System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting personal information of a user; means for collecting learning history, interests, and abilities; means for analyzing the collected data and generating an optimized learning course; means for providing various media such as visual, text, and audio; and means for generating feedback and providing appropriate guidance based on learning progress.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] Conventional educational platforms struggle to provide an optimized learning experience tailored to the needs and learning styles of individual learners. Specifically, it is difficult to provide personalized content that takes into account learning history, interests, and abilities, which prevents learning effectiveness from being maximized. Furthermore, they are often restricted by time and location, making it impossible to provide a flexible learning environment. To solve these problems, a system is needed that provides learners with immediate and appropriate feedback and provides appropriate guidance based on their learning progress. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means described in the claims.

[0006] 1. The means for entering your personal information allows the learning platform to collect your basic information and create a personalized profile.

[0007] 2. Means of collecting learning history, interests, and abilities provide a basis for collecting and analyzing learners' past behavioral data and learning results.

[0008] 3. The means of analyzing collected data and generating optimized learning courses uses AI algorithms to identify and suggest the most suitable learning content for learners, providing a personalized learning experience.

[0009] 4. A variety of media delivery methods, including visual, text, and audio, accommodates learners' different learning styles and provides a variety of materials to maximize learning outcomes.

[0010] 5. Means of generating feedback and providing appropriate guidance based on learning progress will assess learners' progress in real time and provide appropriate feedback and advice as needed, enabling learners to reach their full potential.

[0011] This allows us to provide learners with a flexible and effective learning environment, enabling them to have an individually optimized learning experience.

[0012] "Means for inputting user personal information" refers to the interface on the learning platform that collects basic information about the user and creates a personalized profile.

[0013] "Means for collecting learning history, interests, and abilities" refers to a mechanism that automatically collects learners' past behavioral data and learning results and sends them to a server for analysis.

[0014] "Means for analyzing collected data and generating optimized learning courses" refers to a method that uses AI algorithms to analyze collected data on learning history, interests, and abilities, and automatically creates optimal learning courses and teaching materials based on that data.

[0015] "Means of providing a variety of media, including visual, text, and audio," refers to a system that provides learning materials in multiple formats, such as visual, reading, and auditory, to accommodate learners' different learning styles.

[0016] "Means for generating feedback based on learning progress and providing appropriate guidance" refers to a system that evaluates a learner's learning progress in real time and automatically generates and provides appropriate feedback and advice during or at the end of the learning process.

[0017] A "server" is a computer system that receives, stores, and analyzes learner data, and generates and distributes optimized learning courses and teaching materials.

[0018] "Device" means the electronic device used by a learner to access the learning platform, view learning materials, and receive feedback. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the most appropriate learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[0041] System overview and processing flow

[0042] 1. User registration and profile creation

[0043] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0044] 2. Collection of learning history and abilities

[0045] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0046] 3. Providing individually optimized learning courses

[0047] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. For example, if Taro Tanaka completes "Basic Business Conversation" and the quiz results show an 80% accuracy rate, the server will recommend "Intermediate Business Conversation" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[0048] 4. Providing learning using a variety of media

[0049] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0050] 5. Feedback and Guidance

[0051] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation" and suggests a pronunciation practice course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[0052] Specific examples

[0053] Flow when a user is learning English

[0054] 1. User registration and profile creation

[0055] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[0056] The device sends this information to the server, which stores it in a database.

[0057] 2. Collection of learning history and abilities

[0058] Users begin learning "Business Conversation" and use visual materials and quizzes.

[0059] The device records usage data in real time and transmits it to a server.

[0060] 3. Providing individually optimized learning courses

[0061] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results.

[0062] The device notifies the user of new courses and prompts them for the next learning step.

[0063] 4. Providing learning using a variety of media

[0064] The server prepares a variety of media teaching materials and distributes them to the terminals.

[0065] The terminal displays or plays the educational material, allowing the user to study.

[0066] 5. Feedback and Guidance

[0067] The server analyzes the learning progress and generates feedback such as "pronunciation practice is needed."

[0068] The device displays feedback to the user in real time and offers additional learning steps.

[0069] This system allows users to receive a learning course that is optimized for them and progress effectively with their studies.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[0073] Step 2:

[0074] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[0075] Step 3:

[0076] A user logs in to a learning application and selects, for example, a "Business Conversation" course. The terminal records data such as the user's selection, the start time of the study, and the type of learning material.

[0077] Step 4:

[0078] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[0079] Step 5:

[0080] The server runs an AI algorithm to analyze the collected data, which evaluates the user's learning patterns, accuracy rate, areas of interest, etc. to identify the optimal learning course.

[0081] Step 6:

[0082] Based on the analysis results, the server recommends a course, for example, "Intermediate Business Conversation," to the user. The device displays a notification of the new learning course to the user, guiding them to proceed to the next learning step.

[0083] Step 7:

[0084] The server prepares visual, audio, and text materials for the "Intermediate Business Conversation" course, and distributes these materials to the terminals.

[0085] Step 8:

[0086] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[0087] Step 9:

[0088] As the user continues to study, the device records their progress in real time and sends the data to the server, which then evaluates their learning progress in real time.

[0089] Step 10:

[0090] The server generates feedback based on the user's learning progress. For example, if the user makes many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation." The device displays this feedback to the user in real time.

[0091] Step 11:

[0092] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[0093] Through these steps, the learning platform can provide users with a personalized learning experience and maximize learning effectiveness.

[0094] Example 1

[0095] 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."

[0096] Conventional learning systems have problems in that they are unable to respond to individual users' learning needs and progress, preventing effective learning. Furthermore, they lack real-time feedback, making it difficult to maximize users' learning outcomes. In particular, the lack of diverse learning media and the lack of optimal course recommendations based on collected learning data limits users' learning experiences.

[0097] 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.

[0098] In this invention, the server includes a means for inputting personal information through a user's input device, a means for collecting learning content usage history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning program, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, and a means for recording the user's learning behavior in real time and periodically transmitting the recording to the server, thereby enabling the server to respond to the individual learning needs of each user and provide real-time feedback, thereby providing an effective learning experience.

[0099] "User" refers to an individual who uses this system to carry out learning activities.

[0100] "Input device" refers to a device used by a user to input personal information, such as a keyboard or touchscreen.

[0101] "Learning content usage history" refers to the history of learning materials and content used by a user.

[0102] "Interest" refers to data that indicates a user's interest or curiosity in learning.

[0103] "Ability" refers to data that indicates a user's learning skills and knowledge level.

[0104] "Collected Data" refers to data such as your personal information, learning history, interests, and abilities.

[0105] "Analysis" refers to the process of analyzing collected data and generating the optimal learning course for the user.

[0106] "Optimized Learning Program" refers to a customized learning course that is individually provided to a user based on the analysis results.

[0107] "Visual" refers to visual teaching materials such as images and videos.

[0108] "Text" refers to teaching materials that consist of sentences or written information.

[0109] "Audio" refers to audio instructional materials.

[0110] "Multiple media" refers to educational materials in a variety of formats, including visual, text, and audio.

[0111] "Learning progress" refers to the progress a user makes as they progress through their learning.

[0112] "Feedback" refers to assessments and guidance generated based on learning progress.

[0113] "Real-time" refers to the almost instantaneous transmission and reception of information and data.

[0114] "Server" refers to a computer system that analyzes and stores data, distributes educational materials, etc.

[0115] The system of the present invention aims to provide a user with an individually optimized learning experience, and includes a means for inputting a user's personal information, a means for collecting a user's learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on the learning progress and providing appropriate guidance, and a means for recording a user's learning behavior in real time and periodically transmitting the recording to a server.

[0116] The main hardware components of the system consist of the user's device and the server. The user's device includes input devices such as PCs, smartphones, and tablets, and is responsible for inputting and recording the user's personal information and learning data. The server, on the other hand, is a high-performance computer that performs processes such as data analysis, storage, and distribution of learning materials. This server uses a database (e.g., MySQL, PostgreSQL) and an AI algorithm (e.g., machine learning model) to analyze the user's learning data and generate an optimal learning course.

[0117] When a user accesses the account creation screen using a device and enters basic information (e.g., name, age, learning purpose), the device sends this information to the server. The server stores the received data in a database and creates an individual profile for the user. This allows the user's personal information to be managed appropriately and used for subsequent learning activities.

[0118] When a user logs into the learning application and begins studying, the device records their learning behavior (e.g., selection of study materials, quiz answer results, study time) in real time and periodically sends it to the server. The server accumulates this data and evaluates the user's learning history and ability. An AI algorithm is used for analysis, so the most suitable learning course for the user is generated.

[0119] For example, if a user has completed the "Basic Business Conversation" course and has an 80% success rate in answering questions, the server will recommend the "Intermediate Business Conversation" course. The terminal will notify the user of this information and encourage them to take a new learning course.

[0120] The server then prepares and distributes visual, audio, and text learning materials based on the selected learning course. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts. This maximizes learning effectiveness through a variety of media.

[0121] Based on the analysis of learning progress, the server generates feedback as needed. For example, if there are many pronunciation errors, the server generates feedback such as "Pronunciation practice is required" and suggests a pronunciation practice course as the next learning step. The device displays this feedback in real time to support the user's learning.

[0122] As a concrete example, consider a user learning English. The user enters basic information on an account creation screen, logs into a learning application, and starts a "Business Conversation" course. The device records usage data in real time and periodically sends it to the server. Based on the analysis results, the server generates and recommends an "Intermediate Business Conversation" course. The device notifies the user of the new course and displays or plays various media materials for learning.

[0123] An example of an input prompt for a generative AI model is, "Please suggest the best learning course for the user to learn business English. For example, if a user already has basic business conversation skills, please suggest the intermediate course they should take next."

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1:

[0126] The user uses a terminal to access the account creation screen and enters personal information (such as name, age, and learning objectives). The entered data is: name: "Taro Tanaka", age: "25 years old", learning objective: "learn business English". The terminal sends this data to the server. The input data is sent via an HTTP request. The server stores this information in a database and builds an individual profile for the user. For example, the information is stored in a MySQL database using an INSERT statement.

[0127] Step 2:

[0128] The user logs in to the learning application. To log in, a username and password are required. For example, the user logs in with username: "tanaka_taro" and password: "password123". This information is sent from the terminal to the server, and the server checks it against the authentication information in the database. If authentication is successful, the server generates a session ID and returns it to the terminal. The session ID is used as the user's identifier for subsequent operations.

[0129] Step 3:

[0130] The user uses the device to select the "Business Conversation" course. This selection is completed by clicking or tapping on the course name. The device sends this selection to the server. The server retrieves the details of the selected course and returns a list of appropriate learning materials to the device. For example, it returns a list of learning materials included in the "Business Conversation" course in JSON format.

[0131] Step 4:

[0132] The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, study time, etc.) in real time. For example, if the user answers a quiz correctly, the result is temporarily stored in the device's cache. Periodically (e.g., every 5 minutes), the device sends this data to the server in batches. The sent data is related to learning history, interests, and abilities.

[0133] Step 5:

[0134] The server stores the received data in a database and performs analysis based on the accumulated data. An AI algorithm (e.g., machine learning model) is used for the analysis to calculate the optimal learning course for each user. For example, the next recommended course is determined based on the user's quiz accuracy rate and study time. The server generates the results and notifies the device of the recommended course.

[0135] Step 6:

[0136] The server prepares visual, audio, and text learning materials based on the optimized learning course generated. For example, it retrieves the path to the necessary learning material files from the database and generates a list of URLs for distribution to the device. The server returns this learning material information to the device in JSON format.

[0137] Step 7:

[0138] The device analyzes the learning material list received from the server and displays or plays the learning materials to the user. For example, for the "Intermediate Business Conversation" course, video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts are displayed. When the user plays the video materials, the device launches a streaming video player and displays the content.

[0139] Step 8:

[0140] The server analyzes the user's learning progress in real time and generates feedback as needed. For example, if the user makes many pronunciation errors, the server generates an analysis result saying, "You need to practice pronunciation." The server sends this feedback to the device and suggests a pronunciation practice course as the next learning step.

[0141] Step 9:

[0142] The device displays the feedback received from the server in real time and offers the user additional learning steps. For example, it displays a pop-up message on the screen saying "Pronunciation practice needed" and provides a link to a pronunciation practice course. When the user clicks the link, a new learning course will begin.

[0143] (Application example 1)

[0144] 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."

[0145] Conventional learning systems have difficulty generating optimal learning courses based on individual users' learning progress and interests, resulting in a uniform learning content. They also lack the ability to provide individually optimized learning experiences for workers with the aim of improving their skills. Furthermore, they lack the ability to provide learning using a variety of media and provide real-time feedback.

[0146] 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.

[0147] In this invention, the server includes a means for inputting personal information of users, a means for recording work history and operational behavior, and a means for analyzing the collected data and generating an optimized learning course, which makes it possible to provide an optimal learning course in real time according to the learning progress and skill improvement of each worker.

[0148] (definition statement)

[0149] "Means for entering user's personal information" refers to means for entering basic information such as the user's name, age, and purpose of learning.

[0150] "Means for collecting learning history, interests, and abilities" refers to means for recording and collecting a user's past learning history, interests, and current abilities.

[0151] "Means for analyzing collected data and generating optimized learning courses" refers to means for generating optimal learning courses for users based on collected data using AI algorithms, etc.

[0152] "Means for providing a variety of media, including visual, text, and audio" refers to means for providing users with a variety of learning content, including video, text, and audio.

[0153] The "means for generating feedback based on learning progress and providing appropriate guidance" is a means for analyzing a user's learning progress and providing appropriate feedback and guidance based on the analysis.

[0154] "Means for recording work history and operational actions for the purpose of improving worker skills" refers to means for recording the operations performed by workers during work and the results thereof, and for supporting skill improvement.

[0155] The "means for generating an optimal work skill course based on recorded data" refers to a means for analyzing the recorded work history and operational behavior data and generating a learning course for improving the skills of the worker that is optimal for the worker.

[0156] The system for implementing this invention aims to provide a learning experience that is individually optimized for each user. Specific embodiments thereof will be described below.

[0157] Overall system overview

[0158] The system consists of a terminal and a server. The terminal records the user's information input and learning activities, while the server analyzes and processes the collected data to generate the optimal learning course. Users access the system using a terminal (such as a smartphone or tablet), and the server provides feedback and guidance in real time.

[0159] Hardware used

[0160] The following hardware is mainly used:

[0161] Smartphone

[0162] tablet

[0163] PC

[0164] Software used

[0165] The following software is mainly used:

[0166] Python

[0167] JSON

[0168] Video Player

[0169] Audio Player

[0170] AI algorithms

[0171] Specific examples of processing

[0172] User registration and profile creation

[0173] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning objectives. For example, a user may enter their name as "Yamada Hanako," their age as "30," and their learning objective as "learning machine operation skills." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0174] Collection of learning history and abilities

[0175] A user logs into a learning application and selects, for example, an "Introduction to Machine Operation Course." The device records the user's learning behavior (e.g., selection of learning materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0176] Providing individually optimized learning courses

[0177] The server analyzes the collected data and uses AI algorithms to generate the most suitable learning course for the user. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will recommend the "Intermediate Machine Operation Course" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[0178] Providing learning using a variety of media

[0179] The server prepares visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) based on the selected learning course. The server distributes these learning materials to the terminal, and the terminal displays or plays them in an appropriate format for the user. For example, an "Intermediate Machine Operation Course" may include video learning materials on operating procedures, audio operation guides, and reference texts.

[0180] Feedback and guidance

[0181] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure review course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[0182] Specific prompt examples

[0183] An example of a prompt sentence when using a generative AI model is as follows:

[0184] "Please suggest the best course for a user to learn factory robot operation techniques. The user's learning history is as follows: viewing visual materials, answering quizzes, and encountering errors (operation procedures). The user's name is Hanako Yamada, and she is 30 years old."

[0185] This system allows workers to receive learning courses that are optimized for them, enabling them to effectively acquire skills.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] A user accesses the account creation screen using a device and enters basic information such as name, age, and learning goals. This information is sent from the device to the server. The server stores the received information in a database and builds an individual profile for the user. At this stage, the input is the user's personal information, and the output is the profile data stored in the database.

[0189] Step 2:

[0190] A user logs into a learning application and selects a specific learning course (e.g., an introductory course in machine operation). The terminal sends the user's selection to the server, which records the selection. In addition, learning behavior (e.g., selection of learning materials, quiz answer results, and study time) is also periodically recorded and this data is sent to the server. At this stage, the input is the user's learning behavior data, and the output is the learning history stored in the database.

[0191] Step 3:

[0192] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. Specifically, it selects an appropriate course based on the user's learning history, interests, and abilities. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will generate the "Intermediate Machine Operation Course." At this stage, the input is the learning history stored in the database and the AI ​​algorithm, and the output is the generated optimized learning course.

[0193] Step 4:

[0194] Based on the learning course selected by the server, visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) are prepared and distributed to the terminal. The terminal then displays or plays the received learning materials in an appropriate format, providing the user with a learning opportunity. At this stage, the input is the generated optimized learning course and the learning materials based on it, and the output is the learning content displayed to the user.

