system

Abstract

We provide the system. [Solution] A task generation method that proposes individual tasks to stimulate children's interest in academic content and cultivate their creative thinking and problem-solving abilities in their learning, A progress management system that manages the progress of proposed tasks and provides real-time advice based on the progress status, An evaluation method that assesses user interest and technical level and uses it to select challenges, A user interface means that can be operated by the user through educational equipment, and a feedback means that can create an interactive learning environment, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern children spend a lot of time on online videos, games, etc., and tend to lack study time. Also, parents have a heavy burden in managing their children's learning, and it is difficult to maintain the motivation for children to continue learning independently. As a result, there is a problem that it is difficult to sustain children's interest in science, technology, engineering, and mathematics (STEM), and to effectively cultivate creativity and problem-solving abilities.

Means for Solving the Problems

[0005] This invention provides a generation means that can individually propose projects or tasks tailored to each child's interests and skill level. This effectively stimulates user interest and promotes continuous learning. Furthermore, by managing the progress of the proposed projects or tasks through a progress management means and providing real-time feedback according to the progress, it enhances understanding during learning. In addition, by continuously evaluating the user's interests and skills through an evaluation means and reflecting the results in the selection of projects or tasks, it is possible to optimize the individual learning experience and effectively cultivate creativity and problem-solving abilities.

[0006] A "generation method" refers to a system or method for individually proposing projects or tasks tailored to the user's interests and skill level.

[0007] A "progress management tool" is a system or method for tracking the progress of a proposed project or task and providing real-time feedback based on the progress.

[0008] An "evaluation tool" is a system or method that analyzes a user's interests and skill level and uses the results to select the most suitable project or task.

[0009] A "project or assignment" is a specific learning activity or challenge that a user should engage in to improve their skills and interests.

[0010] "Feedback" refers to advice and information provided based on the user's progress, aimed at identifying areas for improvement and facilitating success. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This system aims to provide children with personalized learning experiences. Children, as users, first access the system through a terminal and input information related to their interests and skill levels. The server then creates a user profile and, through a generation mechanism, suggests projects or assignments optimized for their interests and skills.

[0033] The generated projects are designed to capture children's interest and provide motivation to continue learning. Once a user starts a project, the device records their progress sequentially and sends it to the server. The server analyzes the data using progress management tools to determine what kind of support is needed at each step.

[0034] Real-time feedback is provided to the user via the device. This feedback includes advice tailored to the problems the user is facing during learning and their progress, as well as suggestions on how to approach the next task. For example, if the user is writing program code, and an error is detected, the device will provide hints and explanations showing the correct way to do it.

[0035] As a concrete example, if a child user is working on the task of "creating a program for robot movements," the device first assesses their current skills through a basic knowledge quiz about robot movements. Next, a specific programming task tailored to their skill level is proposed by a generation mechanism. The user then begins writing the program and records their progress on the device.

[0036] Progress management is performed based on data received by the server, and users receive immediate feedback on their devices regarding any steps they get stuck on or points that need improvement. Embodiments of the present invention effectively support children's independent learning through this process.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The device displays a login screen to the user, prompting them to enter their name, age, and areas of interest.

[0040] Step 2:

[0041] The server receives user information sent from the terminal, stores it in the database, and creates a user profile.

[0042] Step 3:

[0043] The device displays surveys and quizzes to assess the user's interests and skills.

[0044] Step 4:

[0045] Users answer surveys and quizzes and send their answers to the server via their devices.

[0046] Step 5:

[0047] The server analyzes the received response data to evaluate the user's interests and skill level.

[0048] Step 6:

[0049] The server uses a generation mechanism to generate projects or tasks based on the evaluation results and sends a list of proposals to the terminal.

[0050] Step 7:

[0051] The user selects an item of interest from a list of projects or tasks displayed on the device.

[0052] Step 8:

[0053] The terminal notifies the server of the user's selection and displays the details of the selected project.

[0054] Step 9:

[0055] As users progress through a project, they record their progress on their device.

[0056] Step 10:

[0057] The terminal sends recorded progress information to the server, and the server analyzes the data using progress management tools.

[0058] Step 11:

[0059] Based on the progress, the server generates real-time feedback and interactive commentary as needed.

[0060] Step 12:

[0061] The terminal displays feedback from the server to the user and provides any other necessary supplementary information or hints.

[0062] (Example 1)

[0063] 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."

[0064] In today's learning environment, providing individualized learning opportunities tailored to each child is crucial, but traditional education systems have failed to adequately meet this need. In particular, there is a lack of means to immediately propose appropriate projects and assignments based on each child's interests and skill level, and to provide real-time feedback on their progress.

[0065] 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.

[0066] In this invention, the server includes terminal means that provides an individualized learning experience by inputting information related to the user's interests and ability level; generation means that creates a user profile and derives an appropriate project or task based on the profile; progress management means that records the user's progress, analyzes the data, and determines support; and feedback means that provides real-time feedback to the user and suggests specific advice and next steps. This enables effective education tailored to individual learning needs.

[0067] A "terminal device" is a device used by a user to input information related to their interests and skill levels and transmit that information to a server.

[0068] A "generation means" is a device or system that has the function of deriving the optimal project or task based on the user profile.

[0069] A "progress management system" is a system equipped with the function of recording user progress data, analyzing it, and determining what kind of support is needed.

[0070] A "feedback mechanism" is a device or system that provides users with specific advice and next steps in real time based on analyzed data.

[0071] A "user profile" is data that records a user's interests, skill level, and past learning history, and is used to generate appropriate learning projects.

[0072] This invention is a system designed to support the learning of individual children, providing a learning experience tailored to the user's interests and abilities. The system is implemented by users accessing it using a terminal and inputting information. Tablets and laptops are used as terminals, and information is transmitted to the server via a dedicated application or web interface.

[0073] The server uses a database management system to create a user profile based on information received from the user. This profile organizes and stores information about the user's interests and skill level. The server uses this profile and a generative AI model to suggest appropriate learning projects or assignments. A large-scale language model is used as the generative AI model, and assignments are generated based on prompt statements.

[0074] The generated project is sent to the device, and the device records the user's progress while they work on the project. The device sends the progress data to the server, which analyzes it to determine the necessary feedback. Data analysis tools such as Python and R are used for the analysis. The feedback generated by the server is provided to the user in real time through the device to assist in learning.

[0075] As a concrete example, when a primary school student (the user) tackles the task of "developing a simple game," the device first presents a quiz on basic knowledge to evaluate the user's skills. Then, a generative AI model suggests a programming task appropriate to the user's level. As the user creates the program, the device records their progress, and if an error occurs, the server provides real-time feedback on how to resolve it. The objective of this invention is to provide educational support through such a process.

[0076] Example prompt: "Assuming the user's skill level is beginner, generate a simple game development project."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users access the system by opening a dedicated application or web interface using their terminal. Users input their areas of interest and self-assessed skill levels. This information is sent from the terminal to the server and treated as initial input.

[0080] Step 2:

[0081] The server stores information received from users in a database. Using a database management system, data regarding interests and skill levels is structured to create user profiles. The input data consists of areas of interest and skill assessments, and based on this, a profile optimized for the user is generated.

[0082] Step 3:

[0083] The server uses a generative AI model to generate projects or tasks based on the user profile. The prompt "Generate a simple project assuming the user's skill level is beginner" is entered to drive the authoritative generative model. This process generates tasks that are interesting to the user and match their skills, and outputs them to the terminal.

[0084] Step 4:

[0085] The user receives the project presented on the terminal and begins working on it. The terminal continuously records the user's activities and captures user action data. This includes program progress, quiz answers, and activity logs.

[0086] Step 5:

[0087] The terminal periodically sends recorded progress data to the server. The server receives this data and analyzes the progress using data analysis tools such as Python or R. The main purpose of the analysis is to identify where the user is experiencing difficulties. The input data consists of progress frequency and activity logs, and the analysis results are generated based on this data.

[0088] Step 6:

[0089] The server generates feedback based on the results obtained from the analysis. Specifically, it constructs feedback that includes how to correct errors, the next steps in learning, or supplementary advice. This feedback is sent to the terminal and presented to the user in real time.

[0090] Step 7:

[0091] The user receives feedback from the device and continues learning based on it. The feedback provided by the device allows the user to understand their learning progress and decide on their next course of action. Specifically, the user might modify the program according to the feedback and try running it again.

[0092] (Application Example 1)

[0093] 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."

[0094] In today's educational setting, it is crucial to provide learning experiences tailored to each child's interests and skill level. However, uniform teaching methods make it difficult to stimulate individual interests and maximize creative thinking and problem-solving abilities. Furthermore, a challenge exists in that children do not receive adequate support during the learning process due to insufficient opportunities for real-time feedback.

[0095] 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.

[0096] In this invention, the server includes task generation means, progress management means, evaluation means, and user interface means. This enables the suggestion of customized tasks based on individual learning needs and the provision of real-time progress advice.

[0097] A "task generation method" is a mechanism that generates individually customized learning tasks based on a child's interests and skill level.

[0098] A "progress management system" is a mechanism for tracking the progress of learning assignments in real time and providing advice and feedback at the appropriate time.

[0099] An "evaluation tool" is a mechanism that analyzes users' interests and skill levels and uses this information to select and propose appropriate learning tasks.

[0100] A "user interface means" is a mechanism that provides an interface that can be operated by a user through educational equipment, thereby creating an interactive learning environment.

[0101] The system for implementing this invention operates in conjunction with educational equipment and a server. The server uses a generative artificial intelligence model as a task generation means to generate customized learning tasks tailored to the child's interests and skill level. It also includes a progress management means to monitor the user's learning progress in real time and provide advice as needed. An evaluation means analyzes the user's input data and uses it to make appropriate learning suggestions.

[0102] The educational equipment uses Raspberry Pi, enabling interactive interaction with users through its display and voice recognition capabilities. For feedback, it utilizes the Python Flask framework to visually display progress information. On the server side, a Django server is installed on AWS® EC2 for data management and analysis.

[0103] As a concrete example, when a child learns the basic movements of a robot, the server generates a task such as "program the robot to rotate" and presents it to the child via a Raspberry Pi. The child tries out the program through the interface, and their progress data is constantly sent to the server, providing timely and accurate feedback.

[0104] Examples of prompt statements are shown below.

[0105] "Please come up with challenges for a robot dance program that children might find interesting. The robot can learn choreography that involves repeating three basic movements, and will be supported with real-time feedback."

[0106] In this way, we provide a learning environment that makes the challenges users actually face interesting and allows them to acquire practical skills.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user logs into their device and enters their interests and skill level. This data is used to create the user's profile and is sent from the device to the server. The server receives this data and performs initial profiling.

