system

Abstract

We provide the system. [Solution] A device for evaluating the progress of multiple educational participants in real time, A device for forming educational participants into an optimal combination or group based on the aforementioned progress evaluation, A device for providing feedback to educational participants in real time, A device for providing an extended virtual environment for educational participants, A device for sharing information and promoting collaborative work among educational participants through two-way communication, A system that includes this.

Description

Technical Field

[0001] The technology of this 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 and includes 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 the modern educational environment, while individual learning is emphasized, there is a problem that the communication and collaboration among learners to deepen understanding are insufficient. Also, there is a problem that it is difficult to evaluate the progress of individual learners in real time and form appropriate learning pairs or groups based on it. In such a situation, the learning efficiency is not maximized, and social skills are not fully developed, causing problems.

Means for Solving the Problems

[0005] This invention provides a system that has means for evaluating the progress of multiple learners in real time and forms optimal pairs or groups based on the evaluation results. Furthermore, it provides real-time feedback to learners, improving learning efficiency. This system also includes means for promoting communication among learners and improving social skills by utilizing an augmented reality environment. This makes it possible to maximize learning effectiveness while deepening mutual understanding among learners.

[0006] A "learner" refers to an individual who participates in educational activities and aims to improve their knowledge and skills.

[0007] "Progress" refers to the current state or process by which learners are achieving their set learning goals and tasks.

[0008] "Real-time" refers to a situation where data is processed at the very moment an event occurs, allowing for immediate responses and feedback.

[0009] "Evaluation" is the act of objectively analyzing a learner's progress and achievements, and measuring their performance and level of understanding.

[0010] A "pair or group" refers to a collection of learners organized to share and accomplish common learning objectives or tasks.

[0011] "Feedback" refers to providing learners with specific areas for improvement and results regarding their actions and achievements, and offering information that will be useful for their future learning.

[0012] A "system" refers to a collection of interrelated elements or components designed to achieve a specific purpose.

[0013] An "augmented reality (AR) environment" refers to a technology that merges the physical and digital worlds by overlaying computer-generated visual information onto images of the real world.

[0014] "Communication" refers to the process of mutually transmitting information and opinions and sharing understanding.

[0015] "Social skills" refer to the abilities and behavioral patterns necessary to successfully communicate smoothly with others and collaborate.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The system implementing this invention consists of a server, a terminal, and learner interaction. Specifically, it operates as follows:

[0038] When a learner logs in, the server verifies their authentication information, references their user profile, and retrieves their learning history. Based on this data, the server selects the most suitable learning content for the learner and delivers it to their device.

[0039] The device displays received learning content in an augmented reality (AR) environment. This display allows learners to understand information more intuitively and gain a more immersive learning experience.

[0040] Users complete tasks through an AR environment, and their progress is transmitted from their device to the server in real time. The server analyzes the progress data to identify the learner's strengths and weaknesses. Based on this analysis, an AI agent forms the optimal pair or group and notifies the device of this information via the server.

[0041] The device provides learners with feedback from the AI ​​agent in both audio and visual ways. For example, if a user is struggling with a particular task, the device will display visual instructions such as "Focus on this part" as an AR overlay.

[0042] Furthermore, this system features a function that facilitates communication among learners. The devices synchronize, allowing learners to interact with the same AR object in real time. This collaborative work improves learners' social skills and enhances their learning engagement.

[0043] In this way, this system combines an augmented reality environment with AI technology to deepen learners' understanding and maximize learning effectiveness.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user logs into the system. The terminal displays an interface for entering a user ID and password, and the user enters the information. Next, the terminal encrypts the entered authentication information and sends it to the server.

[0047] Step 2:

[0048] The server compares the received authentication information with the database to authenticate the user. If authentication is successful, the server starts a session for the user and retrieves the user's learning history. Then, it sends this information, along with the session ID, to the terminal.

[0049] Step 3:

[0050] The server selects appropriate learning content based on the user's learning history. The selected content is immediately sent to the device.

[0051] Step 4:

[0052] The device displays the received learning content in an augmented reality (AR) environment. Through this AR environment, the user begins working on the designated learning tasks.

[0053] Step 5:

[0054] The device continuously collects user progress data and sends it to the server in real time. This data includes the user's operation speed and the number of incorrect answers.

[0055] Step 6:

[0056] The server analyzes the received progress data and evaluates the user's learning progress. An AI agent is used to identify the user's strengths and weaknesses.

[0057] Step 7:

[0058] The AI ​​agent calculates the optimal training pair or group based on the analysis results. The results are then notified to each terminal from the server.

[0059] Step 8:

[0060] The device provides users with real-time visual and auditory feedback from the AI ​​agent. It also displays an interface that suggests new group learning opportunities to the user.

[0061] Step 9:

[0062] The device utilizes features that facilitate communication between users, enabling collaborative work in an AR environment. This promotes information sharing and problem-solving among learners.

[0063] This system is designed to support learners' understanding and the development of their social skills through this series of steps.

[0064] (Example 1)

[0065] 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."

[0066] In today's educational environment, there is a demand for efficient instruction tailored to the individual needs of learners, as well as the development of social skills through communication. However, traditional systems have challenges in real-time progress assessment and optimal group formation, as well as insufficient use of effective feedback and augmented reality environments.

[0067] 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.

[0068] In this invention, the server includes means for evaluating the behavioral information of multiple users in real time, means for forming users into optimal pairs or groups, means for providing response information to users in real time, means for providing an augmented reality environment for displaying digital information, means for creating information optimized for users using generative AI technology, and means for displaying the information on a user interface. This makes it possible to provide a comprehensive learning environment that combines instruction optimized for each learner, real-time monitoring of learning progress, and improvement of social skills.

[0069] "Action information" refers to data related to user behavior and operations, and is evaluated in real time.

[0070] "Real-time" refers to the moment a user's action or operation occurs, and indicates that evaluation and response should be performed immediately.

[0071] "Response information" refers to information provided based on the user's actions and progress, including data related to guidance and areas for improvement.

[0072] An "augmented reality environment" is an environment that uses technology to enhance the user experience by overlaying digital information onto the real world.

[0073] "Generative AI technology" refers to methods for automatically creating content and information using artificial intelligence technology.

[0074] "User interface" refers to the medium, such as screens or control panels, that users use to interact with a system.

[0075] "Optimization" means adjusting something to be most effective or efficient under specific conditions.

[0076] A "learning environment" refers to a physical or digital space provided for learners to acquire knowledge and improve their skills.

[0077] The embodiment of this invention mainly consists of a server, a terminal, and a user. The server uses a database to manage the user's individual authentication information and learning progress information. In the authentication process, the server verifies the authentication information entered by the user through the terminal and compares it with the information in the database. After proper authentication is performed, the server refers to the user's learning history and uses AI technology to generate individually optimized learning content. This generation process uses a generative AI model to dynamically create information that matches the learner's needs.

[0078] The device receives content sent from the server and displays it to the user in an augmented reality (AR) environment. Specifically, it utilizes the device's camera and display technology to integrate digital information into physical space, creating an intuitive interface. This AR display allows users to understand the subject matter more concretely. For example, in a biology class, a 3D model of the heart can be displayed in AR, allowing students to visually learn the details of its internal structure.

[0079] Users engage in learning activities through the AR environment provided by their device, and their progress is transmitted to the server in real time. The server analyzes the received progress data to identify the user's strengths and weaknesses. Based on this analysis, an AI agent forms the optimal pair or group and provides feedback to the user through voice and visual means. This allows users to continue learning and improving.

[0080] Furthermore, this system enables real-time synchronization between devices, supporting users to collaborate and work together in the same AR space. This allows users to hone their social skills through collaborative learning. As a concrete example, a prompt sentence such as "Please provide AR teaching materials to explain the structure of the heart in a biology class" is input into the AI ​​model, and appropriate learning content is generated.

[0081] This system allows for personalized learning experiences while simultaneously promoting the development of social skills through real-time feedback and collaborative work.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server receives authentication information entered by the user from the terminal. This input includes the user ID and password. The server compares this information with the database to determine whether authentication was successful or unsuccessful. If successful, it outputs the user's identification information and prepares to proceed to the next step.

[0085] Step 2:

[0086] The server retrieves user profile information from the database for users who have successfully authenticated. This input includes past learning progress data and areas of strength and weakness. The server uses this data and a generative AI model to generate learning content optimized for the user. The output is the generated content data, which is delivered to the device.

[0087] Step 3:

[0088] The device receives learning content sent from the server. Based on this data, the device uses augmented reality (AR) technology to display the content to the user. Specifically, it uses the device's camera to overlay 3D objects onto the physical environment, providing the user with an interactive visual experience. This output is AR-based visual information.

[0089] Step 4:

[0090] Users engage in learning activities using an AR environment provided on their device. The device records the user's actions and choices. This progress data is sent to the server in real time as input and used to guide the next step.

[0091] Step 5:

[0092] The server receives progress data sent by the user and performs analysis. This analysis examines the user's learning tendencies, strengths, and weaknesses. Based on this information, the server, with the help of an AI agent, generates suitable pairs or groups for the user. The output is information on optimal group formation.

[0093] Step 6:

[0094] The server sends the formed pair or group information back to the terminal. The terminal receives this and provides feedback to the user visually or audibly. At that time, specific advice and instructions tailored to the user's learning progress are presented in AR or audio.

[0095] Step 7:

[0096] The device synchronizes connections in real time, enabling multiple users to collaborate. This feature allows users to communicate while simultaneously manipulating the same AR object. The output is a synchronized experience among users.

[0097] In this way, each step works in conjunction to create a learning experience optimized for each individual learner and enable real-time communication.

[0098] (Application Example 1)

[0099] 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."

[0100] Traditional educational support systems have struggled to provide real-time feedback based on individual progress and facilitate efficient collaborative learning among learners. This is particularly true when providing immersive learning environments utilizing augmented reality technology, where interaction presents significant challenges. There is a need to provide an environment that offers individually optimized learning support while simultaneously improving social skills, given the varying paces and levels of understanding among educational participants.

[0101] 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.

[0102] In this invention, the server includes a device for evaluating the progress of multiple learning participants in real time, a device for forming learning participants into optimal combinations or groups based on the progress evaluation, a device for providing feedback to learning participants in real time, a device for providing an extended virtual environment for learning participants, and a device for sharing information among learning participants and promoting collaborative work through bidirectional communication. This enables individually optimized learning support and smooth collaborative work among learning participants, thereby improving comprehension and promoting social skills.

[0103] An "educational participant" is an individual or group of people who participate in a specific learning program or educational experience.

[0104] "Progress evaluation" is the process of analyzing the learning status and understanding of educational participants in real time and measuring the results.

[0105] "Combination or group" refers to a group or pair of educational participants organized based on specific criteria.

[0106] "Feedback" refers to real-time feedback information regarding the learning progress and understanding of educational participants.

[0107] An "augmented virtual environment" is a technology that provides a virtual learning environment by overlaying digital information onto the real world.

[0108] "Two-way communication" refers to a form of communication that allows for the real-time exchange of information between educational participants and between educational support systems.

[0109] "Collaborative work" refers to the joint implementation of educational activities or tasks by multiple educational participants.

[0110] "Social skills" refer to interpersonal abilities that educational participants should acquire through communication and cooperation.

[0111] This invention is a next-generation educational system that supports individual learning for educational participants and provides an augmented virtual environment. The system consists of a server, terminals, and educational participants.

[0112] When an educational participant logs in, the server verifies their authentication information and references their profile. Based on their past learning history and progress data, it selects the most suitable learning content and delivers it to their device. During this process, a machine learning framework such as TENSORFLOW® is used to evaluate the participant's progress in real time and provide appropriate instructional information.

[0113] The device utilizes Unity and Vuforia to display received learning content in an augmented virtual environment. This allows educators to understand information more intuitively and immersively. Furthermore, it features an interface for sending real-time progress data to a server and providing immediate feedback. This feedback includes audio or visual instructions and is displayed as an AR overlay.

[0114] Participants in the learning program will complete learning tasks through this augmented virtual environment. Two-way communication will utilize WebSocket, enabling participants to share information in real time and collaborate by manipulating the same AR objects. This collaborative work is expected to improve the social skills of the participants.