[0195] Step 5:

[0196] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure check course as the next learning step. This feedback is sent to the terminal in real time and displayed to the user. At this stage, the input is the latest learning history data and the feedback generation algorithm, and the output is the feedback message displayed to the user.

[0197] This series of processes allows workers to receive learning courses that are optimized for them and efficiently acquire skills.

[0198] 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.

[0199] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user, and incorporates an emotion engine that recognizes and utilizes the user's emotions. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the optimal learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[0200] System overview and processing flow

[0201] 1. User registration and profile creation

[0202] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0203] 2. Collection of learning history and abilities

[0204] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0205] 3. Emotion Recognition by Emotion Engine

[0206] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[0207] 4. Providing individually optimized learning courses

[0208] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if Taro Tanaka completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that he is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[0209] 5. Providing learning using a variety of media

[0210] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0211] 6. Feedback and Guidance

[0212] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion engine determines that the user is frustrated, it includes advice to relax. The device displays this feedback to the user in real time, supporting their learning.

[0213] Specific examples

[0214] Flow when a user is learning English

[0215] 1. User registration and profile creation

[0216] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[0217] The device sends this information to the server, which stores it in a database.

[0218] 2. Collection of learning history and abilities

[0219] Users begin learning "Business Conversation" and use visual materials and quizzes.

[0220] The device records usage data in real time and transmits it to a server.

[0221] 3. Emotion Recognition by Emotion Engine

[0222] While the user is learning, the device camera captures facial expression data, which is then analyzed by the emotion engine, which determines, for example, that the user is confused.

[0223] 4. Providing individually optimized learning courses

[0224] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results, adjusting the difficulty and content based on emotional data.

[0225] The device notifies the user of new courses and prompts them to take the next step in their studies.

[0226] 5. Providing learning using a variety of media

[0227] The server prepares a variety of media teaching materials and distributes them to the terminals.

[0228] The terminal displays or plays the educational material, allowing the user to study.

[0229] 6. Feedback and Guidance

[0230] The server analyzes learning progress and emotional data and generates appropriate feedback, e.g., "Pronunciation practice is needed." If confusion is detected, a brief explanation is added.

[0231] The device displays feedback to the user in real time and offers additional learning steps.

[0232] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[0233] The processing flow will be explained below.

[0234] Step 1:

[0235] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[0236] Step 2:

[0237] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[0238] Step 3:

[0239] The user logs in to the learning application and selects the "Business Conversation" course. The device records data such as the user's selection, the start time of the study, and the type of learning material.

[0240] Step 4:

[0241] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[0242] Step 5:

[0243] The device's built-in camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the captured data to recognize the user's emotions in real time, such as whether the user is tired, excited, or confused.

[0244] Step 6:

[0245] The server runs an AI algorithm to analyze the collected learning data and emotional data, and the algorithm comprehensively evaluates the user's learning patterns, accuracy rate, areas of interest, and emotional state to identify the optimal learning course.

[0246] Step 7:

[0247] Based on the analysis results, the server recommends courses such as "Intermediate Business Conversation" to the user. The difficulty and type of learning content is adjusted based on the emotional data. The device notifies the user of new learning courses and guides them to the next learning step.

[0248] Step 8:

[0249] The server prepares visual, audio and text learning materials based on individually optimized learning courses, and delivers these materials to the device.

[0250] Step 9:

[0251] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[0252] Step 10:

[0253] As the user continues to study, the device records the learning progress in real time and sends the data to the server, which then evaluates the learning progress and emotional data in real time.

[0254] Step 11:

[0255] The server generates feedback based on learning progress and emotional data. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion is negative, it also includes advice to relax. The device displays this feedback to the user in real time.

[0256] Step 12:

[0257] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[0258] These detailed steps enable the learning platform to provide users with a personalized learning experience, further leveraging emotion recognition to maximize learning efficiency.

[0259] Example 2

[0260] 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."

[0261] Traditional learning systems have focused on providing a uniform learning course without taking into account the individual circumstances and feelings of each user. This has led to issues such as reduced learning efficiency and insufficient learning outcomes. Furthermore, the lack of real-time feedback and appropriate guidance has hindered the quality of the learning experience.

[0262] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting personal information of the user, a means for collecting learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, a means for capturing the user's facial expressions and voice to recognize emotions, and a means for reflecting the emotion recognition results in the generation of the learning course. This provides an optimal learning experience that takes into account the user's individual situation and emotions, making it possible to improve learning efficiency and effectiveness.

[0263] "User personal information" refers to basic data that identifies a user, such as name, age, and learning purpose.

[0264] "Learning history" is a record of the learning activities that a user has undertaken up to now.

[0265] "Interests" is data about subjects or topics that interest the user.

[0266] "Ability" is data that indicates the user's level of knowledge and skill.

[0267] "Collected Data" includes information about your personal information, learning history, interests, and abilities.

[0268] "Analysis" is the process of interpreting collected data using statistical or machine learning techniques to gain insights.

[0269] An "optimized learning path" is a learning program that is individually tailored to you based on your personal data, learning history, interests, and abilities.

[0270] "Diverse media, including visual, text, and audio" refers to different forms of instructional materials used to convey learning content.

[0271] "Study progress" is data that represents the progress a user has made in a course of study.

[0272] "Feedback" is information that suggests improvements or next steps based on a user's learning activities.

[0273] "Guidance" is the educational advice and support provided to learners.

[0274] "Facial expression" refers to data that indicates the user's facial movements and expressions.

[0275] "Voice" is data that indicates the user's voice.

[0276] "Emotion recognition" refers to identifying a user's emotional state by analyzing facial expressions, voice, etc.

[0277] "Emotion recognition results" are data obtained as analysis results of emotion recognition.

[0278] "Capture" is the act of collecting data using a camera or microphone.

[0279] "Reflect" means incorporating the analysis results into next steps and plans.

[0280] This invention relates to a system that provides a learning experience that is individually optimized for each user, and is characterized in that it uses an emotion engine to recognize the user's emotions and utilizes that information to optimize the learning course.

[0281] The system includes the following main elements:

[0282] 1. User registration and profile creation

[0283] Using a device, a user accesses the account creation screen and enters basic information such as name, age, and learning goals. For example, a user enters their name "Yamada Hanako," their age "30," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database to create an individual user profile.

[0284] 2. Collection of learning history and abilities

[0285] A user logs into the learning application and selects the "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, and study time) in real time and periodically transmits the data to the server. The server then accumulates the collected data and obtains data for analyzing the user's learning history and ability.

[0286] 3. Emotion Recognition by Emotion Engine

[0287] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[0288] 4. Providing individually optimized learning courses

[0289] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if a user completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that the user is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[0290] 5. Providing learning using a variety of media

[0291] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0292] 6. Feedback and Guidance

[0293] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates the feedback "You need to practice your pronunciation." If the emotion engine determines that the user is confused, it adds additional explanations. The device displays this feedback to the user in real time to support their learning.

[0294] Specific examples

[0295] When a user is learning English, the process proceeds as follows:

[0296] 1. The user enters basic information on the account creation screen. For example, the user enters their name "Yamada Hanako," their age "30 years old," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database.

[0297] 2. The user begins learning "Business Conversation" and uses visual materials and quizzes. The device records this usage data in real time and sends it to the server.

[0298] 3. While the user is learning, the device camera captures facial expression data, which the emotion engine analyzes. For example, it determines that the user is confused.

[0299] 4. The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results. The difficulty and content are adjusted based on the emotional data. The device notifies the user of the new course and prompts them to proceed to the next learning step.

[0300] 5. The server prepares various media learning materials and distributes them to the terminals, which then display or play the materials to allow the user to study.

[0301] 6. The server analyzes the learning progress and emotion data and generates appropriate feedback. For example, it may provide feedback such as "You need to practice your pronunciation" and add a brief explanation if the user shows any confusion. The device displays the feedback to the user in real time and offers additional learning steps.

[0302] Example of input prompt for generative AI model

[0303] "What is the best course of action for someone who needs to practice their English pronunciation and is tired?"

[0304] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[0305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0306] Step 1:

[0307] User registration and profile creation

[0308] Input: The user enters basic information such as name, age, and learning purpose into the terminal.

[0309] The server receives basic information entered by the user through the terminal.

[0310] The server stores this information in a database and builds an individual user profile.

[0311] Output: Individual user profile stored in a database.

[0312] Step 2:

[0313] Collection of learning history and abilities

[0314] Input: A user logs into a learning application and selects a course of study.

[0315] The terminal records the user's selected learning course and learning activities (selection of learning materials, quiz answer results, and study time) in real time.

[0316] The server receives the learning behavior data sent from the terminal and stores it in a database.

[0317] Data processing: The server aggregates the collected learning behavior data and analyzes learning history and ability.

[0318] Output: Accumulated and analyzed learning history and ability data.

[0319] Step 3:

[0320] Emotion recognition by emotion engine

[0321] Input: The user's facial expressions and voice are captured by the device's camera and microphone.

[0322] The terminal transmits the captured facial expression and voice data to the emotion engine.

[0323] Data calculation: The emotion engine analyzes facial and voice data to recognize the user's emotional state.

[0324] Output: The emotion recognition result (e.g., whether the user is tired, confused, excited, etc.).

[0325] Step 4:

[0326] Providing individually optimized learning courses

[0327] Input: The server integrates the learning history, interests, abilities and the analysis results of the emotion engine.

[0328] Data calculation: The server uses AI algorithms to comprehensively analyze this data and generate the most suitable learning course for the user.

[0329] The terminal receives the new course of study sent from the server.

[0330] Output: A personalized, optimized learning path.

[0331] Specific operation: For example, the server has finished "Basic Business Conversation" and the quiz result shows an 80% accuracy rate, but the emotion engine determines that the user is tired and recommends lighter learning materials.

[0332] Step 5:

[0333] Providing learning using a variety of media

[0334] Input: The server prepares visual, audio and textual materials based on the new course of study.

[0335] The server distributes the prepared teaching materials to the terminals.

[0336] The terminal displays or plays the distributed educational material in a format appropriate for the user.

[0337] Output: A variety of media materials displayed or played.

[0338] Specific operation: For example, the server prepares video, audio and text learning materials for "intermediate business conversation," and the terminal plays or displays these.

[0339] Step 6:

[0340] Feedback and guidance

[0341] Input: The server receives data based on the user's learning progress and emotion recognition results.

[0342] Data Calculation: The server analyzes these data and generates appropriate feedback and guidance.

[0343] The terminal displays the generated feedback to the user in real time.

[0344] Output: Feedback and guidance provided to the user in real time.

[0345] Specific Actions: For example, if there are many pronunciation errors, feedback such as "You need to practice your pronunciation" is provided, and additional explanations are added if the user appears confused.

[0346] (Application example 2)

[0347] 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."

[0348] Conventional online learning systems are composed of uniform content without considering the user's emotional state, making it difficult to provide an optimal learning experience for each user. Furthermore, it is difficult for virtual stores and services to provide appropriate feedback and support in real time according to the user's emotional state. This leads to issues such as reduced learning effectiveness and service satisfaction, and a decrease in users' willingness to continue using the service.

[0349] 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 recognizing the emotional state of the user and providing appropriate feedback and support based on that, means for navigating and supporting within the virtual environment, and means for providing a variety of media. This enables each user to have an individually optimized learning experience and to receive optimal services within the virtual store.

[0350] "Means for inputting user's personal information" refers to the methods and devices for inputting and collecting the user's name, age, interests, and other personal information.

[0351] "Means for collecting learning history, interests, and abilities" refers to methods and devices for recording and collecting the content that a user has learned to date, areas of interest, and learning abilities.

[0352] "Means for analyzing collected data and generating an optimized learning course" refers to a method and device for analyzing and creating an optimal learning course based on collected data.

[0353] "Visual, textual, and audio multimedia delivery means" refers to methods and devices that provide users with learning materials in different formats, such as visual, written, and audio materials.

[0354] The "means for generating feedback based on learning progress and providing appropriate guidance" refers to a method and apparatus for generating feedback based on the learning progress of a user and providing appropriate guidance.

[0355] "Means for recognizing the user's emotional state and providing appropriate feedback and support based on that" refers to a method and device that analyzes the user's emotional state from their facial expressions and voice, and provides appropriate feedback and support based on that information.

[0356] "Means for navigation and support within a virtual environment" refers to methods and devices that guide and assist users to move and operate efficiently within a virtual space.

[0357] As an embodiment of the present invention, a system including a server, a user terminal, and an emotion engine is proposed, which recognizes a user's emotions and provides appropriate feedback and support based on the emotions to provide an individually optimized learning experience for the user.

[0358] System Configuration

[0359] The system consists of the following main components:

[0360] 1. User device: A device such as a smartphone, tablet, PC, or head-mounted display. These devices are equipped with a camera and microphone to capture the user's facial expressions and voice.

[0361] 2. Server: A computer system that works in conjunction with a database management system (e.g., MySQL) to analyze training data and emotion data. AI algorithms and generative AI models are used for the specific analysis.

[0362] 3. Emotion engine: Uses emotion recognition APIs such as Google Cloud Vision and Amazon Rekognition to recognize the user's emotional state from facial and voice data.

[0363] Program processing

[0364] The server comprehensively analyzes the emotional state recognized by the emotion engine, the learning data collected from the user's device, and other personal information. Based on the analysis results, optimal feedback and support is generated and sent to the user's device in real time. For example, if the user is confused, the system will provide detailed explanations or simplified materials. If the user is excited, the system will encourage the user to move on to the next learning stage.

[0365] Specific use cases

[0366] For example, if a user is browsing a smartphone in a virtual store, and the emotion engine detects confusion in the user's facial expression, the server generates appropriate feedback such as "Would you like to know more about this smartphone?" and displays it on the user's device.

[0367] Example prompt for a generative AI model:

[0368] "When a user is feeling confused, generate feedback using the following template: 'Hey {username}, can you share more information about this {product name}?'"

[0369] Data items: User name, product name

[0370] By realizing this system, each user will be able to enjoy learning experiences and services that are optimized for them, which is expected to improve overall effectiveness and satisfaction.

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] This is the phase where the user's personal information is entered into the user's device. The user enters basic information such as name, age, interests, and learning objectives, and sends this data to the server. The entered data is saved on the server, and a user profile is created.

[0374] Input: Name, age, interests, learning objectives

[0375] Output: User profile data

[0376] Specific operation: When a user enters information on the account creation screen and clicks the "Submit" button, the device sends the information to the server.

[0377] Step 2:

[0378] This is the phase in which the user selects a learning subject and the learning session begins. The device records the user's learning behavior (selection of learning materials, quiz answers, and study time) in real time and periodically sends this data to the server.

[0379] Input: Learning subject, learning history data

[0380] Output: Learning behavior data

[0381] Specific operation: The user selects "Business English," browses the learning materials, and answers the quiz. This behavior is recorded on the device and periodically sent to the server.

[0382] Step 3:

[0383] In this phase, the emotion engine captures the user's facial expressions and voice data and analyzes their emotional state. The emotion engine analyzes the data acquired using the camera and microphone, and recognizes emotions such as confusion or excitement.

[0384] Input: facial expression data, voice data

[0385] Output: Emotional state data

[0386] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. The data is sent to the emotion engine, and the resulting emotional state is sent to the server.

[0387] Step 4:

[0388] In this phase, the server analyzes learning history data, personal profile data, and emotional state data in an integrated manner to generate optimal learning courses and support. The generated results are provided to the user in the form of learning courses and feedback appropriate for the user.

[0389] Input: learning history data, personal profile data, emotional state data

[0390] Output: Optimized learning course, feedback

[0391] How it works: The server analyzes various data and generates optimal learning courses using machine learning algorithms. For example, if the user is confused, it suggests simple learning materials.

[0392] Step 5:

[0393] This is the phase where the server delivers selected learning materials and feedback to the user's device in various media formats to support learning. The learning materials are provided in visual, text, audio, and other formats, and the user studies them.

[0394] Input: Optimized learning path, feedback

[0395] Output: Diverse media materials

[0396] Specific operation: The server sends the selected teaching materials to the terminal in the form of visual, audio, text, etc., and the terminal displays or plays them.

[0397] Step 6:

[0398] This is the phase where the user's device displays real-time feedback to support the user's learning progress. Appropriate feedback is displayed to the user at the appropriate time, encouraging them to move on to the next step.

[0399] Input: Real-time feedback

[0400] Output: Displayed feedback

[0401] Specific operation: The device immediately displays the generated feedback and instructs the user on how to proceed with the learning and what the next step should be. The user continues learning based on the feedback.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Second embodiment]

[0406] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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."