[0110] Step 2:

[0111] The server analyzes the received interest and skill level data and uses task generation tools to generate optimal learning tasks for the user. This involves using a generative artificial intelligence model, and candidate projects are sent to the terminal. The tasks are designed to engage the user's interest.

[0112] Step 3:

[0113] The user selects a task presented via their device and begins learning. User actions are performed on the device, and progress is recorded sequentially. This data is also sent to the server.

[0114] Step 4:

[0115] The server analyzes the received progress data using a progress management system. It determines which step the user is encountering problems at and provides advice and hints as needed. This is done in real time, and feedback is sent to the terminal.

[0116] Step 5:

[0117] Users receive feedback and retry the task based on that feedback. This cyclical process reinforces their learning and provides a sense of accomplishment. Progress is continuously monitored, and the server indicates when it's time to move on to the next step.

[0118] This processing flow allows the system to provide users with a learning experience that is individually optimized for their needs.

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

[0120] This invention is a system that combines an emotional engine to enrich children's learning experiences. When a child user accesses this system, the terminal first displays a profile based on their login information and performs initial setup. Based on this profile information, the server uses a generation mechanism to generate projects and tasks that are suitable for the user's interests and skill level.

[0121] While the user selects and runs a project, an emotion engine recognizes the user's emotions in real time through the device's camera and microphone. The server analyzes this emotion data and combines it with progress management tools to generate feedback tailored to the user's psychological state and emotions. For example, if the user shows a confused expression, the emotion engine recognizes this emotion, and the server provides encouraging messages or hints as needed.

[0122] Emotional data is also used to assess user interests and skills, providing valuable information when selecting projects based on the user's state. Interactive explanations also adapt to the user's emotions. For example, if the device detects a lack of confidence in an answer, it can provide more detailed explanations and display additional materials to aid understanding.

[0123] For example, if a user is working on a math puzzle, the server identifies where the user is getting stuck based on their progress and sentiment data. The device then provides a detailed explanation of the specific steps needed to solve the problem, facilitating understanding.

[0124] Thus, this form of invention, which combines an emotion engine, grasps the learner's emotions in real time and provides an optimal learning environment tailored to their individual needs and psychological state.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The terminal displays a login screen to the user, who then enters the required information.

[0128] Step 2:

[0129] The server receives login information sent from the terminal, retrieves the user's profile from the database, and performs the initial setup.

[0130] Step 3:

[0131] The server uses a generation mechanism to create an optimal project or assignment based on the user's interests and skill level, and provides it to the terminal.

[0132] Step 4:

[0133] The user selects the project or task they want to do from those displayed on the device.

[0134] Step 5:

[0135] While the user works on a project of their choice, the device uses its camera and microphone to recognize the user's emotions in real time through its emotion engine.

[0136] Step 6:

[0137] The user's emotional data, obtained from the emotion engine, is sent to the server via the terminal.

[0138] Step 7:

[0139] The server combines and analyzes emotional data and progress data to generate feedback that corresponds to the user's psychological state and current emotions.

[0140] Step 8:

[0141] The device displays messages and hints to the user at the appropriate time based on feedback from the server.

[0142] Step 9:

[0143] We will integrate emotional data with user skill and interest assessments and use it to select the next project or task.

[0144] Step 10:

[0145] The device continuously records the user's progress and emotional changes, and sends this data to the server to further optimize the learning experience.

[0146] (Example 2)

[0147] 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".

[0148] Traditional learning systems struggle to grasp the emotional and psychological states of individual learners in real time and provide appropriate feedback accordingly. This presents a challenge: they may fail to adequately support learners when they face difficulties, potentially undermining their motivation to learn.

[0149] 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.

[0150] In this invention, the server includes generation means for generating personalized projects or tasks using learner profile information, emotion analysis means for recognizing and collecting the learner's emotional state in real time during execution, and feedback provision means for providing feedback that corresponds to the learner's psychological state and emotions based on the results of the emotion analysis. This enables optimal feedback and support that meets the individual needs of the learner.

[0151] "Learner profile information" refers to information about individual learners, including their past learning history, skill level, and interests.

[0152] "Generative means" refers to methods and techniques for creating individualized projects or assignments using learner profile information.

[0153] "Emotional analysis methods" refer to techniques and technologies for recognizing and analyzing a learner's emotional state in real time from their facial expressions, voice, and other factors.

[0154] "Feedback provision methods" refer to techniques and technologies for providing feedback tailored to the learner's psychological state and emotions, based on the results of an emotional analysis of the learner.

[0155] "Interactive explanations" refer to explanations and descriptions that dynamically change according to the learner's progress and emotional state.

[0156] "Visual cues" are clues or advice provided in a visual form, such as images or videos, to help learners understand more easily.

[0157] A "generative AI model" is a machine learning model designed for tasks such as natural language processing and data generation.

[0158] This invention is a system that improves the learning experience by understanding learners' emotions in real time and proposing personalized learning projects and tasks based on those emotions.

[0159] The server receives the learner's login information and retrieves profile information from the database system. This profile information includes past learning history, skill level, and interests. The server uses a generative AI model to generate projects and assignments tailored to the learner's profile. Natural language processing models and data generation models are used in this generation process. The generated projects are sent to the terminal via the network.

[0160] The device uses its built-in camera and microphone to collect facial expressions and voice while the learner is working on a project, and analyzes this data using emotion analysis tools. Based on the analyzed emotion data, the server provides the learner with the most appropriate feedback. This feedback is displayed on the device as encouraging messages and specific advice. Generative AI models are also utilized in this process.

[0161] For example, if a learner shows a confused expression while working on a math problem, the device can provide a visual guide along with a hint such as "Let's review the calculation steps." In this way, it is possible to provide an environment where learners can concentrate on their studies with peace of mind.

[0162] An example of a prompt for a generative AI model might be: "The user is attempting a complex math puzzle. They seem unsure of the solution. Generate encouraging messages and hints to support the user." Based on this prompt, appropriate feedback is generated, enabling efficient learning.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] When a user logs into the system, the server retrieves profile information from the database based on their authentication credentials. This process takes the user's login credentials as input and retrieves their past learning history and skill level as output. Specifically, it queries the user database and extracts the corresponding user's records.

[0166] Step 2:

[0167] Based on the acquired profile information, the server utilizes a generative AI model to generate projects and tasks suitable for the user. Here, the profile information serves as input data, and the generated personalized projects are the output. Specifically, the generative AI model analyzes the profile information and performs a generation process to suggest appropriate learning themes.

[0168] Step 3:

[0169] The terminal presents the generated project to the user and accepts the user's action to start the project. The user's action triggers the display of project details. Project information is sent as input, and the output is the project content presented to the user.

[0170] Step 4:

[0171] During project execution, the device uses its camera and microphone to input the user's facial expressions and voice into the emotion analysis system. Specifically, real-time audio and video data is collected and becomes the input data. The output is information about the user's emotional state obtained through emotion analysis.

[0172] Step 5:

[0173] The server analyzes emotional state information sent from the terminal and generates appropriate feedback for the user using a feedback provision system. The input is emotional state information, and the output is a feedback message tailored to the user's psychological state. A generation AI model is used to perform specific actions that generate encouragement and hints adapted to the situation.

[0174] Step 6:

[0175] The terminal displays feedback received from the server to the user. The input here is the feedback message from the server, and the output is the feedback content displayed on the screen for the user. Specifically, the terminal's display shows the message.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] In children's learning, there is a need to provide an optimal learning experience that is tailored to their emotions and circumstances. However, conventional systems have struggled to recognize users' emotions in real time and adjust feedback accordingly. As a result, there has been a problem in that learners do not receive appropriate support according to their psychological state and difficulties.

[0179] 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.

[0180] In this invention, the server includes a suggestion means for individually proposing projects or tasks, a progress management means for managing progress and providing real-time feedback, and an emotion recognition means for analyzing the user's emotions and providing feedback. This enables learning support tailored to each user's psychological state and skill level.

[0181] "Suggestion method" refers to a function that individually generates and provides projects and assignments tailored to the user's learning needs.

[0182] A "progress management tool" is a function that monitors the user's learning progress in real time and provides appropriate feedback based on that progress.

[0183] "Measurement tools" refer to functions that evaluate users' interests and skill levels, and use that information to appropriately select projects and tasks.

[0184] "Emotion recognition means" refers to a function that analyzes the user's emotions and adjusts the learning content and feedback according to that data.

[0185] An "interactive explanation tool" is a function that detects the user's learning difficulties and provides additional support and supplementary information tailored to that situation.

[0186] This invention is a system designed to enrich children's learning experiences. The system is configured as follows:

[0187] The server includes a proposal mechanism, a progress management mechanism, and an emotion recognition mechanism. The proposal mechanism uses a generative AI model to generate projects and tasks based on the user's profile information and learning history.

[0188] The device uses a high-precision camera and microphone (e.g., a typical high-resolution camera and condenser microphone) to collect the user's facial expressions and voice in real time. This allows emotion recognition to analyze the user's psychological state, and the server uses this information to provide feedback messages and modified learning content.

[0189] For example, if a user shows a confused expression while solving a math problem, the server uses the analyzed emotion data to instruct the device to display an encouraging message and detailed steps for solving the problem.

[0190] Furthermore, the interactive explanation system provides supplementary materials and encouraging messages as needed, according to the user's skill level and progress. This process is designed to maintain the user's motivation to learn and to facilitate understanding.

[0191] Examples of prompts include, "Analyze this child's facial expressions and voice data to recognize their emotions and generate appropriate feedback," and "Generate projects that the child will be interested in. Suggest them based on their profile data."

[0192] This allows the device to constantly provide a learning experience adapted to the user's situation, enabling interactive and effective education tailored to individual needs.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The terminal receives the user's login information and sends the current learning status and interests to the server based on that profile. The input consists of the user's login information and profile data, and based on this, the terminal prepares recommended learning content.

[0196] Step 2:

[0197] The server uses the proposed method to run a generative AI model and analyze the user's profile information and past learning history. The input data consists of profile information and learning history, which the generative AI model processes to generate projects and tasks that are optimal for the user.

[0198] Step 3:

[0199] The generated projects and assignments are sent from the server to the terminal, which then presents them to the user. The output is a list of projects or assignments best suited to the user. The displayed content includes learning objectives and exaggerated background information.

[0200] Step 4:

[0201] While the user is working on a task, the device uses its camera and microphone to collect facial expressions and voice in real time and send them to the server. The input consists of the user's voice and video data, which are then analyzed by emotion recognition technology.

[0202] Step 5:

[0203] The server uses emotion recognition means to analyze the user's emotions from the acquired data. The output is the analyzed emotion data, and the progress management means generates appropriate feedback according to the user's emotional state.

