system

Abstract

We provide the system. [Solution] A means of creating personalized educational programs using generative artificial intelligence, A means of providing learning materials while adapting to multiple languages ​​in real time, A means of presenting supplementary information to deepen understanding through natural language interaction with the user, A means of recording educational progress and providing individual feedback, A means of providing a community feature that allows users to share knowledge with each other, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional educational systems, there are significant disparities in access to and the quality of education depending on the region and individual circumstances. In particular, education in regions with limited resources only is difficult to meet individual needs, exacerbating inequality in learning opportunities. Also, due to overcrowded education and insufficient multilingual support, it is difficult to achieve efficient educational outcomes. As a result, it has become difficult for students with diverse backgrounds to obtain opportunities to fully发挥 their maximum talents and abilities.

Means for Solving the Problems

[0005] This invention aims to eliminate educational disparities by using generative artificial intelligence to create and provide personalized educational programs based on each user's profile information. Furthermore, this invention provides learning materials that support multiple languages ​​in real time and provides supplementary information to deepen the user's understanding through natural language interaction. It also promotes continuous and efficient learning by recording educational progress and providing individual feedback. In addition, it includes means to improve the quality of education by promoting mutual learning with other users through a community function that enables knowledge sharing.

[0006] "Generative artificial intelligence" is a technology that uses natural language processing and data analysis to generate and provide educational content optimized for the user.

[0007] A "personalized education program" is an educational curriculum customized based on each user's profile information and learning progress.

[0008] "Real-time adaptation" refers to a process that responds immediately to user requests and inputs, providing necessary information and learning materials on the spot.

[0009] "Natural language interaction" is a means for users to gain a deeper understanding of educational content by interacting with the system in the form of everyday conversation.

[0010] "Recording progress and providing feedback" means continuously tracking the user's learning progress, evaluating their achievements and areas for improvement, and providing recommendations and advice for the next stage.

[0011] The "community function" is a platform that allows users to share information with other users and engage in discussions and collaborations regarding learning content. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system for providing personalized education using generative artificial intelligence. The system includes a terminal distributed to the user and a server for managing learning data.

[0034] The device functions as an educational platform accessible to the user, identifying the user through profile settings and authentication. This allows the user to access learning programs tailored to their profile on the device. Generative artificial intelligence generates learning materials based on the user's input and learning history, enabling real-time adaptation. The device displays learning materials suited to the individual's learning situation, and the user can gain a deeper understanding by interacting with the system in natural language.

[0035] The server centrally manages all users' profile data and learning history, enhancing the generation of learning programs. The server also manages each user's progress, generates feedback, and sends it to their device. Furthermore, the generative artificial intelligence supports user learning by referencing appropriate information to generate explanations in response to user questions.

[0036] As a concrete example, when a student is studying mathematics, multiple lectures and practice problems related to mathematics are presented on the terminal. If a question arises during learning, the user enters the question through the terminal. The generating AI then finds the answer to the question and presents a clear explanation to the user. Furthermore, the server analyzes the user's accuracy rate and tendency of incorrect answers and recommends what to learn next. This allows the user to learn efficiently at their own pace.

[0037] A key feature of this system is that it provides each user with the most suitable educational environment through their learning experience, and by combining multilingual support and natural language processing, it realizes a flexible educational system that can be used anywhere in the world.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] When the device is powered on, the initial setup wizard appears. Through the wizard, the user configures the Wi-Fi network and selects their preferred language. The user then enters their profile information and submits it to the server.

[0041] Step 2:

[0042] The server receives the user's profile information and generates a personalized educational program based on it. The generated program is then sent to the terminal.

[0043] Step 3:

[0044] The terminal receives the learning program from the server and presents it to the user. The user then begins learning using the presented learning materials.

[0045] Step 4:

[0046] If a user has questions during the learning process, they input the question in natural language through their device. Generative artificial intelligence analyzes the question and generates an appropriate answer. The answer is displayed on the device for the user to review.

[0047] Step 5:

[0048] The server tracks learning progress and evaluates the user's accuracy and progress after each learning session. Based on the evaluation, information suggesting what to learn next is generated and sent to the device.

[0049] Step 6:

[0050] The device displays feedback from the server to the user, providing guidance for the next learning step. This allows the user to continue learning efficiently.

[0051] Step 7:

[0052] Users can utilize the device's community features to share knowledge with other users. Discussions here can also receive additional support from generative artificial intelligence, allowing for deeper learning.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Traditional education systems often provide uniform content to individual learners, making it difficult to deliver effective education tailored to each learner's level of understanding and progress. Furthermore, the lack of multilingual support and immediate adaptability means that adequate support cannot be provided to learners with diverse language backgrounds. Additionally, the insufficient function for knowledge exchange and communication among learners makes sharing and collaborating on learning challenging.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for creating personalized educational content using generative artificial intelligence, means for displaying educational resources while adapting to multiple languages ​​in real time, and means for providing supplementary data to deepen understanding through natural language interaction with the user. This enables the provision of individually optimized education for each learner and flexible adaptation to multilingual environments. Furthermore, it facilitates mutual knowledge sharing and improves the quality of learning.

[0058] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to dynamically generate and present educational materials and information tailored to individual needs.

[0059] "Personalized educational content" refers to learning materials and programs optimized based on each learner's level of understanding and progress.

[0060] "A means of displaying educational resources while adapting to multiple languages ​​in real time" refers to a technology that instantly translates and displays learning materials in a way that is accessible to users with different language backgrounds.

[0061] "Natural language dialogue" is a method of communication between humans and machines that allows users to speak directly with a computer using the language they use in everyday life.

[0062] "Supplementary data" refers to additional information and explanatory materials provided in addition to the main teaching materials to support learners' understanding.

[0063] "Means for recording learning activities and generating individualized evaluation information" refers to technologies for tracking learners' behavior and outcomes and generating feedback based on that information.

[0064] The "communication function" is a function that supports learners in sharing knowledge and opinions and interacting with each other.

[0065] "A means of dynamically adapting and changing teaching materials based on questions and answers" refers to a technology that appropriately modifies and provides teaching material content based on questions from learners.

[0066] This invention constructs a system for providing personalized educational experiences. The main components of the system include a terminal provided to the user and a server for managing learning data.

[0067] The server is equipped with an advanced generative artificial intelligence model that analyzes user profile information to generate optimal educational content. Specifically, it generates and provides learning materials based on each user's past learning history and progress data. Furthermore, the server supports multiple languages ​​and handles real-time translation and content delivery. The server also records the user's learning activity, analyzes the obtained data, and generates feedback. The generated feedback is sent to the device as personalized advice, suggesting guidelines for the next learning step.

[0068] The device allows users to access the educational platform and engage in interactive learning. Users can input natural language prompts through the device to resolve their questions. For example, if a user inputs the prompt "What is the next math topic to learn?", the generative AI will provide explanations and recommendations in response to that question.

[0069] As a concrete example, consider a user learning English. The user provides a prompt using their device, such as "Explain the use of the present perfect tense again," and the AI ​​instantly generates an explanation and example sentences, displaying them on the device. The server also tracks the user's progress and suggests the most suitable topics for future lessons. This entire process takes place in real time, providing the user with a stress-free learning experience.

[0070] The implementation of this system allows users to receive education in a way that is personalized to their learning needs and goals, thus enabling efficient and effective learning.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user starts up the device.

[0074] The terminal displays a user interface and presents the user with an authentication screen. The user enters their login information and sends profile data. The server performs user authentication based on the received authentication information and profile data, and outputs the authentication status to the terminal.

[0075] Step 2:

[0076] The server retrieves user profile data and generates optimized learning content.

[0077] The server references the user's learning history and profile, and uses a generative AI model to create personalized learning materials. The input is the user's past learning data and current learning goals, and the output is a set of educational content tailored to the user.

[0078] Step 3:

[0079] The device displays customized educational content for the user.

[0080] The terminal displays educational resources received from the server on its screen. The user begins learning based on this, and enters prompts as needed. The input is content data from the server, and the output is the educational display presented to the user.

[0081] Step 4:

[0082] The user enters any questions that arise during the learning process as prompts into the terminal.

[0083] The user sends questions that arise during the learning process to the terminal in natural language. The input is the prompt text entered by the user, which the terminal sends to the server.

[0084] Step 5:

[0085] The server uses a generated AI model to produce answers to user questions.

[0086] The server parses the prompt message, aggregates the necessary information, and generates a response. The input is the prompt message from the user, and the output is the response message.

[0087] Step 6:

[0088] The terminal receives the response from the server and presents it to the user.

[0089] The terminal displays the generated response to the user. The output is response information presented in a user-friendly format.

[0090] Step 7:

[0091] The server receives the user's learning data and analyzes their progress.

[0092] The server analyzes user activity data to provide feedback and optimize the next learning content. The input is user data acquired during learning, and the output is an improved learning plan and feedback.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In modern manufacturing environments, there is a need to improve worker capabilities and productivity through efficient skills training and immediate support. However, traditional training methods lack adaptability and responsiveness to individual workers, hindering efficiency improvements.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for creating personalized educational processes using a generation system, means for presenting supplementary information to deepen understanding through natural language dialogue with the user, and means for displaying learning guidance in real time via a visual device to allow the user to immediately resolve questions during work. This enables workers in the manufacturing field to improve their skills in a way that is relevant to their actual work while receiving individually tailored education.

[0098] A "generation system" is a program that utilizes artificial intelligence to automatically create educational curricula tailored to individual users.

[0099] "Real-time adaptation" refers to the ability to instantly change and present learning materials in response to different language settings and user needs.

[0100] "Natural language dialogue" is a method of communicating with a system using the language that users use in their daily lives.

[0101] "Providing supplementary information" refers to the act of providing additional information or explanations necessary for users to deepen their understanding.

[0102] "Providing feedback" means offering individualized feedback based on the user's educational progress and performance.

[0103] "Community features" refer to mechanisms that allow users to share information with each other through the system and deepen their knowledge together.

[0104] "Display via a visual device" refers to a method of presenting information to the user's field of vision using devices such as smart glasses.

[0105] "Resolving issues immediately" means providing prompt and accurate answers to users' questions and concerns.

[0106] This invention provides a learning system that utilizes visual devices to efficiently conduct skills training in manufacturing sites. The server employs a generative AI model and creates an individualized training process based on each user's profile information and real-time data input. Natural language processing is performed to provide supplementary information and feedback immediately in response to learning progress and questions from the user.

[0107] The visual device, specifically smart glasses, displays learning instructions in real time within the user's field of vision. This allows for immediate and intuitive access to necessary information even while working. Furthermore, the system supports multiple languages, making it widely usable in international manufacturing environments.

[0108] If a user has a question while working, they can input it using voice or text. The server generates an appropriate answer to that question and presents it to the user immediately, minimizing interruptions to their work. This question-answering process utilizes natural language processing technology and the capabilities of generated AI.

[0109] As a concrete example, suppose a user learning how to operate a new piece of equipment in a factory has a question about the equipment's settings. If the user enters the prompt "Please tell me the correct initial setup procedure for this equipment," the server will use a generative AI model to gather relevant information and display an easy-to-understand answer in the user's view. This advanced educational system is expected to dramatically improve users' skill acquisition.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user puts on smart glasses and begins working. Before starting, the system sends the user's profile information (past learning history and skill level) to the server. Based on this input, the server generates an educational process adapted to the user. At this point, appropriate learning objectives and procedures are displayed in the user's field of vision. This display is customized by a generating AI model.

[0113] Step 2:

[0114] During the process, if the user has a question, they input it via voice or text. The terminal receives this input and sends it to the server. This data is analyzed through natural language processing. The input is the user's question, and based on this, the AI ​​searches the database and generates the best answer. The output as an answer is specific instructions or explanations.