[0115] As a concrete example, when children attempt a math problem, the server selects an appropriate problem, the device displays it using augmented reality (AR), and tracks each participant's progress. Based on this progress, hints and additional explanations are provided in real time to enhance learning comprehension.

[0116] An example of a prompt might be, "How can AR displays be used to show concrete examples when children are learning the concept of numbers?"

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] When an educational participant logs in, the server uses their authentication credentials to retrieve their profile and past learning history. This serves as input, and the server performs calculations to extract relevant information from the database, preparing it for the selection of optimized learning content.

[0120] Step 2:

[0121] The server uses generative AI models such as TensorFlow to evaluate progress based on the acquired learning history data. In this step, it selects learning content suitable for the educator by comparing past performance with goals. This calculation outputs the selected learning content, which is then sent to the terminal.

[0122] Step 3:

[0123] The device receives learning content delivered from the server and displays it in an augmented virtual environment using Unity and Vuforia. The received data is processed as AR content and displayed visually to the educational participants. This processing ensures that information is presented intuitively.

[0124] Step 4:

[0125] Participants in the educational program complete tasks provided via a device and input data indicating their progress. The device captures this input and sends progress information to the server in real time. This data transmission is carried out efficiently based on a communication protocol.

[0126] Step 5:

[0127] The server analyzes the received progress information and generates feedback and supplementary information. In this step, the progress data is evaluated by a generating AI model, and data processing is performed to design optimal feedback. The generated feedback is sent back to the terminal.

[0128] Step 6:

[0129] The device displays feedback received from the server to the educational participants as audio and visual instructions. This is provided using technologies such as AR overlays. The presentation of feedback is designed to deepen participants' understanding.

[0130] Step 7:

[0131] To facilitate collaboration among educational participants, devices will be synchronized via WebSocket, allowing them to share the same educational experience. This communication path will enable real-time information exchange among participants, facilitating smooth collaborative work.

[0132] 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.

[0133] This invention improves learning efficiency by combining an emotion engine with a system that evaluates learners' progress in real time. A specific embodiment is described below.

[0134] Users log into the system and work on tasks in an augmented reality (AR) environment at the start of their learning. The device provides this work through an AR interface and monitors the user's actions in real time.

[0135] The emotion engine uses the camera and microphone built into the device to analyze the user's emotions from their facial expressions and voice. For example, if a user is confused about a problem, the emotion engine will detect "confusion" from their facial expressions and sighs.

[0136] The server receives and analyzes the collected learning progress data and sentiment data. The AI ​​agent generates optimal feedback, taking into account the user's progress and emotional state. This feedback is displayed to the user via the device as voice or text, offering encouragement and advice.

[0137] For example, if a user expresses anxiety, the device will provide a voice message such as, "Let's proceed calmly and without rushing." This allows the user to continue learning while receiving emotional support.

[0138] Furthermore, based on the analysis results of the emotion engine, the server reorganizes pairs or groups. For example, if there are significant differences in mental states such as "motivation" or "concentration" among users, the AI ​​agent will reorganize them to provide an environment where they can collaborate comfortably.

[0139] Thus, this system aims to comprehensively understand the learner's real-time state and optimize the learning experience by combining it with an emotion engine. Through feedback tailored to individual learner needs and flexible group configurations, the system simultaneously improves learning efficiency and social skills.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user logs into the system. The terminal provides an input interface for the user ID and password, and the user enters the information. The entered information is encrypted and transmitted from the terminal to the server.

[0143] Step 2:

[0144] The server authenticates the user using the received authentication information in the database. If authentication is successful, the server retrieves the user's learning history and sends that information along with the session ID to the terminal.

[0145] Step 3:

[0146] The device displays learning content tailored to the user in an AR environment, based on the user's learning history from the server. The user then begins learning using this content.

[0147] Step 4:

[0148] During learning, the emotion engine collects the user's facial expressions and voice through the device's built-in camera and microphone. The emotion engine analyzes this data to identify the user's emotional state. For example, if the user is frowning and appears confused, it will be identified as "confused."

[0149] Step 5:

[0150] The device sends identified emotion data, along with progress data, to the server in real time.

[0151] Step 6:

[0152] The server comprehensively analyzes the received progress data and sentiment data, and the AI ​​agent generates optimal feedback. This feedback supports the learning process and includes instructions tailored to the user's emotions.

[0153] Step 7:

[0154] The device presents the generated feedback to the user visually or audibly. For example, if the device analyzes that the user is anxious, it might deliver a voice message such as, "Relax and get ready to move on to the next step."

[0155] Step 8:

[0156] The server re-evaluates the initially formed learning groups, taking into account the user's emotional state. If necessary, the AI ​​agent rearranges the pairs and groups of learners, and the server notifies the terminal of this information.

[0157] Step 9:

[0158] The device displays the reorganized groups in the AR environment and instructs the user to continue learning with new learning pairs or groups.

[0159] Through this process, the system monitors learners' progress and emotional state in real time, providing appropriate feedback and group assignments to create an effective learning experience.

[0160] (Example 2)

[0161] 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".

[0162] This invention relates to a system that simultaneously achieves efficient learning progress and emotional support for learners. Conventional learning support systems have problems in that it is difficult to grasp the actual learning progress and the emotional state of learners in real time, making it impossible to provide optimal feedback and group formation according to the learners' needs. This invention aims to solve this problem.

[0163] 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.

[0164] In this invention, the server includes means for evaluating the learner's progress in real time, means for analyzing the learner's emotional state from facial expressions and voice data, and means for generating optimal feedback for the learner based on the progress evaluation and emotional analysis. This enables personalized, real-time support and an efficient learning experience for each learner.

[0165] A "learner" refers to an individual engaged in an educational program or assignment, whose progress and emotional state are the subjects of evaluation.

[0166] "Real-time assessment" refers to a process of immediately and continuously monitoring learners' progress, which enables rapid feedback.

[0167] "Analyzing emotional states from facial expressions and voice data" refers to a technology that determines a learner's emotions based on data acquired using input devices such as cameras and microphones.

[0168] "Generating feedback" refers to the process of analyzing collected progress and sentiment data to create useful information and advice for learners.

[0169] "Reorganizing into pairs or groups" refers to the process of rearranging personnel to optimize collaboration among learners based on their emotional state and progress.

[0170] An "augmented reality environment" refers to a technological environment that overlays digital information onto the real world and presents it to the user, enriching the learning experience.

[0171] This invention is a system that evaluates learners' progress and emotions in real time and provides optimal feedback based on that evaluation. The system primarily consists of a server, terminals, and an emotion engine.

[0172] The device functions as an interface for users engaging in learning. It includes a display for an augmented reality (AR) environment, a camera for recognizing user movements and facial expressions, and a microphone for collecting audio data. The data acquired through these devices is used to monitor the user's learning progress and emotional state.

[0173] The emotion engine analyzes data acquired from the camera and microphone to identify the user's emotions. For example, if the user makes a confused expression or sighs, it detects emotions such as "confused" or "anxious."

[0174] The server receives and analyzes progress and sentiment data transmitted from the terminal. Using a generative AI model, the server generates optimal feedback based on this data. This feedback is communicated to the user via text or voice through the terminal. For example, if the user indicates "I don't know what to do next" as a prompt, the server provides specific advice using instructions to the generative AI model such as "How can I advise the user on the next step?"

[0175] Furthermore, the server also has the ability to reorganize learners into optimal pairs or groups while taking into account the users' emotional states. This allows learners to learn in an environment where they can easily cooperate with each other, and simultaneously improve their social skills.

[0176] In this way, the present invention is a system that enables the provision of real-time support tailored to learners, thereby significantly improving the learning experience.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The user logs into the system and begins a learning session. Logging in requires a username and password, and the output displays an augmented reality (AR) learning environment on the device. The user then works on assignments within this environment. In the AR environment, learning content is presented visually, and the user can interact with it.

[0180] Step 2:

[0181] The device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. The input consists of the user's facial expressions and voice data. Based on this data, an emotion engine operates, analyzing the user's emotional state (e.g., "confused," "anxious," etc.) as output. This process utilizes image processing and speech recognition technology to perform calculations that analyze emotions from the acquired data.

[0182] Step 3:

[0183] The server receives learning progress data and sentiment data sent from the terminal. Input data includes logs of learning status and the user's sentiment state. The server analyzes this data using a generative AI model and generates optimal feedback for the user as output. This analysis process employs machine learning algorithms to make predictions and evaluations based on the input data.

[0184] Step 4:

[0185] The device receives feedback from the server and notifies the user of it via voice or text. This feedback may include specific advice or encouraging messages. For example, a message such as "Let's proceed calmly and without rushing" might be displayed. Text display and speech synthesis technologies are used to provide this feedback to the user.

[0186] Step 5:

[0187] The server reorganizes learners into optimal pairs or groups, taking sentiment data into consideration. All user progress and sentiment data are processed as input, and reconstructed group information is generated as output. In this step, a clustering algorithm is used to perform dynamic group formation based on user state.

[0188] Through these steps, the system adaptively responds to learners in real time, maximizing the learning experience.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Traditional education systems have struggled to grasp learners' progress and emotional states in real time and provide personalized, optimal feedback. Furthermore, there were insufficient means to facilitate communication among participants and improve the educational experience. There was also a need for an effective system to enhance the quality of educational experiences in physical stores.

[0192] 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.

[0193] In this invention, the server includes means for evaluating learners' progress in real time, means for forming participants into appropriate collaborative groups, and means for evaluating comprehension based on sentiment analysis and generating appropriate advice. This enables flexible feedback and improved experience tailored to the individual needs of learners.

[0194] A "learner" is an individual whose purpose is to absorb educational content and grow.

[0195] "Progress" is an indicator that shows the extent to which learners have achieved the set educational content.

[0196] "Real-time" refers to a state of having the ability to grasp and process events as they occur.

[0197] "Evaluation" is the act of assessing performance, such as progress and emotional state, based on specific criteria.

[0198] A "collaborative group" is a group of learners organized to achieve a common goal.

[0199] "Feedback" is a response that provides appropriate advice and confirmation of correctness according to the learner's progress and level of understanding.

[0200] Augmented reality is a technology that overlays digital information onto physical reality.

[0201] "Emotion analysis" is a technique that identifies emotional states from a learner's facial expressions and voice.

[0202] "Comprehension level" refers to the degree to which learners understand and remember educational content.

[0203] "Advice" refers to guidance or suggestions provided to learners to encourage improvement and growth.

[0204] The system for implementing this invention analyzes the user's progress and emotions in real time to support learning and provides appropriate feedback. Specific embodiments are shown below.

[0205] The server is responsible for evaluating learning progress and emotional state based on data sent by the user. Specifically, the collected data is processed on a cloud-based server, where software and AI agents for emotion analysis operate. Emotion analysis engines such as Microsoft® Azure® Emotion Recognition API are used for emotion analysis. The server also generates and provides feedback to the user using OpenAI® GPT models and other tools.

[0206] The devices are used to monitor the user's learning behavior and collect data. These devices include smart glasses and tablet computers. These devices are equipped with cameras and microphones to capture the user's facial expressions and voice.

[0207] Users learn within an augmented reality environment and receive feedback from their device. If a user is confused, the device detects this confusion, and the server analyzes it and provides feedback such as, "Shall we review this point again?"

[0208] As a concrete example, consider using the system in a cooking class held at a physical store. If a participant shows signs of low understanding, the terminal will display a message such as "Let's review the steps again," providing calm instructions to the user. An example of a prompt message in this case could be, "The participant is confused, so please generate a message in a gentle tone encouraging them to review the steps again."

[0209] Thus, the present invention is a system for optimizing the learning experience by providing real-time feedback that takes into account the user's emotions and progress.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It acquires image data of the user's face and voice data as input data. Specifically, smart glasses or a tablet device are used.

[0213] Step 2:

[0214] The device transmits the acquired image and audio data to the server via a pre-configured secure protocol. The input consists of the user's facial expressions and audio data, while the output consists of this data sent to the server.

[0215] Step 3:

[0216] The server analyzes the received data using an emotion analysis engine (such as the Microsoft Azure Emotion Recognition API). The input consists of user facial image data and audio data, which are analyzed to output emotional states such as "confused," "anxious," and "relaxed."