[0418] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the most appropriate learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[0419] System overview and processing flow

[0420] 1. User registration and profile creation

[0421] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0422] 2. Collection of learning history and abilities

[0423] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0424] 3. Providing individually optimized learning courses

[0425] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. For example, if Taro Tanaka completes "Basic Business Conversation" and the quiz results show an 80% accuracy rate, the server will recommend "Intermediate Business Conversation" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[0426] 4. Providing learning using a variety of media

[0427] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0428] 5. Feedback and Guidance

[0429] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation" and suggests a pronunciation practice course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[0430] Specific examples

[0431] Flow when a user is learning English

[0432] 1. User registration and profile creation

[0433] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[0434] The device sends this information to the server, which stores it in a database.

[0435] 2. Collection of learning history and abilities

[0436] Users begin learning "Business Conversation" and use visual materials and quizzes.

[0437] The device records usage data in real time and transmits it to a server.

[0438] 3. Providing individually optimized learning courses

[0439] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results.

[0440] The device notifies the user of new courses and prompts them for the next learning step.

[0441] 4. Providing learning using a variety of media

[0442] The server prepares a variety of media teaching materials and distributes them to the terminals.

[0443] The terminal displays or plays the educational material, allowing the user to study.

[0444] 5. Feedback and Guidance

[0445] The server analyzes the learning progress and generates feedback such as "pronunciation practice is needed."

[0446] The device displays feedback to the user in real time and offers additional learning steps.

[0447] This system allows users to receive a learning course that is optimized for them and progress effectively with their studies.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[0451] Step 2:

[0452] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[0453] Step 3:

[0454] A user logs in to a learning application and selects, for example, a "Business Conversation" course. The terminal records data such as the user's selection, the start time of the study, and the type of learning material.

[0455] Step 4:

[0456] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[0457] Step 5:

[0458] The server runs an AI algorithm to analyze the collected data, which evaluates the user's learning patterns, accuracy rate, areas of interest, etc. to identify the optimal learning course.

[0459] Step 6:

[0460] Based on the analysis results, the server recommends a course, for example, "Intermediate Business Conversation," to the user. The device displays a notification of the new learning course to the user, guiding them to proceed to the next learning step.

[0461] Step 7:

[0462] The server prepares visual, audio, and text materials for the "Intermediate Business Conversation" course, and distributes these materials to the terminals.

[0463] Step 8:

[0464] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[0465] Step 9:

[0466] As the user continues to study, the device records their progress in real time and sends the data to the server, which then evaluates their learning progress in real time.

[0467] Step 10:

[0468] The server generates feedback based on the user's learning progress. For example, if the user makes many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation." The device displays this feedback to the user in real time.

[0469] Step 11:

[0470] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[0471] Through these steps, the learning platform can provide users with a personalized learning experience and maximize learning effectiveness.

[0472] Example 1

[0473] 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."

[0474] Conventional learning systems have problems in that they are unable to respond to individual users' learning needs and progress, preventing effective learning. Furthermore, they lack real-time feedback, making it difficult to maximize users' learning outcomes. In particular, the lack of diverse learning media and the lack of optimal course recommendations based on collected learning data limits users' learning experiences.

[0475] 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.

[0476] In this invention, the server includes a means for inputting personal information through a user's input device, a means for collecting learning content usage history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning program, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, and a means for recording the user's learning behavior in real time and periodically transmitting the recording to the server, thereby enabling the server to respond to the individual learning needs of each user and provide real-time feedback, thereby providing an effective learning experience.

[0477] "User" refers to an individual who uses this system to carry out learning activities.

[0478] "Input device" refers to a device used by a user to input personal information, such as a keyboard or touchscreen.

[0479] "Learning content usage history" refers to the history of learning materials and content used by a user.

[0480] "Interest" refers to data that indicates a user's interest or curiosity in learning.

[0481] "Ability" refers to data that indicates a user's learning skills and knowledge level.

[0482] "Collected Data" refers to data such as your personal information, learning history, interests, and abilities.

[0483] "Analysis" refers to the process of analyzing collected data and generating the optimal learning course for the user.

[0484] "Optimized Learning Program" refers to a customized learning course that is individually provided to a user based on the analysis results.

[0485] "Visual" refers to visual teaching materials such as images and videos.

[0486] "Text" refers to teaching materials that consist of sentences or written information.

[0487] "Audio" refers to audio instructional materials.

[0488] "Multiple media" refers to educational materials in a variety of formats, including visual, text, and audio.

[0489] "Learning progress" refers to the progress a user makes as they progress through their learning.

[0490] "Feedback" refers to assessments and guidance generated based on learning progress.

[0491] "Real-time" refers to the almost instantaneous transmission and reception of information and data.

[0492] "Server" refers to a computer system that analyzes and stores data, distributes educational materials, etc.

[0493] The system of the present invention aims to provide a user with an individually optimized learning experience, and includes a means for inputting a user's personal information, a means for collecting a user's learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on the learning progress and providing appropriate guidance, and a means for recording a user's learning behavior in real time and periodically transmitting the recording to a server.

[0494] The main hardware components of the system consist of the user's device and the server. The user's device includes input devices such as PCs, smartphones, and tablets, and is responsible for inputting and recording the user's personal information and learning data. The server, on the other hand, is a high-performance computer that performs processes such as data analysis, storage, and distribution of learning materials. This server uses a database (e.g., MySQL, PostgreSQL) and an AI algorithm (e.g., machine learning model) to analyze the user's learning data and generate an optimal learning course.

[0495] When a user accesses the account creation screen using a device and enters basic information (e.g., name, age, learning purpose), the device sends this information to the server. The server stores the received data in a database and creates an individual profile for the user. This allows the user's personal information to be managed appropriately and used for subsequent learning activities.

[0496] When a user logs into the learning application and begins studying, the device records their learning behavior (e.g., selection of study materials, quiz answer results, study time) in real time and periodically sends it to the server. The server accumulates this data and evaluates the user's learning history and ability. An AI algorithm is used for analysis, so the most suitable learning course for the user is generated.

[0497] For example, if a user has completed the "Basic Business Conversation" course and has an 80% success rate in answering questions, the server will recommend the "Intermediate Business Conversation" course. The terminal will notify the user of this information and encourage them to take a new learning course.

[0498] The server then prepares and distributes visual, audio, and text learning materials based on the selected learning course. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts. This maximizes learning effectiveness through a variety of media.

[0499] Based on the analysis of learning progress, the server generates feedback as needed. For example, if there are many pronunciation errors, the server generates feedback such as "Pronunciation practice is required" and suggests a pronunciation practice course as the next learning step. The device displays this feedback in real time to support the user's learning.

[0500] As a concrete example, consider a user learning English. The user enters basic information on an account creation screen, logs into a learning application, and starts a "Business Conversation" course. The device records usage data in real time and periodically sends it to the server. Based on the analysis results, the server generates and recommends an "Intermediate Business Conversation" course. The device notifies the user of the new course and displays or plays various media materials for learning.

[0501] An example of an input prompt for a generative AI model is, "Please suggest the best learning course for the user to learn business English. For example, if a user already has basic business conversation skills, please suggest the intermediate course they should take next."

[0502] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0503] Step 1:

[0504] The user uses a terminal to access the account creation screen and enters personal information (such as name, age, and learning objectives). The entered data is: name: "Taro Tanaka", age: "25 years old", learning objective: "learn business English". The terminal sends this data to the server. The input data is sent via an HTTP request. The server stores this information in a database and builds an individual profile for the user. For example, the information is stored in a MySQL database using an INSERT statement.

[0505] Step 2:

[0506] The user logs in to the learning application. To log in, a username and password are required. For example, the user logs in with username: "tanaka_taro" and password: "password123". This information is sent from the terminal to the server, and the server checks it against the authentication information in the database. If authentication is successful, the server generates a session ID and returns it to the terminal. The session ID is used as the user's identifier for subsequent operations.

[0507] Step 3:

[0508] The user uses the device to select the "Business Conversation" course. This selection is completed by clicking or tapping on the course name. The device sends this selection to the server. The server retrieves the details of the selected course and returns a list of appropriate learning materials to the device. For example, it returns a list of learning materials included in the "Business Conversation" course in JSON format.

[0509] Step 4:

[0510] The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, study time, etc.) in real time. For example, if the user answers a quiz correctly, the result is temporarily stored in the device's cache. Periodically (e.g., every 5 minutes), the device sends this data to the server in batches. The sent data is related to learning history, interests, and abilities.

[0511] Step 5:

[0512] The server stores the received data in a database and performs analysis based on the accumulated data. An AI algorithm (e.g., machine learning model) is used for the analysis to calculate the optimal learning course for each user. For example, the next recommended course is determined based on the user's quiz accuracy rate and study time. The server generates the results and notifies the device of the recommended course.

[0513] Step 6:

[0514] The server prepares visual, audio, and text learning materials based on the optimized learning course generated. For example, it retrieves the path to the necessary learning material files from the database and generates a list of URLs for distribution to the device. The server returns this learning material information to the device in JSON format.

[0515] Step 7:

[0516] The device analyzes the learning material list received from the server and displays or plays the learning materials to the user. For example, for the "Intermediate Business Conversation" course, video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts are displayed. When the user plays the video materials, the device launches a streaming video player and displays the content.

[0517] Step 8:

[0518] The server analyzes the user's learning progress in real time and generates feedback as needed. For example, if the user makes many pronunciation errors, the server generates an analysis result saying, "You need to practice pronunciation." The server sends this feedback to the device and suggests a pronunciation practice course as the next learning step.

[0519] Step 9:

[0520] The device displays the feedback received from the server in real time and offers the user additional learning steps. For example, it displays a pop-up message on the screen saying "Pronunciation practice needed" and provides a link to a pronunciation practice course. When the user clicks the link, a new learning course will begin.

[0521] (Application example 1)

[0522] 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."

[0523] Conventional learning systems have difficulty generating optimal learning courses based on individual users' learning progress and interests, resulting in a uniform learning content. They also lack the ability to provide individually optimized learning experiences for workers with the aim of improving their skills. Furthermore, they lack the ability to provide learning using a variety of media and provide real-time feedback.

[0524] 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.

[0525] In this invention, the server includes a means for inputting personal information of users, a means for recording work history and operational behavior, and a means for analyzing the collected data and generating an optimized learning course, which makes it possible to provide an optimal learning course in real time according to the learning progress and skill improvement of each worker.

[0526] (definition statement)

[0527] "Means for entering user's personal information" refers to means for entering basic information such as the user's name, age, and purpose of learning.

[0528] "Means for collecting learning history, interests, and abilities" refers to means for recording and collecting a user's past learning history, interests, and current abilities.

[0529] "Means for analyzing collected data and generating optimized learning courses" refers to means for generating optimal learning courses for users based on collected data using AI algorithms, etc.

[0530] "Means for providing a variety of media, including visual, text, and audio" refers to means for providing users with a variety of learning content, including video, text, and audio.

[0531] The "means for generating feedback based on learning progress and providing appropriate guidance" is a means for analyzing a user's learning progress and providing appropriate feedback and guidance based on the analysis.

[0532] "Means for recording work history and operational actions for the purpose of improving worker skills" refers to means for recording the operations performed by workers during work and the results thereof, and for supporting skill improvement.

[0533] The "means for generating an optimal work skill course based on recorded data" refers to a means for analyzing the recorded work history and operational behavior data and generating a learning course for improving the skills of the worker that is optimal for the worker.

[0534] The system for implementing this invention aims to provide a learning experience that is individually optimized for each user. Specific embodiments thereof will be described below.

[0535] Overall system overview

[0536] The system consists of a terminal and a server. The terminal records the user's information input and learning activities, while the server analyzes and processes the collected data to generate the optimal learning course. Users access the system using a terminal (such as a smartphone or tablet), and the server provides feedback and guidance in real time.

[0537] Hardware used

[0538] The following hardware is mainly used:

[0539] Smartphone

[0540] tablet

[0541] PC

[0542] Software used

[0543] The following software is mainly used:

[0544] Python

[0545] JSON

[0546] Video Player

[0547] Audio Player

[0548] AI algorithms

[0549] Specific examples of processing

[0550] User registration and profile creation

[0551] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning objectives. For example, a user may enter their name as "Yamada Hanako," their age as "30," and their learning objective as "learning machine operation skills." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0552] Collection of learning history and abilities

[0553] A user logs into a learning application and selects, for example, an "Introduction to Machine Operation Course." The device records the user's learning behavior (e.g., selection of learning materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0554] Providing individually optimized learning courses

[0555] The server analyzes the collected data and uses AI algorithms to generate the most suitable learning course for the user. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will recommend the "Intermediate Machine Operation Course" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[0556] Providing learning using a variety of media

[0557] The server prepares visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) based on the selected learning course. The server distributes these learning materials to the terminal, and the terminal displays or plays them in an appropriate format for the user. For example, an "Intermediate Machine Operation Course" may include video learning materials on operating procedures, audio operation guides, and reference texts.

[0558] Feedback and guidance

[0559] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure review course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[0560] Specific prompt examples

[0561] An example of a prompt sentence when using a generative AI model is as follows:

[0562] "Please suggest the best course for a user to learn factory robot operation techniques. The user's learning history is as follows: viewing visual materials, answering quizzes, and encountering errors (operation procedures). The user's name is Hanako Yamada, and she is 30 years old."

[0563] This system allows workers to receive learning courses that are optimized for them, enabling them to effectively acquire skills.

[0564] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0565] Step 1:

[0566] A user accesses the account creation screen using a device and enters basic information such as name, age, and learning goals. This information is sent from the device to the server. The server stores the received information in a database and builds an individual profile for the user. At this stage, the input is the user's personal information, and the output is the profile data stored in the database.

[0567] Step 2:

[0568] A user logs into a learning application and selects a specific learning course (e.g., an introductory course in machine operation). The terminal sends the user's selection to the server, which records the selection. In addition, learning behavior (e.g., selection of learning materials, quiz answer results, and study time) is also periodically recorded and this data is sent to the server. At this stage, the input is the user's learning behavior data, and the output is the learning history stored in the database.

[0569] Step 3:

[0570] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. Specifically, it selects an appropriate course based on the user's learning history, interests, and abilities. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will generate the "Intermediate Machine Operation Course." At this stage, the input is the learning history stored in the database and the AI ​​algorithm, and the output is the generated optimized learning course.

[0571] Step 4:

[0572] Based on the learning course selected by the server, visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) are prepared and distributed to the terminal. The terminal then displays or plays the received learning materials in an appropriate format, providing the user with a learning opportunity. At this stage, the input is the generated optimized learning course and the learning materials based on it, and the output is the learning content displayed to the user.

[0573] Step 5:

[0574] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure check course as the next learning step. This feedback is sent to the terminal in real time and displayed to the user. At this stage, the input is the latest learning history data and the feedback generation algorithm, and the output is the feedback message displayed to the user.

[0575] This series of processes allows workers to receive learning courses that are optimized for them and efficiently acquire skills.

[0576] 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.

[0577] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user, and incorporates an emotion engine that recognizes and utilizes the user's emotions. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the optimal learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[0578] System overview and processing flow

[0579] 1. User registration and profile creation

[0580] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0581] 2. Collection of learning history and abilities

[0582] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0583] 3. Emotion Recognition by Emotion Engine

[0584] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[0585] 4. Providing individually optimized learning courses

[0586] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if Taro Tanaka completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that he is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[0587] 5. Providing learning using a variety of media

[0588] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0589] 6. Feedback and Guidance

[0590] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion engine determines that the user is frustrated, it includes advice to relax. The device displays this feedback to the user in real time, supporting their learning.

[0591] Specific examples

[0592] Flow when a user is learning English

[0593] 1. User registration and profile creation

[0594] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[0595] The device sends this information to the server, which stores it in a database.

[0596] 2. Collection of learning history and abilities

[0597] Users begin learning "Business Conversation" and use visual materials and quizzes.

[0598] The device records usage data in real time and transmits it to a server.

[0599] 3. Emotion Recognition by Emotion Engine

[0600] While the user is learning, the device camera captures facial expression data, which is then analyzed by the emotion engine, which determines, for example, that the user is confused.

[0601] 4. Providing individually optimized learning courses

[0602] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results, adjusting the difficulty and content based on emotional data.

[0603] The device notifies the user of new courses and prompts them to take the next step in their studies.

[0604] 5. Providing learning using a variety of media

[0605] The server prepares a variety of media teaching materials and distributes them to the terminals.

[0606] The terminal displays or plays the educational material, allowing the user to study.

[0607] 6. Feedback and Guidance

[0608] The server analyzes learning progress and emotional data and generates appropriate feedback, e.g., "Pronunciation practice is needed." If confusion is detected, a brief explanation is added.

[0609] The device displays feedback to the user in real time and offers additional learning steps.

[0610] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[0614] Step 2:

[0615] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[0616] Step 3:

[0617] The user logs in to the learning application and selects the "Business Conversation" course. The device records data such as the user's selection, the start time of the study, and the type of learning material.

[0618] Step 4:

[0619] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[0620] Step 5:

[0621] The device's built-in camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the captured data to recognize the user's emotions in real time, such as whether the user is tired, excited, or confused.

