system

Abstract

We provide the system. [Solution] Methods for obtaining user learning profile information, A means for setting learning objectives and generating a learning plan to achieve them, based on acquired learning profile information. A means of providing appropriate learning content according to the generated learning plan, A means for monitoring the user's learning progress and generating feedback based on that progress, Means for adjusting learning plans and content in response to generated feedback, A means of regularly notifying parents and reporting on progress and the next steps, 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 as a 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] Existing learning systems have a problem in that they do not draw out children's true learning motivation and curiosity by focusing on the progress of learning on obtaining scores and levels. In particular, children in the lower grades tend to lose the joy of learning itself and have a problem that the learning effect decreases because they cannot maintain their interest.

Means for Solving the Problems

[0005] This invention uses each user's learning profile information as a starting point to set individual learning goals and generate a learning plan to achieve those goals. By providing learning content in various formats based on the resulting plan, it aims to capture the user's interest. It monitors learning progress, generates feedback through AI analysis, and adjusts the learning plan and content accordingly. Furthermore, by building a system that regularly reports the user's learning progress and next steps to parents, it enables the development of a learning environment that fosters enjoyment and creativity without being solely focused on scores.

[0006] "User learning profile information" refers to individual data about the user, including their age, areas of learning interest, strengths and weaknesses in learning, and desired learning pace.

[0007] "Learning objectives" are the final knowledge and skill goals that users should achieve through specific learning activities or processes.

[0008] A "learning plan" is a plan that outlines specific learning content and steps in chronological order in order to achieve set learning objectives.

[0009] "Learning content" refers to educational materials and activities provided to users to facilitate their learning, and is a collection of information presented in different formats.

[0010] "Learning progress information" refers to data that shows the status of a user's learning activities, including the progress of learning, the steps achieved, the accuracy rate, and the level of understanding.

[0011] "Feedback" refers to instructions and information generated based on the user's learning progress to indicate areas for improvement and the next steps to take.

[0012] "AI analysis" is a process that uses artificial intelligence technology to analyze a user's learning progress and derive results tailored to their individual learning needs. [Brief explanation of the drawing]

[0013] [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]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that acquires user learning profile information, sets learning objectives, generates a learning plan based on those objectives, and provides learning content. This system consists of a server, a terminal, and the user.

[0035] First, the user inputs the child's individual learning profile information through the device. This information includes elements such as the child's age, areas of interest, strengths and weaknesses, and desired learning pace. The device then sends this information to the server.

[0036] Based on learning profile information obtained from users, the server uses AI technology to set learning objectives tailored to each child and automatically generates a learning plan to achieve them. This learning plan consists of specific learning content and steps to learn it, and is provided in various formats to engage the user's interest. For example, it incorporates features that make learning fun for children, such as game formats and interactive video content.

[0037] Users engage with the learning content presented on their devices, and learning progress information obtained during this process is continuously transmitted from the device to the server. Importantly, this progress information includes detailed records of what the child understands and in which areas they are struggling.

[0038] The server uses AI to analyze this progress information and evaluate the child's learning status. Based on the results, it generates feedback on the next steps to take and adjusts the learning content accordingly. This feedback and adjusted content are then provided to the user again via the device.

[0039] Furthermore, the server sends regular notifications to parents, reporting on the child's learning progress and next steps. This makes it easier for parents to understand their child's learning situation and provide appropriate support.

[0040] For example, if a child shows interest in history, a game-based learning plan is generated based on their profile information, allowing them to learn the basics of history in a fun way. If progress information reveals that the child lacks understanding of a particular ancient civilization, the plan is adjusted by suggesting more easily understandable video materials. This allows children to continue learning without getting bored, and parents can keep track of their child's progress at each stage. In this way, the present invention provides a means to advance learning in a fun and effective manner.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses a device to enter the child's learning profile information. This includes age, interests, subjects they excel at and struggle with, and their preferred learning pace. The device then sends this information to the server.