[0217] Step 4:

[0218] The server uses a generative AI model (such as OpenAI's GPT model) to generate appropriate feedback text data based on the sentiment analysis results. The input includes sentiment data and learning progress data, and the output is feedback text data. Specifically, the prompt message "The participant is confused, so generate a message in a calm tone to reconfirm the procedure" is input to the model.

[0219] Step 5:

[0220] The server sends the generated feedback text to the terminal. The input is the feedback text, and the output is the data that transfers it to the terminal.

[0221] Step 6:

[0222] The device provides the user with received feedback via audio or text display. The input is text data of the feedback, and the user receives visual or auditory feedback. For example, it might display "Shall we review this point again?" through the speaker or screen.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] The system implementing this invention consists of a server, a terminal, and learner interaction. Specifically, it operates as follows:

[0240] When a learner logs in, the server verifies their authentication information, references their user profile, and retrieves their learning history. Based on this data, the server selects the most suitable learning content for the learner and delivers it to their device.

[0241] The device displays received learning content in an augmented reality (AR) environment. This display allows learners to understand information more intuitively and gain a more immersive learning experience.

[0242] Users complete tasks through an AR environment, and their progress is transmitted from their device to the server in real time. The server analyzes the progress data to identify the learner's strengths and weaknesses. Based on this analysis, an AI agent forms the optimal pair or group and notifies the device of this information via the server.

[0243] The device provides learners with feedback from the AI ​​agent in both audio and visual ways. For example, if a user is struggling with a particular task, the device will display visual instructions such as "Focus on this part" as an AR overlay.

[0244] Furthermore, this system features a function that facilitates communication among learners. The devices synchronize, allowing learners to interact with the same AR object in real time. This collaborative work improves learners' social skills and enhances their learning engagement.

[0245] In this way, this system combines an augmented reality environment with AI technology to deepen learners' understanding and maximize learning effectiveness.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The user logs into the system. The terminal displays an interface for entering a user ID and password, and the user enters the information. Next, the terminal encrypts the entered authentication information and sends it to the server.

[0249] Step 2:

[0250] The server compares the received authentication information with the database to authenticate the user. If authentication is successful, the server starts a session for the user and retrieves the user's learning history. Then, it sends this information, along with the session ID, to the terminal.

[0251] Step 3:

[0252] The server selects appropriate learning content based on the user's learning history. The selected content is immediately sent to the device.

[0253] Step 4:

[0254] The device displays the received learning content in an augmented reality (AR) environment. Through this AR environment, the user begins working on the designated learning tasks.

[0255] Step 5:

[0256] The device continuously collects user progress data and sends it to the server in real time. This data includes the user's operation speed and the number of incorrect answers.

[0257] Step 6:

[0258] The server analyzes the received progress data and evaluates the user's learning progress. An AI agent is used to identify the user's strengths and weaknesses.

[0259] Step 7:

[0260] The AI ​​agent calculates the optimal training pair or group based on the analysis results. The results are then notified to each terminal from the server.

[0261] Step 8:

[0262] The device provides users with real-time visual and auditory feedback from the AI ​​agent. It also displays an interface that suggests new group learning opportunities to the user.

[0263] Step 9:

[0264] The device utilizes features that facilitate communication between users, enabling collaborative work in an AR environment. This promotes information sharing and problem-solving among learners.

[0265] This system is designed to support learners' understanding and the development of their social skills through this series of steps.

[0266] (Example 1)

[0267] 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."

[0268] In today's educational environment, there is a demand for efficient instruction tailored to the individual needs of learners, as well as the development of social skills through communication. However, traditional systems have challenges in real-time progress assessment and optimal group formation, as well as insufficient use of effective feedback and augmented reality environments.

[0269] 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.

[0270] In this invention, the server includes means for evaluating the behavioral information of multiple users in real time, means for forming users into optimal pairs or groups, means for providing response information to users in real time, means for providing an augmented reality environment for displaying digital information, means for creating information optimized for users using generative AI technology, and means for displaying the information on a user interface. This makes it possible to provide a comprehensive learning environment that combines instruction optimized for each learner, real-time monitoring of learning progress, and improvement of social skills.

[0271] "Action information" refers to data related to user behavior and operations, and is evaluated in real time.

[0272] "Real-time" refers to the moment a user's action or operation occurs, and indicates that evaluation and response should be performed immediately.

[0273] "Response information" refers to information provided based on the user's actions and progress, including data related to guidance and areas for improvement.

[0274] An "augmented reality environment" is an environment that uses technology to enhance the user experience by overlaying digital information onto the real world.

[0275] "Generative AI technology" refers to methods for automatically creating content and information using artificial intelligence technology.

[0276] "User interface" refers to the medium, such as screens or control panels, that users use to interact with a system.

[0277] "Optimization" means adjusting something to be most effective or efficient under specific conditions.

[0278] A "learning environment" refers to a physical or digital space provided for learners to acquire knowledge and improve their skills.

[0279] The embodiment of this invention mainly consists of a server, a terminal, and a user. The server uses a database to manage the user's individual authentication information and learning progress information. In the authentication process, the server verifies the authentication information entered by the user through the terminal and compares it with the information in the database. After proper authentication is performed, the server refers to the user's learning history and uses AI technology to generate individually optimized learning content. This generation process uses a generative AI model to dynamically create information that matches the learner's needs.

[0280] The terminal receives the content sent from the server and displays it to the user in an augmented reality (AR) environment. Specifically, by leveraging the terminal's camera and display technology, digital information is integrated onto the physical space to realize an intuitive interface. Through this AR display, users can understand the learning object more concretely. For example, in a biology class, a 3D model of the heart can be displayed in AR so that the details of its internal structure can be visually learned.

[0281] Users conduct learning activities through the AR environment provided by the terminal, and the progress is sent to the server in real time. The server analyzes the received progress data to identify the strengths and weaknesses of the users. Based on the analysis results, the AI agent forms an optimal pair or group and provides the feedback to the users audibly or visually. Thus, users can proceed with continuous learning and improvement.

[0282] Furthermore, this system enables real-time synchronization between terminals and supports users to cooperate and work in the same AR space. Through this, social skills can be honed through collaborative learning. As a specific example, a prompt sentence such as "Please provide an AR teaching material for explaining the structure of the heart in a biology class." is input into the generative AI model, and appropriate learning content is generated.

[0283] This system can provide a learning experience optimized for individuals and at the same time promote the development of social capabilities through real-time feedback and collaborative work.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The server receives the authentication information input by the user from the terminal. This input is the user ID and password. The server compares this with the information in the database to determine the success or failure of authentication. If successful, it outputs the user's identification information and prepares to proceed to the next process.

[0287] Step 2:

[0288] The server obtains the profile information of the user who has successfully authenticated from the database. This input includes past learning progress data and areas of strength and weakness. The server uses this data to generate learning content optimized for the user using a generative AI model. The output is the generated content data, which is distributed to the terminal.

[0289] Step 3:

[0290] The terminal receives the learning content sent from the server. Based on this data, the terminal uses augmented reality (AR) technology to display the content to the user. Specifically, it uses the terminal's camera to overlay 3D objects on the physical environment, providing the user with an interactive visual experience. This output is visual information by AR.

[0291] Step 4:

[0292] The user conducts learning activities using the AR environment provided on the terminal. The user's actions and selected content are recorded by the terminal. This progress data is sent to the server in real time as input and used for the next step.

[0293] Step 5:

[0294] The server receives the progress data sent from the user and performs analysis. In this analysis, the user's learning tendencies, strengths, and weaknesses are analyzed. Based on this information, the server generates pairs or groups suitable for the user with the help of an AI agent. The output is optimal group formation information.

[0295] Step 6:

[0296] The server sends the formed pair or group information back to the terminal. The terminal receives this and provides feedback to the user visually or audibly. At that time, specific advice and instructions tailored to the user's learning progress are presented in AR or audio.

[0297] Step 7:

[0298] The device synchronizes connections in real time, enabling multiple users to collaborate. This feature allows users to communicate while simultaneously manipulating the same AR object. The output is a synchronized experience among users.

[0299] In this way, each step works in conjunction to create a learning experience optimized for each individual learner and enable real-time communication.

[0300] (Application Example 1)

[0301] 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."

[0302] Traditional educational support systems have struggled to provide real-time feedback based on individual progress and facilitate efficient collaborative learning among learners. This is particularly true when providing immersive learning environments utilizing augmented reality technology, where interaction presents significant challenges. There is a need to provide an environment that offers individually optimized learning support while simultaneously improving social skills, given the varying paces and levels of understanding among educational participants.

[0303] 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.

[0304] In this invention, the server includes: a device for evaluating the progress of multiple education participants in real time, a device for forming education participants into an optimal combination or group based on the progress evaluation, a device for providing opinions to education participants in real time, a device for providing an extended virtual environment for education participants, and a device for sharing information among education participants through two-way communication to promote collaborative work. Thereby, individually optimized learning support and smooth collaborative work among education participants become possible, and it becomes possible to achieve improved understanding and the promotion of social skills.

[0305] An "education participant" is an individual or group that participates in a specific learning program or educational experience.

[0306] "Progress evaluation" is the process of analyzing the learning situation and understanding level of education participants in real time and measuring their achievements.

[0307] "Combination or group" refers to a group or pair formed by organizing education participants based on specific criteria.

[0308] "Opinion" refers to real-time feedback information regarding the learning situation and understanding level of education participants.

[0309] "Extended virtual environment" is a technology that provides a virtual learning environment by overlaying digital information on the real world.

[0310] "Two-way communication" is a communication form in which information is exchanged in real time among education participants or between an education support system and education participants.

[0311] "Collaborative work" refers to the implementation of educational activities or tasks jointly by multiple education participants.

[0312] "Social skills" are interpersonal relationship abilities that education participants should acquire through communication and cooperation.

[0313] This invention is a next-generation educational system that supports individual learning for educational participants and provides an augmented virtual environment. The system consists of a server, terminals, and educational participants.

[0314] When an educational participant logs in, the server verifies their authentication information and accesses their profile. Based on their past learning history and progress data, it selects the most suitable learning content and delivers it to their device. During this process, machine learning frameworks such as TensorFlow are used to assess the participant's progress in real time and provide appropriate guidance.

[0315] The device utilizes Unity and Vuforia to display received learning content in an augmented virtual environment. This allows educators to understand information more intuitively and immersively. Furthermore, it features an interface for sending real-time progress data to a server and providing immediate feedback. This feedback includes audio or visual instructions and is displayed as an AR overlay.

[0316] Participants in the learning program will complete learning tasks through this augmented virtual environment. Two-way communication will utilize WebSocket, enabling participants to share information in real time and collaborate by manipulating the same AR objects. This collaborative work is expected to improve the social skills of the participants.

[0317] As a concrete example, when children attempt a math problem, the server selects an appropriate problem, the device displays it using augmented reality (AR), and tracks each participant's progress. Based on this progress, hints and additional explanations are provided in real time to enhance learning comprehension.

[0318] An example of a prompt might be, "How can AR displays be used to show concrete examples when children are learning the concept of numbers?"

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] When an educational participant logs in, the server uses their authentication credentials to retrieve their profile and past learning history. This serves as input, and the server performs calculations to extract relevant information from the database, preparing it for the selection of optimized learning content.

[0322] Step 2:

[0323] The server uses generative AI models such as TensorFlow to evaluate progress based on the acquired learning history data. In this step, it selects learning content suitable for the educator by comparing past performance with goals. This calculation outputs the selected learning content, which is then sent to the terminal.

[0324] Step 3:

[0325] The device receives learning content delivered from the server and displays it in an augmented virtual environment using Unity and Vuforia. The received data is processed as AR content and displayed visually to the educational participants. This processing ensures that information is presented intuitively.

[0326] Step 4:

[0327] Participants in the educational program complete tasks provided via a device and input data indicating their progress. The device captures this input and sends progress information to the server in real time. This data transmission is carried out efficiently based on a communication protocol.

[0328] Step 5:

[0329] The server analyzes the received progress information and generates feedback and supplementary information. In this step, the progress data is evaluated by a generating AI model, and data processing is performed to design optimal feedback. The generated feedback is sent back to the terminal.