[0204] Step 6:

[0205] The generated feedback is sent to the device and presented to the user. This feedback includes encouraging messages and detailed explanations. The server observes how the user responds to the task and uses this data as input for the next step.

[0206] Step 7:

[0207] Once a user completes a learning assignment, the device records their progress and provides hints and reminders for the next learning session. The output is data that guides the next learning session and is saved as an optimized learning plan through communication with the server.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This system aims to provide children with personalized learning experiences. Children, as users, first access the system through a terminal and input information related to their interests and skill levels. The server then creates a user profile and, through a generation mechanism, suggests projects or assignments optimized for their interests and skills.

[0225] The generated projects are designed to capture children's interest and provide motivation to continue learning. Once a user starts a project, the device records their progress sequentially and sends it to the server. The server analyzes the data using progress management tools to determine what kind of support is needed at each step.

[0226] Real-time feedback is provided to the user via the device. This feedback includes advice tailored to the problems the user is facing during learning and their progress, as well as suggestions on how to approach the next task. For example, if the user is writing program code, and an error is detected, the device will provide hints and explanations showing the correct way to do it.

[0227] As a concrete example, if a child user is working on the task of "creating a program for robot movements," the device first assesses their current skills through a basic knowledge quiz about robot movements. Next, a specific programming task tailored to their skill level is proposed by a generation mechanism. The user then begins writing the program and records their progress on the device.

[0228] Progress management is performed based on data received by the server, and users receive immediate feedback on their devices regarding any steps they get stuck on or points that need improvement. Embodiments of the present invention effectively support children's independent learning through this process.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The device displays a login screen to the user, prompting them to enter their name, age, and areas of interest.

[0232] Step 2:

[0233] The server receives user information sent from the terminal, stores it in the database, and creates a user profile.

[0234] Step 3:

[0235] The device displays surveys and quizzes to assess the user's interests and skills.

[0236] Step 4:

[0237] Users answer surveys and quizzes and send their answers to the server via their devices.

[0238] Step 5:

[0239] The server analyzes the received response data to evaluate the user's interests and skill level.

[0240] Step 6:

[0241] The server uses a generation mechanism to generate projects or tasks based on the evaluation results and sends a list of proposals to the terminal.

[0242] Step 7:

[0243] The user selects an item of interest from a list of projects or tasks displayed on the device.

[0244] Step 8:

[0245] The terminal notifies the server of the user's selection and displays the details of the selected project.

[0246] Step 9:

[0247] As users progress through a project, they record their progress on their device.

[0248] Step 10:

[0249] The terminal sends recorded progress information to the server, and the server analyzes the data using progress management tools.

[0250] Step 11:

[0251] Based on the progress, the server generates real-time feedback and interactive commentary as needed.

[0252] Step 12:

[0253] The terminal displays feedback from the server to the user and provides any other necessary supplementary information or hints.

[0254] (Example 1)

[0255] 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."

[0256] In today's learning environment, providing individualized learning opportunities tailored to each child is crucial, but traditional education systems have failed to adequately meet this need. In particular, there is a lack of means to immediately propose appropriate projects and assignments based on each child's interests and skill level, and to provide real-time feedback on their progress.

[0257] 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.

[0258] In this invention, the server includes terminal means that provides an individualized learning experience by inputting information related to the user's interests and ability level; generation means that creates a user profile and derives an appropriate project or task based on the profile; progress management means that records the user's progress, analyzes the data, and determines support; and feedback means that provides real-time feedback to the user and suggests specific advice and next steps. This enables effective education tailored to individual learning needs.

[0259] A "terminal device" is a device used by a user to input information related to their interests and skill levels and transmit that information to a server.

[0260] A "generation means" is a device or system that has the function of deriving the optimal project or task based on the user profile.

[0261] A "progress management system" is a system equipped with the function of recording user progress data, analyzing it, and determining what kind of support is needed.

[0262] A "feedback mechanism" is a device or system that provides users with specific advice and next steps in real time based on analyzed data.

[0263] A "user profile" is data that records a user's interests, skill level, and past learning history, and is used to generate appropriate learning projects.

[0264] This invention is a system designed to support the learning of individual children, providing a learning experience tailored to the user's interests and abilities. The system is implemented by users accessing it using a terminal and inputting information. Tablets and laptops are used as terminals, and information is transmitted to the server via a dedicated application or web interface.

[0265] The server uses a database management system to create a user profile based on information received from the user. This profile organizes and stores information about the user's interests and skill level. The server uses this profile and a generative AI model to suggest appropriate learning projects or assignments. A large-scale language model is used as the generative AI model, and assignments are generated based on prompt statements.

[0266] The generated project is sent to the device, and the device records the user's progress while they work on the project. The device sends the progress data to the server, which analyzes it to determine the necessary feedback. Data analysis tools such as Python and R are used for the analysis. The feedback generated by the server is provided to the user in real time through the device to assist in learning.

[0267] As a concrete example, when a primary school student (the user) tackles the task of "developing a simple game," the device first presents a quiz on basic knowledge to evaluate the user's skills. Then, a generative AI model suggests a programming task appropriate to the user's level. As the user creates the program, the device records their progress, and if an error occurs, the server provides real-time feedback on how to resolve it. The objective of this invention is to provide educational support through such a process.

[0268] Example prompt: "Assuming the user's skill level is beginner, generate a simple game development project."

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] Users access the system by opening a dedicated application or web interface using their terminal. Users input their areas of interest and self-assessed skill levels. This information is sent from the terminal to the server and treated as initial input.

[0272] Step 2:

[0273] The server stores information received from users in a database. Using a database management system, data regarding interests and skill levels is structured to create user profiles. The input data consists of areas of interest and skill assessments, and based on this, a profile optimized for the user is generated.

[0274] Step 3:

[0275] The server uses a generative AI model to generate projects or tasks based on the user profile. The prompt "Generate a simple project assuming the user's skill level is beginner" is entered to drive the authoritative generative model. This process generates tasks that are interesting to the user and match their skills, and outputs them to the terminal.

[0276] Step 4:

[0277] The user receives the project presented on the terminal and begins working on it. The terminal continuously records the user's activities and captures user action data. This includes program progress, quiz answers, and activity logs.

[0278] Step 5:

[0279] The terminal periodically sends the recorded progress data to the server. The server receives this data and analyzes the progress using data analysis tools such as Python and R. The main purpose of the analysis is to identify which parts the user is having difficulty with. The input data is the progress frequency and activity log, and based on this, the analysis results are generated.

[0280] Step 6:

[0281] Based on the results obtained from the analysis, the server generates feedback. Specifically, it constructs feedback that includes methods for correcting errors, the next steps in learning, or supplementary advice. This feedback is sent to the terminal and presented to the user in real time.

[0282] Step 7:

[0283] The user receives the feedback from the terminal and continues learning based on it. With the feedback presented by the terminal, the user can understand their learning situation and decide on the next actions. Specific actions include that the user can modify the program according to the feedback and try to execute it again.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In modern educational settings, it is important to provide a learning experience tailored to the interests and skill levels of individual children. However, with a uniform teaching method, it is difficult to draw out the interest of each person and maximize creative thinking and problem-solving abilities. Also, due to the lack of sufficient opportunities to obtain real-time feedback, there is an issue that children cannot receive appropriate support during the learning process.

[0287] 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.

[0288] In this invention, the server includes task generation means, progress management means, evaluation means, and user interface means. This enables the suggestion of customized tasks based on individual learning needs and the provision of real-time progress advice.

[0289] A "task generation method" is a mechanism that generates individually customized learning tasks based on a child's interests and skill level.

[0290] A "progress management system" is a mechanism for tracking the progress of learning assignments in real time and providing advice and feedback at the appropriate time.

[0291] An "evaluation tool" is a mechanism that analyzes users' interests and skill levels and uses this information to select and propose appropriate learning tasks.

[0292] A "user interface means" is a mechanism that provides an interface that can be operated by a user through educational equipment, thereby creating an interactive learning environment.

[0293] The system for implementing this invention operates in conjunction with educational equipment and a server. The server uses a generative artificial intelligence model as a task generation means to generate customized learning tasks tailored to the child's interests and skill level. It also includes a progress management means to monitor the user's learning progress in real time and provide advice as needed. An evaluation means analyzes the user's input data and uses it to make appropriate learning suggestions.

[0294] The educational equipment uses Raspberry Pi, enabling interactive interaction with users through a display and voice recognition capabilities. For feedback, the Flask framework in Python is used to visually present progress information. On the server side, a Django server is set up on AWS EC2 for data management and analysis.

[0295] As a concrete example, when a child learns the basic movements of a robot, the server generates a task such as "program the robot to rotate" and presents it to the child via a Raspberry Pi. The child tries out the program through the interface, and their progress data is constantly sent to the server, providing timely and accurate feedback.

[0296] Examples of prompt statements are shown below.

[0297] "Please come up with challenges for a robot dance program that children might find interesting. The robot can learn choreography that involves repeating three basic movements, and will be supported with real-time feedback."

[0298] In this way, we provide a learning environment that makes the challenges users actually face interesting and allows them to acquire practical skills.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user logs into their device and enters their interests and skill level. This data is used to create the user's profile and is sent from the device to the server. The server receives this data and performs initial profiling.

[0302] Step 2:

[0303] The server analyzes the received data on interests and technical levels, and uses a problem generation means to generate optimal learning tasks for the user. For this, a generation artificial intelligence model is used, and the generated project candidates are transmitted to the terminal. The tasks are designed to arouse the user's interest.

[0304] Step 3:

[0305] The user selects the tasks presented via the terminal and starts learning. The user's operations are performed on the terminal, and the progress is sequentially recorded. This data is also transmitted to the server.

[0306] Step 4:

[0307] The server analyzes the received progress data using a progress management means. It determines at which step the user has problems and provides advice or hints as needed. This is done in real time, and feedback is transmitted to the terminal.

[0308] Step 5:

[0309] The user receives the feedback and challenges the tasks again based on it. Through this cyclic process, the learning content is strengthened, and a sense of achievement can be obtained. Also, the progress is continuously monitored, and the server indicates the timing to proceed to the next step.

[0310] Through such a processing flow, the system can provide an individually optimized learning experience for the user.

[0311] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0312] This invention is a system that combines an emotional engine to enrich children's learning experiences. When a child user accesses this system, the terminal first displays a profile based on their login information and performs initial setup. Based on this profile information, the server uses a generation mechanism to generate projects and tasks that are suitable for the user's interests and skill level.

[0313] While the user selects and runs a project, an emotion engine recognizes the user's emotions in real time through the device's camera and microphone. The server analyzes this emotion data and combines it with progress management tools to generate feedback tailored to the user's psychological state and emotions. For example, if the user shows a confused expression, the emotion engine recognizes this emotion, and the server provides encouraging messages or hints as needed.