[0044] Step 2:

[0045] The server analyzes the received profile information and uses AI to set individual learning goals. Based on these goals, the server generates a customized learning plan for each user and sends it to the device.

[0046] Step 3:

[0047] The device provides the user with appropriate learning content based on the learning plan received from the server. This content is displayed in game or interactive video formats to easily capture the user's interest.

[0048] Step 4:

[0049] Users engage with learning content presented via their device. The device collects progress information during the learning activity and periodically sends it to the server. This progress information includes steps completed and level of understanding.

[0050] Step 5:

[0051] The server uses AI to analyze the received progress information and evaluate the user's current learning status. If problems are detected, the server suggests the next learning steps to take and generates feedback.

[0052] Step 6:

[0053] The server sends analysis results and feedback to the terminal and adjusts the learning content if necessary. The terminal then presents this information to the user to encourage further learning.

[0054] Step 7:

[0055] The server periodically generates learning progress reports for parents and sends them via email or in-app notifications. These reports include the current learning status, achieved goals, and next steps.

[0056] (Example 1)

[0057] 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."

[0058] Traditional learning support systems have had the problem of difficulty in providing flexible educational plans tailored to the individual learning characteristics of each user. Furthermore, the lack of means to manage feedback on learning progress and appropriately adjust educational resources based on that feedback made efficient learning support difficult. In particular, the challenge was to engage users' interest while addressing their individual learning needs.

[0059] 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.

[0060] In this invention, the server includes means for acquiring user learning characteristics information, means for setting goals and generating an educational plan to achieve those goals based on the acquired learning characteristics information, and means for providing appropriate educational resources according to the generated educational plan. This enables efficient and engaging learning support by providing educational resources and plans tailored to each user's individual learning characteristics.

[0061] "User" refers to an individual who uses an educational system to learn.

[0062] "Learning characteristics information" refers to data that includes information such as an individual's age, areas of interest, subjects they excel at and struggle with, and their learning pace.

[0063] "Goals" refer to the specific learning outcomes that learners should achieve.

[0064] An "educational plan" refers to a plan that details the learning steps and activities necessary to achieve a goal.

[0065] "Educational resources" refers to all educational materials, including learning content, teaching materials, and learning support tools.

[0066] "Providing educational resources" refers to presenting learning content and materials in a format that learners can use.

[0067] "Improvement instructions" refer to feedback and guidance for the next steps provided to learners based on their learning progress.

[0068] "Artificial intelligence analysis" refers to the process where a computer system automatically analyzes collected data.

[0069] "Guardian" refers to a parent or equivalent person who is in a position to support the learner.

[0070] To implement this system, the user must first input learner characteristics information using a terminal. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, learning pace, etc., and is collected using an input form on the terminal and transmitted to the server. Smartphones, tablets, and personal computers can be used as terminals.

[0071] The server receives learning characteristics information sent by the user and stores it in a database. The software used here is built with an AI model using a programming language such as Python, and generates individual learning objectives suitable for the learner. This generative AI model uses a pre-trained algorithm to design the optimal learning plan based on the learner's characteristics.

[0072] The server generates educational resources in multiple formats based on the created learning plan. These resources are presented in various forms, such as games and interactive videos, and are designed to engage learners. This allows learners to progress towards achieving their goals while enjoying the learning process.

[0073] The terminal plays a crucial role in providing educational resources. The terminal receives learning plans and educational resources transmitted from the server and presents them to the learner through user interaction. As the learner utilizes the educational resources, their progress is collected in real time by the terminal and fed back to the server.

[0074] As a concrete example, a user might enter a prompt message such as, "I am a 10-year-old learner who is interested in mathematics, and especially wants to learn the basics of arithmetic." Based on this information, the server creates an educational plan to provide the learner with the basics of mathematics in a game format.