[0115] Step 3:

[0116] The server uses a generative AI model to generate answers to user questions and sends them to the terminal. During this process, it consults a training database in the cloud as needed to obtain additional information. The server's output is a detailed answer to the user's question, including any relevant supplementary materials.

[0117] Step 4:

[0118] The terminal displays the response received from the server within the user's field of view. The visual device presents the information in an intuitively understandable format. The displayed information is tailored to the user's task, visually indicating solutions to questions and the next steps. As a result, the user can solve the problem immediately.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention provides an educational system that combines generative artificial intelligence and an emotion engine to realize a personalized learning experience for each user. This system consists of terminals distributed to users and a server that manages the data.

[0121] The device features profile settings and authentication functions to support the user's learning progress. Users access the learning program through the device, and an emotion engine monitors the user's emotional state in real time. It recognizes emotions from the user's facial expressions and voice, and evaluates the user's stress levels and focus during the learning process.

[0122] The server dynamically adjusts the learning program based on data obtained from the emotion engine. The generative artificial intelligence generates educational content that corresponds to the user's emotional state and adapts it as needed, such as slowing the pace. In addition, it collects the user's emotional data and generates an individual lesson plan as feedback, which is then sent to the terminal.

[0123] As a concrete example, suppose a student is studying chemistry. The device, using an emotion engine, recognizes that the student finds the content difficult. The server analyzes this emotion data, generates learning materials with adjusted difficulty levels, and presents them in a format that is easy for the student to understand. Furthermore, the generated materials are tailored to the student's profile and learning history using generative artificial intelligence. If the user has a question, they can ask it in natural language on the device, and an answer is provided instantly. The server also manages the learning progress and provides feedback and suggestions for the next session after each session.

[0124] Through this mechanism, the system provides a flexible learning environment that is tailored to the user's emotions, aiming to bridge the educational gap. Furthermore, the community function allows for information exchange with other users, deepening the understanding of the learning material.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] When the device is powered on, the user is presented with an initial setup wizard. The user connects to a Wi-Fi network and enters their language and profile information. The entered information is then sent to the server.

[0128] Step 2:

[0129] Based on the received profile information, the server generates an optimal educational program for the user. This program is individually customized by the generating artificial intelligence and sent to the terminal.

[0130] Step 3:

[0131] The terminal displays the generated educational program to the user. The user follows the displayed program and begins learning.

[0132] Step 4:

[0133] During learning, the emotion engine built into the device analyzes the user's facial expressions and voice in real time. This allows the system to determine the user's emotional state.

[0134] Step 5:

[0135] Emotional data is sent from the device to the server, which uses this data to analyze the current learning program. If necessary, the difficulty level is adjusted, or the learning materials are modified to reduce user stress.

[0136] Step 6:

[0137] If a user has any questions or points of confusion, they input their questions in natural language through their device. The server uses generative artificial intelligence to generate appropriate explanations for the questions and displays them on the device.

[0138] Step 7:

[0139] The server tracks learning progress and generates feedback at the end of each learning session. The feedback and recommended learning content for the next session are sent to the device and displayed to the user.

[0140] Step 8:

[0141] Users utilize the device's community features to share their learning experiences with other users. Discussions within this community are supported by generative artificial intelligence, allowing users to deepen their knowledge.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In the field of education, there is a demand for personalized educational programs tailored to each user's learning style and level of understanding. However, conventional systems have struggled to analyze users' emotions and learning progress in real time and generate dynamic educational content based on those results. Furthermore, they have been unable to respond immediately to the learning stress and difficulties users experience, leading to a decline in educational efficiency.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for collecting and analyzing data in real time using an emotion engine that analyzes user emotions, means for dynamically generating and adjusting personalized educational programs based on the analyzed emotion data using generative artificial intelligence, and means for immediately responding when the user interacts with the generated educational content in natural language. This makes it possible to provide a flexible and effective learning process that is tailored to the user's learning progress.

[0147] "User authentication" is the process required to verify the identity of a user accessing a system, and is usually performed using profile information or similar methods.

[0148] "Profile settings" refers to the preparatory stage for providing a personalized learning experience based on data such as the user's personal information, learning history, and learning style, which is then registered on the device.

[0149] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to evaluate their emotional state, and detects and collects this data in real time.

[0150] "Generative artificial intelligence" is an AI technology that automatically and dynamically generates educational content tailored to user needs based on collected data.

[0151] "Dynamic adjustment of educational programs" is a process that optimizes educational content in real time based on user sentiment data and learning progress, providing a learning experience tailored to the user.

[0152] "Natural language interaction" refers to the ability for users to communicate with the system using text or voice through their device, and to receive immediate responses to questions about generated educational content.

[0153] "Recording educational progress" is the process of tracking how far a user has progressed in their learning and using that data to provide next learning opportunities and feedback.

[0154] "Communication features" are functions that users use to share information with other users and to collaborate on learning, aiming to share knowledge and improve educational effectiveness.

[0155] Modes for carrying out the invention

[0156] This invention is an educational system for providing users with individualized learning experiences. This system consists of terminals distributed to users and a server that manages data. Specific embodiments of this system are described below.

[0157] Users access the learning program using a device. The device is equipped with features that authenticate the user based on their profile information and prepare them for the learning program. The specific hardware of the device includes a camera and microphone, which allows for real-time monitoring of the user's facial expressions and voice.

[0158] The device uses an emotion engine to evaluate the user's emotional state from collected facial expressions and voice data. The analyzed emotion data is immediately sent to the server. Based on the received emotion data, the server dynamically generates and adjusts educational content using generative artificial intelligence. This generative AI optimizes the educational program to match the user's individual learning history and profile data.

[0159] The generated learning materials are sent to the device in real time and presented to the user. Users can ask questions about the presented educational content using natural language. The server provides an immediate response, resolving the user's questions. This interaction allows users to learn more effectively.

[0160] For example, if a student is studying chemistry, the device analyzes the student's emotions during learning and detects if they find the content difficult. The server analyzes this data and generates and provides chemistry materials with adjusted difficulty levels to make them easier to understand. Also, if the student enters a prompt such as, "Please explain the mechanism of this chemical reaction in detail," a detailed explanation corresponding to that request is instantly provided.

[0161] This system aims to improve educational efficiency by providing a flexible learning environment tailored to the user's learning progress.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user logs into the terminal and enters their profile information. The terminal uses this information to authenticate the user. The authentication process verifies that the user has the necessary permissions to access the system. The input data is the username and password, and the output is the authentication result. If authentication is successful, the user can proceed to the learning program.

[0165] Step 2:

[0166] When a user begins learning, the device's camera and microphone are activated to monitor the user's facial expressions and voice in real time. The device sends this data to an emotion engine to analyze the user's emotional state. The input is facial expressions and voice data, and the output is the analyzed emotional information. The device evaluates the user's emotions and determines their emotional state.

[0167] Step 3:

[0168] The device sends analyzed emotional data to the server. The server processes the emotional data and uses generative artificial intelligence to generate educational content optimized for the user. The input is emotional data and user profile information, and the output is personalized educational content. The server dynamically adjusts the educational program and optimizes the teaching materials.

[0169] Step 4:

[0170] The generated educational content is sent from the server to the terminal and presented to the user. The user can then use the materials to progress with their learning. If a question arises during learning, the user sends a question in natural language to the server by entering a prompt. The input is a prompt, and the output is the answer from the server.

[0171] Step 5:

[0172] Upon receiving a user prompt, the server searches for relevant information and generates an appropriate response using a generative AI model. The server then sends the response to the terminal, providing it to the user quickly. This response resolves the user's question and improves the quality of learning.

[0173] Step 6:

[0174] When a learning session ends, the terminal sends learning progress data to the server, which records it. Based on the progress data, the server suggests the next learning content and provides feedback to the user. The input is learning progress data, and the output is the next learning suggestion and feedback.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0177] Traditional education systems often fail to adequately address individual needs and provide effective learning environments because they do not consider learners' emotional states. Furthermore, learners' stress and decreased interest frequently negatively impact learning efficiency. In particular, in the workplace, generic training programs that disregard individual emotional states are common, highlighting the need for training that takes into account the understanding and emotional needs of individual workers.

[0178] The specific processing performed 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 creating an individualized educational program using generative artificial intelligence, means for detecting the emotional state of the worker and dynamically adjusting the learning content according to that state, and means for presenting supplementary information to deepen understanding through natural language interaction with the user. This provides a flexible training environment that is tailored to the emotional state of the learner or worker, enabling individual skill improvement and stress reduction.

[0179] "Generative artificial intelligence" refers to artificial intelligence technology that includes algorithms for generating personalized educational programs and content based on user data.

[0180] A "personalized education program" is educational content that is tailored to dynamically provide an appropriate learning pace and content, taking into account each user's profile and emotional state.

[0181] "Emotional state" refers to a psychological state determined in real time by analyzing the user's facial expressions and voice, and is information that quantifies the learner's stress and interests.

[0182] "Means of dynamically adjusting learning content" refers to a function that automatically changes the content, difficulty level, and pace of the educational program in real time according to the user's emotional state.

[0183] "Natural language interaction" is a communication method in which users interact with a system using the language they normally use, deepening their understanding through questions and answers.

[0184] "Supplemental information" refers to additional data and explanations provided to help users understand the learning material, and is automatically presented by the system as needed.

[0185] The "community function" is a feature that allows learners to share knowledge with other users and deepen their understanding through exchanging opinions and collaborative learning.

[0186] This system consists of a server and learner terminals, and specifically incorporates generative artificial intelligence, an emotion engine, and a natural language processing module. The server uses generative artificial intelligence to generate educational programs tailored to individual learners. In this process, learner profile data is collected during the initial setup and used as the foundation for the generation process.

[0187] The device is equipped with an emotion engine that acquires the learner's facial expressions and voice data in real time through the camera and microphone. This data is processed by an emotion analysis model developed using Python (utilizing TENSORFLOW® and PyTorch) to identify the learner's emotional state. The emotion data is sent to a server, which then adjusts the learning program appropriately based on this data.

[0188] Users interact with the system through natural language interaction, and if they have questions during learning, they can immediately ask them via text or voice through their device. Natural language processing is performed by a generative AI model, providing rapid responses.

[0189] As a concrete example, imagine a new worker in a factory learning to operate new equipment. If the terminal detects the worker's confused expression, the server automatically slows the pace of the training and adds visual guidance. An example of a prompt used in this process is, "Generate training content to present when the worker's understanding declines while learning how to operate the new machine." In this way, learners can learn at their own individual pace.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The device acquires the learner's facial expressions and voice data in real time through its camera and microphone, and inputs this data into an emotion analysis model. This allows the learner's emotional state to be quantified and recognized.

[0193] Step 2:

[0194] The server integrates emotional state data sent from the terminal with learner profile information obtained during initial setup to generate an appropriate educational program. Using a generative AI model, it dynamically adjusts the difficulty and pace of the learning content using prompt messages.

[0195] Step 3:

[0196] The server generates a personalized educational program and sends it to the terminal. The terminal then presents the program to the learner and begins training with visual guidance as needed.

[0197] Step 4:

[0198] During training, users can ask the system questions in natural language, and the device sends these questions as text data to the server. The server analyzes the questions, uses a generative AI model to instantly generate answers, and sends them back to the device.

[0199] Step 5:

[0200] The terminal displays the responses received from the server to the user and continuously monitors the progress of the training. If necessary, it repeats the process of re-analyzing the sentiment data and fine-tuning the learning program.

[0201] Step 6:

[0202] Based on the emotions and learning data recorded after the training session, the server generates a plan for the next learning session and sends it to the user's device as feedback along with their progress.