[0330] Step 6:

[0331] The device displays feedback received from the server to the educational participants as audio and visual instructions. This is provided using technologies such as AR overlays. The presentation of feedback is designed to deepen participants' understanding.

[0332] Step 7:

[0333] To facilitate collaboration among educational participants, devices will be synchronized via WebSocket, allowing them to share the same educational experience. This communication path will enable real-time information exchange among participants, facilitating smooth collaborative work.

[0334] 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.

[0335] This invention improves learning efficiency by combining an emotion engine with a system that evaluates learners' progress in real time. A specific embodiment is described below.

[0336] Users log into the system and work on tasks in an augmented reality (AR) environment at the start of their learning. The device provides this work through an AR interface and monitors the user's actions in real time.

[0337] The emotion engine uses the camera and microphone built into the device to analyze the user's emotions from their facial expressions and voice. For example, if a user is confused about a problem, the emotion engine will detect "confusion" from their facial expressions and sighs.

[0338] The server receives and analyzes the collected learning progress data and sentiment data. The AI ​​agent generates optimal feedback, taking into account the user's progress and emotional state. This feedback is displayed to the user via the device as voice or text, offering encouragement and advice.

[0339] For example, if a user expresses anxiety, the device will provide a voice message such as, "Let's proceed calmly and without rushing." This allows the user to continue learning while receiving emotional support.

[0340] Furthermore, based on the analysis results of the emotion engine, the server reorganizes pairs or groups. For example, if there are significant differences in mental states such as "motivation" or "concentration" among users, the AI ​​agent will reorganize them to provide an environment where they can collaborate comfortably.

[0341] Thus, this system aims to comprehensively understand the learner's real-time state and optimize the learning experience by combining it with an emotion engine. Through feedback tailored to individual learner needs and flexible group configurations, the system simultaneously improves learning efficiency and social skills.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user logs into the system. The terminal provides an input interface for the user ID and password, and the user enters the information. The entered information is encrypted and transmitted from the terminal to the server.

[0345] Step 2:

[0346] The server authenticates the user using the received authentication information in the database. If authentication is successful, the server retrieves the user's learning history and sends that information along with the session ID to the terminal.

[0347] Step 3:

[0348] The device displays learning content tailored to the user in an AR environment, based on the user's learning history from the server. The user then begins learning using this content.

[0349] Step 4:

[0350] During learning, the emotion engine collects the user's facial expressions and voice through the device's built-in camera and microphone. The emotion engine analyzes this data to identify the user's emotional state. For example, if the user is frowning and appears confused, it will be identified as "confused."

[0351] Step 5:

[0352] The device sends identified emotion data, along with progress data, to the server in real time.

[0353] Step 6:

[0354] The server comprehensively analyzes the received progress data and sentiment data, and the AI ​​agent generates optimal feedback. This feedback supports the learning process and includes instructions tailored to the user's emotions.

[0355] Step 7:

[0356] The device presents the generated feedback to the user visually or audibly. For example, if the device analyzes that the user is anxious, it might deliver a voice message such as, "Relax and get ready to move on to the next step."

[0357] Step 8:

[0358] The server re-evaluates the initially formed learning groups, taking into account the user's emotional state. If necessary, the AI ​​agent rearranges the pairs and groups of learners, and the server notifies the terminal of this information.

[0359] Step 9:

[0360] The device displays the reorganized groups in the AR environment and instructs the user to continue learning with new learning pairs or groups.

[0361] Through this process, the system monitors learners' progress and emotional state in real time, providing appropriate feedback and group assignments to create an effective learning experience.

[0362] (Example 2)

[0363] 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".

[0364] This invention relates to a system that simultaneously achieves efficient learning progress and emotional support for learners. Conventional learning support systems have problems in that it is difficult to grasp the actual learning progress and the emotional state of learners in real time, making it impossible to provide optimal feedback and group formation according to the learners' needs. This invention aims to solve this problem.

[0365] 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.

[0366] In this invention, the server includes means for evaluating the learner's progress in real time, means for analyzing the learner's emotional state from facial expressions and voice data, and means for generating optimal feedback for the learner based on the progress evaluation and emotional analysis. This enables personalized, real-time support and an efficient learning experience for each learner.

[0367] A "learner" refers to an individual engaged in an educational program or assignment, whose progress and emotional state are the subjects of evaluation.

[0368] "Real-time assessment" refers to a process of immediately and continuously monitoring learners' progress, which enables rapid feedback.

[0369] "Analyzing emotional states from facial expressions and voice data" refers to a technology that determines a learner's emotions based on data acquired using input devices such as cameras and microphones.

[0370] "Generating feedback" refers to the process of analyzing collected progress and sentiment data to create useful information and advice for learners.

[0371] "Reorganizing into pairs or groups" refers to the process of rearranging personnel to optimize collaboration among learners based on their emotional state and progress.

[0372] An "augmented reality environment" refers to a technological environment that overlays digital information onto the real world and presents it to the user, enriching the learning experience.

[0373] This invention is a system that evaluates learners' progress and emotions in real time and provides optimal feedback based on that evaluation. The system primarily consists of a server, terminals, and an emotion engine.

[0374] The device functions as an interface for users engaging in learning. It includes a display for an augmented reality (AR) environment, a camera for recognizing user movements and facial expressions, and a microphone for collecting audio data. The data acquired through these devices is used to monitor the user's learning progress and emotional state.

[0375] The emotion engine analyzes data acquired from the camera and microphone to identify the user's emotions. For example, if the user makes a confused expression or sighs, it detects emotions such as "confused" or "anxious."

[0376] The server receives and analyzes progress and sentiment data transmitted from the terminal. Using a generative AI model, the server generates optimal feedback based on this data. This feedback is communicated to the user via text or voice through the terminal. For example, if the user indicates "I don't know what to do next" as a prompt, the server provides specific advice using instructions to the generative AI model such as "How can I advise the user on the next step?"

[0377] Furthermore, the server also has the ability to reorganize learners into optimal pairs or groups while taking into account the users' emotional states. This allows learners to learn in an environment where they can easily cooperate with each other, and simultaneously improve their social skills.

[0378] In this way, the present invention is a system that enables the provision of real-time support tailored to learners, thereby significantly improving the learning experience.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The user logs into the system and begins a learning session. Logging in requires a username and password, and the output displays an augmented reality (AR) learning environment on the device. The user then works on assignments within this environment. In the AR environment, learning content is presented visually, and the user can interact with it.

[0382] Step 2:

[0383] The device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. The input consists of the user's facial expressions and voice data. Based on this data, an emotion engine operates, analyzing the user's emotional state (e.g., "confused," "anxious," etc.) as output. This process utilizes image processing and speech recognition technology to perform calculations that analyze emotions from the acquired data.

[0384] Step 3:

[0385] The server receives learning progress data and sentiment data sent from the terminal. Input data includes logs of learning status and the user's sentiment state. The server analyzes this data using a generative AI model and generates optimal feedback for the user as output. This analysis process employs machine learning algorithms to make predictions and evaluations based on the input data.

[0386] Step 4:

[0387] The device receives feedback from the server and notifies the user of it via voice or text. This feedback may include specific advice or encouraging messages. For example, a message such as "Let's proceed calmly and without rushing" might be displayed. Text display and speech synthesis technologies are used to provide this feedback to the user.

[0388] Step 5:

[0389] The server reorganizes learners into optimal pairs or groups, taking sentiment data into consideration. All user progress and sentiment data are processed as input, and reconstructed group information is generated as output. In this step, a clustering algorithm is used to perform dynamic group formation based on user state.

[0390] Through these steps, the system adaptively responds to learners in real time, maximizing the learning experience.

[0391] (Application Example 2)

[0392] 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 will be referred to as the "terminal."

[0393] Traditional education systems have struggled to grasp learners' progress and emotional states in real time and provide personalized, optimal feedback. Furthermore, there were insufficient means to facilitate communication among participants and improve the educational experience. There was also a need for an effective system to enhance the quality of educational experiences in physical stores.

[0394] 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.

[0395] In this invention, the server includes means for evaluating learners' progress in real time, means for forming participants into appropriate collaborative groups, and means for evaluating comprehension based on sentiment analysis and generating appropriate advice. This enables flexible feedback and improved experience tailored to the individual needs of learners.

[0396] A "learner" is an individual whose purpose is to absorb educational content and grow.

[0397] "Progress" is an indicator that shows the extent to which learners have achieved the set educational content.

[0398] "Real-time" refers to a state of having the ability to grasp and process events as they occur.

[0399] "Evaluation" is the act of assessing performance, such as progress and emotional state, based on specific criteria.

[0400] A "collaborative group" is a group of learners organized to achieve a common goal.

[0401] "Feedback" is a response that provides appropriate advice and confirmation of correctness according to the learner's progress and level of understanding.

[0402] Augmented reality is a technology that overlays digital information onto physical reality.

[0403] "Emotion analysis" is a technique that identifies emotional states from a learner's facial expressions and voice.

[0404] "Comprehension level" refers to the degree to which learners understand and remember educational content.

[0405] "Advice" refers to guidance or suggestions provided to learners to encourage improvement and growth.

[0406] The system for implementing this invention analyzes the user's progress and emotions in real time to support learning and provides appropriate feedback. Specific embodiments are shown below.

[0407] The server is responsible for evaluating learning progress and emotional state based on data sent by the user. Specifically, the collected data is processed on a cloud-based server, where software and AI agents for emotion analysis run. Emotion analysis engines such as the Microsoft Azure Emotion Recognition API are used for emotion analysis. The server also generates and provides feedback to the user using OpenAI's GPT model, among others.

[0408] The devices are used to monitor the user's learning behavior and collect data. These devices include smart glasses and tablet computers. These devices are equipped with cameras and microphones to capture the user's facial expressions and voice.

[0409] Users learn within an augmented reality environment and receive feedback from their device. If a user is confused, the device detects this confusion, and the server analyzes it and provides feedback such as, "Shall we review this point again?"

[0410] As a concrete example, consider using the system in a cooking class held at a physical store. If a participant shows signs of low understanding, the terminal will display a message such as "Let's review the steps again," providing calm instructions to the user. An example of a prompt message in this case could be, "The participant is confused, so please generate a message in a gentle tone encouraging them to review the steps again."

[0411] Thus, the present invention is a system for optimizing the learning experience by providing real-time feedback that takes into account the user's emotions and progress.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It acquires image data of the user's face and voice data as input data. Specifically, smart glasses or a tablet device are used.

[0415] Step 2:

[0416] The device transmits the acquired image and audio data to the server via a pre-configured secure protocol. The input consists of the user's facial expressions and audio data, while the output consists of this data sent to the server.

[0417] Step 3:

[0418] The server analyzes the received data using an emotion analysis engine (such as the Microsoft Azure Emotion Recognition API). The input consists of user facial image data and audio data, which are analyzed to output emotional states such as "confused," "anxious," and "relaxed."

[0419] Step 4:

[0420] The server uses a generative AI model (such as OpenAI's GPT model) to generate appropriate feedback text data based on the sentiment analysis results. The input includes sentiment data and learning progress data, and the output is feedback text data. Specifically, the prompt message "The participant is confused, so generate a message in a calm tone to reconfirm the procedure" is input to the model.

[0421] Step 5:

[0422] The server sends the generated feedback text to the terminal. The input is the feedback text, and the output is the data that transfers it to the terminal.

[0423] Step 6:

[0424] The device provides the user with received feedback via audio or text display. The input is text data of the feedback, and the user receives visual or auditory feedback. For example, it might display "Shall we review this point again?" through the speaker or screen.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] The system implementing this invention consists of a server, a terminal, and learner interaction. Specifically, it operates as follows:

[0442] When a learner logs in, the server verifies their authentication information, references their user profile, and retrieves their learning history. Based on this data, the server selects the most suitable learning content for the learner and delivers it to their device.

[0443] The device displays received learning content in an augmented reality (AR) environment. This display allows learners to understand information more intuitively and gain a more immersive learning experience.

[0444] Users complete tasks through an AR environment, and their progress is transmitted from their device to the server in real time. The server analyzes the progress data to identify the learner's strengths and weaknesses. Based on this analysis, an AI agent forms the optimal pair or group and notifies the device of this information via the server.