[0075] Furthermore, based on progress information, the server performs AI-powered analysis and adjusts the educational plan and resources accordingly. This ensures that feedback and new learning materials are provided in a timely manner, tailored to the learner's understanding and areas of difficulty.

[0076] Therefore, this system can provide an effective learning environment tailored to the individual needs of each learner.

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

[0078] Step 1:

[0079] The user uses a device to input information about the learner's learning characteristics. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, and learning pace. The entered information is sent to the server via a form on the device. Specifically, the user fills out profile data by answering questions on the device screen and completes the information input by pressing the submit button.

[0080] Step 2:

[0081] The server receives learning characteristics information sent from the terminal and stores it in the database. During this process, the server writes the information to the database and organizes the data by associating it with learner IDs and other relevant information. The input is learning characteristics information from the terminal, and the output is the information stored in the database. Specifically, the server verifies the accuracy of the data and registers it in the database using SQL commands.

[0082] Step 3:

[0083] The server uses a generative AI model to design learning objectives appropriate for the learner based on the received data. The model uses a pre-trained algorithm to analyze learning characteristics information and set optimal objectives. The input is learning characteristics information, and the output is the set learning objectives. Specifically, the AI ​​model uses a Python script to analyze the information and generate the objectives.

[0084] Step 4:

[0085] The server designs a learning plan based on the generated learning objectives. This plan includes educational resources and learning steps. A generative AI model automatically creates the plan and optimizes it using training data. The input is the learning objectives, and the output is the specific learning plan. The server stores the generated plan in a database for later access.

[0086] Step 5:

[0087] The server sends the designed learning plan and educational resources to the terminal and provides them to the user. Here, the educational resources are presented in a user-friendly format, such as games or interactive videos. The input is the learning plan, and the output is the transmission of data to the terminal. Specifically, the application on the terminal triggers and reflects the reception of data from the server.

[0088] Step 6:

[0089] Users engage with educational resources provided via a device, and their progress is recorded in real time by the device. The device sends this progress data to a server. The input is the user's learning activity, and the output is the progress information recorded by the device. The device automatically collects and transmits user actions and responses using sensors and logging systems.

[0090] Step 7:

[0091] The server analyzes progress information sent from the terminal and uses AI to evaluate the learner's understanding and necessary improvements. Based on the analysis results, it generates specific improvement instructions for the next step. The input is progress information, and the output is improvement instructions and adjusted educational resources. The AI ​​model evaluates the input data through this analysis and generates feedback.

[0092] Step 8:

[0093] The server sends generated improvement instructions and tailored educational resources to the device and provides them to the user. Simultaneously, it regularly notifies parents of learning progress and next steps. The input is improvement instructions and tailored educational resources, and the output is notifications to the device and parents. Notifications to parents are delivered via email and app push notifications.

[0094] (Application Example 1)

[0095] 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."

[0096] The challenge lies in providing an educational system that allows students to learn efficiently while stimulating their interest, by setting optimal learning goals based on their individual learning profiles, continuously monitoring their progress, and providing timely feedback. Traditional educational systems struggle to dynamically adjust learning content to match individual students' progress and interests, and lack a rapid and adaptive mechanism for incorporating current feedback into learning plans.

[0097] 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.

[0098] In this invention, the server includes means for acquiring user learning profile information, means for setting learning objectives and generating a learning plan based on the acquired information, and means for dynamically providing appropriate educational content in various formats according to the generated learning plan. This makes it possible to provide a learning program customized for each child and to quickly adjust the content according to their progress at any given time.

[0099] A "user" is an entity that provides learning profile information and receives educational content through the system.

[0100] "Learning profile information" refers to attribute information such as the user's age, strengths and weaknesses, areas of interest, and desired learning pace.

[0101] "Learning objectives" are the final destinations or indicators of learning, set based on the user's learning profile information.

[0102] A "learning plan" is a detailed plan that outlines the learning content and procedures necessary to achieve learning objectives.