[0203] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0219] This invention provides a system for providing personalized education using generative artificial intelligence. The system includes a terminal distributed to the user and a server for managing learning data.

[0220] The device functions as an educational platform accessible to the user, identifying the user through profile settings and authentication. This allows the user to access learning programs tailored to their profile on the device. Generative artificial intelligence generates learning materials based on the user's input and learning history, enabling real-time adaptation. The device displays learning materials suited to the individual's learning situation, and the user can gain a deeper understanding by interacting with the system in natural language.

[0221] The server centrally manages all users' profile data and learning history, enhancing the generation of learning programs. The server also manages each user's progress, generates feedback, and sends it to their device. Furthermore, the generative artificial intelligence supports user learning by referencing appropriate information to generate explanations in response to user questions.

[0222] As a concrete example, when a student is studying mathematics, multiple lectures and practice problems related to mathematics are presented on the terminal. If a question arises during learning, the user enters the question through the terminal. The generating AI then finds the answer to the question and presents a clear explanation to the user. Furthermore, the server analyzes the user's accuracy rate and tendency of incorrect answers and recommends what to learn next. This allows the user to learn efficiently at their own pace.

[0223] A key feature of this system is that it provides each user with the most suitable educational environment through their learning experience, and by combining multilingual support and natural language processing, it realizes a flexible educational system that can be used anywhere in the world.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] When the device is powered on, the initial setup wizard appears. Through the wizard, the user configures the Wi-Fi network and selects their preferred language. The user then enters their profile information and submits it to the server.

[0227] Step 2:

[0228] The server receives the user's profile information and generates a personalized educational program based on it. The generated program is then sent to the terminal.

[0229] Step 3:

[0230] The terminal receives the learning program from the server and presents it to the user. The user then begins learning using the presented learning materials.

[0231] Step 4:

[0232] If a user has questions during the learning process, they input the question in natural language through their device. Generative artificial intelligence analyzes the question and generates an appropriate answer. The answer is displayed on the device for the user to review.

[0233] Step 5:

[0234] The server tracks learning progress and evaluates the user's accuracy and progress after each learning session. Based on the evaluation, information suggesting what to learn next is generated and sent to the device.

[0235] Step 6:

[0236] The device displays feedback from the server to the user, providing guidance for the next learning step. This allows the user to continue learning efficiently.

[0237] Step 7:

[0238] Users can utilize the device's community features to share knowledge with other users. Discussions here can also receive additional support from generative artificial intelligence, allowing for deeper learning.

[0239] (Example 1)

[0240] Next, we will describe Example 1. 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."

[0241] Traditional education systems often provide uniform content to individual learners, making it difficult to deliver effective education tailored to each learner's level of understanding and progress. Furthermore, the lack of multilingual support and immediate adaptability means that adequate support cannot be provided to learners with diverse language backgrounds. Additionally, the insufficient function for knowledge exchange and communication among learners makes sharing and collaborating on learning challenging.

[0242] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0243] In this invention, the server includes means for creating personalized educational content using generative artificial intelligence, means for displaying educational resources while adapting to multiple languages ​​in real time, and means for providing supplementary data to deepen understanding through natural language interaction with the user. This enables the provision of individually optimized education for each learner and flexible adaptation to multilingual environments. Furthermore, it facilitates mutual knowledge sharing and improves the quality of learning.

[0244] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to dynamically generate and present educational materials and information tailored to individual needs.

[0245] "Personalized educational content" refers to learning materials and programs optimized based on each learner's level of understanding and progress.

[0246] "A means of displaying educational resources while adapting to multiple languages ​​in real time" refers to a technology that instantly translates and displays learning materials in a way that is accessible to users with different language backgrounds.

[0247] "Natural language dialogue" is a method of communication between humans and machines that allows users to speak directly with a computer using the language they use in everyday life.

[0248] "Supplementary data" refers to additional information and explanatory materials provided in addition to the main teaching materials to support learners' understanding.

[0249] "Means for recording learning activities and generating individualized evaluation information" refers to technologies for tracking learners' behavior and outcomes and generating feedback based on that information.

[0250] The "communication function" is a function that supports learners in sharing knowledge and opinions and interacting with each other.

[0251] "A means of dynamically adapting and changing teaching materials based on questions and answers" refers to a technology that appropriately modifies and provides teaching material content based on questions from learners.

[0252] This invention constructs a system for providing personalized educational experiences. The main components of the system include a terminal provided to the user and a server for managing learning data.

[0253] The server is equipped with an advanced generative artificial intelligence model that analyzes user profile information to generate optimal educational content. Specifically, it generates and provides learning materials based on each user's past learning history and progress data. Furthermore, the server supports multiple languages ​​and handles real-time translation and content delivery. The server also records the user's learning activity, analyzes the obtained data, and generates feedback. The generated feedback is sent to the device as personalized advice, suggesting guidelines for the next learning step.

[0254] The device allows users to access the educational platform and engage in interactive learning. Users can input natural language prompts through the device to resolve their questions. For example, if a user inputs the prompt "What is the next math topic to learn?", the generative AI will provide explanations and recommendations in response to that question.

[0255] As a concrete example, consider a user learning English. The user provides a prompt using their device, such as "Explain the use of the present perfect tense again," and the AI ​​instantly generates an explanation and example sentences, displaying them on the device. The server also tracks the user's progress and suggests the most suitable topics for future lessons. This entire process takes place in real time, providing the user with a stress-free learning experience.

[0256] The implementation of this system allows users to receive education in a way that is personalized to their learning needs and goals, thus enabling efficient and effective learning.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The user starts up the device.

[0260] The terminal displays a user interface and presents the user with an authentication screen. The user enters their login information and sends profile data. The server performs user authentication based on the received authentication information and profile data, and outputs the authentication status to the terminal.

[0261] Step 2:

[0262] The server retrieves user profile data and generates optimized learning content.

[0263] The server references the user's learning history and profile, and uses a generative AI model to create personalized learning materials. The input is the user's past learning data and current learning goals, and the output is a set of educational content tailored to the user.

[0264] Step 3:

[0265] The device displays customized educational content for the user.

[0266] The terminal displays educational resources received from the server on its screen. The user begins learning based on this, and enters prompts as needed. The input is content data from the server, and the output is the educational display presented to the user.

[0267] Step 4:

[0268] The user enters any questions that arise during the learning process as prompts into the terminal.

[0269] The user sends questions that arise during the learning process to the terminal in natural language. The input is the prompt text entered by the user, which the terminal sends to the server.

[0270] Step 5:

[0271] The server uses a generated AI model to produce answers to user questions.

[0272] The server parses the prompt message, aggregates the necessary information, and generates a response. The input is the prompt message from the user, and the output is the response message.

[0273] Step 6:

[0274] The terminal receives the response from the server and presents it to the user.

[0275] The terminal displays the generated response to the user. The output is response information presented in a user-friendly format.

[0276] Step 7:

[0277] The server receives the user's learning data and analyzes their progress.

[0278] The server analyzes user activity data to provide feedback and optimize the next learning content. The input is user data acquired during learning, and the output is an improved learning plan and feedback.

[0279] (Application Example 1)

[0280] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0281] In modern manufacturing environments, there is a need to improve worker capabilities and productivity through efficient skills training and immediate support. However, traditional training methods lack adaptability and responsiveness to individual workers, hindering efficiency improvements.

[0282] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes means for creating an individualized educational process using a generation system, means for presenting supplementary information for deepening understanding through natural language interaction with users, and means for displaying real-time learning guidance via a visual device to immediately resolve questions of users during work. This enables workers at the manufacturing site to improve their skills in line with actual work while receiving individually tailored education.

[0284] The "generation system" is a program that utilizes artificial intelligence to automatically create an educational process tailored to individual users.

[0285] "Adapting in real time" refers to the ability to immediately change and present learning materials according to different language settings and user needs.

[0286] "Dialogue in natural language" is a method of communicating with the system using the language that users use in daily life.

[0287] "Presenting supplementary information" refers to the act of providing additional information and explanations necessary for users to deepen their understanding.

[0288] "Giving feedback" means providing individual feedback based on the educational progress and performance of users.

[0289] The "community function" is a mechanism for sharing information with other users through the system and deepening knowledge with each other.

[0290] "Display via a visual device" is a method of presenting information within the user's field of vision using devices such as smart glasses.

[0291] "Resolving immediately" means providing a quick and accurate answer to the questions and inquiries of users.

[0292] This invention provides a learning system that utilizes visual devices to efficiently conduct skills training in manufacturing sites. The server employs a generative AI model and creates an individualized training process based on each user's profile information and real-time data input. Natural language processing is performed to provide supplementary information and feedback immediately in response to learning progress and questions from the user.

[0293] The visual device, specifically smart glasses, displays learning instructions in real time within the user's field of vision. This allows for immediate and intuitive access to necessary information even while working. Furthermore, the system supports multiple languages, making it widely usable in international manufacturing environments.

[0294] If a user has a question while working, they can input it using voice or text. The server generates an appropriate answer to that question and presents it to the user immediately, minimizing interruptions to their work. This question-answering process utilizes natural language processing technology and the capabilities of generated AI.

[0295] As a concrete example, suppose a user learning how to operate a new piece of equipment in a factory has a question about the equipment's settings. If the user enters the prompt "Please tell me the correct initial setup procedure for this equipment," the server will use a generative AI model to gather relevant information and display an easy-to-understand answer in the user's view. This advanced educational system is expected to dramatically improve users' skill acquisition.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The user puts on smart glasses and begins working. Before starting, the system sends the user's profile information (past learning history and skill level) to the server. Based on this input, the server generates an educational process adapted to the user. At this point, appropriate learning objectives and procedures are displayed in the user's field of vision. This display is customized by a generating AI model.

[0299] Step 2:

[0300] During the process, if the user has a question, they input it via voice or text. The terminal receives this input and sends it to the server. This data is analyzed through natural language processing. The input is the user's question, and based on this, the AI ​​searches the database and generates the best answer. The output as an answer is specific instructions or explanations.

[0301] Step 3:

[0302] The server uses a generative AI model to generate answers to user questions and sends them to the terminal. During this process, it consults a training database in the cloud as needed to obtain additional information. The server's output is a detailed answer to the user's question, including any relevant supplementary materials.

[0303] Step 4:

[0304] The terminal displays the response received from the server within the user's field of view. The visual device presents the information in an intuitively understandable format. The displayed information is tailored to the user's task, visually indicating solutions to questions and the next steps. As a result, the user can solve the problem immediately.

[0305] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0306] The present invention provides an educational system that combines a generative artificial intelligence and an emotion engine, and realizes an individualized learning experience for each user. This system consists of a terminal distributed to users and a server that manages data.

[0307] The terminal has a profile setting and an authentication function to assist the user's learning progress. The user accesses the learning program through the terminal, and the emotion engine monitors the user's emotional state in real time. It recognizes emotions from the user's facial expressions and voice, and evaluates the user's stress and focus state during the learning process.

[0308] Based on the data obtained from the emotion engine, the server dynamically adjusts the learning program. The generative artificial intelligence generates educational content according to the user's emotional state, and makes adaptations such as slowing down the pace if necessary. In addition, it aggregates the user's emotion data, generates an individualized guidance plan as feedback, and transmits it to the terminal.

[0309] As a specific example, suppose a student is learning chemistry. The terminal recognizes through the emotion engine that the student feels the content is difficult. The server analyzes this emotion data, generates teaching materials with adjusted difficulty levels, and presents them in a form that is easy for the student to understand. Furthermore, the generated teaching materials are tailored to the student's profile and learning history by the generative artificial intelligence. If the user has questions, they can ask questions in natural language on the terminal, and answers are provided instantaneously. The server also manages the progress of learning and presents feedback and suggestions for the next session after each session.