[0314] Emotional data is also used to assess user interests and skills, providing valuable information when selecting projects based on the user's state. Interactive explanations also adapt to the user's emotions. For example, if the device detects a lack of confidence in an answer, it can provide more detailed explanations and display additional materials to aid understanding.

[0315] For example, if a user is working on a math puzzle, the server identifies where the user is getting stuck based on their progress and sentiment data. The device then provides a detailed explanation of the specific steps needed to solve the problem, facilitating understanding.

[0316] Thus, this form of invention, which combines an emotion engine, grasps the learner's emotions in real time and provides an optimal learning environment tailored to their individual needs and psychological state.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The terminal displays a login screen to the user, who then enters the required information.

[0320] Step 2:

[0321] The server receives login information sent from the terminal, retrieves the user's profile from the database, and performs the initial setup.

[0322] Step 3:

[0323] The server uses a generation mechanism to create an optimal project or assignment based on the user's interests and skill level, and provides it to the terminal.

[0324] Step 4:

[0325] The user selects the project or task they want to do from those displayed on the device.

[0326] Step 5:

[0327] While the user works on a project of their choice, the device uses its camera and microphone to recognize the user's emotions in real time through its emotion engine.

[0328] Step 6:

[0329] The user's emotional data, obtained from the emotion engine, is sent to the server via the terminal.

[0330] Step 7:

[0331] The server combines and analyzes emotional data and progress data to generate feedback that corresponds to the user's psychological state and current emotions.

[0332] Step 8:

[0333] The device displays messages and hints to the user at the appropriate time based on feedback from the server.

[0334] Step 9:

[0335] We will integrate emotional data with user skill and interest assessments and use it to select the next project or task.

[0336] Step 10:

[0337] The device continuously records the user's progress and emotional changes, and sends this data to the server to further optimize the learning experience.

[0338] (Example 2)

[0339] 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".

[0340] Traditional learning systems struggle to grasp the emotional and psychological states of individual learners in real time and provide appropriate feedback accordingly. This presents a challenge: they may fail to adequately support learners when they face difficulties, potentially undermining their motivation to learn.

[0341] 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.

[0342] In this invention, the server includes generation means for generating personalized projects or tasks using learner profile information, emotion analysis means for recognizing and collecting the learner's emotional state in real time during execution, and feedback provision means for providing feedback that corresponds to the learner's psychological state and emotions based on the results of the emotion analysis. This enables optimal feedback and support that meets the individual needs of the learner.

[0343] "Learner profile information" refers to information about individual learners, including their past learning history, skill level, and interests.

[0344] "Generative means" refers to methods and techniques for creating individualized projects or assignments using learner profile information.

[0345] "Emotional analysis methods" refer to techniques and technologies for recognizing and analyzing a learner's emotional state in real time from their facial expressions, voice, and other factors.

[0346] "Feedback provision methods" refer to techniques and technologies for providing feedback tailored to the learner's psychological state and emotions, based on the results of an emotional analysis of the learner.

[0347] "Interactive explanations" refer to explanations and descriptions that dynamically change according to the learner's progress and emotional state.

[0348] "Visual cues" are clues or advice provided in a visual form, such as images or videos, to help learners understand more easily.

[0349] A "generative AI model" is a machine learning model designed for tasks such as natural language processing and data generation.

[0350] This invention is a system that improves the learning experience by understanding learners' emotions in real time and proposing personalized learning projects and tasks based on those emotions.

[0351] The server receives the learner's login information and retrieves profile information from the database system. This profile information includes past learning history, skill level, and interests. The server uses a generative AI model to generate projects and assignments tailored to the learner's profile. Natural language processing models and data generation models are used in this generation process. The generated projects are sent to the terminal via the network.

[0352] The device uses its built-in camera and microphone to collect facial expressions and voice while the learner is working on a project, and analyzes this data using emotion analysis tools. Based on the analyzed emotion data, the server provides the learner with the most appropriate feedback. This feedback is displayed on the device as encouraging messages and specific advice. Generative AI models are also utilized in this process.

[0353] For example, if a learner shows a confused expression while working on a math problem, the device can provide a visual guide along with a hint such as "Let's review the calculation steps." In this way, it is possible to provide an environment where learners can concentrate on their studies with peace of mind.

[0354] An example of a prompt for a generative AI model might be: "The user is attempting a complex math puzzle. They seem unsure of the solution. Generate encouraging messages and hints to support the user." Based on this prompt, appropriate feedback is generated, enabling efficient learning.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] When a user logs into the system, the server retrieves profile information from the database based on their authentication credentials. This process takes the user's login credentials as input and retrieves their past learning history and skill level as output. Specifically, it queries the user database and extracts the corresponding user's records.

[0358] Step 2:

[0359] Based on the acquired profile information, the server utilizes a generative AI model to generate projects and tasks suitable for the user. Here, the profile information serves as input data, and the generated personalized projects are the output. Specifically, the generative AI model analyzes the profile information and performs a generation process to suggest appropriate learning themes.

[0360] Step 3:

[0361] The terminal presents the generated project to the user and accepts the user's action to start the project. The user's action triggers the display of project details. Project information is sent as input, and the output is the project content presented to the user.

[0362] Step 4:

[0363] During project execution, the device uses its camera and microphone to input the user's facial expressions and voice into the emotion analysis system. Specifically, real-time audio and video data is collected and becomes the input data. The output is information about the user's emotional state obtained through emotion analysis.

[0364] Step 5:

[0365] The server analyzes emotional state information sent from the terminal and generates appropriate feedback for the user using a feedback provision system. The input is emotional state information, and the output is a feedback message tailored to the user's psychological state. A generation AI model is used to perform specific actions that generate encouragement and hints adapted to the situation.

[0366] Step 6:

[0367] The terminal displays feedback received from the server to the user. The input here is the feedback message from the server, and the output is the feedback content displayed on the screen for the user. Specifically, the terminal's display shows the message.

[0368] (Application Example 2)

[0369] 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."

[0370] In children's learning, there is a need to provide an optimal learning experience that is tailored to their emotions and circumstances. However, conventional systems have struggled to recognize users' emotions in real time and adjust feedback accordingly. As a result, there has been a problem in that learners do not receive appropriate support according to their psychological state and difficulties.

[0371] 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.

[0372] In this invention, the server includes a suggestion means for individually proposing projects or tasks, a progress management means for managing progress and providing real-time feedback, and an emotion recognition means for analyzing the user's emotions and providing feedback. This enables learning support tailored to each user's psychological state and skill level.

[0373] "Suggestion method" refers to a function that individually generates and provides projects and assignments tailored to the user's learning needs.

[0374] A "progress management tool" is a function that monitors the user's learning progress in real time and provides appropriate feedback based on that progress.

[0375] "Measurement tools" refer to functions that evaluate users' interests and skill levels, and use that information to appropriately select projects and tasks.

[0376] "Emotion recognition means" refers to a function that analyzes the user's emotions and adjusts the learning content and feedback according to that data.

[0377] An "interactive explanation tool" is a function that detects the user's learning difficulties and provides additional support and supplementary information tailored to that situation.

[0378] This invention is a system designed to enrich children's learning experiences. The system is configured as follows:

[0379] The server includes a proposal mechanism, a progress management mechanism, and an emotion recognition mechanism. The proposal mechanism uses a generative AI model to generate projects and tasks based on the user's profile information and learning history.

[0380] The device uses a high-precision camera and microphone (e.g., a typical high-resolution camera and condenser microphone) to collect the user's facial expressions and voice in real time. This allows emotion recognition to analyze the user's psychological state, and the server uses this information to provide feedback messages and modified learning content.

[0381] For example, if a user shows a confused expression while solving a math problem, the server uses the analyzed emotion data to instruct the device to display an encouraging message and detailed steps for solving the problem.

[0382] Furthermore, the interactive explanation system provides supplementary materials and encouraging messages as needed, according to the user's skill level and progress. This process is designed to maintain the user's motivation to learn and to facilitate understanding.

[0383] Examples of prompts include, "Analyze this child's facial expressions and voice data to recognize their emotions and generate appropriate feedback," and "Generate projects that the child will be interested in. Suggest them based on their profile data."

[0384] This allows the device to constantly provide a learning experience adapted to the user's situation, enabling interactive and effective education tailored to individual needs.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The terminal receives the user's login information and sends the current learning status and interests to the server based on that profile. The input consists of the user's login information and profile data, and based on this, the terminal prepares recommended learning content.

[0388] Step 2:

[0389] The server uses the proposed method to run a generative AI model and analyze the user's profile information and past learning history. The input data consists of profile information and learning history, which the generative AI model processes to generate projects and tasks that are optimal for the user.

[0390] Step 3:

[0391] The generated projects and assignments are sent from the server to the terminal, which then presents them to the user. The output is a list of projects or assignments best suited to the user. The displayed content includes learning objectives and exaggerated background information.

[0392] Step 4:

[0393] While the user is working on a task, the device uses its camera and microphone to collect facial expressions and voice in real time and send them to the server. The input consists of the user's voice and video data, which are then analyzed by emotion recognition technology.

[0394] Step 5:

[0395] The server uses emotion recognition means to analyze the user's emotions from the acquired data. The output is the analyzed emotion data, and the progress management means generates appropriate feedback according to the user's emotional state.

[0396] Step 6:

[0397] The generated feedback is sent to the device and presented to the user. This feedback includes encouraging messages and detailed explanations. The server observes how the user responds to the task and uses this data as input for the next step.

[0398] Step 7:

[0399] Once a user completes a learning assignment, the device records their progress and provides hints and reminders for the next learning session. The output is data that guides the next learning session and is saved as an optimized learning plan through communication with the server.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] This system aims to provide children with personalized learning experiences. Children, as users, first access the system through a terminal and input information related to their interests and skill levels. The server then creates a user profile and, through a generation mechanism, suggests projects or assignments optimized for their interests and skills.

[0417] The generated projects are designed to capture children's interest and provide motivation to continue learning. Once a user starts a project, the device records their progress sequentially and sends it to the server. The server analyzes the data using progress management tools to determine what kind of support is needed at each step.

[0418] Real-time feedback is provided to the user via the device. This feedback includes advice tailored to the problems the user is facing during learning and their progress, as well as suggestions on how to approach the next task. For example, if the user is writing program code, and an error is detected, the device will provide hints and explanations showing the correct way to do it.

[0419] As a concrete example, if a child user is working on the task of "creating a program for robot movements," the device first assesses their current skills through a basic knowledge quiz about robot movements. Next, a specific programming task tailored to their skill level is proposed by a generation mechanism. The user then begins writing the program and records their progress on the device.