[0103] "Educational content" refers to information, including teaching materials and learning resources necessary for carrying out a learning plan, and is provided in a variety of formats.

[0104] "Feedback" refers to evaluations and suggestions for the next steps generated based on what the user has learned and their progress.

[0105] A "supervisor" refers to an individual or organization responsible for monitoring a user's learning progress and next steps, and providing appropriate support.

[0106] "Artificial intelligence analysis" is a technology that uses the user's learning progress information to highly analyze and design the next learning step.

[0107] "Diverse formats" refers to educational content being delivered through different media and methods, depending on the learner's interests and level of understanding.

[0108] The system based on this invention acquires the user's learning profile information, sets personalized learning goals based on that information, and generates a learning plan. This information, provided through the terminal, records the user's attributes and interests in detail. The server uses artificial intelligence technology to analyze this information and generate the optimal learning program. Frameworks such as TENSORFLOW® and PyTorch are used for this AI analysis.

[0109] The server sends the generated learning plan and educational content to the device, which the user receives via smartphone or smart glasses to proceed with their learning. These devices have applications built using React Native installed, presenting the content to the user in an interactive and engaging way. The educational content is dynamically adjusted according to progress and delivered in diverse formats. This enhances learning effectiveness and helps maintain sustained interest.

[0110] Furthermore, the server monitors learning progress and generates appropriate feedback. This feedback is also analyzed by AI and used to precisely adjust the next learning process. Supervisors are regularly reported on the user's progress and next steps, allowing them to understand the learning situation in real time.

[0111] For example, if a user expresses interest in history, the server can provide history content tailored to that user in a game format. If the user encounters difficulties understanding ancient civilizations, the learning content can be adjusted by suggesting additional video materials or quizzes.

[0112] Examples of prompts include, "I want to learn about medieval European culture. Please recommend some learning materials," and "Please generate a quiz to learn more about ancient Egyptian civilization." Based on these prompts, the system is structured to prepare for effective learning support for the user.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user inputs learning profile information using a terminal. This information includes age, areas of interest, strengths and weaknesses, and desired learning pace. The terminal stores this information in a database and sends it to the server. Input here is direct input by the user, while output is the transfer of information to the server.

[0116] Step 2:

[0117] The server analyzes the received learning profile information using artificial intelligence technology. Specifically, it uses TensorFlow or PyTorch to set individual learning goals for the AI ​​model. In this step, the input is the user's profile information, and the output is the set learning goals.

[0118] Step 3:

[0119] The server generates a learning plan based on the set learning objectives. This generation process designs step-by-step content while reflecting past learning data and user interests. The generated learning plan is then sent to the terminal as output.

[0120] Step 4:

[0121] The device displays educational content to the user based on a learning plan received from the server. React Native is used here to provide interactive content. The input is the learning plan from the server, and the output is the display of visual content to the user.

[0122] Step 5:

[0123] Users progress through their learning based on the provided educational content. Progress is continuously transmitted from the device to the server as feedback information. Input is the user's response to the learning content, and output is the collection and transmission of progress information.

[0124] Step 6:

[0125] The server receives progress information and generates feedback through AI analysis. It evaluates what the user understands and where they are struggling, and makes necessary adjustments for the next stage. Based on this analysis, data processing is used to dynamically design the next learning plan. As output, the adjusted feedback is provided back to the device.

[0126] Step 7:

[0127] The server periodically reports to the supervisor on the user's learning progress and next steps. This information is provided via email and notification functions. The input is accumulated learning data, and the output is notifications to the supervisor.

[0128] 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.

[0129] This invention is a system that incorporates an emotion engine into a learning support system to take into account the user's emotional state and provide an individually tailored learning experience. The system consists of a server, a terminal, the emotion engine, and the user.

[0130] Users enter learning profile information on their device. This includes age, areas of interest, subjects they excel at and struggle with, and their desired learning pace. This data is then sent from the device to the server.