[0310] With such a mechanism, the system provides a flexible learning environment that adapts to the user's emotions, aiming to eliminate educational disparities. In addition, through the community function, information exchange with other users can be carried out, further deepening the understanding of the learning content.

[0311] The following explains the processing flow.

[0312] Step 1:

[0313] When the device is powered on, the user is presented with an initial setup wizard. The user connects to a Wi-Fi network and enters their language and profile information. The entered information is then sent to the server.

[0314] Step 2:

[0315] Based on the received profile information, the server generates an optimal educational program for the user. This program is individually customized by the generating artificial intelligence and sent to the terminal.

[0316] Step 3:

[0317] The terminal displays the generated educational program to the user. The user follows the displayed program and begins learning.

[0318] Step 4:

[0319] During learning, the emotion engine built into the device analyzes the user's facial expressions and voice in real time. This allows the system to determine the user's emotional state.

[0320] Step 5:

[0321] Emotional data is sent from the device to the server, which uses this data to analyze the current learning program. If necessary, the difficulty level is adjusted, or the learning materials are modified to reduce user stress.

[0322] Step 6:

[0323] If a user has any questions or points of confusion, they input their questions in natural language through their device. The server uses generative artificial intelligence to generate appropriate explanations for the questions and displays them on the device.

[0324] Step 7:

[0325] The server tracks learning progress and generates feedback at the end of each learning session. The feedback and recommended learning content for the next session are sent to the device and displayed to the user.

[0326] Step 8:

[0327] Users utilize the device's community features to share their learning experiences with other users. Discussions within this community are supported by generative artificial intelligence, allowing users to deepen their knowledge.

[0328] (Example 2)

[0329] Next, we will describe Example 2. 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".

[0330] In the field of education, there is a demand for personalized educational programs tailored to each user's learning style and level of understanding. However, conventional systems have struggled to analyze users' emotions and learning progress in real time and generate dynamic educational content based on those results. Furthermore, they have been unable to respond immediately to the learning stress and difficulties users experience, leading to a decline in educational efficiency.

[0331] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0332] In this invention, the server includes means for collecting and analyzing data in real time using an emotion engine that analyzes user emotions, means for dynamically generating and adjusting personalized educational programs based on the analyzed emotion data using generative artificial intelligence, and means for immediately responding when the user interacts with the generated educational content in natural language. This makes it possible to provide a flexible and effective learning process that is tailored to the user's learning progress.

[0333] "User authentication" is the process required to verify the identity of a user accessing a system, and is usually performed using profile information or similar methods.

[0334] "Profile settings" refers to the preparatory stage for providing a personalized learning experience based on data such as the user's personal information, learning history, and learning style, which is then registered on the device.

[0335] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to evaluate their emotional state, and detects and collects this data in real time.

[0336] "Generative artificial intelligence" is an AI technology that automatically and dynamically generates educational content tailored to user needs based on collected data.

[0337] "Dynamic adjustment of educational programs" is a process that optimizes educational content in real time based on user sentiment data and learning progress, providing a learning experience tailored to the user.

[0338] "Natural language interaction" refers to the ability for users to communicate with the system using text or voice through their device, and to receive immediate responses to questions about generated educational content.

[0339] "Recording educational progress" is the process of tracking how far a user has progressed in their learning and using that data to provide next learning opportunities and feedback.

[0340] "Communication features" are functions that users use to share information with other users and to collaborate on learning, aiming to share knowledge and improve educational effectiveness.

[0341] Modes for carrying out the invention

[0342] This invention is an educational system for providing users with individualized learning experiences. This system consists of terminals distributed to users and a server that manages data. Specific embodiments of this system are described below.

[0343] Users access the learning program using a device. The device is equipped with features that authenticate the user based on their profile information and prepare them for the learning program. The specific hardware of the device includes a camera and microphone, which allows for real-time monitoring of the user's facial expressions and voice.

[0344] The device uses an emotion engine to evaluate the user's emotional state from collected facial expressions and voice data. The analyzed emotion data is immediately sent to the server. Based on the received emotion data, the server dynamically generates and adjusts educational content using generative artificial intelligence. This generative AI optimizes the educational program to match the user's individual learning history and profile data.

[0345] The generated learning materials are sent to the device in real time and presented to the user. Users can ask questions about the presented educational content using natural language. The server provides an immediate response, resolving the user's questions. This interaction allows users to learn more effectively.

[0346] For example, if a student is studying chemistry, the device analyzes the student's emotions during learning and detects if they find the content difficult. The server analyzes this data and generates and provides chemistry materials with adjusted difficulty levels to make them easier to understand. Also, if the student enters a prompt such as, "Please explain the mechanism of this chemical reaction in detail," a detailed explanation corresponding to that request is instantly provided.

[0347] This system aims to improve educational efficiency by providing a flexible learning environment tailored to the user's learning progress.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The user logs into the terminal and enters their profile information. The terminal uses this information to authenticate the user. The authentication process verifies that the user has the necessary permissions to access the system. The input data is the username and password, and the output is the authentication result. If authentication is successful, the user can proceed to the learning program.

[0351] Step 2:

[0352] When a user begins learning, the device's camera and microphone are activated to monitor the user's facial expressions and voice in real time. The device sends this data to an emotion engine to analyze the user's emotional state. The input is facial expressions and voice data, and the output is the analyzed emotional information. The device evaluates the user's emotions and determines their emotional state.

[0353] Step 3:

[0354] The device sends analyzed emotional data to the server. The server processes the emotional data and uses generative artificial intelligence to generate educational content optimized for the user. The input is emotional data and user profile information, and the output is personalized educational content. The server dynamically adjusts the educational program and optimizes the teaching materials.

[0355] Step 4:

[0356] The generated educational content is sent from the server to the terminal and presented to the user. The user can then use the materials to progress with their learning. If a question arises during learning, the user sends a question in natural language to the server by entering a prompt. The input is a prompt, and the output is the answer from the server.

[0357] Step 5:

[0358] Upon receiving a user prompt, the server searches for relevant information and generates an appropriate response using a generative AI model. The server then sends the response to the terminal, providing it to the user quickly. This response resolves the user's question and improves the quality of learning.

[0359] Step 6:

[0360] When a learning session ends, the terminal sends learning progress data to the server, which records it. Based on the progress data, the server suggests the next learning content and provides feedback to the user. The input is learning progress data, and the output is the next learning suggestion and feedback.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0363] Traditional education systems often fail to adequately address individual needs and provide effective learning environments because they do not consider learners' emotional states. Furthermore, learners' stress and decreased interest frequently negatively impact learning efficiency. In particular, in the workplace, generic training programs that disregard individual emotional states are common, highlighting the need for training that takes into account the understanding and emotional needs of individual workers.

[0364] The specific processing performed 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 creating an individualized educational program using generative artificial intelligence, means for detecting the emotional state of the worker and dynamically adjusting the learning content according to that state, and means for presenting supplementary information to deepen understanding through natural language interaction with the user. This provides a flexible training environment that is tailored to the emotional state of the learner or worker, enabling individual skill improvement and stress reduction.

[0365] "Generative artificial intelligence" refers to artificial intelligence technology that includes algorithms for generating personalized educational programs and content based on user data.

[0366] A "personalized education program" is educational content that is tailored to dynamically provide an appropriate learning pace and content, taking into account each user's profile and emotional state.

[0367] "Emotional state" refers to a psychological state determined in real time by analyzing the user's facial expressions and voice, and is information that quantifies the learner's stress and interests.

[0368] "Means of dynamically adjusting learning content" refers to a function that automatically changes the content, difficulty level, and pace of the educational program in real time according to the user's emotional state.

[0369] "Natural language interaction" is a communication method in which users interact with a system using the language they normally use, deepening their understanding through questions and answers.

[0370] "Supplemental information" refers to additional data and explanations provided to help users understand the learning material, and is automatically presented by the system as needed.

[0371] The "community function" is a feature that allows learners to share knowledge with other users and deepen their understanding through exchanging opinions and collaborative learning.

[0372] This system consists of a server and learner terminals, and specifically incorporates generative artificial intelligence, an emotion engine, and a natural language processing module. The server uses generative artificial intelligence to generate educational programs tailored to individual learners. In this process, learner profile data is collected during the initial setup and used as the foundation for the generation process.

[0373] The device is equipped with an emotion engine that acquires the learner's facial expressions and voice data in real time through the camera and microphone. This data is processed by an emotion analysis model developed using Python (utilizing TensorFlow and PyTorch) to identify the learner's emotional state. The emotion data is sent to a server, which then adjusts the learning program accordingly.

[0374] Users interact with the system through natural language interaction, and if they have questions during learning, they can immediately ask them via text or voice through their device. Natural language processing is performed by a generative AI model, providing rapid responses.

[0375] As a concrete example, imagine a new worker in a factory learning to operate new equipment. If the terminal detects the worker's confused expression, the server automatically slows the pace of the training and adds visual guidance. An example of a prompt used in this process is, "Generate training content to present when the worker's understanding declines while learning how to operate the new machine." In this way, learners can learn at their own individual pace.

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The device acquires the learner's facial expressions and voice data in real time through its camera and microphone, and inputs this data into an emotion analysis model. This allows the learner's emotional state to be quantified and recognized.

[0379] Step 2:

[0380] The server integrates emotional state data sent from the terminal with learner profile information obtained during initial setup to generate an appropriate educational program. Using a generative AI model, it dynamically adjusts the difficulty and pace of the learning content using prompt messages.

[0381] Step 3:

[0382] The server generates a personalized educational program and sends it to the terminal. The terminal then presents the program to the learner and begins training with visual guidance as needed.

[0383] Step 4:

[0384] During training, users can ask the system questions in natural language, and the device sends these questions as text data to the server. The server analyzes the questions, uses a generative AI model to instantly generate answers, and sends them back to the device.

[0385] Step 5:

[0386] The terminal displays the responses received from the server to the user and continuously monitors the progress of the training. If necessary, it repeats the process of re-analyzing the sentiment data and fine-tuning the learning program.

[0387] Step 6:

[0388] Based on the emotions and learning data recorded after the training session, the server generates a plan for the next learning session and sends it to the user's device as feedback along with their progress.

[0389] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0390] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0391] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0395] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0396] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0397] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0398] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0399] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0400] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0401] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0402] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0403] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0404] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0405] This invention provides a system for providing personalized education using generative artificial intelligence. The system includes a terminal distributed to the user and a server for managing learning data.

[0406] The device functions as an educational platform accessible to the user, identifying the user through profile settings and authentication. This allows the user to access learning programs tailored to their profile on the device. Generative artificial intelligence generates learning materials based on the user's input and learning history, enabling real-time adaptation. The device displays learning materials suited to the individual's learning situation, and the user can gain a deeper understanding by interacting with the system in natural language.

[0407] The server centrally manages all users' profile data and learning history, enhancing the generation of learning programs. The server also manages each user's progress, generates feedback, and sends it to their device. Furthermore, the generative artificial intelligence supports user learning by referencing appropriate information to generate explanations in response to user questions.

[0408] As a concrete example, when a student is studying mathematics, multiple lectures and practice problems related to mathematics are presented on the terminal. If a question arises during learning, the user enters the question through the terminal. The generating AI then finds the answer to the question and presents a clear explanation to the user. Furthermore, the server analyzes the user's accuracy rate and tendency of incorrect answers and recommends what to learn next. This allows the user to learn efficiently at their own pace.

[0409] A key feature of this system is that it provides each user with the most suitable educational environment through their learning experience, and by combining multilingual support and natural language processing, it realizes a flexible educational system that can be used anywhere in the world.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] When the device is powered on, the initial setup wizard appears. Through the wizard, the user configures the Wi-Fi network and selects their preferred language. The user then enters their profile information and submits it to the server.