[0622] Step 6:

[0623] The server runs an AI algorithm to analyze the collected learning data and emotional data, and the algorithm comprehensively evaluates the user's learning patterns, accuracy rate, areas of interest, and emotional state to identify the optimal learning course.

[0624] Step 7:

[0625] Based on the analysis results, the server recommends courses such as "Intermediate Business Conversation" to the user. The difficulty and type of learning content is adjusted based on the emotional data. The device notifies the user of new learning courses and guides them to the next learning step.

[0626] Step 8:

[0627] The server prepares visual, audio and text learning materials based on individually optimized learning courses, and delivers these materials to the device.

[0628] Step 9:

[0629] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[0630] Step 10:

[0631] As the user continues to study, the device records the learning progress in real time and sends the data to the server, which then evaluates the learning progress and emotional data in real time.

[0632] Step 11:

[0633] The server generates feedback based on learning progress and emotional data. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion is negative, it also includes advice to relax. The device displays this feedback to the user in real time.

[0634] Step 12:

[0635] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[0636] These detailed steps enable the learning platform to provide users with a personalized learning experience, further leveraging emotion recognition to maximize learning efficiency.

[0637] Example 2

[0638] 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."

[0639] Traditional learning systems have focused on providing a uniform learning course without taking into account the individual circumstances and feelings of each user. This has led to issues such as reduced learning efficiency and insufficient learning outcomes. Furthermore, the lack of real-time feedback and appropriate guidance has hindered the quality of the learning experience.

[0640] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting personal information of the user, a means for collecting learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, a means for capturing the user's facial expressions and voice to recognize emotions, and a means for reflecting the emotion recognition results in the generation of the learning course. This provides an optimal learning experience that takes into account the user's individual situation and emotions, making it possible to improve learning efficiency and effectiveness.

[0641] "User personal information" refers to basic data that identifies a user, such as name, age, and learning purpose.

[0642] "Learning history" is a record of the learning activities that a user has undertaken up to now.

[0643] "Interests" is data about subjects or topics that interest the user.

[0644] "Ability" is data that indicates the user's level of knowledge and skill.

[0645] "Collected Data" includes information about your personal information, learning history, interests, and abilities.

[0646] "Analysis" is the process of interpreting collected data using statistical or machine learning techniques to gain insights.

[0647] An "optimized learning path" is a learning program that is individually tailored to you based on your personal data, learning history, interests, and abilities.

[0648] "Diverse media, including visual, text, and audio" refers to different forms of instructional materials used to convey learning content.

[0649] "Study progress" is data that represents the progress a user has made in a course of study.

[0650] "Feedback" is information that suggests improvements or next steps based on a user's learning activities.

[0651] "Guidance" is the educational advice and support provided to learners.

[0652] "Facial expression" refers to data that indicates the user's facial movements and expressions.

[0653] "Voice" is data that indicates the user's voice.

[0654] "Emotion recognition" refers to identifying a user's emotional state by analyzing facial expressions, voice, etc.

[0655] "Emotion recognition results" are data obtained as analysis results of emotion recognition.

[0656] "Capture" is the act of collecting data using a camera or microphone.

[0657] "Reflect" means incorporating the analysis results into next steps and plans.

[0658] This invention relates to a system that provides a learning experience that is individually optimized for each user, and is characterized in that it uses an emotion engine to recognize the user's emotions and utilizes that information to optimize the learning course.

[0659] The system includes the following main elements:

[0660] 1. User registration and profile creation

[0661] Using a device, a user accesses the account creation screen and enters basic information such as name, age, and learning goals. For example, a user enters their name "Yamada Hanako," their age "30," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database to create an individual user profile.

[0662] 2. Collection of learning history and abilities

[0663] A user logs into the learning application and selects the "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, and study time) in real time and periodically transmits the data to the server. The server then accumulates the collected data and obtains data for analyzing the user's learning history and ability.

[0664] 3. Emotion Recognition by Emotion Engine

[0665] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[0666] 4. Providing individually optimized learning courses

[0667] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if a user completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that the user is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[0668] 5. Providing learning using a variety of media

[0669] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0670] 6. Feedback and Guidance

[0671] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates the feedback "You need to practice your pronunciation." If the emotion engine determines that the user is confused, it adds additional explanations. The device displays this feedback to the user in real time to support their learning.

[0672] Specific examples

[0673] When a user is learning English, the process proceeds as follows:

[0674] 1. The user enters basic information on the account creation screen. For example, the user enters their name "Yamada Hanako," their age "30 years old," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database.

[0675] 2. The user begins learning "Business Conversation" and uses visual materials and quizzes. The device records this usage data in real time and sends it to the server.

[0676] 3. While the user is learning, the device camera captures facial expression data, which the emotion engine analyzes. For example, it determines that the user is confused.

[0677] 4. The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results. The difficulty and content are adjusted based on the emotional data. The device notifies the user of the new course and prompts them to proceed to the next learning step.

[0678] 5. The server prepares various media learning materials and distributes them to the terminals, which then display or play the materials to allow the user to study.

[0679] 6. The server analyzes the learning progress and emotion data and generates appropriate feedback. For example, it may provide feedback such as "You need to practice your pronunciation" and add a brief explanation if the user shows any confusion. The device displays the feedback to the user in real time and offers additional learning steps.

[0680] Example of input prompt for generative AI model

[0681] "What is the best course of action for someone who needs to practice their English pronunciation and is tired?"

[0682] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[0683] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0684] Step 1:

[0685] User registration and profile creation

[0686] Input: The user enters basic information such as name, age, and learning purpose into the terminal.

[0687] The server receives basic information entered by the user through the terminal.

[0688] The server stores this information in a database and builds an individual user profile.

[0689] Output: Individual user profile stored in a database.

[0690] Step 2:

[0691] Collection of learning history and abilities

[0692] Input: A user logs into a learning application and selects a course of study.

[0693] The terminal records the user's selected learning course and learning activities (selection of learning materials, quiz answer results, and study time) in real time.

[0694] The server receives the learning behavior data sent from the terminal and stores it in a database.

[0695] Data processing: The server aggregates the collected learning behavior data and analyzes learning history and ability.

[0696] Output: Accumulated and analyzed learning history and ability data.

[0697] Step 3:

[0698] Emotion recognition by emotion engine

[0699] Input: The user's facial expressions and voice are captured by the device's camera and microphone.

[0700] The terminal transmits the captured facial expression and voice data to the emotion engine.

[0701] Data calculation: The emotion engine analyzes facial and voice data to recognize the user's emotional state.

[0702] Output: The emotion recognition result (e.g., whether the user is tired, confused, excited, etc.).

[0703] Step 4:

[0704] Providing individually optimized learning courses

[0705] Input: The server integrates the learning history, interests, abilities and the analysis results of the emotion engine.

[0706] Data calculation: The server uses AI algorithms to comprehensively analyze this data and generate the most suitable learning course for the user.

[0707] The terminal receives the new course of study sent from the server.

[0708] Output: A personalized, optimized learning path.

[0709] Specific operation: For example, the server has finished "Basic Business Conversation" and the quiz result shows an 80% accuracy rate, but the emotion engine determines that the user is tired and recommends lighter learning materials.

[0710] Step 5:

[0711] Providing learning using a variety of media

[0712] Input: The server prepares visual, audio and textual materials based on the new course of study.

[0713] The server distributes the prepared teaching materials to the terminals.

[0714] The terminal displays or plays the distributed educational material in a format appropriate for the user.

[0715] Output: A variety of media materials displayed or played.

[0716] Specific operation: For example, the server prepares video, audio and text learning materials for "intermediate business conversation," and the terminal plays or displays these.

[0717] Step 6:

[0718] Feedback and guidance

[0719] Input: The server receives data based on the user's learning progress and emotion recognition results.

[0720] Data Calculation: The server analyzes these data and generates appropriate feedback and guidance.

[0721] The terminal displays the generated feedback to the user in real time.

[0722] Output: Feedback and guidance provided to the user in real time.

[0723] Specific Actions: For example, if there are many pronunciation errors, feedback such as "You need to practice your pronunciation" is provided, and additional explanations are added if the user appears confused.

[0724] (Application example 2)

[0725] 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."

[0726] Conventional online learning systems are composed of uniform content without considering the user's emotional state, making it difficult to provide an optimal learning experience for each user. Furthermore, it is difficult for virtual stores and services to provide appropriate feedback and support in real time according to the user's emotional state. This leads to issues such as reduced learning effectiveness and service satisfaction, and a decrease in users' willingness to continue using the service.

[0727] 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 recognizing the emotional state of the user and providing appropriate feedback and support based on that, means for navigating and supporting within the virtual environment, and means for providing a variety of media. This enables each user to have an individually optimized learning experience and to receive optimal services within the virtual store.

[0728] "Means for inputting user's personal information" refers to the methods and devices for inputting and collecting the user's name, age, interests, and other personal information.

[0729] "Means for collecting learning history, interests, and abilities" refers to methods and devices for recording and collecting the content that a user has learned to date, areas of interest, and learning abilities.

[0730] "Means for analyzing collected data and generating an optimized learning course" refers to a method and device for analyzing and creating an optimal learning course based on collected data.

[0731] "Visual, textual, and audio multimedia delivery means" refers to methods and devices that provide users with learning materials in different formats, such as visual, written, and audio materials.

[0732] The "means for generating feedback based on learning progress and providing appropriate guidance" refers to a method and apparatus for generating feedback based on the learning progress of a user and providing appropriate guidance.

[0733] "Means for recognizing the user's emotional state and providing appropriate feedback and support based on that" refers to a method and device that analyzes the user's emotional state from their facial expressions and voice, and provides appropriate feedback and support based on that information.

[0734] "Means for navigation and support within a virtual environment" refers to methods and devices that guide and assist users to move and operate efficiently within a virtual space.

[0735] As an embodiment of the present invention, a system including a server, a user terminal, and an emotion engine is proposed, which recognizes a user's emotions and provides appropriate feedback and support based on the emotions to provide an individually optimized learning experience for the user.

[0736] System Configuration

[0737] The system consists of the following main components:

[0738] 1. User device: A device such as a smartphone, tablet, PC, or head-mounted display. These devices are equipped with a camera and microphone to capture the user's facial expressions and voice.

[0739] 2. Server: A computer system that works in conjunction with a database management system (e.g., MySQL) to analyze training data and emotion data. AI algorithms and generative AI models are used for the specific analysis.

[0740] 3. Emotion engine: Uses emotion recognition APIs such as Google Cloud Vision and Amazon Rekognition to recognize the user's emotional state from facial and voice data.

[0741] Program processing

[0742] The server comprehensively analyzes the emotional state recognized by the emotion engine, the learning data collected from the user's device, and other personal information. Based on the analysis results, optimal feedback and support is generated and sent to the user's device in real time. For example, if the user is confused, the system will provide detailed explanations or simplified materials. If the user is excited, the system will encourage the user to move on to the next learning stage.

[0743] Specific use cases

[0744] For example, if a user is browsing a smartphone in a virtual store, and the emotion engine detects confusion in the user's facial expression, the server generates appropriate feedback such as "Would you like to know more about this smartphone?" and displays it on the user's device.

[0745] Example prompt for a generative AI model:

[0746] "When a user is feeling confused, generate feedback using the following template: 'Hey {username}, can you share more information about this {product name}?'"

[0747] Data items: User name, product name

[0748] By realizing this system, each user will be able to enjoy learning experiences and services that are optimized for them, which is expected to improve overall effectiveness and satisfaction.

[0749] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0750] Step 1:

[0751] This is the phase where the user's personal information is entered into the user's device. The user enters basic information such as name, age, interests, and learning objectives, and sends this data to the server. The entered data is saved on the server, and a user profile is created.

[0752] Input: Name, age, interests, learning objectives

[0753] Output: User profile data

[0754] Specific operation: When a user enters information on the account creation screen and clicks the "Submit" button, the device sends the information to the server.

[0755] Step 2:

[0756] This is the phase in which the user selects a learning subject and the learning session begins. The device records the user's learning behavior (selection of learning materials, quiz answers, and study time) in real time and periodically sends this data to the server.

[0757] Input: Learning subject, learning history data

[0758] Output: Learning behavior data

[0759] Specific operation: The user selects "Business English," browses the learning materials, and answers the quiz. This behavior is recorded on the device and periodically sent to the server.

[0760] Step 3:

[0761] In this phase, the emotion engine captures the user's facial expressions and voice data and analyzes their emotional state. The emotion engine analyzes the data acquired using the camera and microphone, and recognizes emotions such as confusion or excitement.

[0762] Input: facial expression data, voice data

[0763] Output: Emotional state data

[0764] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. The data is sent to the emotion engine, and the resulting emotional state is sent to the server.

[0765] Step 4:

[0766] In this phase, the server analyzes learning history data, personal profile data, and emotional state data in an integrated manner to generate optimal learning courses and support. The generated results are provided to the user in the form of learning courses and feedback appropriate for the user.

[0767] Input: learning history data, personal profile data, emotional state data

[0768] Output: Optimized learning course, feedback

[0769] How it works: The server analyzes various data and generates optimal learning courses using machine learning algorithms. For example, if the user is confused, it suggests simple learning materials.

[0770] Step 5:

[0771] This is the phase where the server delivers selected learning materials and feedback to the user's device in various media formats to support learning. The learning materials are provided in visual, text, audio, and other formats, and the user studies them.

[0772] Input: Optimized learning path, feedback

[0773] Output: Diverse media materials

[0774] Specific operation: The server sends the selected teaching materials to the terminal in the form of visual, audio, text, etc., and the terminal displays or plays them.

[0775] Step 6:

[0776] This is the phase where the user's device displays real-time feedback to support the user's learning progress. Appropriate feedback is displayed to the user at the appropriate time, encouraging them to move on to the next step.

[0777] Input: Real-time feedback

[0778] Output: Displayed feedback

[0779] Specific operation: The device immediately displays the generated feedback and instructs the user on how to proceed with the learning and what the next step should be. The user continues learning based on the feedback.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] [Third embodiment]

[0784] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0785] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0786] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0787] The 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.

[0788] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0789] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0790] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 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.

[0791] 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.

[0792] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0793] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0794] In the 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.

[0795] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0796] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the most appropriate learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[0797] System overview and processing flow

[0798] 1. User registration and profile creation

[0799] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0800] 2. Collection of learning history and abilities

[0801] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0802] 3. Providing individually optimized learning courses

[0803] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. For example, if Taro Tanaka completes "Basic Business Conversation" and the quiz results show an 80% accuracy rate, the server will recommend "Intermediate Business Conversation" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[0804] 4. Providing learning using a variety of media

[0805] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0806] 5. Feedback and Guidance

[0807] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation" and suggests a pronunciation practice course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[0808] Specific examples

[0809] Flow when a user is learning English

[0810] 1. User registration and profile creation

[0811] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[0812] The device sends this information to the server, which stores it in a database.

[0813] 2. Collection of learning history and abilities

[0814] Users begin learning "Business Conversation" and use visual materials and quizzes.

[0815] The device records usage data in real time and transmits it to a server.

[0816] 3. Providing individually optimized learning courses

[0817] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results.

[0818] The device notifies the user of new courses and prompts them for the next learning step.

[0819] 4. Providing learning using a variety of media

[0820] The server prepares a variety of media teaching materials and distributes them to the terminals.

[0821] The terminal displays or plays the educational material, allowing the user to study.

[0822] 5. Feedback and Guidance

[0823] The server analyzes the learning progress and generates feedback such as "pronunciation practice is needed."

[0824] The device displays feedback to the user in real time and offers additional learning steps.

[0825] This system allows users to receive a learning course that is optimized for them and progress effectively with their studies.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[0829] Step 2:

[0830] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[0831] Step 3:

[0832] A user logs in to a learning application and selects, for example, a "Business Conversation" course. The terminal records data such as the user's selection, the start time of the study, and the type of learning material.

[0833] Step 4:

[0834] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[0835] Step 5:

[0836] The server runs an AI algorithm to analyze the collected data, which evaluates the user's learning patterns, accuracy rate, areas of interest, etc. to identify the optimal learning course.

[0837] Step 6:

[0838] Based on the analysis results, the server recommends a course, for example, "Intermediate Business Conversation," to the user. The device displays a notification of the new learning course to the user, guiding them to proceed to the next learning step.

[0839] Step 7:

[0840] The server prepares visual, audio, and text materials for the "Intermediate Business Conversation" course, and distributes these materials to the terminals.

[0841] Step 8:

[0842] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[0843] Step 9:

[0844] As the user continues to study, the device records their progress in real time and sends the data to the server, which then evaluates their learning progress in real time.

[0845] Step 10:

[0846] The server generates feedback based on the user's learning progress. For example, if the user makes many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation." The device displays this feedback to the user in real time.

[0847] Step 11:

[0848] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[0849] Through these steps, the learning platform can provide users with a personalized learning experience and maximize learning effectiveness.

[0850] Example 1

[0851] 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."

[0852] Conventional learning systems have problems in that they are unable to respond to individual users' learning needs and progress, preventing effective learning. Furthermore, they lack real-time feedback, making it difficult to maximize users' learning outcomes. In particular, the lack of diverse learning media and the lack of optimal course recommendations based on collected learning data limits users' learning experiences.