[0445] The device provides learners with feedback from the AI ​​agent in both audio and visual ways. For example, if a user is struggling with a particular task, the device will display visual instructions such as "Focus on this part" as an AR overlay.

[0446] Furthermore, this system features a function that facilitates communication among learners. The devices synchronize, allowing learners to interact with the same AR object in real time. This collaborative work improves learners' social skills and enhances their learning engagement.

[0447] In this way, this system combines an augmented reality environment with AI technology to deepen learners' understanding and maximize learning effectiveness.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The user logs into the system. The terminal displays an interface for entering a user ID and password, and the user enters the information. Next, the terminal encrypts the entered authentication information and sends it to the server.

[0451] Step 2:

[0452] The server compares the received authentication information with the database to authenticate the user. If authentication is successful, the server starts a session for the user and retrieves the user's learning history. Then, it sends this information, along with the session ID, to the terminal.

[0453] Step 3:

[0454] The server selects appropriate learning content based on the user's learning history. The selected content is immediately sent to the device.

[0455] Step 4:

[0456] The device displays the received learning content in an augmented reality (AR) environment. Through this AR environment, the user begins working on the designated learning tasks.

[0457] Step 5:

[0458] The device continuously collects user progress data and sends it to the server in real time. This data includes the user's operation speed and the number of incorrect answers.

[0459] Step 6:

[0460] The server analyzes the received progress data and evaluates the user's learning progress. An AI agent is used to identify the user's strengths and weaknesses.

[0461] Step 7:

[0462] The AI ​​agent calculates the optimal training pair or group based on the analysis results. The results are then notified to each terminal from the server.

[0463] Step 8:

[0464] The device provides users with real-time visual and auditory feedback from the AI ​​agent. It also displays an interface that suggests new group learning opportunities to the user.

[0465] Step 9:

[0466] The device utilizes features that facilitate communication between users, enabling collaborative work in an AR environment. This promotes information sharing and problem-solving among learners.

[0467] This system is designed to support learners' understanding and the development of their social skills through this series of steps.

[0468] (Example 1)

[0469] 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."

[0470] In today's educational environment, there is a demand for efficient instruction tailored to the individual needs of learners, as well as the development of social skills through communication. However, traditional systems have challenges in real-time progress assessment and optimal group formation, as well as insufficient use of effective feedback and augmented reality environments.

[0471] 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.

[0472] In this invention, the server includes means for evaluating the behavioral information of multiple users in real time, means for forming users into optimal pairs or groups, means for providing response information to users in real time, means for providing an augmented reality environment for displaying digital information, means for creating information optimized for users using generative AI technology, and means for displaying the information on a user interface. This makes it possible to provide a comprehensive learning environment that combines instruction optimized for each learner, real-time monitoring of learning progress, and improvement of social skills.

[0473] "Action information" refers to data related to user behavior and operations, and is evaluated in real time.

[0474] "Real-time" refers to the moment a user's action or operation occurs, and indicates that evaluation and response should be performed immediately.

[0475] "Response information" refers to information provided based on the user's actions and progress, including data related to guidance and areas for improvement.

[0476] An "augmented reality environment" is an environment that uses technology to enhance the user experience by overlaying digital information onto the real world.

[0477] "Generative AI technology" refers to methods for automatically creating content and information using artificial intelligence technology.

[0478] "User interface" refers to the medium, such as screens or control panels, that users use to interact with a system.

[0479] "Optimization" means adjusting something to be most effective or efficient under specific conditions.

[0480] A "learning environment" refers to a physical or digital space provided for learners to acquire knowledge and improve their skills.

[0481] The embodiment of this invention mainly consists of a server, a terminal, and a user. The server uses a database to manage the user's individual authentication information and learning progress information. In the authentication process, the server verifies the authentication information entered by the user through the terminal and compares it with the information in the database. After proper authentication is performed, the server refers to the user's learning history and uses AI technology to generate individually optimized learning content. This generation process uses a generative AI model to dynamically create information that matches the learner's needs.

[0482] The device receives content sent from the server and displays it to the user in an augmented reality (AR) environment. Specifically, it utilizes the device's camera and display technology to integrate digital information into physical space, creating an intuitive interface. This AR display allows users to understand the subject matter more concretely. For example, in a biology class, a 3D model of the heart can be displayed in AR, allowing students to visually learn the details of its internal structure.

[0483] Users engage in learning activities through the AR environment provided by their device, and their progress is transmitted to the server in real time. The server analyzes the received progress data to identify the user's strengths and weaknesses. Based on this analysis, an AI agent forms the optimal pair or group and provides feedback to the user through voice and visual means. This allows users to continue learning and improving.

[0484] Furthermore, this system enables real-time synchronization between devices, supporting users to collaborate and work together in the same AR space. This allows users to hone their social skills through collaborative learning. As a concrete example, a prompt sentence such as "Please provide AR teaching materials to explain the structure of the heart in a biology class" is input into the AI ​​model, and appropriate learning content is generated.

[0485] This system allows for personalized learning experiences while simultaneously promoting the development of social skills through real-time feedback and collaborative work.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The server receives authentication information entered by the user from the terminal. This input includes the user ID and password. The server compares this information with the database to determine whether authentication was successful or unsuccessful. If successful, it outputs the user's identification information and prepares to proceed to the next step.

[0489] Step 2:

[0490] The server retrieves user profile information from the database for users who have successfully authenticated. This input includes past learning progress data and areas of strength and weakness. The server uses this data and a generative AI model to generate learning content optimized for the user. The output is the generated content data, which is delivered to the device.

[0491] Step 3:

[0492] The device receives learning content sent from the server. Based on this data, the device uses augmented reality (AR) technology to display the content to the user. Specifically, it uses the device's camera to overlay 3D objects onto the physical environment, providing the user with an interactive visual experience. This output is AR-based visual information.

[0493] Step 4:

[0494] Users engage in learning activities using an AR environment provided on their device. The device records the user's actions and choices. This progress data is sent to the server in real time as input and used to guide the next step.

[0495] Step 5:

[0496] The server receives progress data sent by the user and performs analysis. This analysis examines the user's learning tendencies, strengths, and weaknesses. Based on this information, the server, with the help of an AI agent, generates suitable pairs or groups for the user. The output is information on optimal group formation.

[0497] Step 6:

[0498] The server sends the formed pair or group information back to the terminal. The terminal receives this and provides feedback to the user visually or audibly. At that time, specific advice and instructions tailored to the user's learning progress are presented in AR or audio.

[0499] Step 7:

[0500] The device synchronizes connections in real time, enabling multiple users to collaborate. This feature allows users to communicate while simultaneously manipulating the same AR object. The output is a synchronized experience among users.

[0501] In this way, each step works in conjunction to create a learning experience optimized for each individual learner and enable real-time communication.

[0502] (Application Example 1)

[0503] 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."

[0504] Traditional educational support systems have struggled to provide real-time feedback based on individual progress and facilitate efficient collaborative learning among learners. This is particularly true when providing immersive learning environments utilizing augmented reality technology, where interaction presents significant challenges. There is a need to provide an environment that offers individually optimized learning support while simultaneously improving social skills, given the varying paces and levels of understanding among educational participants.

[0505] 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.

[0506] In this invention, the server includes a device for evaluating the progress of multiple learning participants in real time, a device for forming learning participants into optimal combinations or groups based on the progress evaluation, a device for providing feedback to learning participants in real time, a device for providing an extended virtual environment for learning participants, and a device for sharing information among learning participants and promoting collaborative work through bidirectional communication. This enables individually optimized learning support and smooth collaborative work among learning participants, thereby improving comprehension and promoting social skills.

[0507] An "educational participant" is an individual or group of people who participate in a specific learning program or educational experience.

[0508] "Progress evaluation" is the process of analyzing the learning status and understanding of educational participants in real time and measuring the results.

[0509] "Combination or group" refers to a group or pair of educational participants organized based on specific criteria.

[0510] "Feedback" refers to real-time feedback information regarding the learning progress and understanding of educational participants.

[0511] An "augmented virtual environment" is a technology that provides a virtual learning environment by overlaying digital information onto the real world.

[0512] "Two-way communication" refers to a form of communication that allows for the real-time exchange of information between educational participants and between educational support systems.

[0513] "Collaborative work" refers to the joint implementation of educational activities or tasks by multiple educational participants.

[0514] "Social skills" refer to interpersonal abilities that educational participants should acquire through communication and cooperation.

[0515] This invention is a next-generation educational system that supports individual learning for educational participants and provides an augmented virtual environment. The system consists of a server, terminals, and educational participants.

[0516] When an educational participant logs in, the server verifies their authentication information and accesses their profile. Based on their past learning history and progress data, it selects the most suitable learning content and delivers it to their device. During this process, machine learning frameworks such as TensorFlow are used to assess the participant's progress in real time and provide appropriate guidance.

[0517] The device utilizes Unity and Vuforia to display received learning content in an augmented virtual environment. This allows educators to understand information more intuitively and immersively. Furthermore, it features an interface for sending real-time progress data to a server and providing immediate feedback. This feedback includes audio or visual instructions and is displayed as an AR overlay.

[0518] Participants in the learning program will complete learning tasks through this augmented virtual environment. Two-way communication will utilize WebSocket, enabling participants to share information in real time and collaborate by manipulating the same AR objects. This collaborative work is expected to improve the social skills of the participants.

[0519] As a concrete example, when children attempt a math problem, the server selects an appropriate problem, the device displays it using augmented reality (AR), and tracks each participant's progress. Based on this progress, hints and additional explanations are provided in real time to enhance learning comprehension.

[0520] An example of a prompt might be, "How can AR displays be used to show concrete examples when children are learning the concept of numbers?"

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] When an educational participant logs in, the server uses their authentication credentials to retrieve their profile and past learning history. This serves as input, and the server performs calculations to extract relevant information from the database, preparing it for the selection of optimized learning content.

[0524] Step 2:

[0525] The server uses generative AI models such as TensorFlow to evaluate progress based on the acquired learning history data. In this step, it selects learning content suitable for the educator by comparing past performance with goals. This calculation outputs the selected learning content, which is then sent to the terminal.

[0526] Step 3:

[0527] The device receives learning content delivered from the server and displays it in an augmented virtual environment using Unity and Vuforia. The received data is processed as AR content and displayed visually to the educational participants. This processing ensures that information is presented intuitively.

[0528] Step 4:

[0529] Participants in the educational program complete tasks provided via a device and input data indicating their progress. The device captures this input and sends progress information to the server in real time. This data transmission is carried out efficiently based on a communication protocol.

[0530] Step 5:

[0531] The server analyzes the received progress information and generates feedback and supplementary information. In this step, the progress data is evaluated by a generating AI model, and data processing is performed to design optimal feedback. The generated feedback is sent back to the terminal.

[0532] Step 6:

[0533] The device displays feedback received from the server to the educational participants as audio and visual instructions. This is provided using technologies such as AR overlays. The presentation of feedback is designed to deepen participants' understanding.

[0534] Step 7:

[0535] To facilitate collaboration among educational participants, devices will be synchronized via WebSocket, allowing them to share the same educational experience. This communication path will enable real-time information exchange among participants, facilitating smooth collaborative work.

[0536] 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.

[0537] This invention improves learning efficiency by combining an emotion engine with a system that evaluates learners' progress in real time. A specific embodiment is described below.

[0538] Users log into the system and work on tasks in an augmented reality (AR) environment at the start of their learning. The device provides this work through an AR interface and monitors the user's actions in real time.

[0539] The emotion engine uses the camera and microphone built into the device to analyze the user's emotions from their facial expressions and voice. For example, if a user is confused about a problem, the emotion engine will detect "confusion" from their facial expressions and sighs.

[0540] The server receives and analyzes the collected learning progress data and sentiment data. The AI ​​agent generates optimal feedback, taking into account the user's progress and emotional state. This feedback is displayed to the user via the device as voice or text, offering encouragement and advice.

[0541] For example, if a user expresses anxiety, the device will provide a voice message such as, "Let's proceed calmly and without rushing." This allows the user to continue learning while receiving emotional support.

[0542] Furthermore, based on the analysis results of the emotion engine, the server reorganizes pairs or groups. For example, if there are significant differences in mental states such as "motivation" or "concentration" among users, the AI ​​agent will reorganize them to provide an environment where they can collaborate comfortably.