[0413] Step 2:

[0414] The server receives the user's profile information and generates a personalized educational program based on it. The generated program is then sent to the terminal.

[0415] Step 3:

[0416] The terminal receives the learning program from the server and presents it to the user. The user then begins learning using the presented learning materials.

[0417] Step 4:

[0418] If a user has questions during the learning process, they input the question in natural language through their device. Generative artificial intelligence analyzes the question and generates an appropriate answer. The answer is displayed on the device for the user to review.

[0419] Step 5:

[0420] The server tracks learning progress and evaluates the user's accuracy and progress after each learning session. Based on the evaluation, information suggesting what to learn next is generated and sent to the device.

[0421] Step 6:

[0422] The device displays feedback from the server to the user, providing guidance for the next learning step. This allows the user to continue learning efficiently.

[0423] Step 7:

[0424] Users can utilize the device's community features to share knowledge with other users. Discussions here can also receive additional support from generative artificial intelligence, allowing for deeper learning.

[0425] (Example 1)

[0426] Next, we will describe Example 1. 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."

[0427] Traditional education systems often provide uniform content to individual learners, making it difficult to deliver effective education tailored to each learner's level of understanding and progress. Furthermore, the lack of multilingual support and immediate adaptability means that adequate support cannot be provided to learners with diverse language backgrounds. Additionally, the insufficient function for knowledge exchange and communication among learners makes sharing and collaborating on learning challenging.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes means for creating personalized educational content using generative artificial intelligence, means for displaying educational resources while adapting to multiple languages ​​in real time, and means for providing supplementary data to deepen understanding through natural language interaction with the user. This enables the provision of individually optimized education for each learner and flexible adaptation to multilingual environments. Furthermore, it facilitates mutual knowledge sharing and improves the quality of learning.

[0430] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to dynamically generate and present educational materials and information tailored to individual needs.

[0431] "Personalized educational content" refers to learning materials and programs optimized based on each learner's level of understanding and progress.

[0432] "A means of displaying educational resources while adapting to multiple languages ​​in real time" refers to a technology that instantly translates and displays learning materials in a way that is accessible to users with different language backgrounds.

[0433] "Natural language dialogue" is a method of communication between humans and machines that allows users to speak directly with a computer using the language they use in everyday life.

[0434] "Supplementary data" refers to additional information and explanatory materials provided in addition to the main teaching materials to support learners' understanding.

[0435] "Means for recording learning activities and generating individualized evaluation information" refers to technologies for tracking learners' behavior and outcomes and generating feedback based on that information.

[0436] The "communication function" is a function that supports learners in sharing knowledge and opinions and interacting with each other.

[0437] "A means of dynamically adapting and changing teaching materials based on questions and answers" refers to a technology that appropriately modifies and provides teaching material content based on questions from learners.

[0438] This invention constructs a system for providing personalized educational experiences. The main components of the system include a terminal provided to the user and a server for managing learning data.

[0439] The server is equipped with an advanced generative artificial intelligence model that analyzes user profile information to generate optimal educational content. Specifically, it generates and provides learning materials based on each user's past learning history and progress data. Furthermore, the server supports multiple languages ​​and handles real-time translation and content delivery. The server also records the user's learning activity, analyzes the obtained data, and generates feedback. The generated feedback is sent to the device as personalized advice, suggesting guidelines for the next learning step.

[0440] The device allows users to access the educational platform and engage in interactive learning. Users can input natural language prompts through the device to resolve their questions. For example, if a user inputs the prompt "What is the next math topic to learn?", the generative AI will provide explanations and recommendations in response to that question.

[0441] As a concrete example, consider a user learning English. The user provides a prompt using their device, such as "Explain the use of the present perfect tense again," and the AI ​​instantly generates an explanation and example sentences, displaying them on the device. The server also tracks the user's progress and suggests the most suitable topics for future lessons. This entire process takes place in real time, providing the user with a stress-free learning experience.

[0442] The implementation of this system allows users to receive education in a way that is personalized to their learning needs and goals, thus enabling efficient and effective learning.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The user starts up the device.

[0446] The terminal displays a user interface and presents the user with an authentication screen. The user enters their login information and sends profile data. The server performs user authentication based on the received authentication information and profile data, and outputs the authentication status to the terminal.

[0447] Step 2:

[0448] The server retrieves user profile data and generates optimized learning content.

[0449] The server references the user's learning history and profile, and uses a generative AI model to create personalized learning materials. The input is the user's past learning data and current learning goals, and the output is a set of educational content tailored to the user.

[0450] Step 3:

[0451] The device displays customized educational content for the user.

[0452] The terminal displays educational resources received from the server on its screen. The user begins learning based on this, and enters prompts as needed. The input is content data from the server, and the output is the educational display presented to the user.

[0453] Step 4:

[0454] The user enters any questions that arise during the learning process as prompts into the terminal.

[0455] The user sends questions that arise during the learning process to the terminal in natural language. The input is the prompt text entered by the user, which the terminal sends to the server.

[0456] Step 5:

[0457] The server uses a generated AI model to produce answers to user questions.

[0458] The server parses the prompt message, aggregates the necessary information, and generates a response. The input is the prompt message from the user, and the output is the response message.

[0459] Step 6:

[0460] The terminal receives the response from the server and presents it to the user.

[0461] The terminal displays the generated response to the user. The output is response information presented in a user-friendly format.

[0462] Step 7:

[0463] The server receives the user's learning data and analyzes their progress.

[0464] The server analyzes user activity data to provide feedback and optimize the next learning content. The input is user data acquired during learning, and the output is an improved learning plan and feedback.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, 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."

[0467] In modern manufacturing environments, there is a need to improve worker capabilities and productivity through efficient skills training and immediate support. However, traditional training methods lack adaptability and responsiveness to individual workers, hindering efficiency improvements.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes means for creating personalized educational processes using a generation system, means for presenting supplementary information to deepen understanding through natural language dialogue with the user, and means for displaying learning guidance in real time via a visual device to allow the user to immediately resolve questions during work. This enables workers in the manufacturing field to improve their skills in a way that is relevant to their actual work while receiving individually tailored education.

[0470] A "generation system" is a program that utilizes artificial intelligence to automatically create educational curricula tailored to individual users.

[0471] "Real-time adaptation" refers to the ability to instantly change and present learning materials in response to different language settings and user needs.

[0472] "Natural language dialogue" is a method of communicating with a system using the language that users use in their daily lives.

[0473] "Providing supplementary information" refers to the act of providing additional information or explanations necessary for users to deepen their understanding.

[0474] "Providing feedback" means offering individualized feedback based on the user's educational progress and performance.

[0475] "Community features" refer to mechanisms that allow users to share information with each other through the system and deepen their knowledge together.

[0476] "Display via a visual device" refers to a method of presenting information to the user's field of vision using devices such as smart glasses.

[0477] "Resolving issues immediately" means providing prompt and accurate answers to users' questions and concerns.

[0478] This invention provides a learning system that utilizes visual devices to efficiently conduct skills training in manufacturing sites. The server employs a generative AI model and creates an individualized training process based on each user's profile information and real-time data input. Natural language processing is performed to provide supplementary information and feedback immediately in response to learning progress and questions from the user.

[0479] The visual device, specifically smart glasses, displays learning instructions in real time within the user's field of vision. This allows for immediate and intuitive access to necessary information even while working. Furthermore, the system supports multiple languages, making it widely usable in international manufacturing environments.

[0480] If a user has a question while working, they can input it using voice or text. The server generates an appropriate answer to that question and presents it to the user immediately, minimizing interruptions to their work. This question-answering process utilizes natural language processing technology and the capabilities of generated AI.

[0481] As a concrete example, suppose a user learning how to operate a new piece of equipment in a factory has a question about the equipment's settings. If the user enters the prompt "Please tell me the correct initial setup procedure for this equipment," the server will use a generative AI model to gather relevant information and display an easy-to-understand answer in the user's view. This advanced educational system is expected to dramatically improve users' skill acquisition.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The user puts on smart glasses and begins working. Before starting, the system sends the user's profile information (past learning history and skill level) to the server. Based on this input, the server generates an educational process adapted to the user. At this point, appropriate learning objectives and procedures are displayed in the user's field of vision. This display is customized by a generating AI model.

[0485] Step 2:

[0486] During the process, if the user has a question, they input it via voice or text. The terminal receives this input and sends it to the server. This data is analyzed through natural language processing. The input is the user's question, and based on this, the AI ​​searches the database and generates the best answer. The output as an answer is specific instructions or explanations.

[0487] Step 3:

[0488] The server uses a generative AI model to generate answers to user questions and sends them to the terminal. During this process, it consults a training database in the cloud as needed to obtain additional information. The server's output is a detailed answer to the user's question, including any relevant supplementary materials.

[0489] Step 4:

[0490] The terminal displays the response received from the server within the user's field of view. The visual device presents the information in an intuitively understandable format. The displayed information is tailored to the user's task, visually indicating solutions to questions and the next steps. As a result, the user can solve the problem immediately.

[0491] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0492] This invention provides an educational system that combines generative artificial intelligence and an emotion engine to realize a personalized learning experience for each user. This system consists of terminals distributed to users and a server that manages the data.

[0493] The device features profile settings and authentication functions to support the user's learning progress. Users access the learning program through the device, and an emotion engine monitors the user's emotional state in real time. It recognizes emotions from the user's facial expressions and voice, and evaluates the user's stress levels and focus during the learning process.

[0494] The server dynamically adjusts the learning program based on data obtained from the emotion engine. The generative artificial intelligence generates educational content that corresponds to the user's emotional state and adapts it as needed, such as slowing the pace. In addition, it collects the user's emotional data and generates an individual lesson plan as feedback, which is then sent to the terminal.

[0495] As a concrete example, suppose a student is studying chemistry. The device, using an emotion engine, recognizes that the student finds the content difficult. The server analyzes this emotion data, generates learning materials with adjusted difficulty levels, and presents them in a format that is easy for the student to understand. Furthermore, the generated materials are tailored to the student's profile and learning history using generative artificial intelligence. If the user has a question, they can ask it in natural language on the device, and an answer is provided instantly. The server also manages the learning progress and provides feedback and suggestions for the next session after each session.

[0496] Through this mechanism, the system provides a flexible learning environment that is tailored to the user's emotions, aiming to bridge the educational gap. Furthermore, the community function allows for information exchange with other users, deepening the understanding of the learning material.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] When the device is powered on, the user is presented with an initial setup wizard. The user connects to a Wi-Fi network and enters their language and profile information. The entered information is then sent to the server.

[0500] Step 2:

[0501] Based on the received profile information, the server generates an optimal educational program for the user. This program is individually customized by the generating artificial intelligence and sent to the terminal.

[0502] Step 3:

[0503] The terminal displays the generated educational program to the user. The user follows the displayed program and begins learning.

[0504] Step 4:

[0505] During learning, the emotion engine built into the device analyzes the user's facial expressions and voice in real time. This allows the system to determine the user's emotional state.

[0506] Step 5:

[0507] Emotional data is sent from the device to the server, which uses this data to analyze the current learning program. If necessary, the difficulty level is adjusted, or the learning materials are modified to reduce user stress.

[0508] Step 6:

[0509] If a user has any questions or points of confusion, they input their questions in natural language through their device. The server uses generative artificial intelligence to generate appropriate explanations for the questions and displays them on the device.

[0510] Step 7:

[0511] The server tracks learning progress and generates feedback at the end of each learning session. The feedback and recommended learning content for the next session are sent to the device and displayed to the user.

