instructional videos or questions in a different format. The device parses the received data again and displays it through the user interface.

[1656] In this way, users are continually provided with an optimal learning experience tailored to their individual skill level and emotional state.

[1657] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1658] 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.

[1659] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1660] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1661] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1662] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1663] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1664] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1665] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1666] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1667] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1668] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1669] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1670] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1671] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1672] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1673] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1674] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1675] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1676] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1677] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1678] The following is further disclosed regarding the above embodiment.

[1679] (Claim 1)

[1680] a terminal for inputting user information;

[1681] a server that stores user information transmitted from the terminal;

[1682] A means for the server to analyze the stored user information and propose an optimal learning method;

[1683] means for transmitting learning content to the terminal based on the proposed learning method;

[1684] means for collecting and transmitting user learning progress and feedback to said server;

[1685] means for analyzing the collected data and adjusting the learning methodology as necessary;

[1686] A system including:

[1687] (Claim 2)

[1688] 10. The system of claim 1, wherein the user information includes the user's name, age, areas of interest, and current understanding level.

[1689] (Claim 3)

[1690] 2. The system of claim 1, wherein the suggested learning methods are optimized based on the user's interests and personality.

[1691] "Example 1"

[1692] (Claim 1)

[1693] a device for inputting user information;

[1694] a storage device for storing user information transmitted from the device;

[1695] means for analyzing the user information stored in the storage device and proposing an optimal education method;

[1696] means for transmitting educational resources to the device based on the proposed educational method;

[1697] means for collecting and transmitting to said storage device the user's educational progress and opinions;

[1698] means for analyzing the collected data and adjusting teaching methods as necessary;

[1699] A system including:

[1700] (Claim 2)

[1701] 10. The system of claim 1, wherein the user information includes the user's name, age, areas of interest, and current understanding level.

[1702] (Claim 3)

[1703] 10. The system of claim 1, wherein the educational suggestions are optimized based on the user's interests and personality.

[1704] "Application Example 1"

[1705] (Claim 1)

[1706] a terminal for inputting user information;

[1707] a server that stores user information transmitted from the terminal;

[1708] A means for the server to analyze the stored user information and propose an optimal learning method;

[1709] means for transmitting learning content to the terminal based on the proposed learning method;

[1710] means for collecting and transmitting user learning progress and feedback to said server;

[1711] means for analyzing the collected data and adjusting the learning methodology as necessary;

[1712] A means to suggest that the learning method provided be in the form of a game or video explanation;

[1713] A system including:

[1714] (Claim 2)

[1715] 10. The system of claim 1, wherein the user information includes the user's identity, age, areas of interest, and current level of understanding.

[1716] (Claim 3)

[1717] 2. The system of claim 1, wherein the suggested learning methods are optimized based on the user's interests and characteristics.

[1718] "Example 2: Combining Emotion Engines"

[1719] (Claim 1)

[1720] a terminal for inputting user information;

[1721] a server that stores user information transmitted from the terminal;

[1722] A means for the server to analyze the stored user information and propose an optimal learning method;

[1723] means for transmitting learning content to the terminal based on the proposed learning method;

[1724] means for collecting and transmitting user learning progress and feedback to said server;

[1725] means for analyzing the collected data and adjusting the learning methodology as necessary;

[1726] The terminal is provided with sensors including a camera and a microphone, and means for collecting the user's facial expressions and tone of voice in real time;

[1727] means for analyzing emotion data obtained from the sensor and determining an emotional state of a user;

[1728] a means for selecting learning content based on the emotion data and learning progress data and retransmitting the learning content to the terminal;

[1729] A system including:

[1730] (Claim 2)

[1731] 10. The system of claim 1, wherein the user information includes the user's name, age, areas of interest, and current understanding level.

[1732] (Claim 3)

[1733] 2. The system of claim 1, wherein the suggested learning methods are optimized based on the user's interests and personality.

[1734] "Application example 2 when combining emotion engines"

[1735] (Claim 1)

[1736] a terminal for inputting user information;

[1737] a server that stores user information transmitted from the terminal;

[1738] A means for the server to analyze the stored user information and propose an optimal learning method;

[1739] means for transmitting learning content to the terminal based on the proposed learning method;

[1740] means for collecting and transmitting user learning progress and feedback to said server;

[1741] means for analyzing the collected data and adjusting the learning methodology as necessary;

[1742] means including an emotion engine for collecting and analyzing user emotion data in real time;

[1743] means for further optimizing the learning experience based on said emotional data;

[1744] A system including:

[1745] (Claim 2)

[1746] 10. The system of claim 1, wherein the user information includes the user's name, age, areas of interest, and current understanding level.

[1747] (Claim 3)

[1748] The system according to claim 1, characterized in that the learning method suggestions are optimized based on the user's interests and personality, and further characterized in that the suggestions are adjusted based on emotional data. [Explanation of symbols]

[1749] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal for inputting user information; a server that stores user information transmitted from the terminal; A means for the server to analyze the stored user information and propose an optimal learning method; means for transmitting learning content to the terminal based on the proposed learning method; means for collecting and transmitting user learning progress and feedback to said server; means for analyzing the collected data and adjusting the learning methodology as necessary; A system including:

2. 2. The system of claim 1, wherein the user information includes the user's name, age, areas of interest, and current level of understanding.

3. 10. The system of claim 1, wherein the suggested learning methods are optimized based on the user's interests and personality.

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