[0853] 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.

[0854] In this invention, the server includes a means for inputting personal information through a user's input device, a means for collecting learning content usage history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning program, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, and a means for recording the user's learning behavior in real time and periodically transmitting the recording to the server, thereby enabling the server to respond to the individual learning needs of each user and provide real-time feedback, thereby providing an effective learning experience.

[0855] "User" refers to an individual who uses this system to carry out learning activities.

[0856] "Input device" refers to a device used by a user to input personal information, such as a keyboard or touchscreen.

[0857] "Learning content usage history" refers to the history of learning materials and content used by a user.

[0858] "Interest" refers to data that indicates a user's interest or curiosity in learning.

[0859] "Ability" refers to data that indicates a user's learning skills and knowledge level.

[0860] "Collected Data" refers to data such as your personal information, learning history, interests, and abilities.

[0861] "Analysis" refers to the process of analyzing collected data and generating the optimal learning course for the user.

[0862] "Optimized Learning Program" refers to a customized learning course that is individually provided to a user based on the analysis results.

[0863] "Visual" refers to visual teaching materials such as images and videos.

[0864] "Text" refers to teaching materials that consist of sentences or written information.

[0865] "Audio" refers to audio instructional materials.

[0866] "Multiple media" refers to educational materials in a variety of formats, including visual, text, and audio.

[0867] "Learning progress" refers to the progress a user makes as they progress through their learning.

[0868] "Feedback" refers to assessments and guidance generated based on learning progress.

[0869] "Real-time" refers to the almost instantaneous transmission and reception of information and data.

[0870] "Server" refers to a computer system that analyzes and stores data, distributes educational materials, etc.

[0871] The system of the present invention aims to provide a user with an individually optimized learning experience, and includes a means for inputting a user's personal information, a means for collecting a user's learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on the learning progress and providing appropriate guidance, and a means for recording a user's learning behavior in real time and periodically transmitting the recording to a server.

[0872] The main hardware components of the system consist of the user's device and the server. The user's device includes input devices such as PCs, smartphones, and tablets, and is responsible for inputting and recording the user's personal information and learning data. The server, on the other hand, is a high-performance computer that performs processes such as data analysis, storage, and distribution of learning materials. This server uses a database (e.g., MySQL, PostgreSQL) and an AI algorithm (e.g., machine learning model) to analyze the user's learning data and generate an optimal learning course.

[0873] When a user accesses the account creation screen using a device and enters basic information (e.g., name, age, learning purpose), the device sends this information to the server. The server stores the received data in a database and creates an individual profile for the user. This allows the user's personal information to be managed appropriately and used for subsequent learning activities.

[0874] When a user logs into the learning application and begins studying, the device records their learning behavior (e.g., selection of study materials, quiz answer results, study time) in real time and periodically sends it to the server. The server accumulates this data and evaluates the user's learning history and ability. An AI algorithm is used for analysis, so the most suitable learning course for the user is generated.

[0875] For example, if a user has completed the "Basic Business Conversation" course and has an 80% success rate in answering questions, the server will recommend the "Intermediate Business Conversation" course. The terminal will notify the user of this information and encourage them to take a new learning course.

[0876] The server then prepares and distributes visual, audio, and text learning materials based on the selected learning course. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts. This maximizes learning effectiveness through a variety of media.

[0877] Based on the analysis of learning progress, the server generates feedback as needed. For example, if there are many pronunciation errors, the server generates feedback such as "Pronunciation practice is required" and suggests a pronunciation practice course as the next learning step. The device displays this feedback in real time to support the user's learning.

[0878] As a concrete example, consider a user learning English. The user enters basic information on an account creation screen, logs into a learning application, and starts a "Business Conversation" course. The device records usage data in real time and periodically sends it to the server. Based on the analysis results, the server generates and recommends an "Intermediate Business Conversation" course. The device notifies the user of the new course and displays or plays various media materials for learning.

[0879] An example of an input prompt for a generative AI model is, "Please suggest the best learning course for the user to learn business English. For example, if a user already has basic business conversation skills, please suggest the intermediate course they should take next."

[0880] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0881] Step 1:

[0882] The user uses a terminal to access the account creation screen and enters personal information (such as name, age, and learning objectives). The entered data is: name: "Taro Tanaka", age: "25 years old", learning objective: "learn business English". The terminal sends this data to the server. The input data is sent via an HTTP request. The server stores this information in a database and builds an individual profile for the user. For example, the information is stored in a MySQL database using an INSERT statement.

[0883] Step 2:

[0884] The user logs in to the learning application. To log in, a username and password are required. For example, the user logs in with username: "tanaka_taro" and password: "password123". This information is sent from the terminal to the server, and the server checks it against the authentication information in the database. If authentication is successful, the server generates a session ID and returns it to the terminal. The session ID is used as the user's identifier for subsequent operations.

[0885] Step 3:

[0886] The user uses the device to select the "Business Conversation" course. This selection is completed by clicking or tapping on the course name. The device sends this selection to the server. The server retrieves the details of the selected course and returns a list of appropriate learning materials to the device. For example, it returns a list of learning materials included in the "Business Conversation" course in JSON format.

[0887] Step 4:

[0888] The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, study time, etc.) in real time. For example, if the user answers a quiz correctly, the result is temporarily stored in the device's cache. Periodically (e.g., every 5 minutes), the device sends this data to the server in batches. The sent data is related to learning history, interests, and abilities.

[0889] Step 5:

[0890] The server stores the received data in a database and performs analysis based on the accumulated data. An AI algorithm (e.g., machine learning model) is used for the analysis to calculate the optimal learning course for each user. For example, the next recommended course is determined based on the user's quiz accuracy rate and study time. The server generates the results and notifies the device of the recommended course.

[0891] Step 6:

[0892] The server prepares visual, audio, and text learning materials based on the optimized learning course generated. For example, it retrieves the path to the necessary learning material files from the database and generates a list of URLs for distribution to the device. The server returns this learning material information to the device in JSON format.

[0893] Step 7:

[0894] The device analyzes the learning material list received from the server and displays or plays the learning materials to the user. For example, for the "Intermediate Business Conversation" course, video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts are displayed. When the user plays the video materials, the device launches a streaming video player and displays the content.

[0895] Step 8:

[0896] The server analyzes the user's learning progress in real time and generates feedback as needed. For example, if the user makes many pronunciation errors, the server generates an analysis result saying, "You need to practice pronunciation." The server sends this feedback to the device and suggests a pronunciation practice course as the next learning step.

[0897] Step 9:

[0898] The device displays the feedback received from the server in real time and offers the user additional learning steps. For example, it displays a pop-up message on the screen saying "Pronunciation practice needed" and provides a link to a pronunciation practice course. When the user clicks the link, a new learning course will begin.

[0899] (Application example 1)

[0900] 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."

[0901] Conventional learning systems have difficulty generating optimal learning courses based on individual users' learning progress and interests, resulting in a uniform learning content. They also lack the ability to provide individually optimized learning experiences for workers with the aim of improving their skills. Furthermore, they lack the ability to provide learning using a variety of media and provide real-time feedback.

[0902] 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.

[0903] In this invention, the server includes a means for inputting personal information of users, a means for recording work history and operational behavior, and a means for analyzing the collected data and generating an optimized learning course, which makes it possible to provide an optimal learning course in real time according to the learning progress and skill improvement of each worker.

[0904] (definition statement)

[0905] "Means for entering user's personal information" refers to means for entering basic information such as the user's name, age, and purpose of learning.

[0906] "Means for collecting learning history, interests, and abilities" refers to means for recording and collecting a user's past learning history, interests, and current abilities.

[0907] "Means for analyzing collected data and generating optimized learning courses" refers to means for generating optimal learning courses for users based on collected data using AI algorithms, etc.

[0908] "Means for providing a variety of media, including visual, text, and audio" refers to means for providing users with a variety of learning content, including video, text, and audio.

[0909] The "means for generating feedback based on learning progress and providing appropriate guidance" is a means for analyzing a user's learning progress and providing appropriate feedback and guidance based on the analysis.

[0910] "Means for recording work history and operational actions for the purpose of improving worker skills" refers to means for recording the operations performed by workers during work and the results thereof, and for supporting skill improvement.

[0911] The "means for generating an optimal work skill course based on recorded data" refers to a means for analyzing the recorded work history and operational behavior data and generating a learning course for improving the skills of the worker that is optimal for the worker.

[0912] The system for implementing this invention aims to provide a learning experience that is individually optimized for each user. Specific embodiments thereof will be described below.

[0913] Overall system overview

[0914] The system consists of a terminal and a server. The terminal records the user's information input and learning activities, while the server analyzes and processes the collected data to generate the optimal learning course. Users access the system using a terminal (such as a smartphone or tablet), and the server provides feedback and guidance in real time.

[0915] Hardware used

[0916] The following hardware is mainly used:

[0917] Smartphone

[0918] tablet

[0919] PC

[0920] Software used

[0921] The following software is mainly used:

[0922] Python

[0923] JSON

[0924] Video Player

[0925] Audio Player

[0926] AI algorithms

[0927] Specific examples of processing

[0928] User registration and profile creation

[0929] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning objectives. For example, a user may enter their name as "Yamada Hanako," their age as "30," and their learning objective as "learning machine operation skills." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0930] Collection of learning history and abilities

[0931] A user logs into a learning application and selects, for example, an "Introduction to Machine Operation Course." The device records the user's learning behavior (e.g., selection of learning materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0932] Providing individually optimized learning courses

[0933] The server analyzes the collected data and uses AI algorithms to generate the most suitable learning course for the user. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will recommend the "Intermediate Machine Operation Course" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[0934] Providing learning using a variety of media

[0935] The server prepares visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) based on the selected learning course. The server distributes these learning materials to the terminal, and the terminal displays or plays them in an appropriate format for the user. For example, an "Intermediate Machine Operation Course" may include video learning materials on operating procedures, audio operation guides, and reference texts.

[0936] Feedback and guidance

[0937] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure review course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[0938] Specific prompt examples

[0939] An example of a prompt sentence when using a generative AI model is as follows:

[0940] "Please suggest the best course for a user to learn factory robot operation techniques. The user's learning history is as follows: viewing visual materials, answering quizzes, and encountering errors (operation procedures). The user's name is Hanako Yamada, and she is 30 years old."

[0941] This system allows workers to receive learning courses that are optimized for them, enabling them to effectively acquire skills.

[0942] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0943] Step 1:

[0944] A user accesses the account creation screen using a device and enters basic information such as name, age, and learning goals. This information is sent from the device to the server. The server stores the received information in a database and builds an individual profile for the user. At this stage, the input is the user's personal information, and the output is the profile data stored in the database.

[0945] Step 2:

[0946] A user logs into a learning application and selects a specific learning course (e.g., an introductory course in machine operation). The terminal sends the user's selection to the server, which records the selection. In addition, learning behavior (e.g., selection of learning materials, quiz answer results, and study time) is also periodically recorded and this data is sent to the server. At this stage, the input is the user's learning behavior data, and the output is the learning history stored in the database.

[0947] Step 3:

[0948] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. Specifically, it selects an appropriate course based on the user's learning history, interests, and abilities. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will generate the "Intermediate Machine Operation Course." At this stage, the input is the learning history stored in the database and the AI ​​algorithm, and the output is the generated optimized learning course.

[0949] Step 4:

[0950] Based on the learning course selected by the server, visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) are prepared and distributed to the terminal. The terminal then displays or plays the received learning materials in an appropriate format, providing the user with a learning opportunity. At this stage, the input is the generated optimized learning course and the learning materials based on it, and the output is the learning content displayed to the user.

[0951] Step 5:

[0952] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure check course as the next learning step. This feedback is sent to the terminal in real time and displayed to the user. At this stage, the input is the latest learning history data and the feedback generation algorithm, and the output is the feedback message displayed to the user.

[0953] This series of processes allows workers to receive learning courses that are optimized for them and efficiently acquire skills.

[0954] 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.

[0955] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user, and incorporates an emotion engine that recognizes and utilizes the user's emotions. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the optimal learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[0956] System overview and processing flow

[0957] 1. User registration and profile creation

[0958] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[0959] 2. Collection of learning history and abilities

[0960] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[0961] 3. Emotion Recognition by Emotion Engine

[0962] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[0963] 4. Providing individually optimized learning courses

[0964] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if Taro Tanaka completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that he is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[0965] 5. Providing learning using a variety of media

[0966] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[0967] 6. Feedback and Guidance

[0968] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion engine determines that the user is frustrated, it includes advice to relax. The device displays this feedback to the user in real time, supporting their learning.

[0969] Specific examples

[0970] Flow when a user is learning English

[0971] 1. User registration and profile creation

[0972] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[0973] The device sends this information to the server, which stores it in a database.

[0974] 2. Collection of learning history and abilities

[0975] Users begin learning "Business Conversation" and use visual materials and quizzes.

[0976] The device records usage data in real time and transmits it to a server.

[0977] 3. Emotion Recognition by Emotion Engine

[0978] While the user is learning, the device camera captures facial expression data, which is then analyzed by the emotion engine, which determines, for example, that the user is confused.

[0979] 4. Providing individually optimized learning courses

[0980] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results, adjusting the difficulty and content based on emotional data.

[0981] The device notifies the user of new courses and prompts them to take the next step in their studies.

[0982] 5. Providing learning using a variety of media

[0983] The server prepares a variety of media teaching materials and distributes them to the terminals.

[0984] The terminal displays or plays the educational material, allowing the user to study.

[0985] 6. Feedback and Guidance

[0986] The server analyzes learning progress and emotional data and generates appropriate feedback, e.g., "Pronunciation practice is needed." If confusion is detected, a brief explanation is added.

[0987] The device displays feedback to the user in real time and offers additional learning steps.

[0988] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[0989] The processing flow will be explained below.

[0990] Step 1:

[0991] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[0992] Step 2:

[0993] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[0994] Step 3:

[0995] The user logs in to the learning application and selects the "Business Conversation" course. The device records data such as the user's selection, the start time of the study, and the type of learning material.

[0996] Step 4:

[0997] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[0998] Step 5:

[0999] The device's built-in camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the captured data to recognize the user's emotions in real time, such as whether the user is tired, excited, or confused.

[1000] Step 6:

[1001] The server runs an AI algorithm to analyze the collected learning data and emotional data, and the algorithm comprehensively evaluates the user's learning patterns, accuracy rate, areas of interest, and emotional state to identify the optimal learning course.

[1002] Step 7:

[1003] Based on the analysis results, the server recommends courses such as "Intermediate Business Conversation" to the user. The difficulty and type of learning content is adjusted based on the emotional data. The device notifies the user of new learning courses and guides them to the next learning step.

[1004] Step 8:

[1005] The server prepares visual, audio and text learning materials based on individually optimized learning courses, and delivers these materials to the device.

[1006] Step 9:

[1007] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[1008] Step 10:

[1009] As the user continues to study, the device records the learning progress in real time and sends the data to the server, which then evaluates the learning progress and emotional data in real time.

[1010] Step 11:

[1011] The server generates feedback based on learning progress and emotional data. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion is negative, it also includes advice to relax. The device displays this feedback to the user in real time.

[1012] Step 12:

[1013] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[1014] These detailed steps enable the learning platform to provide users with a personalized learning experience, further leveraging emotion recognition to maximize learning efficiency.

[1015] Example 2

[1016] 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."

[1017] Traditional learning systems have focused on providing a uniform learning course without taking into account the individual circumstances and feelings of each user. This has led to issues such as reduced learning efficiency and insufficient learning outcomes. Furthermore, the lack of real-time feedback and appropriate guidance has hindered the quality of the learning experience.

[1018] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting personal information of the user, a means for collecting learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, a means for capturing the user's facial expressions and voice to recognize emotions, and a means for reflecting the emotion recognition results in the generation of the learning course. This provides an optimal learning experience that takes into account the user's individual situation and emotions, making it possible to improve learning efficiency and effectiveness.

[1019] "User personal information" refers to basic data that identifies a user, such as name, age, and learning purpose.

[1020] "Learning history" is a record of the learning activities that a user has undertaken up to now.

[1021] "Interests" is data about subjects or topics that interest the user.

[1022] "Ability" is data that indicates the user's level of knowledge and skill.

[1023] "Collected Data" includes information about your personal information, learning history, interests, and abilities.

[1024] "Analysis" is the process of interpreting collected data using statistical or machine learning techniques to gain insights.

[1025] An "optimized learning path" is a learning program that is individually tailored to you based on your personal data, learning history, interests, and abilities.

[1026] "Diverse media, including visual, text, and audio" refers to different forms of instructional materials used to convey learning content.

[1027] "Study progress" is data that represents the progress a user has made in a course of study.

[1028] "Feedback" is information that suggests improvements or next steps based on a user's learning activities.

[1029] "Guidance" is the educational advice and support provided to learners.