[0512] Step 8:

[0513] Users utilize the device's community features to share their learning experiences with other users. Discussions within this community are supported by generative artificial intelligence, allowing users to deepen their knowledge.

[0514] (Example 2)

[0515] Next, we will describe Example 2. 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."

[0516] In the field of education, there is a demand for personalized educational programs tailored to each user's learning style and level of understanding. However, conventional systems have struggled to analyze users' emotions and learning progress in real time and generate dynamic educational content based on those results. Furthermore, they have been unable to respond immediately to the learning stress and difficulties users experience, leading to a decline in educational efficiency.

[0517] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0518] In this invention, the server includes means for collecting and analyzing data in real time using an emotion engine that analyzes user emotions, means for dynamically generating and adjusting personalized educational programs based on the analyzed emotion data using generative artificial intelligence, and means for immediately responding when the user interacts with the generated educational content in natural language. This makes it possible to provide a flexible and effective learning process that is tailored to the user's learning progress.

[0519] "User authentication" is the process required to verify the identity of a user accessing a system, and is usually performed using profile information or similar methods.

[0520] "Profile settings" refers to the preparatory stage for providing a personalized learning experience based on data such as the user's personal information, learning history, and learning style, which is then registered on the device.

[0521] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to evaluate their emotional state, and detects and collects this data in real time.

[0522] "Generative artificial intelligence" is an AI technology that automatically and dynamically generates educational content tailored to user needs based on collected data.

[0523] "Dynamic adjustment of educational programs" is a process that optimizes educational content in real time based on user sentiment data and learning progress, providing a learning experience tailored to the user.

[0524] "Natural language interaction" refers to the ability for users to communicate with the system using text or voice through their device, and to receive immediate responses to questions about generated educational content.

[0525] "Recording educational progress" is the process of tracking how far a user has progressed in their learning and using that data to provide next learning opportunities and feedback.

[0526] "Communication features" are functions that users use to share information with other users and to collaborate on learning, aiming to share knowledge and improve educational effectiveness.

[0527] Modes for carrying out the invention

[0528] This invention is an educational system for providing users with individualized learning experiences. This system consists of terminals distributed to users and a server that manages data. Specific embodiments of this system are described below.

[0529] Users access the learning program using a device. The device is equipped with features that authenticate the user based on their profile information and prepare them for the learning program. The specific hardware of the device includes a camera and microphone, which allows for real-time monitoring of the user's facial expressions and voice.

[0530] The device uses an emotion engine to evaluate the user's emotional state from collected facial expressions and voice data. The analyzed emotion data is immediately sent to the server. Based on the received emotion data, the server dynamically generates and adjusts educational content using generative artificial intelligence. This generative AI optimizes the educational program to match the user's individual learning history and profile data.

[0531] The generated learning materials are sent to the device in real time and presented to the user. Users can ask questions about the presented educational content using natural language. The server provides an immediate response, resolving the user's questions. This interaction allows users to learn more effectively.

[0532] For example, if a student is studying chemistry, the device analyzes the student's emotions during learning and detects if they find the content difficult. The server analyzes this data and generates and provides chemistry materials with adjusted difficulty levels to make them easier to understand. Also, if the student enters a prompt such as, "Please explain the mechanism of this chemical reaction in detail," a detailed explanation corresponding to that request is instantly provided.

[0533] This system aims to improve educational efficiency by providing a flexible learning environment tailored to the user's learning progress.

[0534] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0535] Step 1:

[0536] The user logs into the terminal and enters their profile information. The terminal uses this information to authenticate the user. The authentication process verifies that the user has the necessary permissions to access the system. The input data is the username and password, and the output is the authentication result. If authentication is successful, the user can proceed to the learning program.

[0537] Step 2:

[0538] When a user begins learning, the device's camera and microphone are activated to monitor the user's facial expressions and voice in real time. The device sends this data to an emotion engine to analyze the user's emotional state. The input is facial expressions and voice data, and the output is the analyzed emotional information. The device evaluates the user's emotions and determines their emotional state.

[0539] Step 3:

[0540] The device sends analyzed emotional data to the server. The server processes the emotional data and uses generative artificial intelligence to generate educational content optimized for the user. The input is emotional data and user profile information, and the output is personalized educational content. The server dynamically adjusts the educational program and optimizes the teaching materials.

[0541] Step 4:

[0542] The generated educational content is sent from the server to the terminal and presented to the user. The user can then use the materials to progress with their learning. If a question arises during learning, the user sends a question in natural language to the server by entering a prompt. The input is a prompt, and the output is the answer from the server.

[0543] Step 5:

[0544] Upon receiving a user prompt, the server searches for relevant information and generates an appropriate response using a generative AI model. The server then sends the response to the terminal, providing it to the user quickly. This response resolves the user's question and improves the quality of learning.

[0545] Step 6:

[0546] When a learning session ends, the terminal sends learning progress data to the server, which records it. Based on the progress data, the server suggests the next learning content and provides feedback to the user. The input is learning progress data, and the output is the next learning suggestion and feedback.

[0547] (Application Example 2)

[0548] Next, we will explain application example 2. In the following explanation, 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."

[0549] Traditional education systems often fail to adequately address individual needs and provide effective learning environments because they do not consider learners' emotional states. Furthermore, learners' stress and decreased interest frequently negatively impact learning efficiency. In particular, in the workplace, generic training programs that disregard individual emotional states are common, highlighting the need for training that takes into account the understanding and emotional needs of individual workers.

[0550] The specific processing performed 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 creating an individualized educational program using generative artificial intelligence, means for detecting the emotional state of the worker and dynamically adjusting the learning content according to that state, and means for presenting supplementary information to deepen understanding through natural language interaction with the user. This provides a flexible training environment that is tailored to the emotional state of the learner or worker, enabling individual skill improvement and stress reduction.

[0551] "Generative artificial intelligence" refers to artificial intelligence technology that includes algorithms for generating personalized educational programs and content based on user data.

[0552] A "personalized education program" is educational content that is tailored to dynamically provide an appropriate learning pace and content, taking into account each user's profile and emotional state.

[0553] "Emotional state" refers to a psychological state determined in real time by analyzing the user's facial expressions and voice, and is information that quantifies the learner's stress and interests.

[0554] "Means of dynamically adjusting learning content" refers to a function that automatically changes the content, difficulty level, and pace of the educational program in real time according to the user's emotional state.

[0555] "Natural language interaction" is a communication method in which users interact with a system using the language they normally use, deepening their understanding through questions and answers.

[0556] "Supplemental information" refers to additional data and explanations provided to help users understand the learning material, and is automatically presented by the system as needed.

[0557] The "community function" is a feature that allows learners to share knowledge with other users and deepen their understanding through exchanging opinions and collaborative learning.

[0558] This system consists of a server and learner terminals, and specifically incorporates generative artificial intelligence, an emotion engine, and a natural language processing module. The server uses generative artificial intelligence to generate educational programs tailored to individual learners. In this process, learner profile data is collected during the initial setup and used as the foundation for the generation process.

[0559] The device is equipped with an emotion engine that acquires the learner's facial expressions and voice data in real time through the camera and microphone. This data is processed by an emotion analysis model developed using Python (utilizing TensorFlow and PyTorch) to identify the learner's emotional state. The emotion data is sent to a server, which then adjusts the learning program accordingly.

[0560] Users interact with the system through natural language interaction, and if they have questions during learning, they can immediately ask them via text or voice through their device. Natural language processing is performed by a generative AI model, providing rapid responses.

[0561] As a concrete example, imagine a new worker in a factory learning to operate new equipment. If the terminal detects the worker's confused expression, the server automatically slows the pace of the training and adds visual guidance. An example of a prompt used in this process is, "Generate training content to present when the worker's understanding declines while learning how to operate the new machine." In this way, learners can learn at their own individual pace.

[0562] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0563] Step 1:

[0564] The device acquires the learner's facial expressions and voice data in real time through its camera and microphone, and inputs this data into an emotion analysis model. This allows the learner's emotional state to be quantified and recognized.

[0565] Step 2:

[0566] The server integrates emotional state data sent from the terminal with learner profile information obtained during initial setup to generate an appropriate educational program. Using a generative AI model, it dynamically adjusts the difficulty and pace of the learning content using prompt messages.

[0567] Step 3:

[0568] The server generates a personalized educational program and sends it to the terminal. The terminal then presents the program to the learner and begins training with visual guidance as needed.

[0569] Step 4:

[0570] During training, users can ask the system questions in natural language, and the device sends these questions as text data to the server. The server analyzes the questions, uses a generative AI model to instantly generate answers, and sends them back to the device.

[0571] Step 5:

[0572] The terminal displays the responses received from the server to the user and continuously monitors the progress of the training. If necessary, it repeats the process of re-analyzing the sentiment data and fine-tuning the learning program.

[0573] Step 6:

[0574] Based on the emotions and learning data recorded after the training session, the server generates a plan for the next learning session and sends it to the user's device as feedback along with their progress.

[0575] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0576] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0577] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0578] [Fourth Embodiment]

[0579] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0580] As shown in Figure 7, the 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.

[0581] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0582] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0583] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0584] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0585] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0586] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0587] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0588] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0589] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0590] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0591] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0592] This invention provides a system for providing personalized education using generative artificial intelligence. The system includes a terminal distributed to the user and a server for managing learning data.

[0593] The device functions as an educational platform accessible to the user, identifying the user through profile settings and authentication. This allows the user to access learning programs tailored to their profile on the device. Generative artificial intelligence generates learning materials based on the user's input and learning history, enabling real-time adaptation. The device displays learning materials suited to the individual's learning situation, and the user can gain a deeper understanding by interacting with the system in natural language.

[0594] The server centrally manages all users' profile data and learning history, enhancing the generation of learning programs. The server also manages each user's progress, generates feedback, and sends it to their device. Furthermore, the generative artificial intelligence supports user learning by referencing appropriate information to generate explanations in response to user questions.

[0595] As a concrete example, when a student is studying mathematics, multiple lectures and practice problems related to mathematics are presented on the terminal. If a question arises during learning, the user enters the question through the terminal. The generating AI then finds the answer to the question and presents a clear explanation to the user. Furthermore, the server analyzes the user's accuracy rate and tendency of incorrect answers and recommends what to learn next. This allows the user to learn efficiently at their own pace.

[0596] A key feature of this system is that it provides each user with the most suitable educational environment through their learning experience, and by combining multilingual support and natural language processing, it realizes a flexible educational system that can be used anywhere in the world.

[0597] The following describes the processing flow.

[0598] Step 1:

[0599] When the device is powered on, the initial setup wizard appears. Through the wizard, the user configures the Wi-Fi network and selects their preferred language. The user then enters their profile information and submits it to the server.

[0600] Step 2:

[0601] The server receives the user's profile information and generates a personalized educational program based on it. The generated program is then sent to the terminal.

[0602] Step 3:

[0603] The terminal receives the learning program from the server and presents it to the user. The user then begins learning using the presented learning materials.

[0604] Step 4:

[0605] If a user has questions during the learning process, they input the question in natural language through their device. Generative artificial intelligence analyzes the question and generates an appropriate answer. The answer is displayed on the device for the user to review.

[0606] Step 5:

[0607] The server tracks learning progress and evaluates the user's accuracy and progress after each learning session. Based on the evaluation, information suggesting what to learn next is generated and sent to the device.

[0608] Step 6:

[0609] The device displays feedback from the server to the user, providing guidance for the next learning step. This allows the user to continue learning efficiently.

[0610] Step 7:

[0611] Users can utilize the device's community features to share knowledge with other users. Discussions here can also receive additional support from generative artificial intelligence, allowing for deeper learning.