[0131] The server analyzes the transmitted learning profile information and user emotion data obtained from the emotion engine. The emotion engine analyzes micro-expressions, tone of voice, and behavioral patterns while the user is using the learning content, recognizing emotions in real time. Based on this data, the server sets learning objectives optimized for the user and generates a customized learning plan to achieve them.

[0132] The learning plan is dynamically adjusted according to the user's learning progress and emotions. For example, if the user is feeling frustrated, the server will temporarily change the learning content to a game or quiz format to stimulate their motivation. The device provides these changes to the user in real time, encouraging them to continue learning with enthusiasm.

[0133] The user's learning progress and emotional state are constantly transmitted from the device to the server for analysis. Based on this information, the server periodically generates detailed feedback, including the child's learning progress and psychological state, and sends notifications to parents. These notifications include insights into where the user is struggling and what emotional state they are in while learning, allowing parents to adjust their conversations and support with the user based on this information.

[0134] For example, if a child is detected as feeling frustrated while working on math content, the emotion engine will detect this, and the server will provide hints or easier content to simplify the problem. Then, once the user regains their motivation, feedback will be provided to gradually increase the difficulty level. In this way, providing a flexible learning experience that responds to children's emotions creates an effective and enjoyable learning environment.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] Users input learning profile information via their device and send it to the server. This includes age, areas of interest, subjects they excel at and struggle with, and their preferred learning pace.

[0138] Step 2:

[0139] While the user is using the learning content, the device uses an emotion engine to analyze the user's micro-expressions, voice tone, and other characteristics in real time, and acquires emotional data.

[0140] Step 3:

[0141] The device sends the collected emotional data to the server. The server analyzes this data along with the user's learning profile information and sets learning goals tailored to the user.

[0142] Step 4:

[0143] The server generates a customized learning plan based on the learning objectives and sends it to the device. The device then provides the user with learning content in the appropriate format according to this plan.

[0144] Step 5:

[0145] The user engages with learning content. The device continuously monitors the user's progress and emotional state, and sends data to the server as needed.

[0146] Step 6:

[0147] The server analyzes progress information and sentiment data, and adjusts the learning plan as needed. For example, if the user appears confused, the server simplifies the problem and adds voice hints.

[0148] Step 7:

[0149] The server generates notifications for parents and periodically sends reports that include the user's mental state and progress. This allows parents to have a detailed understanding of their child's learning situation.

[0150] Step 8:

[0151] If the user's emotions improve, the device will follow instructions from the server to gradually reduce the difficulty level of the learning content and allow the user to proceed to the next learning step.

[0152] (Example 2)

[0153] 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".

[0154] Modern learning support systems face the challenge of providing flexible learning experiences tailored to the individual needs and emotional states of users. Furthermore, there is a lack of effective means to appropriately inform parents about their child's learning progress and psychological state, and to maintain the user's motivation.

[0155] 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.

[0156] In this invention, the server includes means for acquiring user profile information, means for setting learning objectives and generating an individualized learning plan based on the acquired profile information and emotional data collected in real time, and means for dynamically adjusting the learning plan and content based on the generated feedback. This provides a flexible learning environment that responds to the individual needs and emotional state of the user, enabling effective learning support while maintaining motivation.

[0157] "User profile information" refers to personal learning-related information that users provide to the system, including age, areas of interest, subjects they excel at and struggle with, and their desired learning pace.

[0158] "Emotional data" refers to data about the emotional state a user exhibits during learning, and is collected in real time based on micro-expressions, tone of voice, behavioral patterns, and other factors.

[0159] "Learning objectives" are indicators that show the specific results or states that users are expected to ultimately achieve in their learning.

[0160] "Personalized learning plans" refer to learning schedules and content arrangements designed to meet individual needs, based on user profile information and emotional data.

[0161] "Feedback" refers to information such as evaluations and advice generated based on the user's learning progress and emotional state, with the aim of improving and adjusting their learning.