[1030] "Facial expression" refers to data that indicates the user's facial movements and expressions.

[1031] "Voice" is data that indicates the user's voice.

[1032] "Emotion recognition" refers to identifying a user's emotional state by analyzing facial expressions, voice, etc.

[1033] "Emotion recognition results" are data obtained as analysis results of emotion recognition.

[1034] "Capture" is the act of collecting data using a camera or microphone.

[1035] "Reflect" means incorporating the analysis results into next steps and plans.

[1036] This invention relates to a system that provides a learning experience that is individually optimized for each user, and is characterized in that it uses an emotion engine to recognize the user's emotions and utilizes that information to optimize the learning course.

[1037] The system includes the following main elements:

[1038] 1. User registration and profile creation

[1039] Using a device, a user accesses the account creation screen and enters basic information such as name, age, and learning goals. For example, a user enters their name "Yamada Hanako," their age "30," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database to create an individual user profile.

[1040] 2. Collection of learning history and abilities

[1041] A user logs into the learning application and selects the "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, and study time) in real time and periodically transmits the data to the server. The server then accumulates the collected data and obtains data for analyzing the user's learning history and ability.

[1042] 3. Emotion Recognition by Emotion Engine

[1043] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[1044] 4. Providing individually optimized learning courses

[1045] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if a user completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that the user is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[1046] 5. Providing learning using a variety of media

[1047] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[1048] 6. Feedback and Guidance

[1049] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates the feedback "You need to practice your pronunciation." If the emotion engine determines that the user is confused, it adds additional explanations. The device displays this feedback to the user in real time to support their learning.

[1050] Specific examples

[1051] When a user is learning English, the process proceeds as follows:

[1052] 1. The user enters basic information on the account creation screen. For example, the user enters their name "Yamada Hanako," their age "30 years old," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database.

[1053] 2. The user begins learning "Business Conversation" and uses visual materials and quizzes. The device records this usage data in real time and sends it to the server.

[1054] 3. While the user is learning, the device camera captures facial expression data, which the emotion engine analyzes. For example, it determines that the user is confused.

[1055] 4. The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results. The difficulty and content are adjusted based on the emotional data. The device notifies the user of the new course and prompts them to proceed to the next learning step.

[1056] 5. The server prepares various media learning materials and distributes them to the terminals, which then display or play the materials to allow the user to study.

[1057] 6. The server analyzes the learning progress and emotion data and generates appropriate feedback. For example, it may provide feedback such as "You need to practice your pronunciation" and add a brief explanation if the user shows any confusion. The device displays the feedback to the user in real time and offers additional learning steps.

[1058] Example of input prompt for generative AI model

[1059] "What is the best course of action for someone who needs to practice their English pronunciation and is tired?"

[1060] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[1061] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1062] Step 1:

[1063] User registration and profile creation

[1064] Input: The user enters basic information such as name, age, and learning purpose into the terminal.

[1065] The server receives basic information entered by the user through the terminal.

[1066] The server stores this information in a database and builds an individual user profile.

[1067] Output: Individual user profile stored in a database.

[1068] Step 2:

[1069] Collection of learning history and abilities

[1070] Input: A user logs into a learning application and selects a course of study.

[1071] The terminal records the user's selected learning course and learning activities (selection of learning materials, quiz answer results, and study time) in real time.

[1072] The server receives the learning behavior data sent from the terminal and stores it in a database.

[1073] Data processing: The server aggregates the collected learning behavior data and analyzes learning history and ability.

[1074] Output: Accumulated and analyzed learning history and ability data.

[1075] Step 3:

[1076] Emotion recognition by emotion engine

[1077] Input: The user's facial expressions and voice are captured by the device's camera and microphone.

[1078] The terminal transmits the captured facial expression and voice data to the emotion engine.

[1079] Data calculation: The emotion engine analyzes facial and voice data to recognize the user's emotional state.

[1080] Output: The emotion recognition result (e.g., whether the user is tired, confused, excited, etc.).

[1081] Step 4:

[1082] Providing individually optimized learning courses

[1083] Input: The server integrates the learning history, interests, abilities and the analysis results of the emotion engine.

[1084] Data calculation: The server uses AI algorithms to comprehensively analyze this data and generate the most suitable learning course for the user.

[1085] The terminal receives the new course of study sent from the server.

[1086] Output: A personalized, optimized learning path.

[1087] Specific operation: For example, the server has finished "Basic Business Conversation" and the quiz result shows an 80% accuracy rate, but the emotion engine determines that the user is tired and recommends lighter learning materials.

[1088] Step 5:

[1089] Providing learning using a variety of media

[1090] Input: The server prepares visual, audio and textual materials based on the new course of study.

[1091] The server distributes the prepared teaching materials to the terminals.

[1092] The terminal displays or plays the distributed educational material in a format appropriate for the user.

[1093] Output: A variety of media materials displayed or played.

[1094] Specific operation: For example, the server prepares video, audio and text learning materials for "intermediate business conversation," and the terminal plays or displays these.

[1095] Step 6:

[1096] Feedback and guidance

[1097] Input: The server receives data based on the user's learning progress and emotion recognition results.

[1098] Data Calculation: The server analyzes these data and generates appropriate feedback and guidance.

[1099] The terminal displays the generated feedback to the user in real time.

[1100] Output: Feedback and guidance provided to the user in real time.

[1101] Specific Actions: For example, if there are many pronunciation errors, feedback such as "You need to practice your pronunciation" is provided, and additional explanations are added if the user appears confused.

[1102] (Application example 2)

[1103] 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."

[1104] Conventional online learning systems are composed of uniform content without considering the user's emotional state, making it difficult to provide an optimal learning experience for each user. Furthermore, it is difficult for virtual stores and services to provide appropriate feedback and support in real time according to the user's emotional state. This leads to issues such as reduced learning effectiveness and service satisfaction, and a decrease in users' willingness to continue using the service.

[1105] 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 recognizing the emotional state of the user and providing appropriate feedback and support based on that, means for navigating and supporting within the virtual environment, and means for providing a variety of media. This enables each user to have an individually optimized learning experience and to receive optimal services within the virtual store.

[1106] "Means for inputting user's personal information" refers to the methods and devices for inputting and collecting the user's name, age, interests, and other personal information.

[1107] "Means for collecting learning history, interests, and abilities" refers to methods and devices for recording and collecting the content that a user has learned to date, areas of interest, and learning abilities.

[1108] "Means for analyzing collected data and generating an optimized learning course" refers to a method and device for analyzing and creating an optimal learning course based on collected data.

[1109] "Visual, textual, and audio multimedia delivery means" refers to methods and devices that provide users with learning materials in different formats, such as visual, written, and audio materials.

[1110] The "means for generating feedback based on learning progress and providing appropriate guidance" refers to a method and apparatus for generating feedback based on the learning progress of a user and providing appropriate guidance.

[1111] "Means for recognizing the user's emotional state and providing appropriate feedback and support based on that" refers to a method and device that analyzes the user's emotional state from their facial expressions and voice, and provides appropriate feedback and support based on that information.

[1112] "Means for navigation and support within a virtual environment" refers to methods and devices that guide and assist users to move and operate efficiently within a virtual space.

[1113] As an embodiment of the present invention, a system including a server, a user terminal, and an emotion engine is proposed, which recognizes a user's emotions and provides appropriate feedback and support based on the emotions to provide an individually optimized learning experience for the user.

[1114] System Configuration

[1115] The system consists of the following main components:

[1116] 1. User device: A device such as a smartphone, tablet, PC, or head-mounted display. These devices are equipped with a camera and microphone to capture the user's facial expressions and voice.

[1117] 2. Server: A computer system that works in conjunction with a database management system (e.g., MySQL) to analyze training data and emotion data. AI algorithms and generative AI models are used for the specific analysis.

[1118] 3. Emotion engine: Uses emotion recognition APIs such as Google Cloud Vision and Amazon Rekognition to recognize the user's emotional state from facial and voice data.

[1119] Program processing

[1120] The server comprehensively analyzes the emotional state recognized by the emotion engine, the learning data collected from the user's device, and other personal information. Based on the analysis results, optimal feedback and support is generated and sent to the user's device in real time. For example, if the user is confused, the system will provide detailed explanations or simplified materials. If the user is excited, the system will encourage the user to move on to the next learning stage.

[1121] Specific use cases

[1122] For example, if a user is browsing a smartphone in a virtual store, and the emotion engine detects confusion in the user's facial expression, the server generates appropriate feedback such as "Would you like to know more about this smartphone?" and displays it on the user's device.

[1123] Example prompt for a generative AI model:

[1124] "When a user is feeling confused, generate feedback using the following template: 'Hey {username}, can you share more information about this {product name}?'"

[1125] Data items: User name, product name

[1126] By realizing this system, each user will be able to enjoy learning experiences and services that are optimized for them, which is expected to improve overall effectiveness and satisfaction.

[1127] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1128] Step 1:

[1129] This is the phase where the user's personal information is entered into the user's device. The user enters basic information such as name, age, interests, and learning objectives, and sends this data to the server. The entered data is saved on the server, and a user profile is created.

[1130] Input: Name, age, interests, learning objectives

[1131] Output: User profile data

[1132] Specific operation: When a user enters information on the account creation screen and clicks the "Submit" button, the device sends the information to the server.

[1133] Step 2:

[1134] This is the phase in which the user selects a learning subject and the learning session begins. The device records the user's learning behavior (selection of learning materials, quiz answers, and study time) in real time and periodically sends this data to the server.

[1135] Input: Learning subject, learning history data

[1136] Output: Learning behavior data

[1137] Specific operation: The user selects "Business English," browses the learning materials, and answers the quiz. This behavior is recorded on the device and periodically sent to the server.

[1138] Step 3:

[1139] In this phase, the emotion engine captures the user's facial expressions and voice data and analyzes their emotional state. The emotion engine analyzes the data acquired using the camera and microphone, and recognizes emotions such as confusion or excitement.

[1140] Input: facial expression data, voice data

[1141] Output: Emotional state data

[1142] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. The data is sent to the emotion engine, and the resulting emotional state is sent to the server.

[1143] Step 4:

[1144] In this phase, the server analyzes learning history data, personal profile data, and emotional state data in an integrated manner to generate optimal learning courses and support. The generated results are provided to the user in the form of learning courses and feedback appropriate for the user.

[1145] Input: learning history data, personal profile data, emotional state data

[1146] Output: Optimized learning course, feedback

[1147] How it works: The server analyzes various data and generates optimal learning courses using machine learning algorithms. For example, if the user is confused, it suggests simple learning materials.

[1148] Step 5:

[1149] This is the phase where the server delivers selected learning materials and feedback to the user's device in various media formats to support learning. The learning materials are provided in visual, text, audio, and other formats, and the user studies them.

[1150] Input: Optimized learning path, feedback

[1151] Output: Diverse media materials

[1152] Specific operation: The server sends the selected teaching materials to the terminal in the form of visual, audio, text, etc., and the terminal displays or plays them.

[1153] Step 6:

[1154] This is the phase where the user's device displays real-time feedback to support the user's learning progress. Appropriate feedback is displayed to the user at the appropriate time, encouraging them to move on to the next step.

[1155] Input: Real-time feedback

[1156] Output: Displayed feedback

[1157] Specific operation: The device immediately displays the generated feedback and instructs the user on how to proceed with the learning and what the next step should be. The user continues learning based on the feedback.

[1158] 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.

[1159] 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.

[1160] 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.

[1161] [Fourth embodiment]

[1162] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1163] 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.

[1164] 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).

[1165] 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.

[1166] 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.

[1167] 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).

[1168] 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.

[1169] 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.

[1170] 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.

[1171] 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.

[1172] 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.

[1173] 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.

[1174] 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."

[1175] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the most appropriate learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[1176] System overview and processing flow

[1177] 1. User registration and profile creation

[1178] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[1179] 2. Collection of learning history and abilities

[1180] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[1181] 3. Providing individually optimized learning courses

[1182] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. For example, if Taro Tanaka completes "Basic Business Conversation" and the quiz results show an 80% accuracy rate, the server will recommend "Intermediate Business Conversation" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[1183] 4. Providing learning using a variety of media

[1184] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[1185] 5. Feedback and Guidance

[1186] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation" and suggests a pronunciation practice course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[1187] Specific examples

[1188] Flow when a user is learning English

[1189] 1. User registration and profile creation

[1190] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[1191] The device sends this information to the server, which stores it in a database.

[1192] 2. Collection of learning history and abilities

[1193] Users begin learning "Business Conversation" and use visual materials and quizzes.

[1194] The device records usage data in real time and transmits it to a server.

[1195] 3. Providing individually optimized learning courses

[1196] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results.

[1197] The device notifies the user of new courses and prompts them for the next learning step.

[1198] 4. Providing learning using a variety of media

[1199] The server prepares a variety of media teaching materials and distributes them to the terminals.

[1200] The terminal displays or plays the educational material, allowing the user to study.

[1201] 5. Feedback and Guidance

[1202] The server analyzes the learning progress and generates feedback such as "pronunciation practice is needed."

[1203] The device displays feedback to the user in real time and offers additional learning steps.

[1204] This system allows users to receive a learning course that is optimized for them and progress effectively with their studies.

[1205] The processing flow will be explained below.

[1206] Step 1:

[1207] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[1208] Step 2:

[1209] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[1210] Step 3:

[1211] A user logs in to a learning application and selects, for example, a "Business Conversation" course. The terminal records data such as the user's selection, the start time of the study, and the type of learning material.

[1212] Step 4:

[1213] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[1214] Step 5:

[1215] The server runs an AI algorithm to analyze the collected data, which evaluates the user's learning patterns, accuracy rate, areas of interest, etc. to identify the optimal learning course.

[1216] Step 6:

[1217] Based on the analysis results, the server recommends a course, for example, "Intermediate Business Conversation," to the user. The device displays a notification of the new learning course to the user, guiding them to proceed to the next learning step.

[1218] Step 7:

[1219] The server prepares visual, audio, and text materials for the "Intermediate Business Conversation" course, and distributes these materials to the terminals.

[1220] Step 8:

[1221] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[1222] Step 9:

[1223] As the user continues to study, the device records their progress in real time and sends the data to the server, which then evaluates their learning progress in real time.

[1224] Step 10:

[1225] The server generates feedback based on the user's learning progress. For example, if the user makes many pronunciation errors, the server generates feedback such as "You need to practice your pronunciation." The device displays this feedback to the user in real time.

[1226] Step 11:

[1227] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[1228] Through these steps, the learning platform can provide users with a personalized learning experience and maximize learning effectiveness.

[1229] Example 1

[1230] 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."

[1231] Conventional learning systems have problems in that they are unable to respond to individual users' learning needs and progress, preventing effective learning. Furthermore, they lack real-time feedback, making it difficult to maximize users' learning outcomes. In particular, the lack of diverse learning media and the lack of optimal course recommendations based on collected learning data limits users' learning experiences.

[1232] 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.

[1233] In this invention, the server includes a means for inputting personal information through a user's input device, a means for collecting learning content usage history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning program, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, and a means for recording the user's learning behavior in real time and periodically transmitting the recording to the server, thereby enabling the server to respond to the individual learning needs of each user and provide real-time feedback, thereby providing an effective learning experience.

[1234] "User" refers to an individual who uses this system to carry out learning activities.

[1235] "Input device" refers to a device used by a user to input personal information, such as a keyboard or touchscreen.

[1236] "Learning content usage history" refers to the history of learning materials and content used by a user.

[1237] "Interest" refers to data that indicates a user's interest or curiosity in learning.

[1238] "Ability" refers to data that indicates a user's learning skills and knowledge level.

[1239] "Collected Data" refers to data such as your personal information, learning history, interests, and abilities.

[1240] "Analysis" refers to the process of analyzing collected data and generating the optimal learning course for the user.

[1241] "Optimized Learning Program" refers to a customized learning course that is individually provided to a user based on the analysis results.

[1242] "Visual" refers to visual teaching materials such as images and videos.

[1243] "Text" refers to teaching materials that consist of sentences or written information.

[1244] "Audio" refers to audio instructional materials.

[1245] "Multiple media" refers to educational materials in a variety of formats, including visual, text, and audio.

[1246] "Learning progress" refers to the progress a user makes as they progress through their learning.

[1247] "Feedback" refers to assessments and guidance generated based on learning progress.

[1248] "Real-time" refers to the almost instantaneous transmission and reception of information and data.

[1249] "Server" refers to a computer system that analyzes and stores data, distributes educational materials, etc.

[1250] The system of the present invention aims to provide a user with an individually optimized learning experience, and includes a means for inputting a user's personal information, a means for collecting a user's learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on the learning progress and providing appropriate guidance, and a means for recording a user's learning behavior in real time and periodically transmitting the recording to a server.

[1251] The main hardware components of the system consist of the user's device and the server. The user's device includes input devices such as PCs, smartphones, and tablets, and is responsible for inputting and recording the user's personal information and learning data. The server, on the other hand, is a high-performance computer that performs processes such as data analysis, storage, and distribution of learning materials. This server uses a database (e.g., MySQL, PostgreSQL) and an AI algorithm (e.g., machine learning model) to analyze the user's learning data and generate an optimal learning course.