[0612] (Example 1)

[0613] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0614] Traditional education systems often provide uniform content to individual learners, making it difficult to deliver effective education tailored to each learner's level of understanding and progress. Furthermore, the lack of multilingual support and immediate adaptability means that adequate support cannot be provided to learners with diverse language backgrounds. Additionally, the insufficient function for knowledge exchange and communication among learners makes sharing and collaborating on learning challenging.

[0615] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0616] In this invention, the server includes means for creating personalized educational content using generative artificial intelligence, means for displaying educational resources while adapting to multiple languages ​​in real time, and means for providing supplementary data to deepen understanding through natural language interaction with the user. This enables the provision of individually optimized education for each learner and flexible adaptation to multilingual environments. Furthermore, it facilitates mutual knowledge sharing and improves the quality of learning.

[0617] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to dynamically generate and present educational materials and information tailored to individual needs.

[0618] "Personalized educational content" refers to learning materials and programs optimized based on each learner's level of understanding and progress.

[0619] "A means of displaying educational resources while adapting to multiple languages ​​in real time" refers to a technology that instantly translates and displays learning materials in a way that is accessible to users with different language backgrounds.

[0620] "Natural language dialogue" is a method of communication between humans and machines that allows users to speak directly with a computer using the language they use in everyday life.

[0621] "Supplementary data" refers to additional information and explanatory materials provided in addition to the main teaching materials to support learners' understanding.

[0622] "Means for recording learning activities and generating individualized evaluation information" refers to technologies for tracking learners' behavior and outcomes and generating feedback based on that information.

[0623] The "communication function" is a function that supports learners in sharing knowledge and opinions and interacting with each other.

[0624] "A means of dynamically adapting and changing teaching materials based on questions and answers" refers to a technology that appropriately modifies and provides teaching material content based on questions from learners.

[0625] This invention constructs a system for providing personalized educational experiences. The main components of the system include a terminal provided to the user and a server for managing learning data.

[0626] The server is equipped with an advanced generative artificial intelligence model that analyzes user profile information to generate optimal educational content. Specifically, it generates and provides learning materials based on each user's past learning history and progress data. Furthermore, the server supports multiple languages ​​and handles real-time translation and content delivery. The server also records the user's learning activity, analyzes the obtained data, and generates feedback. The generated feedback is sent to the device as personalized advice, suggesting guidelines for the next learning step.

[0627] The device allows users to access the educational platform and engage in interactive learning. Users can input natural language prompts through the device to resolve their questions. For example, if a user inputs the prompt "What is the next math topic to learn?", the generative AI will provide explanations and recommendations in response to that question.

[0628] As a concrete example, consider a user learning English. The user provides a prompt using their device, such as "Explain the use of the present perfect tense again," and the AI ​​instantly generates an explanation and example sentences, displaying them on the device. The server also tracks the user's progress and suggests the most suitable topics for future lessons. This entire process takes place in real time, providing the user with a stress-free learning experience.

[0629] The implementation of this system allows users to receive education in a way that is personalized to their learning needs and goals, thus enabling efficient and effective learning.

[0630] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0631] Step 1:

[0632] The user starts up the device.

[0633] The terminal displays a user interface and presents the user with an authentication screen. The user enters their login information and sends profile data. The server performs user authentication based on the received authentication information and profile data, and outputs the authentication status to the terminal.

[0634] Step 2:

[0635] The server retrieves user profile data and generates optimized learning content.

[0636] The server references the user's learning history and profile, and uses a generative AI model to create personalized learning materials. The input is the user's past learning data and current learning goals, and the output is a set of educational content tailored to the user.

[0637] Step 3:

[0638] The device displays customized educational content for the user.

[0639] The terminal displays educational resources received from the server on its screen. The user begins learning based on this, and enters prompts as needed. The input is content data from the server, and the output is the educational display presented to the user.

[0640] Step 4:

[0641] The user enters any questions that arise during the learning process as prompts into the terminal.

[0642] The user sends questions that arise during the learning process to the terminal in natural language. The input is the prompt text entered by the user, which the terminal sends to the server.

[0643] Step 5:

[0644] The server uses a generated AI model to produce answers to user questions.

[0645] The server parses the prompt message, aggregates the necessary information, and generates a response. The input is the prompt message from the user, and the output is the response message.

[0646] Step 6:

[0647] The terminal receives the response from the server and presents it to the user.

[0648] The terminal displays the generated response to the user. The output is response information presented in a user-friendly format.

[0649] Step 7:

[0650] The server receives the user's learning data and analyzes their progress.

[0651] The server analyzes user activity data to provide feedback and optimize the next learning content. The input is user data acquired during learning, and the output is an improved learning plan and feedback.

[0652] (Application Example 1)

[0653] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] In modern manufacturing environments, there is a need to improve worker capabilities and productivity through efficient skills training and immediate support. However, traditional training methods lack adaptability and responsiveness to individual workers, hindering efficiency improvements.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0656] In this invention, the server includes means for creating personalized educational processes using a generation system, means for presenting supplementary information to deepen understanding through natural language dialogue with the user, and means for displaying learning guidance in real time via a visual device to allow the user to immediately resolve questions during work. This enables workers in the manufacturing field to improve their skills in a way that is relevant to their actual work while receiving individually tailored education.

[0657] A "generation system" is a program that utilizes artificial intelligence to automatically create educational curricula tailored to individual users.

[0658] "Real-time adaptation" refers to the ability to instantly change and present learning materials in response to different language settings and user needs.

[0659] "Natural language dialogue" is a method of communicating with a system using the language that users use in their daily lives.

[0660] "Providing supplementary information" refers to the act of providing additional information or explanations necessary for users to deepen their understanding.

[0661] "Providing feedback" means offering individualized feedback based on the user's educational progress and performance.

[0662] "Community features" refer to mechanisms that allow users to share information with each other through the system and deepen their knowledge together.

[0663] "Display via a visual device" refers to a method of presenting information to the user's field of vision using devices such as smart glasses.

[0664] "Resolving issues immediately" means providing prompt and accurate answers to users' questions and concerns.

[0665] This invention provides a learning system that utilizes visual devices to efficiently conduct skills training in manufacturing sites. The server employs a generative AI model and creates an individualized training process based on each user's profile information and real-time data input. Natural language processing is performed to provide supplementary information and feedback immediately in response to learning progress and questions from the user.

[0666] The visual device, specifically smart glasses, displays learning instructions in real time within the user's field of vision. This allows for immediate and intuitive access to necessary information even while working. Furthermore, the system supports multiple languages, making it widely usable in international manufacturing environments.

[0667] If a user has a question while working, they can input it using voice or text. The server generates an appropriate answer to that question and presents it to the user immediately, minimizing interruptions to their work. This question-answering process utilizes natural language processing technology and the capabilities of generated AI.

[0668] As a concrete example, suppose a user learning how to operate a new piece of equipment in a factory has a question about the equipment's settings. If the user enters the prompt "Please tell me the correct initial setup procedure for this equipment," the server will use a generative AI model to gather relevant information and display an easy-to-understand answer in the user's view. This advanced educational system is expected to dramatically improve users' skill acquisition.

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The user puts on smart glasses and begins working. Before starting, the system sends the user's profile information (past learning history and skill level) to the server. Based on this input, the server generates an educational process adapted to the user. At this point, appropriate learning objectives and procedures are displayed in the user's field of vision. This display is customized by a generating AI model.

[0672] Step 2:

[0673] During the process, if the user has a question, they input it via voice or text. The terminal receives this input and sends it to the server. This data is analyzed through natural language processing. The input is the user's question, and based on this, the AI ​​searches the database and generates the best answer. The output as an answer is specific instructions or explanations.

[0674] Step 3:

[0675] The server uses a generative AI model to generate answers to user questions and sends them to the terminal. During this process, it consults a training database in the cloud as needed to obtain additional information. The server's output is a detailed answer to the user's question, including any relevant supplementary materials.

[0676] Step 4:

[0677] The terminal displays the response received from the server within the user's field of view. The visual device presents the information in an intuitively understandable format. The displayed information is tailored to the user's task, visually indicating solutions to questions and the next steps. As a result, the user can solve the problem immediately.

[0678] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0679] This invention provides an educational system that combines generative artificial intelligence and an emotion engine to realize a personalized learning experience for each user. This system consists of terminals distributed to users and a server that manages the data.

[0680] The device features profile settings and authentication functions to support the user's learning progress. Users access the learning program through the device, and an emotion engine monitors the user's emotional state in real time. It recognizes emotions from the user's facial expressions and voice, and evaluates the user's stress levels and focus during the learning process.

[0681] The server dynamically adjusts the learning program based on data obtained from the emotion engine. The generative artificial intelligence generates educational content that corresponds to the user's emotional state and adapts it as needed, such as slowing the pace. In addition, it collects the user's emotional data and generates an individual lesson plan as feedback, which is then sent to the terminal.

[0682] As a concrete example, suppose a student is studying chemistry. The device, using an emotion engine, recognizes that the student finds the content difficult. The server analyzes this emotion data, generates learning materials with adjusted difficulty levels, and presents them in a format that is easy for the student to understand. Furthermore, the generated materials are tailored to the student's profile and learning history using generative artificial intelligence. If the user has a question, they can ask it in natural language on the device, and an answer is provided instantly. The server also manages the learning progress and provides feedback and suggestions for the next session after each session.

[0683] Through this mechanism, the system provides a flexible learning environment that is tailored to the user's emotions, aiming to bridge the educational gap. Furthermore, the community function allows for information exchange with other users, deepening the understanding of the learning material.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] When the device is powered on, the user is presented with an initial setup wizard. The user connects to a Wi-Fi network and enters their language and profile information. The entered information is then sent to the server.

[0687] Step 2:

[0688] Based on the received profile information, the server generates an optimal educational program for the user. This program is individually customized by the generating artificial intelligence and sent to the terminal.

[0689] Step 3:

[0690] The terminal displays the generated educational program to the user. The user follows the displayed program and begins learning.

[0691] Step 4:

[0692] During learning, the emotion engine built into the device analyzes the user's facial expressions and voice in real time. This allows the system to determine the user's emotional state.

[0693] Step 5:

[0694] Emotional data is sent from the device to the server, which uses this data to analyze the current learning program. If necessary, the difficulty level is adjusted, or the learning materials are modified to reduce user stress.

[0695] Step 6:

[0696] If a user has any questions or points of confusion, they input their questions in natural language through their device. The server uses generative artificial intelligence to generate appropriate explanations for the questions and displays them on the device.

[0697] Step 7:

[0698] The server tracks learning progress and generates feedback at the end of each learning session. The feedback and recommended learning content for the next session are sent to the device and displayed to the user.

[0699] Step 8:

[0700] Users utilize the device's community features to share their learning experiences with other users. Discussions within this community are supported by generative artificial intelligence, allowing users to deepen their knowledge.

[0701] (Example 2)

[0702] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In the field of education, there is a demand for personalized educational programs tailored to each user's learning style and level of understanding. However, conventional systems have struggled to analyze users' emotions and learning progress in real time and generate dynamic educational content based on those results. Furthermore, they have been unable to respond immediately to the learning stress and difficulties users experience, leading to a decline in educational efficiency.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0705] In this invention, the server includes means for collecting and analyzing data in real time using an emotion engine that analyzes user emotions, means for dynamically generating and adjusting personalized educational programs based on the analyzed emotion data using generative artificial intelligence, and means for immediately responding when the user interacts with the generated educational content in natural language. This makes it possible to provide a flexible and effective learning process that is tailored to the user's learning progress.

[0706] "User authentication" is the process required to verify the identity of a user accessing a system, and is usually performed using profile information or similar methods.

[0707] "Profile settings" refers to the preparatory stage for providing a personalized learning experience based on data such as the user's personal information, learning history, and learning style, which is then registered on the device.