[0162] An "interactive format" refers to a method of providing learning content that enables two-way interaction with users, and includes games, quizzes, simulations, and other similar methods.

[0163] "Dynamic adjustment" refers to the process by which the system automatically modifies learning plans and content based on the user's latest data and circumstances.

[0164] This invention comprises a learning support system including a server, a terminal, and an emotion engine that complements them. In implementing the system, the user first inputs their profile information using a terminal. The terminal can be a general computer, tablet, or smartphone, and data can be entered via a keyboard or touchscreen. This profile information includes the user's age, areas of learning interest, subjects they excel at and struggle with, and desired learning pace. Subsequently, the information collected from the user is transmitted to the server via the internet.

[0165] The server processes the received profile information and combines it with additional emotional data obtained from the emotion engine. The emotion engine uses the device's camera and microphone to collect and analyze the user's micro-expressions, voice tone, and behavioral patterns in real time. This technology utilizes facial recognition software and voice analysis software. Based on the analyzed data, the server sets user-specific learning goals and learning plans.

[0166] Once a learning plan is generated, it is provided to the user via their device. The learning content is delivered in an interactive format, including gamified learning tools and interactive quizzes. Users can effectively progress through their learning by utilizing this content.

[0167] User progress and behavioral data are constantly transmitted from the device to the server. The server analyzes this information and generates feedback using a generative AI model. This feedback is used to dynamically adjust the user's learning plan. In addition, notifications are sent periodically to parents, providing detailed reports on the user's learning progress and emotional state.

[0168] For example, if the emotion engine detects frustration while a user is solving a math problem, the learning plan is immediately adjusted, and hints and simple tasks are provided to help solve the problem. Furthermore, users can input questions into the system, such as, "How can I see in real time how a child expresses emotions in specific content?" Through this series of technical measures, the optimal learning environment for the user is provided.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user enters learning profile information on the device. Specifically, the user uses the device's input interface (keyboard, touchscreen, etc.) to enter their age, areas of interest, subjects they excel and struggle with, and desired learning pace. The entered data is sent from the device to the server. The output here is user profile information for processing by the server.

[0172] Step 2:

[0173] The server receives learning profile information transmitted from the terminal and collects user emotion data in real time through the emotion engine. This emotion data is obtained using the terminal's camera and microphone, capturing the user's micro-expressions, voice tone, and behavioral patterns. By analyzing this data, the server determines the user's current emotional state. The output is the analyzed user emotion state information.

[0174] Step 3:

[0175] The server sets optimized learning objectives based on the user's profile information and analyzed sentiment data. It then generates an individualized learning plan. Specifically, the server's algorithm receives profile and sentiment data as input, calculates the learning path, and outputs the result as a learning plan.

[0176] Step 4:

[0177] The device receives a personalized learning plan sent from the server and provides the user with appropriate learning content accordingly. The learning content uses interactive formats to engage the user's interest, including games and quizzes. The output displays the learning content the user should work on.

[0178] Step 5:

[0179] As users utilize learning content and progress, their devices continuously transmit behavioral data and emotional states to a server. The server analyzes the received data and generates feedback using a generative AI model. Based on the input behavioral and emotional data, feedback output is generated and reflected in the learning plan.

[0180] Step 6:

[0181] The server dynamically adjusts the learning plan and content based on the feedback generated. For example, if a user becomes frustrated with a difficult task, the server adjusts the plan by making the task easier or adding hints. As a result, an optimal learning environment is maintained for the user.

[0182] Step 7:

[0183] The server periodically generates detailed reports including the user's learning progress and emotional state, and sends notifications to parents. These notifications include information about where the user is struggling and their emotional state while learning, allowing parents to provide support. The output is a notification that is sent to the parent's device.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] Traditional learning support systems had the problem of providing only a uniform learning experience without considering the emotional state of individual users. As a result, users were prone to losing motivation for the learning material, making it difficult to achieve effective learning. Furthermore, the lack of flexible feedback tailored to learning progress made it difficult to optimize learning.