[0420] Progress management is performed based on data received by the server, and users receive immediate feedback on their devices regarding any steps they get stuck on or points that need improvement. Embodiments of the present invention effectively support children's independent learning through this process.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The device displays a login screen to the user, prompting them to enter their name, age, and areas of interest.

[0424] Step 2:

[0425] The server receives user information sent from the terminal, stores it in the database, and creates a user profile.

[0426] Step 3:

[0427] The device displays surveys and quizzes to assess the user's interests and skills.

[0428] Step 4:

[0429] Users answer surveys and quizzes and send their answers to the server via their devices.

[0430] Step 5:

[0431] The server analyzes the received response data to evaluate the user's interests and skill level.

[0432] Step 6:

[0433] The server uses a generation mechanism to generate projects or tasks based on the evaluation results and sends a list of proposals to the terminal.

[0434] Step 7:

[0435] The user selects an item of interest from a list of projects or tasks displayed on the device.

[0436] Step 8:

[0437] The terminal notifies the server of the user's selection and displays the details of the selected project.

[0438] Step 9:

[0439] As users progress through a project, they record their progress on their device.

[0440] Step 10:

[0441] The terminal sends recorded progress information to the server, and the server analyzes the data using progress management tools.

[0442] Step 11:

[0443] Based on the progress, the server generates real-time feedback and interactive commentary as needed.

[0444] Step 12:

[0445] The terminal displays feedback from the server to the user and provides any other necessary supplementary information or hints.

[0446] (Example 1)

[0447] 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."

[0448] In today's learning environment, providing individualized learning opportunities tailored to each child is crucial, but traditional education systems have failed to adequately meet this need. In particular, there is a lack of means to immediately propose appropriate projects and assignments based on each child's interests and skill level, and to provide real-time feedback on their progress.

[0449] 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.

[0450] In this invention, the server includes terminal means that provides an individualized learning experience by inputting information related to the user's interests and ability level; generation means that creates a user profile and derives an appropriate project or task based on the profile; progress management means that records the user's progress, analyzes the data, and determines support; and feedback means that provides real-time feedback to the user and suggests specific advice and next steps. This enables effective education tailored to individual learning needs.

[0451] A "terminal device" is a device used by a user to input information related to their interests and skill levels and transmit that information to a server.

[0452] A "generation means" is a device or system that has the function of deriving the optimal project or task based on the user profile.

[0453] A "progress management system" is a system equipped with the function of recording user progress data, analyzing it, and determining what kind of support is needed.

[0454] A "feedback mechanism" is a device or system that provides users with specific advice and next steps in real time based on analyzed data.

[0455] A "user profile" is data that records a user's interests, skill level, and past learning history, and is used to generate appropriate learning projects.

[0456] This invention is a system designed to support the learning of individual children, providing a learning experience tailored to the user's interests and abilities. The system is implemented by users accessing it using a terminal and inputting information. Tablets and laptops are used as terminals, and information is transmitted to the server via a dedicated application or web interface.

[0457] The server uses a database management system to create a user profile based on information received from the user. This profile organizes and stores information about the user's interests and skill level. The server uses this profile and a generative AI model to suggest appropriate learning projects or assignments. A large-scale language model is used as the generative AI model, and assignments are generated based on prompt statements.

[0458] The generated project is sent to the device, and the device records the user's progress while they work on the project. The device sends the progress data to the server, which analyzes it to determine the necessary feedback. Data analysis tools such as Python and R are used for the analysis. The feedback generated by the server is provided to the user in real time through the device to assist in learning.

[0459] As a concrete example, when a primary school student (the user) tackles the task of "developing a simple game," the device first presents a quiz on basic knowledge to evaluate the user's skills. Then, a generative AI model suggests a programming task appropriate to the user's level. As the user creates the program, the device records their progress, and if an error occurs, the server provides real-time feedback on how to resolve it. The objective of this invention is to provide educational support through such a process.

[0460] Example prompt: "Assuming the user's skill level is beginner, generate a simple game development project."

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] Users access the system by opening a dedicated application or web interface using their terminal. Users input their areas of interest and self-assessed skill levels. This information is sent from the terminal to the server and treated as initial input.

[0464] Step 2:

[0465] The server stores information received from users in a database. Using a database management system, data regarding interests and skill levels is structured to create user profiles. The input data consists of areas of interest and skill assessments, and based on this, a profile optimized for the user is generated.

[0466] Step 3:

[0467] The server uses a generative AI model to generate projects or tasks based on the user profile. The prompt "Generate a simple project assuming the user's skill level is beginner" is entered to drive the authoritative generative model. This process generates tasks that are interesting to the user and match their skills, and outputs them to the terminal.

[0468] Step 4:

[0469] The user receives the project presented on the terminal and begins working on it. The terminal continuously records the user's activities and captures user action data. This includes program progress, quiz answers, and activity logs.

[0470] Step 5:

[0471] The terminal periodically sends recorded progress data to the server. The server receives this data and analyzes the progress using data analysis tools such as Python or R. The main purpose of the analysis is to identify where the user is experiencing difficulties. The input data consists of progress frequency and activity logs, and the analysis results are generated based on this data.

[0472] Step 6:

[0473] The server generates feedback based on the results obtained from the analysis. Specifically, it constructs feedback that includes how to correct errors, the next steps in learning, or supplementary advice. This feedback is sent to the terminal and presented to the user in real time.

[0474] Step 7:

[0475] The user receives feedback from the device and continues learning based on it. The feedback provided by the device allows the user to understand their learning progress and decide on their next course of action. Specifically, the user might modify the program according to the feedback and try running it again.

[0476] (Application Example 1)

[0477] 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."

[0478] In today's educational setting, it is crucial to provide learning experiences tailored to each child's interests and skill level. However, uniform teaching methods make it difficult to stimulate individual interests and maximize creative thinking and problem-solving abilities. Furthermore, a challenge exists in that children do not receive adequate support during the learning process due to insufficient opportunities for real-time feedback.

[0479] 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.

[0480] In this invention, the server includes task generation means, progress management means, evaluation means, and user interface means. This enables the suggestion of customized tasks based on individual learning needs and the provision of real-time progress advice.

[0481] A "task generation method" is a mechanism that generates individually customized learning tasks based on a child's interests and skill level.

[0482] A "progress management system" is a mechanism for tracking the progress of learning assignments in real time and providing advice and feedback at the appropriate time.

[0483] An "evaluation tool" is a mechanism that analyzes users' interests and skill levels and uses this information to select and propose appropriate learning tasks.

[0484] A "user interface means" is a mechanism that provides an interface that can be operated by a user through educational equipment, thereby creating an interactive learning environment.

[0485] The system for implementing this invention operates in conjunction with educational equipment and a server. The server uses a generative artificial intelligence model as a task generation means to generate customized learning tasks tailored to the child's interests and skill level. It also includes a progress management means to monitor the user's learning progress in real time and provide advice as needed. An evaluation means analyzes the user's input data and uses it to make appropriate learning suggestions.

[0486] The educational equipment uses Raspberry Pi, enabling interactive interaction with users through a display and voice recognition capabilities. For feedback, the Flask framework in Python is used to visually present progress information. On the server side, a Django server is set up on AWS EC2 for data management and analysis.

[0487] As a concrete example, when a child learns the basic movements of a robot, the server generates a task such as "program the robot to rotate" and presents it to the child via a Raspberry Pi. The child tries out the program through the interface, and their progress data is constantly sent to the server, providing timely and accurate feedback.

[0488] Examples of prompt statements are shown below.

[0489] "Please come up with challenges for a robot dance program that children would find interesting. The robot can learn choreography that involves repeating three basic movements, and will be supported with real-time feedback."

[0490] In this way, we provide a learning environment that makes the challenges users actually face interesting and allows them to acquire practical skills.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The user logs into their device and enters their interests and skill level. This data is used to create the user's profile and is sent from the device to the server. The server receives this data and performs initial profiling.

[0494] Step 2:

[0495] The server analyzes the received interest and skill level data and uses a task generation mechanism to generate the most suitable learning tasks for the user. This uses a generative artificial intelligence model, and candidate projects are sent to the terminal. The tasks are designed to engage the user's interest.

[0496] Step 3:

[0497] The user selects a task presented via their device and begins learning. User actions are performed on the device, and progress is recorded sequentially. This data is also sent to the server.

[0498] Step 4:

[0499] The server analyzes the received progress data using a progress management system. It determines which step the user is encountering problems at and provides advice and hints as needed. This is done in real time, and feedback is sent to the terminal.

[0500] Step 5:

[0501] Users receive feedback and retry the task based on that feedback. This cyclical process reinforces their learning and provides a sense of accomplishment. Progress is continuously monitored, and the server indicates when it's time to move on to the next step.

[0502] This processing flow allows the system to provide users with a learning experience that is individually optimized for their needs.

[0503] 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.

[0504] This invention is a system that combines an emotional engine to enrich children's learning experiences. When a child user accesses this system, the terminal first displays a profile based on their login information and performs initial setup. Based on this profile information, the server uses a generation mechanism to generate projects and tasks that are suitable for the user's interests and skill level.

[0505] While the user selects and runs a project, an emotion engine recognizes the user's emotions in real time through the device's camera and microphone. The server analyzes this emotion data and combines it with progress management tools to generate feedback tailored to the user's psychological state and emotions. For example, if the user shows a confused expression, the emotion engine recognizes this emotion, and the server provides encouraging messages or hints as needed.

[0506] Emotional data is also used to assess user interests and skills, providing valuable information when selecting projects based on the user's state. Interactive explanations also adapt to the user's emotions. For example, if the device detects a lack of confidence in an answer, it can provide more detailed explanations and display additional materials to aid understanding.

[0507] For example, if a user is working on a math puzzle, the server identifies where the user is getting stuck based on their progress and sentiment data. The device then provides a detailed explanation of the specific steps needed to solve the problem, working to facilitate understanding.

[0508] Thus, this form of invention, which combines an emotion engine, grasps the learner's emotions in real time and provides an optimal learning environment tailored to their individual needs and psychological state.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The terminal displays a login screen to the user, who then enters the required information.

[0512] Step 2:

[0513] The server receives login information sent from the terminal, retrieves the user's profile from the database, and performs the initial setup.

[0514] Step 3:

[0515] The server uses a generation mechanism to create an optimal project or assignment based on the user's interests and skill level, and provides it to the terminal.

[0516] Step 4:

[0517] The user selects the project or task they want to do from those displayed on the device.

[0518] Step 5:

[0519] While the user works on a project of their choice, the device uses its camera and microphone to recognize the user's emotions in real time through its emotion engine.

[0520] Step 6:

[0521] The user's emotional data, obtained from the emotion engine, is sent to the server via the terminal.