[0708] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to evaluate their emotional state, and detects and collects this data in real time.

[0709] "Generative artificial intelligence" is an AI technology that automatically and dynamically generates educational content tailored to user needs based on collected data.

[0710] "Dynamic adjustment of educational programs" is a process that optimizes educational content in real time based on user sentiment data and learning progress, providing a learning experience tailored to the user.

[0711] "Natural language interaction" refers to the ability for users to communicate with the system using text or voice through their device, and to receive immediate responses to questions about generated educational content.

[0712] "Recording educational progress" is the process of tracking how far a user has progressed in their learning and using that data to provide next learning opportunities and feedback.

[0713] "Communication features" are functions that users use to share information with other users and to collaborate on learning, aiming to share knowledge and improve educational effectiveness.

[0714] Modes for carrying out the invention

[0715] This invention is an educational system for providing users with individualized learning experiences. This system consists of terminals distributed to users and a server that manages data. Specific embodiments of this system are described below.

[0716] Users access the learning program using a device. The device is equipped with features that authenticate the user based on their profile information and prepare them for the learning program. The specific hardware of the device includes a camera and microphone, which allows for real-time monitoring of the user's facial expressions and voice.

[0717] The device uses an emotion engine to evaluate the user's emotional state from collected facial expressions and voice data. The analyzed emotion data is immediately sent to the server. Based on the received emotion data, the server dynamically generates and adjusts educational content using generative artificial intelligence. This generative AI optimizes the educational program to match the user's individual learning history and profile data.

[0718] The generated learning materials are sent to the device in real time and presented to the user. Users can ask questions about the presented educational content using natural language. The server provides an immediate response, resolving the user's questions. This interaction allows users to learn more effectively.

[0719] For example, if a student is studying chemistry, the device analyzes the student's emotions during learning and detects if they find the content difficult. The server analyzes this data and generates and provides chemistry materials with adjusted difficulty levels to make them easier to understand. Also, if the student enters a prompt such as, "Please explain the mechanism of this chemical reaction in detail," a detailed explanation corresponding to that request is instantly provided.

[0720] This system aims to improve educational efficiency by providing a flexible learning environment tailored to the user's learning progress.

[0721] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0722] Step 1:

[0723] The user logs into the terminal and enters their profile information. The terminal uses this information to authenticate the user. The authentication process verifies that the user has the necessary permissions to access the system. The input data is the username and password, and the output is the authentication result. If authentication is successful, the user can proceed to the learning program.

[0724] Step 2:

[0725] When a user begins learning, the device's camera and microphone are activated to monitor the user's facial expressions and voice in real time. The device sends this data to an emotion engine to analyze the user's emotional state. The input is facial expressions and voice data, and the output is the analyzed emotional information. The device evaluates the user's emotions and determines their emotional state.

[0726] Step 3:

[0727] The device sends analyzed emotional data to the server. The server processes the emotional data and uses generative artificial intelligence to generate educational content optimized for the user. The input is emotional data and user profile information, and the output is personalized educational content. The server dynamically adjusts the educational program and optimizes the teaching materials.

[0728] Step 4:

[0729] The generated educational content is sent from the server to the terminal and presented to the user. The user can then use the materials to progress with their learning. If a question arises during learning, the user sends a question in natural language to the server by entering a prompt. The input is a prompt, and the output is the answer from the server.

[0730] Step 5:

[0731] Upon receiving a user prompt, the server searches for relevant information and generates an appropriate response using a generative AI model. The server then sends the response to the terminal, providing it to the user quickly. This response resolves the user's question and improves the quality of learning.

[0732] Step 6:

[0733] When a learning session ends, the terminal sends learning progress data to the server, which records it. Based on the progress data, the server suggests the next learning content and provides feedback to the user. The input is learning progress data, and the output is the next learning suggestion and feedback.

[0734] (Application Example 2)

[0735] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] Traditional education systems often fail to adequately address individual needs and provide effective learning environments because they do not consider learners' emotional states. Furthermore, learners' stress and decreased interest frequently negatively impact learning efficiency. In particular, in the workplace, generic training programs that disregard individual emotional states are common, highlighting the need for training that takes into account the understanding and emotional needs of individual workers.

[0737] The specific processing performed 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 creating an individualized educational program using generative artificial intelligence, means for detecting the emotional state of the worker and dynamically adjusting the learning content according to that state, and means for presenting supplementary information to deepen understanding through natural language interaction with the user. This provides a flexible training environment that is tailored to the emotional state of the learner or worker, enabling individual skill improvement and stress reduction.

[0738] "Generative artificial intelligence" refers to artificial intelligence technology that includes algorithms for generating personalized educational programs and content based on user data.

[0739] A "personalized education program" is educational content that is tailored to dynamically provide an appropriate learning pace and content, taking into account each user's profile and emotional state.

[0740] "Emotional state" refers to a psychological state determined in real time by analyzing the user's facial expressions and voice, and is information that quantifies the learner's stress and interests.

[0741] "Means of dynamically adjusting learning content" refers to a function that automatically changes the content, difficulty level, and pace of the educational program in real time according to the user's emotional state.

[0742] "Natural language interaction" is a communication method in which users interact with a system using the language they normally use, deepening their understanding through questions and answers.

[0743] "Supplemental information" refers to additional data and explanations provided to help users understand the learning material, and is automatically presented by the system as needed.

[0744] The "community function" is a feature that allows learners to share knowledge with other users and deepen their understanding through exchanging opinions and collaborative learning.

[0745] This system consists of a server and learner terminals, and specifically incorporates generative artificial intelligence, an emotion engine, and a natural language processing module. The server uses generative artificial intelligence to generate educational programs tailored to individual learners. In this process, learner profile data is collected during the initial setup and used as the foundation for the generation process.

[0746] The device is equipped with an emotion engine that acquires the learner's facial expressions and voice data in real time through the camera and microphone. This data is processed by an emotion analysis model developed using Python (utilizing TensorFlow and PyTorch) to identify the learner's emotional state. The emotion data is sent to a server, which then adjusts the learning program accordingly.

[0747] Users interact with the system through natural language interaction, and if they have questions during learning, they can immediately ask them via text or voice through their device. Natural language processing is performed by a generative AI model, providing rapid responses.

[0748] As a concrete example, imagine a new worker in a factory learning to operate new equipment. If the terminal detects the worker's confused expression, the server automatically slows the pace of the training and adds visual guidance. An example of a prompt used in this process is, "Generate training content to present when the worker's understanding declines while learning how to operate the new machine." In this way, learners can learn at their own individual pace.

[0749] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0750] Step 1:

[0751] The device acquires the learner's facial expressions and voice data in real time through its camera and microphone, and inputs this data into an emotion analysis model. This allows the learner's emotional state to be quantified and recognized.

[0752] Step 2:

[0753] The server integrates emotional state data sent from the terminal with learner profile information obtained during initial setup to generate an appropriate educational program. Using a generative AI model, it dynamically adjusts the difficulty and pace of the learning content using prompt messages.

[0754] Step 3:

[0755] The server generates a personalized educational program and sends it to the terminal. The terminal then presents the program to the learner and begins training with visual guidance as needed.

[0756] Step 4:

[0757] During training, users can ask the system questions in natural language, and the device sends these questions as text data to the server. The server analyzes the questions, uses a generative AI model to instantly generate answers, and sends them back to the device.

[0758] Step 5:

[0759] The terminal displays the responses received from the server to the user and continuously monitors the progress of the training. If necessary, it repeats the process of re-analyzing the sentiment data and fine-tuning the learning program.

[0760] Step 6:

[0761] Based on the emotions and learning data recorded after the training session, the server generates a plan for the next learning session and sends it to the user's device as feedback along with their progress.

[0762] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0763] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0764] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0765] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0766] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0767] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0768] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0769] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0770] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0771] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0772] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0773] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0774] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0775] 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.

[0776] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0777] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0778] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0779] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0780] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0781] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0782] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0783] The following is further disclosed regarding the embodiments described above.

[0784] (Claim 1)

[0785] A means of creating personalized educational programs using generative artificial intelligence,

[0786] A means of providing learning materials while adapting to multiple languages ​​in real time,

[0787] A means of presenting supplementary information to deepen understanding through natural language interaction with the user,

[0788] A means of recording educational progress and providing individual feedback,

[0789] A means of providing a community feature that allows users to share knowledge with each other,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for collecting user profile information during initial setup and optimizing the educational program based on that information.

[0793] (Claim 3)

[0794] The system according to claim 1, further comprising means for analyzing the progress of an educational program and suggesting the next learning content tailored to the user.

[0795] "Example 1"

[0796] (Claim 1)

[0797] A means of creating personalized educational content using generative artificial intelligence,

[0798] A means of displaying educational resources while adapting to multiple languages ​​in real time,

[0799] A means of providing supplementary data to deepen understanding through natural language dialogue with the user,

[0800] A means for recording learning activities and generating individual evaluation information,

[0801] A means of providing a communication function that allows users to share information with other users,

[0802] A means of dynamically adapting and changing teaching materials based on user questions and answers,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for collecting user profile data during initial setup and optimizing the teaching method based on that data.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising means for analyzing progress information on educational methods and suggesting the next learning content tailored to the user.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of creating individualized educational processes using a generation system,

[0811] A means of providing learning materials while adapting in real time to multiple different languages,

[0812] A means of providing supplementary information to deepen understanding through natural language dialogue with users,

[0813] A means of recording the progress of education and providing individual feedback,

[0814] A means of providing a community function for sharing knowledge with other users,

[0815] A means of displaying learning instructions in real time via a visual device, allowing users to instantly resolve questions they have while working,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for collecting user profile information during initial setup and optimizing the educational process based on that information.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising means for analyzing the progress of the educational process and suggesting the next learning content suitable for the user.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] A means comprising a device for user authentication and profile setting,

[0824] A means of collecting and analyzing data in real time using an emotion engine that analyzes user emotions,

[0825] A means for dynamically generating and adjusting personalized educational programs based on analyzed emotional data using generative artificial intelligence,

[0826] A means of instantly responding to users interacting with generated educational content using natural language,

[0827] A means of recording educational progress and providing the next learning content according to the user's learning status,

[0828] A means of providing communication functions that enable information sharing with other users,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising means for obtaining profile information from the user during initial setup and optimizing the educational program based on that information.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising means for evaluating the progress of a user's educational program and providing personalized learning suggestions based on the results of analyzing emotional data.

[0834] "Application example 2 of combining emotional engines"

[0835] (Claim 1)

[0836] A means of creating personalized educational programs using generative artificial intelligence,

[0837] A means of providing learning materials while adapting to multiple languages ​​in real time,

[0838] A means of presenting supplementary information to deepen understanding through natural language interaction with the user,

[0839] A means of recording educational progress and providing individual feedback,

[0840] A means of providing a community feature that allows users to share knowledge with each other,

[0841] A means for detecting the emotional state of the worker and dynamically adjusting the learning content according to that state,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for collecting user profile information during initial setup and optimizing the educational program based on that information.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for analyzing the progress of an educational program and suggesting the next learning content tailored to the user. [Explanation of Symbols]

[0847] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of creating personalized educational programs using generative artificial intelligence, A means of providing learning materials while adapting to multiple languages ​​in real time, A means of presenting supplementary information to deepen understanding through natural language interaction with the user, A means of recording educational progress and providing individual feedback, A means of providing a community feature that allows users to share knowledge with each other, A system that includes this.

2. The system according to claim 1, further comprising means for collecting user profile information during initial setup and optimizing the educational program based on that information.

3. The system according to claim 1, further comprising means for analyzing the progress of an educational program and suggesting the next learning content tailored to the user.

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