[0543] Thus, this system aims to comprehensively understand the learner's real-time state and optimize the learning experience by combining it with an emotion engine. Through feedback tailored to individual learner needs and flexible group configurations, the system simultaneously improves learning efficiency and social skills.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The user logs into the system. The terminal provides an input interface for the user ID and password, and the user enters the information. The entered information is encrypted and transmitted from the terminal to the server.

[0547] Step 2:

[0548] The server authenticates the user using the received authentication information in the database. If authentication is successful, the server retrieves the user's learning history and sends that information along with the session ID to the terminal.

[0549] Step 3:

[0550] The device displays learning content tailored to the user in an AR environment, based on the user's learning history from the server. The user then begins learning using this content.

[0551] Step 4:

[0552] During learning, the emotion engine collects the user's facial expressions and voice through the device's built-in camera and microphone. The emotion engine analyzes this data to identify the user's emotional state. For example, if the user is frowning and appears confused, it will be identified as "confused."

[0553] Step 5:

[0554] The device sends identified emotion data, along with progress data, to the server in real time.

[0555] Step 6:

[0556] The server comprehensively analyzes the received progress data and sentiment data, and the AI ​​agent generates optimal feedback. This feedback supports the learning process and includes instructions tailored to the user's emotions.

[0557] Step 7:

[0558] The device presents the generated feedback to the user visually or audibly. For example, if the device analyzes that the user is anxious, it might deliver a voice message such as, "Relax and get ready to move on to the next step."

[0559] Step 8:

[0560] The server re-evaluates the initially formed learning groups, taking into account the user's emotional state. If necessary, the AI ​​agent rearranges the pairs and groups of learners, and the server notifies the terminal of this information.

[0561] Step 9:

[0562] The device displays the reorganized groups in the AR environment and instructs the user to continue learning with new learning pairs or groups.

[0563] Through this process, the system monitors learners' progress and emotional state in real time, providing appropriate feedback and group assignments to create an effective learning experience.

[0564] (Example 2)

[0565] 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."

[0566] This invention relates to a system that simultaneously achieves efficient learning progress and emotional support for learners. Conventional learning support systems have problems in that it is difficult to grasp the actual learning progress and the emotional state of learners in real time, making it impossible to provide optimal feedback and group formation according to the learners' needs. This invention aims to solve this problem.

[0567] 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.

[0568] In this invention, the server includes means for evaluating the learner's progress in real time, means for analyzing the learner's emotional state from facial expressions and voice data, and means for generating optimal feedback for the learner based on the progress evaluation and emotional analysis. This enables personalized, real-time support and an efficient learning experience for each learner.

[0569] A "learner" refers to an individual engaged in an educational program or assignment, whose progress and emotional state are the subjects of evaluation.

[0570] "Real-time assessment" refers to a process of immediately and continuously monitoring learners' progress, which enables rapid feedback.

[0571] "Analyzing emotional states from facial expressions and voice data" refers to a technology that determines a learner's emotions based on data acquired using input devices such as cameras and microphones.

[0572] "Generating feedback" refers to the process of analyzing collected progress and sentiment data to create useful information and advice for learners.

[0573] "Reorganizing into pairs or groups" refers to the process of rearranging personnel to optimize collaboration among learners based on their emotional state and progress.

[0574] An "augmented reality environment" refers to a technological environment that overlays digital information onto the real world and presents it to the user, enriching the learning experience.

[0575] This invention is a system that evaluates learners' progress and emotions in real time and provides optimal feedback based on that evaluation. The system primarily consists of a server, terminals, and an emotion engine.

[0576] The device functions as an interface for users engaging in learning. It includes a display for an augmented reality (AR) environment, a camera for recognizing user movements and facial expressions, and a microphone for collecting audio data. The data acquired through these devices is used to monitor the user's learning progress and emotional state.

[0577] The emotion engine analyzes data acquired from the camera and microphone to identify the user's emotions. For example, if the user makes a confused expression or sighs, it detects emotions such as "confused" or "anxious."

[0578] The server receives and analyzes progress and sentiment data transmitted from the terminal. Using a generative AI model, the server generates optimal feedback based on this data. This feedback is communicated to the user via text or voice through the terminal. For example, if the user indicates "I don't know what to do next" as a prompt, the server provides specific advice using instructions to the generative AI model such as "How can I advise the user on the next step?"

[0579] Furthermore, the server also has the ability to reorganize learners into optimal pairs or groups while taking into account the users' emotional states. This allows learners to learn in an environment where they can easily cooperate with each other, and simultaneously improve their social skills.

[0580] In this way, the present invention is a system that enables the provision of real-time support tailored to learners, thereby significantly improving the learning experience.

[0581] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0582] Step 1:

[0583] The user logs into the system and begins a learning session. Logging in requires a username and password, and the output displays an augmented reality (AR) learning environment on the device. The user then works on assignments within this environment. In the AR environment, learning content is presented visually, and the user can interact with it.

[0584] Step 2:

[0585] The device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. The input consists of the user's facial expressions and voice data. Based on this data, an emotion engine operates, analyzing the user's emotional state (e.g., "confused," "anxious," etc.) as output. This process utilizes image processing and speech recognition technology to perform calculations that analyze emotions from the acquired data.

[0586] Step 3:

[0587] The server receives learning progress data and sentiment data sent from the terminal. Input data includes logs of learning status and the user's sentiment state. The server analyzes this data using a generative AI model and generates optimal feedback for the user as output. This analysis process employs machine learning algorithms to make predictions and evaluations based on the input data.

[0588] Step 4:

[0589] The device receives feedback from the server and notifies the user of it via voice or text. This feedback may include specific advice or encouraging messages. For example, a message such as "Let's proceed calmly and without rushing" might be displayed. Text display and speech synthesis technologies are used to provide this feedback to the user.

[0590] Step 5:

[0591] The server reorganizes learners into optimal pairs or groups, taking sentiment data into consideration. All user progress and sentiment data are processed as input, and reconstructed group information is generated as output. In this step, a clustering algorithm is used to perform dynamic group formation based on user state.

[0592] Through these steps, the system adaptively responds to learners in real time, maximizing the learning experience.

[0593] (Application Example 2)

[0594] 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."

[0595] Traditional education systems have struggled to grasp learners' progress and emotional states in real time and provide personalized, optimal feedback. Furthermore, there were insufficient means to facilitate communication among participants and improve the educational experience. There was also a need for an effective system to enhance the quality of educational experiences in physical stores.

[0596] 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.

[0597] In this invention, the server includes means for evaluating learners' progress in real time, means for forming participants into appropriate collaborative groups, and means for evaluating comprehension based on sentiment analysis and generating appropriate advice. This enables flexible feedback and improved experience tailored to the individual needs of learners.

[0598] A "learner" is an individual whose purpose is to absorb educational content and grow.

[0599] "Progress" is an indicator that shows the extent to which learners have achieved the set educational content.

[0600] "Real-time" refers to a state of having the ability to grasp and process events as they occur.

[0601] "Evaluation" is the act of assessing performance, such as progress and emotional state, based on specific criteria.

[0602] A "collaborative group" is a group of learners organized to achieve a common goal.

[0603] "Feedback" is a response that provides appropriate advice and confirmation of correctness according to the learner's progress and level of understanding.

[0604] Augmented reality is a technology that overlays digital information onto physical reality.

[0605] "Emotion analysis" is a technique that identifies emotional states from a learner's facial expressions and voice.

[0606] "Comprehension level" refers to the degree to which learners understand and remember educational content.

[0607] "Advice" refers to guidance or suggestions provided to learners to encourage improvement and growth.

[0608] The system for implementing this invention analyzes the user's progress and emotions in real time to support learning and provides appropriate feedback. Specific embodiments are shown below.

[0609] The server is responsible for evaluating learning progress and emotional state based on data sent by the user. Specifically, the collected data is processed on a cloud-based server, where software and AI agents for emotion analysis run. Emotion analysis engines such as the Microsoft Azure Emotion Recognition API are used for emotion analysis. The server also generates and provides feedback to the user using OpenAI's GPT model, among others.

[0610] The devices are used to monitor the user's learning behavior and collect data. These devices include smart glasses and tablet computers. These devices are equipped with cameras and microphones to capture the user's facial expressions and voice.

[0611] Users learn within an augmented reality environment and receive feedback from their device. If a user is confused, the device detects this confusion, and the server analyzes it and provides feedback such as, "Shall we review this point again?"

[0612] As a concrete example, consider using the system in a cooking class held at a physical store. If a participant shows signs of low understanding, the terminal will display a message such as "Let's review the steps again," providing calm instructions to the user. An example of a prompt message in this case could be, "The participant is confused, so please generate a message in a gentle tone encouraging them to review the steps again."

[0613] Thus, the present invention is a system for optimizing the learning experience by providing real-time feedback that takes into account the user's emotions and progress.

[0614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0615] Step 1:

[0616] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It acquires image data of the user's face and voice data as input data. Specifically, smart glasses or a tablet device are used.

[0617] Step 2:

[0618] The device transmits the acquired image and audio data to the server via a pre-configured secure protocol. The input consists of the user's facial expressions and audio data, while the output consists of this data sent to the server.

[0619] Step 3:

[0620] The server analyzes the received data using an emotion analysis engine (such as the Microsoft Azure Emotion Recognition API). The input consists of user facial image data and audio data, which are analyzed to output emotional states such as "confused," "anxious," and "relaxed."

[0621] Step 4:

[0622] The server uses a generative AI model (such as OpenAI's GPT model) to generate appropriate feedback text data based on the sentiment analysis results. The input includes sentiment data and learning progress data, and the output is feedback text data. Specifically, the prompt message "The participant is confused, so generate a message in a calm tone to reconfirm the procedure" is input to the model.

[0623] Step 5:

[0624] The server sends the generated feedback text to the terminal. The input is the feedback text, and the output is the data that transfers it to the terminal.

[0625] Step 6:

[0626] The device provides the user with received feedback via audio or text display. The input is text data of the feedback, and the user receives visual or auditory feedback. For example, it might display "Shall we review this point again?" through the speaker or screen.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] The system implementing this invention consists of a server, a terminal, and learner interaction. Specifically, it operates as follows:

[0645] When a learner logs in, the server verifies their authentication information, references their user profile, and retrieves their learning history. Based on this data, the server selects the most suitable learning content for the learner and delivers it to their device.

[0646] The device displays received learning content in an augmented reality (AR) environment. This display allows learners to understand information more intuitively and gain a more immersive learning experience.

[0647] Users complete tasks through an AR environment, and their progress is transmitted from their device to the server in real time. The server analyzes the progress data to identify the learner's strengths and weaknesses. Based on this analysis, an AI agent forms the optimal pair or group and notifies the device of this information via the server.

[0648] The device provides learners with feedback from the AI ​​agent in both audio and visual ways. For example, if a user is struggling with a particular task, the device will display visual instructions such as "Focus on this part" as an AR overlay.

[0649] Furthermore, this system features a function that facilitates communication among learners. The devices synchronize, allowing learners to interact with the same AR object in real time. This collaborative work improves learners' social skills and enhances their learning engagement.

[0650] In this way, this system combines an augmented reality environment with AI technology to deepen learners' understanding and maximize learning effectiveness.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The user logs into the system. The terminal displays an interface for entering a user ID and password, and the user enters the information. Next, the terminal encrypts the entered authentication information and sends it to the server.

[0654] Step 2:

[0655] The server compares the received authentication information with the database to authenticate the user. If authentication is successful, the server starts a session for the user and retrieves the user's learning history. Then, it sends this information, along with the session ID, to the terminal.

[0656] Step 3:

[0657] The server selects appropriate learning content based on the user's learning history. The selected content is immediately sent to the device.

[0658] Step 4:

[0659] The device displays the received learning content in an augmented reality (AR) environment. Through this AR environment, the user begins working on the designated learning tasks.

[0660] Step 5:

[0661] The device continuously collects user progress data and sends it to the server in real time. This data includes the user's operation speed and the number of incorrect answers.

[0662] Step 6:

[0663] The server analyzes the received progress data and evaluates the user's learning progress. An AI agent is used to identify the user's strengths and weaknesses.