[0522] Step 7:

[0523] The server combines and analyzes emotional data and progress data to generate feedback that corresponds to the user's psychological state and current emotions.

[0524] Step 8:

[0525] The device displays messages and hints to the user at the appropriate time based on feedback from the server.

[0526] Step 9:

[0527] We will integrate emotional data with user skill and interest assessments and use it to select the next project or task.

[0528] Step 10:

[0529] The device continuously records the user's progress and emotional changes, and sends this data to the server to further optimize the learning experience.

[0530] (Example 2)

[0531] 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."

[0532] Traditional learning systems struggle to grasp the emotional and psychological states of individual learners in real time and provide appropriate feedback accordingly. This presents a challenge: they may fail to adequately support learners when they face difficulties, potentially undermining their motivation to learn.

[0533] 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.

[0534] In this invention, the server includes generation means for generating personalized projects or tasks using learner profile information, emotion analysis means for recognizing and collecting the learner's emotional state in real time during execution, and feedback provision means for providing feedback that corresponds to the learner's psychological state and emotions based on the results of the emotion analysis. This enables optimal feedback and support that meets the individual needs of the learner.

[0535] "Learner profile information" refers to information about individual learners, including their past learning history, skill level, and interests.

[0536] "Generative means" refers to methods and techniques for creating individualized projects or assignments using learner profile information.

[0537] "Emotional analysis methods" refer to techniques and technologies for recognizing and analyzing a learner's emotional state in real time from their facial expressions, voice, and other factors.

[0538] "Feedback provision methods" refer to techniques and technologies for providing feedback tailored to the learner's psychological state and emotions, based on the results of an emotional analysis of the learner.

[0539] "Interactive explanations" refer to explanations and descriptions that dynamically change according to the learner's progress and emotional state.

[0540] "Visual cues" are clues or advice provided in a visual form, such as images or videos, to help learners understand more easily.

[0541] A "generative AI model" is a machine learning model designed for tasks such as natural language processing and data generation.

[0542] This invention is a system that improves the learning experience by understanding learners' emotions in real time and proposing personalized learning projects and tasks based on those emotions.

[0543] The server receives the learner's login information and retrieves profile information from the database system. This profile information includes past learning history, skill level, and interests. The server uses a generative AI model to generate projects and assignments tailored to the learner's profile. Natural language processing models and data generation models are used in this generation process. The generated projects are sent to the terminal via the network.

[0544] The device uses its built-in camera and microphone to collect facial expressions and voice while the learner is working on a project, and analyzes this data using emotion analysis tools. Based on the analyzed emotion data, the server provides the learner with the most appropriate feedback. This feedback is displayed on the device as encouraging messages and specific advice. Generative AI models are also utilized in this process.

[0545] For example, if a learner shows a confused expression while working on a math problem, the device can provide a visual guide along with a hint such as "Let's review the calculation steps." In this way, it is possible to provide an environment where learners can concentrate on their studies with peace of mind.

[0546] An example of a prompt for a generative AI model might be: "The user is attempting a complex math puzzle. They seem unsure of the solution. Generate encouraging messages and hints to support the user." Based on this prompt, appropriate feedback is generated, enabling efficient learning.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] When a user logs into the system, the server retrieves profile information from the database based on their authentication credentials. This process takes the user's login credentials as input and retrieves their past learning history and skill level as output. Specifically, it queries the user database and extracts the corresponding user's records.

[0550] Step 2:

[0551] Based on the acquired profile information, the server utilizes a generative AI model to generate projects and tasks suitable for the user. Here, the profile information serves as input data, and the generated personalized projects are the output. Specifically, the generative AI model analyzes the profile information and performs a generation process to suggest appropriate learning themes.

[0552] Step 3:

[0553] The terminal presents the generated project to the user and accepts the user's action to start the project. The user's action triggers the display of project details. Project information is sent as input, and the output is the project content presented to the user.

[0554] Step 4:

[0555] During project execution, the device uses its camera and microphone to input the user's facial expressions and voice into the emotion analysis system. Specifically, real-time audio and video data is collected and becomes the input data. The output is information about the user's emotional state obtained through emotion analysis.

[0556] Step 5:

[0557] The server analyzes emotional state information sent from the terminal and generates appropriate feedback for the user using a feedback provision system. The input is emotional state information, and the output is a feedback message tailored to the user's psychological state. A generation AI model is used to perform specific actions that generate encouragement and hints adapted to the situation.

[0558] Step 6:

[0559] The terminal displays feedback received from the server to the user. The input here is the feedback message from the server, and the output is the feedback content displayed on the screen for the user. Specifically, the terminal's display shows the message.

[0560] (Application Example 2)

[0561] 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."

[0562] In children's learning, there is a need to provide an optimal learning experience that is tailored to their emotions and circumstances. However, conventional systems have struggled to recognize users' emotions in real time and adjust feedback accordingly. As a result, there has been a problem in that learners do not receive appropriate support according to their psychological state and difficulties.

[0563] 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.

[0564] In this invention, the server includes a suggestion means for individually proposing projects or tasks, a progress management means for managing progress and providing real-time feedback, and an emotion recognition means for analyzing the user's emotions and providing feedback. This enables learning support tailored to each user's psychological state and skill level.

[0565] "Suggestion method" refers to a function that individually generates and provides projects and assignments tailored to the user's learning needs.

[0566] A "progress management tool" is a function that monitors the user's learning progress in real time and provides appropriate feedback based on that progress.

[0567] "Measurement tools" refer to functions that evaluate users' interests and skill levels, and use that information to appropriately select projects and tasks.

[0568] "Emotion recognition means" refers to a function that analyzes the user's emotions and adjusts the learning content and feedback according to that data.

[0569] An "interactive explanation tool" is a function that detects the user's learning difficulties and provides additional support and supplementary information tailored to that situation.

[0570] This invention is a system designed to enrich children's learning experiences. The system is configured as follows:

[0571] The server includes a proposal mechanism, a progress management mechanism, and an emotion recognition mechanism. The proposal mechanism uses a generative AI model to generate projects and tasks based on the user's profile information and learning history.

[0572] The device uses a high-precision camera and microphone (e.g., a typical high-resolution camera and condenser microphone) to collect the user's facial expressions and voice in real time. This allows emotion recognition to analyze the user's psychological state, and the server uses this information to provide feedback messages and modified learning content.

[0573] For example, if a user shows a confused expression while solving a math problem, the server uses the analyzed emotion data to instruct the device to display an encouraging message and detailed steps for solving the problem.

[0574] Furthermore, the interactive explanation system provides supplementary materials and encouraging messages as needed, according to the user's skill level and progress. This process is designed to maintain the user's motivation to learn and to facilitate understanding.

[0575] Examples of prompts include, "Analyze this child's facial expressions and voice data to recognize their emotions and generate appropriate feedback," and "Generate projects that the child will be interested in. Suggest them based on their profile data."

[0576] This allows the device to constantly provide a learning experience adapted to the user's situation, enabling interactive and effective education tailored to individual needs.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The terminal receives the user's login information and sends the current learning status and interests to the server based on that profile. The input consists of the user's login information and profile data, and based on this, the terminal prepares recommended learning content.

[0580] Step 2:

[0581] The server uses the proposed method to run a generative AI model and analyze the user's profile information and past learning history. The input data consists of profile information and learning history, which the generative AI model processes to generate projects and tasks that are optimal for the user.

[0582] Step 3:

[0583] The generated projects and assignments are sent from the server to the terminal, which then presents them to the user. The output is a list of projects or assignments best suited to the user. The displayed content includes learning objectives and exaggerated background information.

[0584] Step 4:

[0585] While the user is working on a task, the device uses its camera and microphone to collect facial expressions and voice in real time and send them to the server. The input consists of the user's voice and video data, which are then analyzed by an emotion recognition system.

[0586] Step 5:

[0587] The server uses emotion recognition to analyze the user's emotions from the acquired data. The output is the analyzed emotion data, and the progress management system generates appropriate feedback according to the user's emotional state.

[0588] Step 6:

[0589] The generated feedback is sent to the device and presented to the user. This feedback includes encouraging messages and detailed explanations. The server observes how the user responds to the task and uses this data as input for the next step.

[0590] Step 7:

[0591] Once a user completes a learning assignment, the device records their progress and provides hints and reminders for the next learning session. The output is data that guides the next learning session and is saved as an optimized learning plan through communication with the server.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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".

[0609] This system aims to provide children with personalized learning experiences. Children, as users, first access the system through a terminal and input information related to their interests and skill levels. The server then creates a user profile and, through a generation mechanism, suggests projects or assignments optimized for their interests and skills.

[0610] The generated projects are designed to capture children's interest and provide motivation to continue learning. Once a user starts a project, the device records their progress sequentially and sends it to the server. The server analyzes the data using progress management tools to determine what kind of support is needed at each step.

[0611] Real-time feedback is provided to the user via the device. This feedback includes advice tailored to the problems the user is facing during learning and their progress, as well as suggestions on how to approach the next task. For example, if the user is writing program code, and an error is detected, the device will provide hints and explanations showing the correct way to do it.

[0612] As a concrete example, if a child user is working on the task of "creating a program for robot movements," the device first assesses their current skills through a basic knowledge quiz about robot movements. Next, a specific programming task tailored to their skill level is proposed by a generation mechanism. The user then begins writing the program and records their progress on the device.

[0613] Progress management is performed based on data received by the server, and users receive immediate feedback on their devices regarding any steps they get stuck on or points that need improvement. Embodiments of the present invention effectively support children's independent learning through this process.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The device displays a login screen to the user, prompting them to enter their name, age, and areas of interest.

[0617] Step 2:

[0618] The server receives user information sent from the terminal, stores it in the database, and creates a user profile.

[0619] Step 3:

[0620] The device displays surveys and quizzes to assess the user's interests and skills.

[0621] Step 4:

[0622] Users answer surveys and quizzes and send their answers to the server via their devices.

[0623] Step 5:

[0624] The server analyzes the received response data to evaluate the user's interests and skill level.

[0625] Step 6:

[0626] The server uses a generation mechanism to generate projects or tasks based on the evaluation results and sends a list of proposals to the terminal.

[0627] Step 7:

[0628] The user selects an item of interest from a list of projects or tasks displayed on the device.

[0629] Step 8:

[0630] The terminal notifies the server of the user's selection and displays the details of the selected project.

[0631] Step 9:

[0632] As users progress through a project, they record their progress on their device.

[0633] Step 10:

[0634] The terminal sends recorded progress information to the server, and the server analyzes the data using progress management tools.

[0635] Step 11:

[0636] Based on the progress, the server generates real-time feedback and interactive commentary as needed.