[1252] When a user accesses the account creation screen using a device and enters basic information (e.g., name, age, learning purpose), the device sends this information to the server. The server stores the received data in a database and creates an individual profile for the user. This allows the user's personal information to be managed appropriately and used for subsequent learning activities.

[1253] When a user logs into the learning application and begins studying, the device records their learning behavior (e.g., selection of study materials, quiz answer results, study time) in real time and periodically sends it to the server. The server accumulates this data and evaluates the user's learning history and ability. An AI algorithm is used for analysis, so the most suitable learning course for the user is generated.

[1254] For example, if a user has completed the "Basic Business Conversation" course and has an 80% success rate in answering questions, the server will recommend the "Intermediate Business Conversation" course. The terminal will notify the user of this information and encourage them to take a new learning course.

[1255] The server then prepares and distributes visual, audio, and text learning materials based on the selected learning course. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts. This maximizes learning effectiveness through a variety of media.

[1256] Based on the analysis of learning progress, the server generates feedback as needed. For example, if there are many pronunciation errors, the server generates feedback such as "Pronunciation practice is required" and suggests a pronunciation practice course as the next learning step. The device displays this feedback in real time to support the user's learning.

[1257] As a concrete example, consider a user learning English. The user enters basic information on an account creation screen, logs into a learning application, and starts a "Business Conversation" course. The device records usage data in real time and periodically sends it to the server. Based on the analysis results, the server generates and recommends an "Intermediate Business Conversation" course. The device notifies the user of the new course and displays or plays various media materials for learning.

[1258] An example of an input prompt for a generative AI model is, "Please suggest the best learning course for the user to learn business English. For example, if a user already has basic business conversation skills, please suggest the intermediate course they should take next."

[1259] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1260] Step 1:

[1261] The user uses a terminal to access the account creation screen and enters personal information (such as name, age, and learning objectives). The entered data is: name: "Taro Tanaka", age: "25 years old", learning objective: "learn business English". The terminal sends this data to the server. The input data is sent via an HTTP request. The server stores this information in a database and builds an individual profile for the user. For example, the information is stored in a MySQL database using an INSERT statement.

[1262] Step 2:

[1263] The user logs in to the learning application. To log in, a username and password are required. For example, the user logs in with username: "tanaka_taro" and password: "password123". This information is sent from the terminal to the server, and the server checks it against the authentication information in the database. If authentication is successful, the server generates a session ID and returns it to the terminal. The session ID is used as the user's identifier for subsequent operations.

[1264] Step 3:

[1265] The user uses the device to select the "Business Conversation" course. This selection is completed by clicking or tapping on the course name. The device sends this selection to the server. The server retrieves the details of the selected course and returns a list of appropriate learning materials to the device. For example, it returns a list of learning materials included in the "Business Conversation" course in JSON format.

[1266] Step 4:

[1267] The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, study time, etc.) in real time. For example, if the user answers a quiz correctly, the result is temporarily stored in the device's cache. Periodically (e.g., every 5 minutes), the device sends this data to the server in batches. The sent data is related to learning history, interests, and abilities.

[1268] Step 5:

[1269] The server stores the received data in a database and performs analysis based on the accumulated data. An AI algorithm (e.g., machine learning model) is used for the analysis to calculate the optimal learning course for each user. For example, the next recommended course is determined based on the user's quiz accuracy rate and study time. The server generates the results and notifies the device of the recommended course.

[1270] Step 6:

[1271] The server prepares visual, audio, and text learning materials based on the optimized learning course generated. For example, it retrieves the path to the necessary learning material files from the database and generates a list of URLs for distribution to the device. The server returns this learning material information to the device in JSON format.

[1272] Step 7:

[1273] The device analyzes the learning material list received from the server and displays or plays the learning materials to the user. For example, for the "Intermediate Business Conversation" course, video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts are displayed. When the user plays the video materials, the device launches a streaming video player and displays the content.

[1274] Step 8:

[1275] The server analyzes the user's learning progress in real time and generates feedback as needed. For example, if the user makes many pronunciation errors, the server generates an analysis result saying, "You need to practice pronunciation." The server sends this feedback to the device and suggests a pronunciation practice course as the next learning step.

[1276] Step 9:

[1277] The device displays the feedback received from the server in real time and offers the user additional learning steps. For example, it displays a pop-up message on the screen saying "Pronunciation practice needed" and provides a link to a pronunciation practice course. When the user clicks the link, a new learning course will begin.

[1278] (Application example 1)

[1279] 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."

[1280] Conventional learning systems have difficulty generating optimal learning courses based on individual users' learning progress and interests, resulting in a uniform learning content. They also lack the ability to provide individually optimized learning experiences for workers with the aim of improving their skills. Furthermore, they lack the ability to provide learning using a variety of media and provide real-time feedback.

[1281] 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.

[1282] In this invention, the server includes a means for inputting personal information of users, a means for recording work history and operational behavior, and a means for analyzing the collected data and generating an optimized learning course, which makes it possible to provide an optimal learning course in real time according to the learning progress and skill improvement of each worker.

[1283] (definition statement)

[1284] "Means for entering user's personal information" refers to means for entering basic information such as the user's name, age, and purpose of learning.

[1285] "Means for collecting learning history, interests, and abilities" refers to means for recording and collecting a user's past learning history, interests, and current abilities.

[1286] "Means for analyzing collected data and generating optimized learning courses" refers to means for generating optimal learning courses for users based on collected data using AI algorithms, etc.

[1287] "Means for providing a variety of media, including visual, text, and audio" refers to means for providing users with a variety of learning content, including video, text, and audio.

[1288] The "means for generating feedback based on learning progress and providing appropriate guidance" is a means for analyzing a user's learning progress and providing appropriate feedback and guidance based on the analysis.

[1289] "Means for recording work history and operational actions for the purpose of improving worker skills" refers to means for recording the operations performed by workers during work and the results thereof, and for supporting skill improvement.

[1290] The "means for generating an optimal work skill course based on recorded data" refers to a means for analyzing the recorded work history and operational behavior data and generating a learning course for improving the skills of the worker that is optimal for the worker.

[1291] The system for implementing this invention aims to provide a learning experience that is individually optimized for each user. Specific embodiments thereof will be described below.

[1292] Overall system overview

[1293] The system consists of a terminal and a server. The terminal records the user's information input and learning activities, while the server analyzes and processes the collected data to generate the optimal learning course. Users access the system using a terminal (such as a smartphone or tablet), and the server provides feedback and guidance in real time.

[1294] Hardware used

[1295] The following hardware is mainly used:

[1296] Smartphone

[1297] tablet

[1298] PC

[1299] Software used

[1300] The following software is mainly used:

[1301] Python

[1302] JSON

[1303] Video Player

[1304] Audio Player

[1305] AI algorithms

[1306] Specific examples of processing

[1307] User registration and profile creation

[1308] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning objectives. For example, a user may enter their name as "Yamada Hanako," their age as "30," and their learning objective as "learning machine operation skills." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[1309] Collection of learning history and abilities

[1310] A user logs into a learning application and selects, for example, an "Introduction to Machine Operation Course." The device records the user's learning behavior (e.g., selection of learning materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[1311] Providing individually optimized learning courses

[1312] The server analyzes the collected data and uses AI algorithms to generate the most suitable learning course for the user. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will recommend the "Intermediate Machine Operation Course" as the next step. The device will notify the user of the new learning course and encourage them to start studying.

[1313] Providing learning using a variety of media

[1314] The server prepares visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) based on the selected learning course. The server distributes these learning materials to the terminal, and the terminal displays or plays them in an appropriate format for the user. For example, an "Intermediate Machine Operation Course" may include video learning materials on operating procedures, audio operation guides, and reference texts.

[1315] Feedback and guidance

[1316] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure review course as the next learning step. The device displays this feedback to the user in real time to support the user's learning.

[1317] Specific prompt examples

[1318] An example of a prompt sentence when using a generative AI model is as follows:

[1319] "Please suggest the best course for a user to learn factory robot operation techniques. The user's learning history is as follows: viewing visual materials, answering quizzes, and encountering errors (operation procedures). The user's name is Hanako Yamada, and she is 30 years old."

[1320] This system allows workers to receive learning courses that are optimized for them, enabling them to effectively acquire skills.

[1321] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1322] Step 1:

[1323] A user accesses the account creation screen using a device and enters basic information such as name, age, and learning goals. This information is sent from the device to the server. The server stores the received information in a database and builds an individual profile for the user. At this stage, the input is the user's personal information, and the output is the profile data stored in the database.

[1324] Step 2:

[1325] A user logs into a learning application and selects a specific learning course (e.g., an introductory course in machine operation). The terminal sends the user's selection to the server, which records the selection. In addition, learning behavior (e.g., selection of learning materials, quiz answer results, and study time) is also periodically recorded and this data is sent to the server. At this stage, the input is the user's learning behavior data, and the output is the learning history stored in the database.

[1326] Step 3:

[1327] The server analyzes the collected data and uses an AI algorithm to generate the most suitable learning course for the user. Specifically, it selects an appropriate course based on the user's learning history, interests, and abilities. For example, if a user completes the "Introduction to Machine Operation Course" and the quiz results show an 80% accuracy rate, the server will generate the "Intermediate Machine Operation Course." At this stage, the input is the learning history stored in the database and the AI ​​algorithm, and the output is the generated optimized learning course.

[1328] Step 4:

[1329] Based on the learning course selected by the server, visual learning materials (videos), audio learning materials (audio commentary), and text learning materials (manuals) are prepared and distributed to the terminal. The terminal then displays or plays the received learning materials in an appropriate format, providing the user with a learning opportunity. At this stage, the input is the generated optimized learning course and the learning materials based on it, and the output is the learning content displayed to the user.

[1330] Step 5:

[1331] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many errors in a particular operation, the server generates feedback such as "Please check the operation procedure again" and suggests a procedure check course as the next learning step. This feedback is sent to the terminal in real time and displayed to the user. At this stage, the input is the latest learning history data and the feedback generation algorithm, and the output is the feedback message displayed to the user.

[1332] This series of processes allows workers to receive learning courses that are optimized for them and efficiently acquire skills.

[1333] 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.

[1334] The system according to an embodiment of the present invention aims to provide a personalized learning experience for each user, and incorporates an emotion engine that recognizes and utilizes the user's emotions. The system inputs the user's personal information, collects and analyzes their learning history, interests, and abilities, and then provides the optimal learning course based on the collected information. Furthermore, the system maximizes learning effectiveness by using various media, including visual, text, and audio, and provides timely and appropriate feedback and guidance.

[1335] System overview and processing flow

[1336] 1. User registration and profile creation

[1337] The user accesses the account creation screen using a device and enters basic information such as name, age, and learning goal. For example, a user might enter their name as "Taro Tanaka," their age as "25," and their learning goal as "learning business English." The device sends this information to the server, which then stores it in a database to create an individual profile for the user.

[1338] 2. Collection of learning history and abilities

[1339] A user logs into a learning application and selects, for example, a "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answers, and study time) in real time and periodically transmits the data to the server. This allows the server to accumulate the collected data and obtain basic data for analyzing the user's learning history and ability.

[1340] 3. Emotion Recognition by Emotion Engine

[1341] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[1342] 4. Providing individually optimized learning courses

[1343] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if Taro Tanaka completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that he is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[1344] 5. Providing learning using a variety of media

[1345] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[1346] 6. Feedback and Guidance

[1347] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion engine determines that the user is frustrated, it includes advice to relax. The device displays this feedback to the user in real time, supporting their learning.

[1348] Specific examples

[1349] Flow when a user is learning English

[1350] 1. User registration and profile creation

[1351] The user enters basic information on the account creation screen. For example, name "Taro Tanaka," age "25 years old," and learning goal "to learn business English."

[1352] The device sends this information to the server, which stores it in a database.

[1353] 2. Collection of learning history and abilities

[1354] Users begin learning "Business Conversation" and use visual materials and quizzes.

[1355] The device records usage data in real time and transmits it to a server.

[1356] 3. Emotion Recognition by Emotion Engine

[1357] While the user is learning, the device camera captures facial expression data, which is then analyzed by the emotion engine, which determines, for example, that the user is confused.

[1358] 4. Providing individually optimized learning courses

[1359] The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results, adjusting the difficulty and content based on emotional data.

[1360] The device notifies the user of new courses and prompts them to take the next step in their studies.

[1361] 5. Providing learning using a variety of media

[1362] The server prepares a variety of media teaching materials and distributes them to the terminals.

[1363] The terminal displays or plays the educational material, allowing the user to study.

[1364] 6. Feedback and Guidance

[1365] The server analyzes learning progress and emotional data and generates appropriate feedback, e.g., "Pronunciation practice is needed." If confusion is detected, a brief explanation is added.

[1366] The device displays feedback to the user in real time and offers additional learning steps.

[1367] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[1368] The processing flow will be explained below.

[1369] Step 1:

[1370] The user accesses the registration form and enters basic information such as name, age, and learning purpose. For example, the user enters the name "Taro Tanaka," the age "25 years old," and the learning purpose "learn business English." The terminal then sends the entered information to the server.

[1371] Step 2:

[1372] The server stores the received information in a database and creates an individual user profile. Based on the profile, the server performs initial setup and prepares the user's learning environment.

[1373] Step 3:

[1374] The user logs in to the learning application and selects the "Business Conversation" course. The device records data such as the user's selection, the start time of the study, and the type of learning material.

[1375] Step 4:

[1376] The device transmits the user's learning progress, such as quiz answer results and the time the learning was completed, to the server in real time, allowing the server to store the user's learning history and performance data.

[1377] Step 5:

[1378] The device's built-in camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the captured data to recognize the user's emotions in real time, such as whether the user is tired, excited, or confused.

[1379] Step 6:

[1380] The server runs an AI algorithm to analyze the collected learning data and emotional data, and the algorithm comprehensively evaluates the user's learning patterns, accuracy rate, areas of interest, and emotional state to identify the optimal learning course.

[1381] Step 7:

[1382] Based on the analysis results, the server recommends courses such as "Intermediate Business Conversation" to the user. The difficulty and type of learning content is adjusted based on the emotional data. The device notifies the user of new learning courses and guides them to the next learning step.

[1383] Step 8:

[1384] The server prepares visual, audio and text learning materials based on individually optimized learning courses, and delivers these materials to the device.

[1385] Step 9:

[1386] The terminal displays or plays the received learning materials to the user, who then watches the visual learning materials, plays the audio learning materials, and reads the text learning materials.

[1387] Step 10:

[1388] As the user continues to study, the device records the learning progress in real time and sends the data to the server, which then evaluates the learning progress and emotional data in real time.

[1389] Step 11:

[1390] The server generates feedback based on learning progress and emotional data. For example, if there are many pronunciation errors, it generates feedback such as "You need to practice your pronunciation." If the emotion is negative, it also includes advice to relax. The device displays this feedback to the user in real time.

[1391] Step 12:

[1392] The user checks the feedback and follows the next learning step. The server provides new learning content and additional practice questions and guides the user through the terminal.

[1393] These detailed steps enable the learning platform to provide users with a personalized learning experience, further leveraging emotion recognition to maximize learning efficiency.

[1394] Example 2

[1395] 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."

[1396] Traditional learning systems have focused on providing a uniform learning course without taking into account the individual circumstances and feelings of each user. This has led to issues such as reduced learning efficiency and insufficient learning outcomes. Furthermore, the lack of real-time feedback and appropriate guidance has hindered the quality of the learning experience.

[1397] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting personal information of the user, a means for collecting learning history, interests, and abilities, a means for analyzing the collected data and generating an optimized learning course, a means for providing various media such as visual, text, and audio, a means for generating feedback based on learning progress and providing appropriate guidance, a means for capturing the user's facial expressions and voice to recognize emotions, and a means for reflecting the emotion recognition results in the generation of the learning course. This provides an optimal learning experience that takes into account the user's individual situation and emotions, making it possible to improve learning efficiency and effectiveness.

[1398] "User personal information" refers to basic data that identifies a user, such as name, age, and learning purpose.

[1399] "Learning history" is a record of the learning activities that a user has undertaken up to now.

[1400] "Interests" is data about subjects or topics that interest the user.

[1401] "Ability" is data that indicates the user's level of knowledge and skill.

[1402] "Collected Data" includes information about your personal information, learning history, interests, and abilities.

[1403] "Analysis" is the process of interpreting collected data using statistical or machine learning techniques to gain insights.

[1404] An "optimized learning path" is a learning program that is individually tailored to you based on your personal data, learning history, interests, and abilities.

[1405] "Diverse media, including visual, text, and audio" refers to different forms of instructional materials used to convey learning content.

[1406] "Study progress" is data that represents the progress a user has made in a course of study.

[1407] "Feedback" is information that suggests improvements or next steps based on a user's learning activities.

[1408] "Guidance" is the educational advice and support provided to learners.