[0187] 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.

[0188] In this invention, the server includes means for setting learning objectives based on the user's learning profile information and emotional information, and generating a learning plan to reach those objectives; means for monitoring the user's learning progress information and emotional state, and generating feedback based on that data; and means for adjusting the learning plan and content in accordance with the generated feedback and providing them adaptively. This enables the provision of a flexible and personalized learning experience that responds to the user's emotional state, and allows for the optimization of effective learning.

[0189] "User learning profile information" refers to information that indicates the individual learning characteristics of the user, including their age, areas of learning interest, subjects they excel at and struggle with, and their desired learning pace.

[0190] "Emotional information" refers to data that indicates a user's emotional state at a given time, obtained by analyzing micro-expressions, tone of voice, and behavioral patterns exhibited during the learning process.

[0191] "Learning objectives" are standards that indicate the specific level of knowledge and skills that a user should achieve within a particular learning period.

[0192] A "learning plan" is a plan that outlines the steps and resources necessary to achieve learning objectives and designs learning activities optimized for the user.

[0193] "Learning content" refers to educational materials such as text, videos, and interactive quizzes provided to support user learning.

[0194] "Feedback" refers to evaluations and suggestions generated based on the user's learning progress, providing guidance for improving learning and taking the next steps.

[0195] "Emotional state" refers to a user's internal psychological state, and is an emotional state that directly affects their motivation to learn and their ability to concentrate.

[0196] "Stakeholders" refers to individuals who are directly or indirectly involved in the learning progress or outcomes, such as the learner's parents, educators, or supporters.

[0197] The system implementing this invention consists of a server, a terminal, an emotion engine, and a user. The server plays a central role in providing a personalized learning experience based on the user's input data. The detailed configuration of the system is described below.

[0198] The device provides an interface for users to input learning profile information. This information includes the user's age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is transmitted to the server via an internet connection.

[0199] The server analyzes the user's emotional state using an emotion engine. The emotion engine evaluates the user's micro-expressions and voice tone, captured through the device's camera and microphone, in real time to recognize their emotional state. This analysis utilizes Python's OpenCV library and librosa, among others, to extract emotional information from the data.

[0200] The server sets learning objectives based on acquired learning profile information and emotional information. The generated learning plan is dynamically adjusted according to the user's emotional state, and learning content is delivered to the device at the appropriate time. Learning content is provided in multiple formats, including text, video, and interactive quizzes.

[0201] Furthermore, the server constantly monitors the user's learning progress and emotional state, generating timely feedback. This feedback is generated through artificial intelligence analysis and supports the optimization of learning. It also allows for immediate adjustments to the learning plan and content based on the generated feedback.

[0202] For example, if a user shows frustration while tackling a complex problem, the emotion engine can detect this and the server can switch the learning content to easier problems or game formats. This can help rekindle the user's motivation.

[0203] Examples of prompt messages are as follows:

[0204] "Analyze the user's facial expressions in real time to determine if they are experiencing fatigue or frustration."

[0205] "Based on user sentiment data, select and deliver learning content in the most optimal format."

[0206] In this way, the server can provide a flexible learning experience tailored to the individual needs of the user.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] Users enter learning profile information on their device. This input process involves entering detailed information such as age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is formatted by the device and sent to the server.

[0210] Step 2:

[0211] The server receives learning profile information sent from the terminal and begins processing it. Based on this information, the server sets initial learning objectives. Here, it uses database queries to compare with past user data and extract the most appropriate learning objectives.

[0212] Step 3:

[0213] The server uses the terminal's camera and microphone to collect emotional information about the user. An emotion engine analyzes this data to identify the user's emotional state from their micro-expressions and voice tone. It uses the Python OpenCV library to analyze facial expressions and librosa to extract voice features. The analysis results are then sent to the server.