[0637] Step 12:

[0638] The terminal displays feedback from the server to the user and provides any other necessary supplementary information or hints.

[0639] (Example 1)

[0640] 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".

[0641] In today's learning environment, providing individualized learning opportunities tailored to each child is crucial, but traditional education systems have failed to adequately meet this need. In particular, there is a lack of means to immediately propose appropriate projects and assignments based on each child's interests and skill level, and to provide real-time feedback on their progress.

[0642] 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.

[0643] In this invention, the server includes terminal means that provides an individualized learning experience by inputting information related to the user's interests and ability level; generation means that creates a user profile and derives an appropriate project or task based on the profile; progress management means that records the user's progress, analyzes the data, and determines support; and feedback means that provides real-time feedback to the user and suggests specific advice and next steps. This enables effective education tailored to individual learning needs.

[0644] A "terminal device" is a device used by a user to input information related to their interests and skill levels and transmit that information to a server.

[0645] A "generation means" is a device or system that has the function of deriving the optimal project or task based on the user profile.

[0646] A "progress management system" is a system equipped with the function of recording user progress data, analyzing it, and determining what kind of support is needed.

[0647] A "feedback mechanism" is a device or system that provides users with specific advice and next steps in real time based on analyzed data.

[0648] A "user profile" is data that records a user's interests, skill level, and past learning history, and is used to generate appropriate learning projects.

[0649] This invention is a system designed to support the learning of individual children, providing a learning experience tailored to the user's interests and abilities. The system is implemented by users accessing it using a terminal and inputting information. Tablets and laptops are used as terminals, and information is transmitted to the server via a dedicated application or web interface.

[0650] The server uses a database management system to create a user profile based on information received from the user. This profile organizes and stores information about the user's interests and skill level. The server uses this profile and a generative AI model to suggest appropriate learning projects or assignments. A large-scale language model is used as the generative AI model, and assignments are generated based on prompt statements.

[0651] The generated project is sent to the device, and the device records the user's progress while they work on the project. The device sends the progress data to the server, which analyzes it to determine the necessary feedback. Data analysis tools such as Python and R are used for the analysis. The feedback generated by the server is provided to the user in real time through the device to assist in learning.

[0652] As a concrete example, when a primary school student (the user) tackles the task of "developing a simple game," the device first presents a quiz on basic knowledge to evaluate the user's skills. Then, a generative AI model suggests a programming task appropriate to the user's level. As the user creates the program, the device records their progress, and if an error occurs, the server provides real-time feedback on how to resolve it. The objective of this invention is to provide educational support through such a process.

[0653] Example prompt: "Assuming the user's skill level is beginner, generate a simple game development project."

[0654] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0655] Step 1:

[0656] Users access the system by opening a dedicated application or web interface using their terminal. Users input their areas of interest and self-assessed skill levels. This information is sent from the terminal to the server and treated as initial input.

[0657] Step 2:

[0658] The server stores information received from users in a database. Using a database management system, data regarding interests and skill levels is structured to create user profiles. The input data consists of areas of interest and skill assessments, and based on this, a profile optimized for the user is generated.

[0659] Step 3:

[0660] The server uses a generative AI model to generate projects or tasks based on the user profile. The prompt "Generate a simple project assuming the user's skill level is beginner" is entered to drive the authoritative generative model. This process generates tasks that are interesting to the user and match their skills, and outputs them to the terminal.

[0661] Step 4:

[0662] The user receives the project presented on the terminal and begins working on it. The terminal continuously records the user's activities and captures user action data. This includes program progress, quiz answers, and activity logs.

[0663] Step 5:

[0664] The terminal periodically sends recorded progress data to the server. The server receives this data and analyzes the progress using data analysis tools such as Python or R. The main purpose of the analysis is to identify where the user is experiencing difficulties. The input data consists of progress frequency and activity logs, and the analysis results are generated based on this data.

[0665] Step 6:

[0666] The server generates feedback based on the results obtained from the analysis. Specifically, it constructs feedback that includes how to correct errors, the next steps in learning, or supplementary advice. This feedback is sent to the terminal and presented to the user in real time.

[0667] Step 7:

[0668] The user receives feedback from the device and continues learning based on it. The feedback provided by the device allows the user to understand their learning progress and decide on their next course of action. Specifically, the user might modify the program according to the feedback and try running it again.

[0669] (Application Example 1)

[0670] 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".

[0671] In today's educational setting, it is crucial to provide learning experiences tailored to each child's interests and skill level. However, uniform teaching methods make it difficult to stimulate individual interests and maximize creative thinking and problem-solving abilities. Furthermore, a challenge exists in that children do not receive adequate support during the learning process due to insufficient opportunities for real-time feedback.

[0672] 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.

[0673] In this invention, the server includes task generation means, progress management means, evaluation means, and user interface means. This enables the suggestion of customized tasks based on individual learning needs and the provision of real-time progress advice.

[0674] A "task generation method" is a mechanism that generates individually customized learning tasks based on a child's interests and skill level.

[0675] A "progress management system" is a mechanism for tracking the progress of learning assignments in real time and providing advice and feedback at the appropriate time.

[0676] An "evaluation tool" is a mechanism that analyzes users' interests and skill levels and uses this information to select and propose appropriate learning tasks.

[0677] A "user interface means" is a mechanism that provides an interface that can be operated by a user through educational equipment, thereby creating an interactive learning environment.

[0678] The system for implementing this invention operates in conjunction with educational equipment and a server. The server uses a generative artificial intelligence model as a task generation means to generate customized learning tasks tailored to the child's interests and skill level. It also includes a progress management means to monitor the user's learning progress in real time and provide advice as needed. An evaluation means analyzes the user's input data and uses it to make appropriate learning suggestions.

[0679] The educational equipment uses Raspberry Pi, enabling interactive interaction with users through a display and voice recognition capabilities. For feedback, the Flask framework in Python is used to visually present progress information. On the server side, a Django server is set up on AWS EC2 for data management and analysis.

[0680] As a concrete example, when a child learns the basic movements of a robot, the server generates a task such as "program the robot to rotate" and presents it to the child via a Raspberry Pi. The child tries out the program through the interface, and their progress data is constantly sent to the server, providing timely and accurate feedback.

[0681] Examples of prompt statements are shown below.

[0682] "Please come up with challenges for a robot dance program that children would find interesting. The robot can learn choreography that involves repeating three basic movements, and will be supported with real-time feedback."

[0683] In this way, we provide a learning environment that makes the challenges users actually face interesting and allows them to acquire practical skills.

[0684] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0685] Step 1:

[0686] The user logs into their device and enters their interests and skill level. This data is used to create the user's profile and is sent from the device to the server. The server receives this data and performs initial profiling.

[0687] Step 2:

[0688] The server analyzes the received interest and skill level data and uses a task generation mechanism to generate the most suitable learning tasks for the user. This uses a generative artificial intelligence model, and candidate projects are sent to the terminal. The tasks are designed to engage the user's interest.

[0689] Step 3:

[0690] The user selects a task presented via their device and begins learning. User actions are performed on the device, and progress is recorded sequentially. This data is also sent to the server.

[0691] Step 4:

[0692] The server analyzes the received progress data using a progress management system. It determines which step the user is encountering problems at and provides advice and hints as needed. This is done in real time, and feedback is sent to the terminal.

[0693] Step 5:

[0694] Users receive feedback and retry the task based on that feedback. This cyclical process reinforces their learning and provides a sense of accomplishment. Progress is continuously monitored, and the server indicates when it's time to move on to the next step.

[0695] This processing flow allows the system to provide users with a learning experience that is individually optimized for their needs.

[0696] 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.

[0697] This invention is a system that combines an emotional engine to enrich children's learning experiences. When a child user accesses this system, the terminal first displays a profile based on their login information and performs initial setup. Based on this profile information, the server uses a generation mechanism to generate projects and tasks that are suitable for the user's interests and skill level.

[0698] While the user selects and runs a project, an emotion engine recognizes the user's emotions in real time through the device's camera and microphone. The server analyzes this emotion data and combines it with progress management tools to generate feedback tailored to the user's psychological state and emotions. For example, if the user shows a confused expression, the emotion engine recognizes this emotion, and the server provides encouraging messages or hints as needed.

[0699] Emotional data is also used to assess user interests and skills, providing valuable information when selecting projects based on the user's state. Interactive explanations also adapt to the user's emotions. For example, if the device detects a lack of confidence in an answer, it can provide more detailed explanations and display additional materials to aid understanding.

[0700] For example, if a user is working on a math puzzle, the server identifies where the user is getting stuck based on their progress and sentiment data. The device then provides a detailed explanation of the specific steps needed to solve the problem, working to facilitate understanding.

[0701] Thus, this form of invention, which combines an emotion engine, grasps the learner's emotions in real time and provides an optimal learning environment tailored to their individual needs and psychological state.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The terminal displays a login screen to the user, who then enters the required information.

[0705] Step 2:

[0706] The server receives login information sent from the terminal, retrieves the user's profile from the database, and performs the initial setup.

[0707] Step 3:

[0708] The server uses a generation mechanism to create an optimal project or assignment based on the user's interests and skill level, and provides it to the terminal.

[0709] Step 4:

[0710] The user selects the project or task they want to do from those displayed on the device.

[0711] Step 5:

[0712] While the user works on a project of their choice, the device uses its camera and microphone to recognize the user's emotions in real time through its emotion engine.

[0713] Step 6:

[0714] The user's emotional data, obtained from the emotion engine, is sent to the server via the terminal.

[0715] Step 7:

[0716] The server combines and analyzes emotional data and progress data to generate feedback that corresponds to the user's psychological state and current emotions.

[0717] Step 8:

[0718] The device displays messages and hints to the user at the appropriate time based on feedback from the server.

[0719] Step 9:

[0720] We will integrate emotional data with user skill and interest assessments and use it to select the next project or task.

[0721] Step 10:

[0722] The device continuously records the user's progress and emotional changes, and sends this data to the server to further optimize the learning experience.

[0723] (Example 2)

[0724] 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".

[0725] Traditional learning systems struggle to grasp the emotional and psychological states of individual learners in real time and provide appropriate feedback accordingly. This presents a challenge: they may fail to adequately support learners when they face difficulties, potentially undermining their motivation to learn.

[0726] 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.

[0727] In this invention, the server includes generation means for generating personalized projects or tasks using learner profile information, emotion analysis means for recognizing and collecting the learner's emotional state in real time during execution, and feedback provision means for providing feedback that corresponds to the learner's psychological state and emotions based on the results of the emotion analysis. This enables optimal feedback and support that meets the individual needs of the learner.

[0728] "Learner profile information" refers to information about individual learners, including their past learning history, skill level, and interests.

[0729] "Generative means" refers to methods and techniques for creating individualized projects or assignments using learner profile information.