[1409] "Facial expression" refers to data that indicates the user's facial movements and expressions.

[1410] "Voice" is data that indicates the user's voice.

[1411] "Emotion recognition" refers to identifying a user's emotional state by analyzing facial expressions, voice, etc.

[1412] "Emotion recognition results" are data obtained as analysis results of emotion recognition.

[1413] "Capture" is the act of collecting data using a camera or microphone.

[1414] "Reflect" means incorporating the analysis results into next steps and plans.

[1415] This invention relates to a system that provides a learning experience that is individually optimized for each user, and is characterized in that it uses an emotion engine to recognize the user's emotions and utilizes that information to optimize the learning course.

[1416] The system includes the following main elements:

[1417] 1. User registration and profile creation

[1418] Using a device, a user accesses the account creation screen and enters basic information such as name, age, and learning goals. For example, a user enters their name "Yamada Hanako," their age "30," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database to create an individual user profile.

[1419] 2. Collection of learning history and abilities

[1420] A user logs into the learning application and selects the "Business Conversation" course. The device records the user's learning behavior (e.g., selection of study materials, quiz answer results, and study time) in real time and periodically transmits the data to the server. The server then accumulates the collected data and obtains data for analyzing the user's learning history and ability.

[1421] 3. Emotion Recognition by Emotion Engine

[1422] The device's built-in camera and microphone capture the user's facial expressions and voice in real time. The emotion engine analyzes this data and recognizes the user's emotional state. For example, the emotion engine determines whether the user is tired, confused, or excited.

[1423] 4. Providing individually optimized learning courses

[1424] The server uses an AI algorithm to comprehensively analyze the user's learning history, interests, abilities, and the results of the emotion engine's analysis, and generates the most suitable learning course for the user. For example, if a user completes "Basics of Business Conversation" and the quiz results show an 80% accuracy rate, but the emotion engine determines that the user is tired, the server will recommend lighter learning materials. The device will notify the user of the new learning course and encourage them to start studying.

[1425] 5. Providing learning using a variety of media

[1426] The server prepares visual, audio, and text materials based on the selected learning course. The server distributes these materials to the terminal, which then displays or plays them in an appropriate format for the user. For example, the "Intermediate Business Conversation" course includes video materials of dialogue scenes, audio materials for pronunciation practice, and reference texts.

[1427] 6. Feedback and Guidance

[1428] The server analyzes the user's learning progress and generates appropriate feedback and guidance as needed. For example, if there are many pronunciation errors, it generates the feedback "You need to practice your pronunciation." If the emotion engine determines that the user is confused, it adds additional explanations. The device displays this feedback to the user in real time to support their learning.

[1429] Specific examples

[1430] When a user is learning English, the process proceeds as follows:

[1431] 1. The user enters basic information on the account creation screen. For example, the user enters their name "Yamada Hanako," their age "30 years old," and their learning goal "to learn business English." The device sends this information to the server, which then stores it in a database.

[1432] 2. The user begins learning "Business Conversation" and uses visual materials and quizzes. The device records this usage data in real time and sends it to the server.

[1433] 3. While the user is learning, the device camera captures facial expression data, which the emotion engine analyzes. For example, it determines that the user is confused.

[1434] 4. The server generates and recommends an "Intermediate Business Conversation" course based on the analysis results. The difficulty and content are adjusted based on the emotional data. The device notifies the user of the new course and prompts them to proceed to the next learning step.

[1435] 5. The server prepares various media learning materials and distributes them to the terminals, which then display or play the materials to allow the user to study.

[1436] 6. The server analyzes the learning progress and emotion data and generates appropriate feedback. For example, it may provide feedback such as "You need to practice your pronunciation" and add a brief explanation if the user shows any confusion. The device displays the feedback to the user in real time and offers additional learning steps.

[1437] Example of input prompt for generative AI model

[1438] "What is the best course of action for someone who needs to practice their English pronunciation and is tired?"

[1439] This system allows users to receive personalized learning courses and progress effectively. The introduction of an emotion engine will further address individual learning needs and improve the learning experience.

[1440] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1441] Step 1:

[1442] User registration and profile creation

[1443] Input: The user enters basic information such as name, age, and learning purpose into the terminal.

[1444] The server receives basic information entered by the user through the terminal.

[1445] The server stores this information in a database and builds an individual user profile.

[1446] Output: Individual user profile stored in a database.

[1447] Step 2:

[1448] Collection of learning history and abilities

[1449] Input: A user logs into a learning application and selects a course of study.

[1450] The terminal records the user's selected learning course and learning activities (selection of learning materials, quiz answer results, and study time) in real time.

[1451] The server receives the learning behavior data sent from the terminal and stores it in a database.

[1452] Data processing: The server aggregates the collected learning behavior data and analyzes learning history and ability.

[1453] Output: Accumulated and analyzed learning history and ability data.

[1454] Step 3:

[1455] Emotion recognition by emotion engine

[1456] Input: The user's facial expressions and voice are captured by the device's camera and microphone.

[1457] The terminal transmits the captured facial expression and voice data to the emotion engine.

[1458] Data calculation: The emotion engine analyzes facial and voice data to recognize the user's emotional state.

[1459] Output: The emotion recognition result (e.g., whether the user is tired, confused, excited, etc.).

[1460] Step 4:

[1461] Providing individually optimized learning courses

[1462] Input: The server integrates the learning history, interests, abilities and the analysis results of the emotion engine.

[1463] Data calculation: The server uses AI algorithms to comprehensively analyze this data and generate the most suitable learning course for the user.

[1464] The terminal receives the new course of study sent from the server.

[1465] Output: A personalized, optimized learning path.

[1466] Specific operation: For example, the server has finished "Basic Business Conversation" and the quiz result shows an 80% accuracy rate, but the emotion engine determines that the user is tired and recommends lighter learning materials.

[1467] Step 5:

[1468] Providing learning using a variety of media

[1469] Input: The server prepares visual, audio and textual materials based on the new course of study.

[1470] The server distributes the prepared teaching materials to the terminals.

[1471] The terminal displays or plays the distributed educational material in a format appropriate for the user.

[1472] Output: A variety of media materials displayed or played.

[1473] Specific operation: For example, the server prepares video, audio and text learning materials for "intermediate business conversation," and the terminal plays or displays these.

[1474] Step 6:

[1475] Feedback and guidance

[1476] Input: The server receives data based on the user's learning progress and emotion recognition results.

[1477] Data Calculation: The server analyzes these data and generates appropriate feedback and guidance.

[1478] The terminal displays the generated feedback to the user in real time.

[1479] Output: Feedback and guidance provided to the user in real time.

[1480] Specific Actions: For example, if there are many pronunciation errors, feedback such as "You need to practice your pronunciation" is provided, and additional explanations are added if the user appears confused.

[1481] (Application example 2)

[1482] 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."

[1483] Conventional online learning systems are composed of uniform content without considering the user's emotional state, making it difficult to provide an optimal learning experience for each user. Furthermore, it is difficult for virtual stores and services to provide appropriate feedback and support in real time according to the user's emotional state. This leads to issues such as reduced learning effectiveness and service satisfaction, and a decrease in users' willingness to continue using the service.

[1484] 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 recognizing the emotional state of the user and providing appropriate feedback and support based on that, means for navigating and supporting within the virtual environment, and means for providing a variety of media. This enables each user to have an individually optimized learning experience and to receive optimal services within the virtual store.

[1485] "Means for inputting user's personal information" refers to the methods and devices for inputting and collecting the user's name, age, interests, and other personal information.

[1486] "Means for collecting learning history, interests, and abilities" refers to methods and devices for recording and collecting the content that a user has learned to date, areas of interest, and learning abilities.

[1487] "Means for analyzing collected data and generating an optimized learning course" refers to a method and device for analyzing and creating an optimal learning course based on collected data.

[1488] "Visual, textual, and audio multimedia delivery means" refers to methods and devices that provide users with learning materials in different formats, such as visual, written, and audio materials.

[1489] The "means for generating feedback based on learning progress and providing appropriate guidance" refers to a method and apparatus for generating feedback based on the learning progress of a user and providing appropriate guidance.

[1490] "Means for recognizing the user's emotional state and providing appropriate feedback and support based on that" refers to a method and device that analyzes the user's emotional state from their facial expressions and voice, and provides appropriate feedback and support based on that information.

[1491] "Means for navigation and support within a virtual environment" refers to methods and devices that guide and assist users to move and operate efficiently within a virtual space.

[1492] As an embodiment of the present invention, a system including a server, a user terminal, and an emotion engine is proposed, which recognizes a user's emotions and provides appropriate feedback and support based on the emotions to provide an individually optimized learning experience for the user.

[1493] System Configuration

[1494] The system consists of the following main components:

[1495] 1. User device: A device such as a smartphone, tablet, PC, or head-mounted display. These devices are equipped with a camera and microphone to capture the user's facial expressions and voice.

[1496] 2. Server: A computer system that works in conjunction with a database management system (e.g., MySQL) to analyze training data and emotion data. AI algorithms and generative AI models are used for the specific analysis.

[1497] 3. Emotion engine: Uses emotion recognition APIs such as Google Cloud Vision and Amazon Rekognition to recognize the user's emotional state from facial and voice data.

[1498] Program processing

[1499] The server comprehensively analyzes the emotional state recognized by the emotion engine, the learning data collected from the user's device, and other personal information. Based on the analysis results, optimal feedback and support is generated and sent to the user's device in real time. For example, if the user is confused, the system will provide detailed explanations or simplified materials. If the user is excited, the system will encourage the user to move on to the next learning stage.

[1500] Specific use cases

[1501] For example, if a user is browsing a smartphone in a virtual store, and the emotion engine detects confusion in the user's facial expression, the server generates appropriate feedback such as "Would you like to know more about this smartphone?" and displays it on the user's device.

[1502] Example prompt for a generative AI model:

[1503] "When a user is feeling confused, generate feedback using the following template: 'Hey {username}, can you share more information about this {product name}?'"

[1504] Data items: User name, product name

[1505] By realizing this system, each user will be able to enjoy learning experiences and services that are optimized for them, which is expected to improve overall effectiveness and satisfaction.

[1506] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1507] Step 1:

[1508] This is the phase where the user's personal information is entered into the user's device. The user enters basic information such as name, age, interests, and learning objectives, and sends this data to the server. The entered data is saved on the server, and a user profile is created.

[1509] Input: Name, age, interests, learning objectives

[1510] Output: User profile data

[1511] Specific operation: When a user enters information on the account creation screen and clicks the "Submit" button, the device sends the information to the server.

[1512] Step 2:

[1513] This is the phase in which the user selects a learning subject and the learning session begins. The device records the user's learning behavior (selection of learning materials, quiz answers, and study time) in real time and periodically sends this data to the server.

[1514] Input: Learning subject, learning history data

[1515] Output: Learning behavior data

[1516] Specific operation: The user selects "Business English," browses the learning materials, and answers the quiz. This behavior is recorded on the device and periodically sent to the server.

[1517] Step 3:

[1518] In this phase, the emotion engine captures the user's facial expressions and voice data and analyzes their emotional state. The emotion engine analyzes the data acquired using the camera and microphone, and recognizes emotions such as confusion or excitement.

[1519] Input: facial expression data, voice data

[1520] Output: Emotional state data

[1521] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. The data is sent to the emotion engine, and the resulting emotional state is sent to the server.

[1522] Step 4:

[1523] In this phase, the server analyzes learning history data, personal profile data, and emotional state data in an integrated manner to generate optimal learning courses and support. The generated results are provided to the user in the form of learning courses and feedback appropriate for the user.

[1524] Input: learning history data, personal profile data, emotional state data

[1525] Output: Optimized learning course, feedback

[1526] How it works: The server analyzes various data and generates optimal learning courses using machine learning algorithms. For example, if the user is confused, it suggests simple learning materials.

[1527] Step 5:

[1528] This is the phase where the server delivers selected learning materials and feedback to the user's device in various media formats to support learning. The learning materials are provided in visual, text, audio, and other formats, and the user studies them.

[1529] Input: Optimized learning path, feedback

[1530] Output: Diverse media materials

[1531] Specific operation: The server sends the selected teaching materials to the terminal in the form of visual, audio, text, etc., and the terminal displays or plays them.

[1532] Step 6:

[1533] This is the phase where the user's device displays real-time feedback to support the user's learning progress. Appropriate feedback is displayed to the user at the appropriate time, encouraging them to move on to the next step.

[1534] Input: Real-time feedback

[1535] Output: Displayed feedback

[1536] Specific operation: The device immediately displays the generated feedback and instructs the user on how to proceed with the learning and what the next step should be. The user continues learning based on the feedback.

[1537] 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.

[1538] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1539] In the above embodiment, an example 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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).

[1544] 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.

[1545] 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."

[1546] 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.

[1547] 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).

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] The following is further disclosed regarding the above embodiment.

[1559] (Claim 1)

[1560] A means for inputting the user's personal information;

[1561] a means of collecting learning history, interests, and abilities;

[1562] A means for analyzing the collected data and generating an optimized course of study;

[1563] A means of providing a variety of media, including visual, textual, and audio;

[1564] a means for generating feedback based on learning progress and providing appropriate guidance;

[1565] A system including:

[1566] (Claim 2)

[1567] 10. The system of claim 1, further comprising: means for transmitting the collected data to a server.

[1568] (Claim 3)

[1569] 10. The system of claim 1, further comprising: means for displaying the feedback in real time on the user's terminal.

[1570] "Example 1"

[1571] (Claim 1)

[1572] means for inputting personal information through a user's input device;

[1573] a means of collecting learning content usage history, interests, and abilities;

[1574] A means for analyzing the collected data and generating an optimized learning program;

[1575] A means of providing a variety of media, including visual, textual, and audio;

[1576] a means for generating feedback based on learning progress and providing appropriate guidance;

[1577] A means for recording the user's learning behavior in real time and periodically transmitting the recording to a server;

[1578] A system including:

[1579] (Claim 2)

[1580] 10. The system of claim 1, wherein an artificial intelligence algorithm is used to recommend the most suitable course of study to the user based on the collected data.

[1581] (Claim 3)

[1582] 10. The system of claim 1, further comprising: means for displaying the feedback in real time on the user's terminal.

[1583] "Application Example 1"

[1584] (Claim 1)

[1585] A means for inputting the user's personal information;

[1586] a means of collecting learning history, interests, and abilities;

[1587] A means for analyzing the collected data and generating an optimized course of study;

[1588] A means of providing a variety of media, including visual, textual, and audio;

[1589] a means for generating feedback based on learning progress and providing appropriate guidance;

[1590] A means for recording work history and operation behavior for the purpose of improving worker skills;

[1591] A means for generating an optimal work skill course based on the recorded data;

[1592] A system including:

[1593] (Claim 2)

[1594] 10. The system of claim 1, further comprising: means for transmitting the collected data to a server.

[1595] (Claim 3)

[1596] 10. The system of claim 1, further comprising: means for displaying the feedback in real time on the user's terminal.

[1597] "Example 2: Combining Emotion Engines"

[1598] (Claim 1)

[1599] A means for inputting the user's personal information;

[1600] a means of collecting learning history, interests, and abilities;

[1601] A means for analyzing the collected data and generating an optimized course of study;

[1602] A means of providing a variety of media, including visual, textual, and audio;

[1603] a means for generating feedback based on learning progress and providing appropriate guidance;

[1604] A means of capturing the user's facial expressions and voice to recognize their emotions;

[1605] A means for reflecting emotion recognition results in the generation of learning courses;

[1606] A system including:

[1607] (Claim 2)

[1608] 10. The system of claim 1, further comprising: means for transmitting the collected data to a server.

[1609] (Claim 3)

[1610] 10. The system of claim 1, further comprising: means for displaying the feedback in real time on the user's terminal.

[1611] "Application example 2 when combining emotion engines"

[1612] (Claim 1)

[1613] A means for inputting the user's personal information;

[1614] a means of collecting learning history, interests, and abilities;

[1615] A means for analyzing the collected data and generating an optimized course of study;

[1616] A means of providing a variety of media, including visual, textual, and audio;

[1617] a means for generating feedback based on learning progress and providing appropriate guidance;

[1618] A means of recognizing the user's emotional state and providing appropriate feedback and support based on that state;

[1619] a means of navigation and support within the virtual environment;

[1620] A system including:

[1621] (Claim 2)

[1622] 10. The system of claim 1, further comprising: means for transmitting the collected data to a server.

[1623] (Claim 3)

[1624] 10. The system of claim 1, further comprising: means for displaying the feedback in real time on the user's terminal. [Explanation of symbols]

[1625] 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 means for inputting the user's personal information; a means of collecting learning history, interests, and abilities; A means for analyzing the collected data and generating an optimized course of study; A means of providing a variety of media, including visual, textual, and audio; a means for generating feedback based on learning progress and providing appropriate guidance; A system including:

2. The system of claim 1 further comprising means for transmitting the collected data to a server.

3. The system of claim 1 further comprising means for displaying the feedback in real time on the user's terminal.

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