[0664] Step 7:

[0665] The AI ​​agent calculates the optimal training pair or group based on the analysis results. The results are then notified to each terminal from the server.

[0666] Step 8:

[0667] The device provides users with real-time visual and auditory feedback from the AI ​​agent. It also displays an interface that suggests new group learning opportunities to the user.

[0668] Step 9:

[0669] The device utilizes features that facilitate communication between users, enabling collaborative work in an AR environment. This promotes information sharing and problem-solving among learners.

[0670] This system is designed to support learners' understanding and the development of their social skills through this series of steps.

[0671] (Example 1)

[0672] 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".

[0673] In today's educational environment, there is a demand for efficient instruction tailored to the individual needs of learners, as well as the development of social skills through communication. However, traditional systems have challenges in real-time progress assessment and optimal group formation, as well as insufficient use of effective feedback and augmented reality environments.

[0674] 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.

[0675] In this invention, the server includes means for evaluating the behavioral information of multiple users in real time, means for forming users into optimal pairs or groups, means for providing response information to users in real time, means for providing an augmented reality environment for displaying digital information, means for creating information optimized for users using generative AI technology, and means for displaying the information on a user interface. This makes it possible to provide a comprehensive learning environment that combines instruction optimized for each learner, real-time monitoring of learning progress, and improvement of social skills.

[0676] "Action information" refers to data related to user behavior and operations, and is evaluated in real time.

[0677] "Real-time" refers to the moment a user's action or operation occurs, and indicates that evaluation and response should be performed immediately.

[0678] "Response information" refers to information provided based on the user's actions and progress, including data related to guidance and areas for improvement.

[0679] An "augmented reality environment" is an environment that uses technology to enhance the user experience by overlaying digital information onto the real world.

[0680] "Generative AI technology" refers to methods for automatically creating content and information using artificial intelligence technology.

[0681] "User interface" refers to the medium, such as screens or control panels, that users use to interact with a system.

[0682] "Optimization" means adjusting something to be most effective or efficient under specific conditions.

[0683] A "learning environment" refers to a physical or digital space provided for learners to acquire knowledge and improve their skills.

[0684] The embodiment of this invention mainly consists of a server, a terminal, and a user. The server uses a database to manage the user's individual authentication information and learning progress information. In the authentication process, the server verifies the authentication information entered by the user through the terminal and compares it with the information in the database. After proper authentication is performed, the server refers to the user's learning history and uses AI technology to generate individually optimized learning content. This generation process uses a generative AI model to dynamically create information that matches the learner's needs.

[0685] The device receives content sent from the server and displays it to the user in an augmented reality (AR) environment. Specifically, it utilizes the device's camera and display technology to integrate digital information into physical space, creating an intuitive interface. This AR display allows users to understand the subject matter more concretely. For example, in a biology class, a 3D model of the heart can be displayed in AR, allowing students to visually learn the details of its internal structure.

[0686] Users engage in learning activities through the AR environment provided by their device, and their progress is transmitted to the server in real time. The server analyzes the received progress data to identify the user's strengths and weaknesses. Based on this analysis, an AI agent forms the optimal pair or group and provides feedback to the user through voice and visual means. This allows users to continue learning and improving.

[0687] Furthermore, this system enables real-time synchronization between devices, supporting users to collaborate and work together in the same AR space. This allows users to hone their social skills through collaborative learning. As a concrete example, a prompt sentence such as "Please provide AR teaching materials to explain the structure of the heart in a biology class" is input into the AI ​​model, and appropriate learning content is generated.

[0688] This system allows for personalized learning experiences while simultaneously promoting the development of social skills through real-time feedback and collaborative work.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The server receives authentication information entered by the user from the terminal. This input includes the user ID and password. The server compares this information with the database to determine whether authentication was successful or unsuccessful. If successful, it outputs the user's identification information and prepares to proceed to the next step.

[0692] Step 2:

[0693] The server retrieves user profile information from the database for users who have successfully authenticated. This input includes past learning progress data and areas of strength and weakness. The server uses this data and a generative AI model to generate learning content optimized for the user. The output is the generated content data, which is delivered to the device.

[0694] Step 3:

[0695] The device receives learning content sent from the server. Based on this data, the device uses augmented reality (AR) technology to display the content to the user. Specifically, it uses the device's camera to overlay 3D objects onto the physical environment, providing the user with an interactive visual experience. This output is AR-based visual information.

[0696] Step 4:

[0697] Users engage in learning activities using an AR environment provided on their device. The device records the user's actions and choices. This progress data is sent to the server in real time as input and used to guide the next step.

[0698] Step 5:

[0699] The server receives progress data sent by the user and performs analysis. This analysis examines the user's learning tendencies, strengths, and weaknesses. Based on this information, the server, with the help of an AI agent, generates suitable pairs or groups for the user. The output is information on optimal group formation.

[0700] Step 6:

[0701] The server sends the formed pair or group information back to the terminal. The terminal receives this and provides feedback to the user visually or audibly. At that time, specific advice and instructions tailored to the user's learning progress are presented in AR or audio.

[0702] Step 7:

[0703] The device synchronizes connections in real time, enabling multiple users to collaborate. This feature allows users to communicate while simultaneously manipulating the same AR object. The output is a synchronized experience among users.

[0704] In this way, each step works in conjunction to create a learning experience optimized for each individual learner and enable real-time communication.

[0705] (Application Example 1)

[0706] 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".

[0707] Traditional educational support systems have struggled to provide real-time feedback based on individual progress and facilitate efficient collaborative learning among learners. This is particularly true when providing immersive learning environments utilizing augmented reality technology, where interaction presents significant challenges. There is a need to provide an environment that offers individually optimized learning support while simultaneously improving social skills, given the varying paces and levels of understanding among educational participants.

[0708] 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.

[0709] In this invention, the server includes a device for evaluating the progress of multiple learning participants in real time, a device for forming learning participants into optimal combinations or groups based on the progress evaluation, a device for providing feedback to learning participants in real time, a device for providing an extended virtual environment for learning participants, and a device for sharing information among learning participants and promoting collaborative work through bidirectional communication. This enables individually optimized learning support and smooth collaborative work among learning participants, thereby improving comprehension and promoting social skills.

[0710] An "educational participant" is an individual or group of people who participate in a specific learning program or educational experience.

[0711] "Progress evaluation" is the process of analyzing the learning status and understanding of educational participants in real time and measuring the results.

[0712] "Combination or group" refers to a group or pair of educational participants organized based on specific criteria.

[0713] "Feedback" refers to real-time feedback information regarding the learning progress and understanding of educational participants.

[0714] An "augmented virtual environment" is a technology that provides a virtual learning environment by overlaying digital information onto the real world.

[0715] "Two-way communication" refers to a form of communication that allows for the real-time exchange of information between educational participants and between educational support systems.

[0716] "Collaborative work" refers to the joint implementation of educational activities or tasks by multiple educational participants.

[0717] "Social skills" refer to interpersonal abilities that educational participants should acquire through communication and cooperation.

[0718] This invention is a next-generation educational system that supports individual learning for educational participants and provides an augmented virtual environment. The system consists of a server, terminals, and educational participants.

[0719] When an educational participant logs in, the server verifies their authentication information and accesses their profile. Based on their past learning history and progress data, it selects the most suitable learning content and delivers it to their device. During this process, machine learning frameworks such as TensorFlow are used to assess the participant's progress in real time and provide appropriate guidance.

[0720] The device utilizes Unity and Vuforia to display received learning content in an augmented virtual environment. This allows educators to understand information more intuitively and immersively. Furthermore, it features an interface for sending real-time progress data to a server and providing immediate feedback. This feedback includes audio or visual instructions and is displayed as an AR overlay.

[0721] Participants in the learning program will complete learning tasks through this augmented virtual environment. Two-way communication will utilize WebSocket, enabling participants to share information in real time and collaborate by manipulating the same AR objects. This collaborative work is expected to improve the social skills of the participants.

[0722] As a concrete example, when children attempt a math problem, the server selects an appropriate problem, the device displays it using augmented reality (AR), and tracks each participant's progress. Based on this progress, hints and additional explanations are provided in real time to enhance learning comprehension.

[0723] An example of a prompt might be, "How can AR displays be used to show concrete examples when children are learning the concept of numbers?"

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] When an educational participant logs in, the server uses their authentication credentials to retrieve their profile and past learning history. This serves as input, and the server performs calculations to extract relevant information from the database, preparing it for the selection of optimized learning content.

[0727] Step 2:

[0728] The server uses generative AI models such as TensorFlow to evaluate progress based on the acquired learning history data. In this step, it selects learning content suitable for the educator by comparing past performance with goals. This calculation outputs the selected learning content, which is then sent to the terminal.

[0729] Step 3:

[0730] The device receives learning content delivered from the server and displays it in an augmented virtual environment using Unity and Vuforia. The received data is processed as AR content and displayed visually to the educational participants. This processing ensures that information is presented intuitively.

[0731] Step 4:

[0732] Participants in the educational program complete tasks provided via a device and input data indicating their progress. The device captures this input and sends progress information to the server in real time. This data transmission is carried out efficiently based on a communication protocol.

[0733] Step 5:

[0734] The server analyzes the received progress information and generates feedback and supplementary information. In this step, the progress data is evaluated by a generating AI model, and data processing is performed to design optimal feedback. The generated feedback is sent back to the terminal.

[0735] Step 6:

[0736] The device displays feedback received from the server to the educational participants as audio and visual instructions. This is provided using technologies such as AR overlays. The presentation of feedback is designed to deepen participants' understanding.

[0737] Step 7:

[0738] To facilitate collaboration among educational participants, devices will be synchronized via WebSocket, allowing them to share the same educational experience. This communication path will enable real-time information exchange among participants, facilitating smooth collaborative work.

[0739] 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.

[0740] This invention improves learning efficiency by combining an emotion engine with a system that evaluates learners' progress in real time. A specific embodiment is described below.

[0741] Users log into the system and work on tasks in an augmented reality (AR) environment at the start of their learning. The device provides this work through an AR interface and monitors the user's actions in real time.

[0742] The emotion engine uses the camera and microphone built into the device to analyze the user's emotions from their facial expressions and voice. For example, if a user is confused about a problem, the emotion engine will detect "confusion" from their facial expressions and sighs.

[0743] The server receives and analyzes the collected learning progress data and sentiment data. The AI ​​agent generates optimal feedback, taking into account the user's progress and emotional state. This feedback is displayed to the user via the device as voice or text, offering encouragement and advice.

[0744] For example, if a user expresses anxiety, the device will provide a voice message such as, "Let's proceed calmly and without rushing." This allows the user to continue learning while receiving emotional support.

[0745] Furthermore, based on the analysis results of the emotion engine, the server reorganizes pairs or groups. For example, if there are significant differences in mental states such as "motivation" or "concentration" among users, the AI ​​agent will reorganize them to provide an environment where they can collaborate comfortably.

[0746] Thus, this system aims to comprehensively understand the learner's real-time state and optimize the learning experience by combining it with an emotion engine. Through feedback tailored to individual learner needs and flexible group configurations, the system simultaneously improves learning efficiency and social skills.

[0747] The following describes the processing flow.

[0748] Step 1:

[0749] The user logs into the system. The terminal provides an input interface for the user ID and password, and the user enters the information. The entered information is encrypted and transmitted from the terminal to the server.

[0750] Step 2:

[0751] The server authenticates the user using the received authentication information in the database. If authentication is successful, the server retrieves the user's learning history and sends that information along with the session ID to the terminal.

[0752] Step 3:

[0753] The device displays learning content tailored to the user in an AR environment, based on the user's learning history from the server. The user then begins learning using this content.

[0754] Step 4:

[0755] During learning, the emotion engine collects the user's facial expressions and voice through the device's built-in camera and microphone. The emotion engine analyzes this data to identify the user's emotional state. For example, if the user is frowning and appears confused, it will be identified as "confused."

[0756] Step 5:

[0757] The device sends identified emotion data, along with progress data, to the server in real time.

[0758] Step 6:

[0759] The server comprehensively analyzes the received progress data and sentiment data, and the AI ​​agent generates optimal feedback. This feedback supports the learning process and includes instructions tailored to the user's emotions.