[0730] "Emotional analysis methods" refer to techniques and technologies for recognizing and analyzing a learner's emotional state in real time from their facial expressions, voice, and other factors.

[0731] "Feedback provision methods" refer to techniques and technologies for providing feedback tailored to the learner's psychological state and emotions, based on the results of an emotional analysis of the learner.

[0732] "Interactive explanations" refer to explanations and descriptions that dynamically change according to the learner's progress and emotional state.

[0733] "Visual cues" are clues or advice provided in a visual form, such as images or videos, to help learners understand more easily.

[0734] A "generative AI model" is a machine learning model designed for tasks such as natural language processing and data generation.

[0735] This invention is a system that improves the learning experience by understanding learners' emotions in real time and proposing personalized learning projects and tasks based on those emotions.

[0736] The server receives the learner's login information and retrieves profile information from the database system. This profile information includes past learning history, skill level, and interests. The server uses a generative AI model to generate projects and assignments tailored to the learner's profile. Natural language processing models and data generation models are used in this generation process. The generated projects are sent to the terminal via the network.

[0737] The device uses its built-in camera and microphone to collect facial expressions and voice while the learner is working on a project, and analyzes this data using emotion analysis tools. Based on the analyzed emotion data, the server provides the learner with the most appropriate feedback. This feedback is displayed on the device as encouraging messages and specific advice. Generative AI models are also utilized in this process.

[0738] For example, if a learner shows a confused expression while working on a math problem, the device can provide a visual guide along with a hint such as "Let's review the calculation steps." In this way, it is possible to provide an environment where learners can concentrate on their studies with peace of mind.

[0739] An example of a prompt for a generative AI model might be: "The user is attempting a complex math puzzle. They seem unsure of the solution. Generate encouraging messages and hints to support the user." Based on this prompt, appropriate feedback is generated, enabling efficient learning.

[0740] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0741] Step 1:

[0742] When a user logs into the system, the server retrieves profile information from the database based on their authentication credentials. This process takes the user's login credentials as input and retrieves their past learning history and skill level as output. Specifically, it queries the user database and extracts the corresponding user's records.

[0743] Step 2:

[0744] Based on the acquired profile information, the server utilizes a generative AI model to generate projects and tasks suitable for the user. Here, the profile information serves as input data, and the generated personalized projects are the output. Specifically, the generative AI model analyzes the profile information and performs a generation process to suggest appropriate learning themes.

[0745] Step 3:

[0746] The terminal presents the generated project to the user and accepts the user's action to start the project. The user's action triggers the display of project details. Project information is sent as input, and the output is the project content presented to the user.

[0747] Step 4:

[0748] During project execution, the device uses its camera and microphone to input the user's facial expressions and voice into the emotion analysis system. Specifically, real-time audio and video data is collected and becomes the input data. The output is information about the user's emotional state obtained through emotion analysis.

[0749] Step 5:

[0750] The server analyzes emotional state information sent from the terminal and generates appropriate feedback for the user using a feedback provision system. The input is emotional state information, and the output is a feedback message tailored to the user's psychological state. A generation AI model is used to perform specific actions that generate encouragement and hints adapted to the situation.

[0751] Step 6:

[0752] The terminal displays feedback received from the server to the user. The input here is the feedback message from the server, and the output is the feedback content displayed on the screen for the user. Specifically, the terminal's display shows the message.

[0753] (Application Example 2)

[0754] 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".

[0755] In children's learning, there is a need to provide an optimal learning experience that is tailored to their emotions and circumstances. However, conventional systems have struggled to recognize users' emotions in real time and adjust feedback accordingly. As a result, there has been a problem in that learners do not receive appropriate support according to their psychological state and difficulties.

[0756] 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.

[0757] In this invention, the server includes a suggestion means for individually proposing projects or tasks, a progress management means for managing progress and providing real-time feedback, and an emotion recognition means for analyzing the user's emotions and providing feedback. This enables learning support tailored to each user's psychological state and skill level.

[0758] "Suggestion method" refers to a function that individually generates and provides projects and assignments tailored to the user's learning needs.

[0759] A "progress management tool" is a function that monitors the user's learning progress in real time and provides appropriate feedback based on that progress.

[0760] "Measurement tools" refer to functions that evaluate users' interests and skill levels, and use that information to appropriately select projects and tasks.

[0761] "Emotion recognition means" refers to a function that analyzes the user's emotions and adjusts the learning content and feedback according to that data.

[0762] An "interactive explanation tool" is a function that detects the user's learning difficulties and provides additional support and supplementary information tailored to that situation.

[0763] This invention is a system designed to enrich children's learning experiences. The system is configured as follows:

[0764] The server includes a proposal mechanism, a progress management mechanism, and an emotion recognition mechanism. The proposal mechanism uses a generative AI model to generate projects and tasks based on the user's profile information and learning history.

[0765] The device uses a high-precision camera and microphone (e.g., a typical high-resolution camera and condenser microphone) to collect the user's facial expressions and voice in real time. This allows emotion recognition to analyze the user's psychological state, and the server uses this information to provide feedback messages and modified learning content.

[0766] For example, if a user shows a confused expression while solving a math problem, the server uses the analyzed emotion data to instruct the device to display an encouraging message and detailed steps for solving the problem.

[0767] Furthermore, the interactive explanation system provides supplementary materials and encouraging messages as needed, according to the user's skill level and progress. This process is designed to maintain the user's motivation to learn and to facilitate understanding.

[0768] Examples of prompts include, "Analyze this child's facial expressions and voice data to recognize their emotions and generate appropriate feedback," and "Generate projects that the child will be interested in. Suggest them based on their profile data."

[0769] This allows the device to constantly provide a learning experience adapted to the user's situation, enabling interactive and effective education tailored to individual needs.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The terminal receives the user's login information and sends the current learning status and interests to the server based on that profile. The input consists of the user's login information and profile data, and based on this, the terminal prepares recommended learning content.

[0773] Step 2:

[0774] The server uses the proposed method to run a generative AI model and analyze the user's profile information and past learning history. The input data consists of profile information and learning history, which the generative AI model processes to generate projects and tasks that are optimal for the user.

[0775] Step 3:

[0776] The generated projects and assignments are sent from the server to the terminal, which then presents them to the user. The output is a list of projects or assignments best suited to the user. The displayed content includes learning objectives and exaggerated background information.

[0777] Step 4:

[0778] While the user is working on a task, the device uses its camera and microphone to collect facial expressions and voice in real time and send them to the server. The input consists of the user's voice and video data, which are then analyzed by an emotion recognition system.

[0779] Step 5:

[0780] The server uses emotion recognition to analyze the user's emotions from the acquired data. The output is the analyzed emotion data, and the progress management system generates appropriate feedback according to the user's emotional state.

[0781] Step 6:

[0782] The generated feedback is sent to the device and presented to the user. This feedback includes encouraging messages and detailed explanations. The server observes how the user responds to the task and uses this data as input for the next step.

[0783] Step 7:

[0784] Once a user completes a learning assignment, the device records their progress and provides hints and reminders for the next learning session. The output is data that guides the next learning session and is saved as an optimized learning plan through communication with the server.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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."

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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 to be incorporated by reference.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A generator for individually proposing projects or tasks that stimulate children's interest in science, technology, engineering, and mathematics, and that foster creativity and problem-solving skills in their learning.

[0809] A progress management system that manages the progress of a proposed project or task and provides real-time feedback based on the progress,

[0810] An evaluation method that assesses users' interests and skill levels and uses this information to select projects or tasks,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, comprising explanatory means that provide feedback and interactive explanations based on the user's progress.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the generation means for generating projects or tasks optimized for the user's interests and skill level uses a generation model.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A terminal device that provides an individualized learning experience by inputting information related to the user's interests and skill level,

[0819] A generation means for creating a user profile and deriving an appropriate project or task based on that profile,

[0820] A progress management system that records the user's progress, analyzes the data, and determines whether to provide support.

[0821] A feedback mechanism that provides real-time feedback to users, offering specific advice and suggesting the next steps,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising means for performing analysis based on progress data and providing support to the user.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the generation means for generating projects or tasks optimized for the user's interests and skill level uses a generation model.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] A task generation method that proposes individual tasks to stimulate children's interest in academic content and cultivate their creative thinking and problem-solving abilities in their learning,

[0830] A progress management system that manages the progress of proposed tasks and provides real-time advice based on the progress status,

[0831] An evaluation method that assesses user interest and technical level and uses it to select challenges,

[0832] A user interface means that can be operated by the user through educational equipment, and a feedback means that can create an interactive learning environment,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising an explanatory means that provides feedback and interactive explanations based on the user's progress.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the task generation means for generating tasks optimized to the user's interests and skill level uses a generation artificial intelligence model.

[0838] "Example 2 of combining an emotion engine"

[0839] (Claim 1)

[0840] In children's learning, a generation method for generating individualized projects or assignments using learner profile information,

[0841] A sentiment analysis means for recognizing and collecting learners' emotional states in real time while they are performing a generated project or assignment,

[0842] A feedback provision method that provides feedback tailored to the learner's psychological state and emotions based on the results of emotion analysis,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, comprising an explanatory means that provides interactive explanations and visual hints based on the learner's emotional state.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the emotion analysis means analyzes the emotional state of a learner using a database system and a generative AI model.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A proposal method for individually suggesting projects or tasks to stimulate children's interest in science, technology, engineering, and mathematics, and to cultivate their creativity and problem-solving abilities in their learning.

[0851] A progress management system that manages the progress of a proposed project or task and provides real-time feedback based on the progress,

[0852] Measurement methods to evaluate users' interests and skill levels and use them in selecting projects or tasks,

[0853] An emotion recognition means for analyzing user emotions and providing feedback according to the emotion data,

[0854] An interactive explanatory means that provides supplementary information when a user encounters difficulties in learning tasks,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the proposal means for generating projects or tasks optimized for the user's interests and skill level uses a generation model.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein an emotion recognition means and an interactive explanation means work together to perform learning adjustments according to the user's psychological state. [Explanation of Symbols]

[0860] 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 task generation method that proposes individual tasks to stimulate children's interest in academic content and cultivate their creative thinking and problem-solving abilities in their learning, A progress management system that manages the progress of proposed tasks and provides real-time advice based on the progress status, An evaluation method that assesses user interest and technical level and uses it to select challenges, A user interface means that can be operated by the user through educational equipment, and a feedback means that can create an interactive learning environment, A system that includes this.

2. The system according to claim 1, comprising an explanatory means that provides feedback and interactive explanations based on the user's progress.

3. The system according to claim 1, wherein the task generation means for generating tasks optimized to the user's interests and skill level uses a generation artificial intelligence model.

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