[0760] Step 7:

[0761] The device presents the generated feedback to the user visually or audibly. For example, if the device analyzes that the user is anxious, it might deliver a voice message such as, "Relax and get ready to move on to the next step."

[0762] Step 8:

[0763] The server re-evaluates the initially formed learning groups, taking into account the user's emotional state. If necessary, the AI ​​agent rearranges the pairs and groups of learners, and the server notifies the terminal of this information.

[0764] Step 9:

[0765] The device displays the reorganized groups in the AR environment and instructs the user to continue learning with new learning pairs or groups.

[0766] Through this process, the system monitors learners' progress and emotional state in real time, providing appropriate feedback and group assignments to create an effective learning experience.

[0767] (Example 2)

[0768] 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".

[0769] This invention relates to a system that simultaneously achieves efficient learning progress and emotional support for learners. Conventional learning support systems have problems in that it is difficult to grasp the actual learning progress and the emotional state of learners in real time, making it impossible to provide optimal feedback and group formation according to the learners' needs. This invention aims to solve this problem.

[0770] 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.

[0771] In this invention, the server includes means for evaluating the learner's progress in real time, means for analyzing the learner's emotional state from facial expressions and voice data, and means for generating optimal feedback for the learner based on the progress evaluation and emotional analysis. This enables personalized, real-time support and an efficient learning experience for each learner.

[0772] A "learner" refers to an individual engaged in an educational program or assignment, whose progress and emotional state are the subjects of evaluation.

[0773] "Real-time assessment" refers to a process of immediately and continuously monitoring learners' progress, which enables rapid feedback.

[0774] "Analyzing emotional states from facial expressions and voice data" refers to a technology that determines a learner's emotions based on data acquired using input devices such as cameras and microphones.

[0775] "Generating feedback" refers to the process of analyzing collected progress and sentiment data to create useful information and advice for learners.

[0776] "Reorganizing into pairs or groups" refers to the process of rearranging personnel to optimize collaboration among learners based on their emotional state and progress.

[0777] An "augmented reality environment" refers to a technological environment that overlays digital information onto the real world and presents it to the user, enriching the learning experience.

[0778] This invention is a system that evaluates learners' progress and emotions in real time and provides optimal feedback based on that evaluation. The system primarily consists of a server, terminals, and an emotion engine.

[0779] The device functions as an interface for users engaging in learning. It includes a display for an augmented reality (AR) environment, a camera for recognizing user movements and facial expressions, and a microphone for collecting audio data. The data acquired through these devices is used to monitor the user's learning progress and emotional state.

[0780] The emotion engine analyzes data acquired from the camera and microphone to identify the user's emotions. For example, if the user makes a confused expression or sighs, it detects emotions such as "confused" or "anxious."

[0781] The server receives and analyzes progress and sentiment data transmitted from the terminal. Using a generative AI model, the server generates optimal feedback based on this data. This feedback is communicated to the user via text or voice through the terminal. For example, if the user indicates "I don't know what to do next" as a prompt, the server provides specific advice using instructions to the generative AI model such as "How can I advise the user on the next step?"

[0782] Furthermore, the server also has the ability to reorganize learners into optimal pairs or groups while taking into account the users' emotional states. This allows learners to learn in an environment where they can easily cooperate with each other, and simultaneously improve their social skills.

[0783] In this way, the present invention is a system that enables the provision of real-time support tailored to learners, thereby significantly improving the learning experience.

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] The user logs into the system and begins a learning session. Logging in requires a username and password, and the output displays an augmented reality (AR) learning environment on the device. The user then works on assignments within this environment. In the AR environment, learning content is presented visually, and the user can interact with it.

[0787] Step 2:

[0788] The device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. The input consists of the user's facial expressions and voice data. Based on this data, an emotion engine operates, analyzing the user's emotional state (e.g., "confused," "anxious," etc.) as output. This process utilizes image processing and speech recognition technology to perform calculations that analyze emotions from the acquired data.

[0789] Step 3:

[0790] The server receives learning progress data and sentiment data sent from the terminal. Input data includes logs of learning status and the user's sentiment state. The server analyzes this data using a generative AI model and generates optimal feedback for the user as output. This analysis process employs machine learning algorithms to make predictions and evaluations based on the input data.

[0791] Step 4:

[0792] The device receives feedback from the server and notifies the user of it via voice or text. This feedback may include specific advice or encouraging messages. For example, a message such as "Let's proceed calmly and without rushing" might be displayed. Text display and speech synthesis technologies are used to provide this feedback to the user.

[0793] Step 5:

[0794] The server reorganizes learners into optimal pairs or groups, taking sentiment data into consideration. All user progress and sentiment data are processed as input, and reconstructed group information is generated as output. In this step, a clustering algorithm is used to perform dynamic group formation based on user state.

[0795] Through these steps, the system adaptively responds to learners in real time, maximizing the learning experience.

[0796] (Application Example 2)

[0797] 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".

[0798] Traditional education systems have struggled to grasp learners' progress and emotional states in real time and provide personalized, optimal feedback. Furthermore, there were insufficient means to facilitate communication among participants and improve the educational experience. There was also a need for an effective system to enhance the quality of educational experiences in physical stores.

[0799] 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.

[0800] In this invention, the server includes means for evaluating learners' progress in real time, means for forming participants into appropriate collaborative groups, and means for evaluating comprehension based on sentiment analysis and generating appropriate advice. This enables flexible feedback and improved experience tailored to the individual needs of learners.

[0801] A "learner" is an individual whose purpose is to absorb educational content and grow.

[0802] "Progress" is an indicator that shows the extent to which learners have achieved the set educational content.

[0803] "Real-time" refers to a state of having the ability to grasp and process events as they occur.

[0804] "Evaluation" is the act of assessing performance, such as progress and emotional state, based on specific criteria.

[0805] A "collaborative group" is a group of learners organized to achieve a common goal.

[0806] "Feedback" is a response that provides appropriate advice and confirmation of correctness according to the learner's progress and level of understanding.

[0807] Augmented reality is a technology that overlays digital information onto physical reality.

[0808] "Emotion analysis" is a technique that identifies emotional states from a learner's facial expressions and voice.

[0809] "Comprehension level" refers to the degree to which learners understand and remember educational content.

[0810] "Advice" refers to guidance or suggestions provided to learners to encourage improvement and growth.

[0811] The system for implementing this invention analyzes the user's progress and emotions in real time to support learning and provides appropriate feedback. Specific embodiments are shown below.

[0812] The server is responsible for evaluating learning progress and emotional state based on data sent by the user. Specifically, the collected data is processed on a cloud-based server, where software and AI agents for emotion analysis run. Emotion analysis engines such as the Microsoft Azure Emotion Recognition API are used for emotion analysis. The server also generates and provides feedback to the user using OpenAI's GPT model, among others.

[0813] The devices are used to monitor the user's learning behavior and collect data. These devices include smart glasses and tablet computers. These devices are equipped with cameras and microphones to capture the user's facial expressions and voice.

[0814] Users learn within an augmented reality environment and receive feedback from their device. If a user is confused, the device detects this confusion, and the server analyzes it and provides feedback such as, "Shall we review this point again?"

[0815] As a concrete example, consider using the system in a cooking class held at a physical store. If a participant shows signs of low understanding, the terminal will display a message such as "Let's review the steps again," providing calm instructions to the user. An example of a prompt message in this case could be, "The participant is confused, so please generate a message in a gentle tone encouraging them to review the steps again."

[0816] Thus, the present invention is a system for optimizing the learning experience by providing real-time feedback that takes into account the user's emotions and progress.

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It acquires image data of the user's face and voice data as input data. Specifically, smart glasses or a tablet device are used.

[0820] Step 2:

[0821] The device transmits the acquired image and audio data to the server via a pre-configured secure protocol. The input consists of the user's facial expressions and audio data, while the output consists of this data sent to the server.

[0822] Step 3:

[0823] The server analyzes the received data using an emotion analysis engine (such as the Microsoft Azure Emotion Recognition API). The input consists of user facial image data and audio data, which are analyzed to output emotional states such as "confused," "anxious," and "relaxed."

[0824] Step 4:

[0825] The server uses a generative AI model (such as OpenAI's GPT model) to generate appropriate feedback text data based on the sentiment analysis results. The input includes sentiment data and learning progress data, and the output is feedback text data. Specifically, the prompt message "The participant is confused, so generate a message in a calm tone to reconfirm the procedure" is input to the model.

[0826] Step 5:

[0827] The server sends the generated feedback text to the terminal. The input is the feedback text, and the output is the data that transfers it to the terminal.

[0828] Step 6:

[0829] The device provides the user with received feedback via audio or text display. The input is text data of the feedback, and the user receives visual or auditory feedback. For example, it might display "Shall we review this point again?" through the speaker or screen.

[0830] 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.

[0831] 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.

[0832] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] A means of evaluating the progress of multiple learners in real time,

[0854] Means for forming learners into optimal pairs or groups based on the aforementioned progress evaluation,

[0855] A means of providing real-time feedback to learners,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for providing an augmented reality environment for learners.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising means for promoting communication among learners and improving social skills by forming the aforementioned pairs or groups.

[0861] "Example 1"

[0862] (Claim 1)

[0863] A means of evaluating the behavior information of multiple users in real time,

[0864] Means for forming users into optimal pairs or groups based on the aforementioned performance evaluation,

[0865] A means of providing response information to users in real time,

[0866] A means of providing an augmented reality environment for displaying digital information,

[0867] A means of creating user-optimized information using generative AI technology,

[0868] Means for displaying the aforementioned information on a user interface,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, further comprising means for promoting information exchange among users and improving social skills.

[0872] (Claim 3)

[0873] The system according to claim 1, further comprising means for enabling multiple users to operate the same 3D display in real time via terminal devices.

[0874] "Application Example 1"

[0875] (Claim 1)

[0876] A device for evaluating the progress of multiple educational participants in real time,

[0877] A device for forming educational participants into an optimal combination or group based on the aforementioned progress evaluation,

[0878] A device for providing feedback to educational participants in real time,

[0879] A device for providing an extended virtual environment for educational participants,

[0880] A device for sharing information and promoting collaborative work among educational participants through two-way communication,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, which uses a machine computing model for educational participants to individually learn information and immediately provide analysis results.

[0884] (Claim 3)

[0885] The system according to claim 1, further comprising a device for improving the social skills of educational participants through collaborative work.

[0886] "Example 2 of combining an emotion engine"

[0887] (Claim 1)

[0888] A means of evaluating learners' progress in real time,

[0889] Methods for analyzing emotional states from facial expressions and voice data,

[0890] A means for generating optimal feedback for learners based on the aforementioned progress evaluation and sentiment analysis,

[0891] Means of providing feedback in audio or text,

[0892] Means for reorganizing learners into optimal pairs or groups based on the aforementioned progress evaluation and sentiment analysis,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, further comprising means for presenting a task to a learner in an augmented reality environment.

[0896] (Claim 3)

[0897] The system according to claim 1, further comprising means for providing advice to help learners continue with tasks while receiving emotional support.

[0898] "Application example 2 when combining with an emotional engine"

[0899] (Claim 1)

[0900] A means of evaluating learners' progress in real time,

[0901] A means for forming learners into appropriate collaborative groups based on the aforementioned progress evaluation,

[0902] A means of providing real-time feedback to learners,

[0903] A means of providing a learning environment using augmented reality,

[0904] A means of evaluating learners' comprehension based on emotion analysis and generating appropriate advice,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, further comprising means for analyzing the emotional state of participants and providing individualized feedback in order to provide an educational experience in a physical store.

[0908] (Claim 3)

[0909] The system according to claim 1, further comprising means for promoting communication among learners and improving social skills through the formation of the aforementioned collaborative groups. [Explanation of Symbols]

[0910] 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 device for evaluating the progress of multiple educational participants in real time, A device for forming educational participants into an optimal combination or group based on the aforementioned progress evaluation, A device for providing feedback to educational participants in real time, A device for providing an extended virtual environment for educational participants, A device for sharing information and promoting collaborative work among educational participants through two-way communication, A system that includes this.

2. The system according to claim 1, which uses a machine computing model for educational participants to individually learn information and to immediately provide analysis results.

3. The system according to claim 1, further comprising a device for improving the social skills of educational participants through collaborative work.

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