[0214] Step 4:

[0215] The server combines received sentiment information and learning profile information to generate a learning plan optimized for the user. This plan determines the list of necessary content and the order in which each piece of content is presented, according to the learning objectives. An AI model is used to fine-tune the plan to achieve the maximum learning effect.

[0216] Step 5:

[0217] The device presents the user with appropriate learning content according to the learning plan received from the server. This content includes text, video, and quiz formats, incorporating features designed to enhance the user's motivation to learn. The device then records how the user responds to this content.

[0218] Step 6:

[0219] The system monitors the user's learning progress and emotional state, periodically sending this data from the device to the server. The server then generates feedback based on this data. This feedback identifies areas of performance that are strong and areas that need improvement, and guides the user through the next steps. This feedback is generated using artificial intelligence analysis, and the analysis results are utilized.

[0220] Step 7:

[0221] The server adjusts the learning plan and content in real time based on the generated feedback, adapting them to create a more effective learning experience. This adjustment is then sent back to the terminal, providing the user with a new learning direction. This process may also involve using prompts to generate new suggestions.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] This invention is a system that acquires user learning profile information, sets learning objectives, generates a learning plan based on those objectives, and provides learning content. This system consists of a server, a terminal, and the user.

[0239] First, the user inputs the child's individual learning profile information through the device. This information includes elements such as the child's age, areas of interest, strengths and weaknesses, and desired learning pace. The device then sends this information to the server.

[0240] Based on learning profile information obtained from users, the server uses AI technology to set learning objectives tailored to each child and automatically generates a learning plan to achieve them. This learning plan consists of specific learning content and steps to learn it, and is provided in various formats to engage the user's interest. For example, it incorporates features that make learning fun for children, such as game formats and interactive video content.

[0241] Users engage with the learning content presented on their devices, and learning progress information obtained during this process is continuously transmitted from the device to the server. Importantly, this progress information includes detailed records of what the child understands and in which areas they are struggling.

[0242] The server uses AI to analyze this progress information and evaluate the child's learning status. Based on the results, it generates feedback on the next steps to take and adjusts the learning content accordingly. This feedback and adjusted content are then provided to the user again via the device.

[0243] Furthermore, the server sends regular notifications to parents, reporting on the child's learning progress and next steps. This makes it easier for parents to understand their child's learning situation and provide appropriate support.

[0244] For example, if a child shows interest in history, a game-based learning plan is generated based on their profile information, allowing them to learn the basics of history in a fun way. If progress information reveals that the child lacks understanding of a particular ancient civilization, the plan is adjusted by suggesting more easily understandable video materials. This allows children to continue learning without getting bored, and parents can keep track of their child's progress at each stage. In this way, the present invention provides a means to advance learning in a fun and effective manner.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user uses a device to enter the child's learning profile information. This includes age, interests, subjects they excel at and struggle with, and their preferred learning pace. The device then sends this information to the server.

[0248] Step 2:

[0249] The server analyzes the received profile information and uses AI to set individual learning goals. Based on these goals, the server generates a customized learning plan for each user and sends it to the device.

[0250] Step 3:

[0251] The device provides the user with appropriate learning content based on the learning plan received from the server. This content is displayed in game or interactive video formats to easily capture the user's interest.

[0252] Step 4:

[0253] Users engage with learning content presented via their device. The device collects progress information during the learning activity and periodically sends it to the server. This progress information includes steps completed and level of understanding.

[0254] Step 5:

[0255] The server uses AI to analyze the received progress information and evaluate the user's current learning status. If problems are detected, the server suggests the next learning steps to take and generates feedback.

[0256] Step 6:

[0257] The server sends analysis results and feedback to the terminal and adjusts the learning content if necessary. The terminal then presents this information to the user to encourage further learning.

[0258] Step 7:

[0259] The server periodically generates learning progress reports for parents and sends them via email or in-app notifications. These reports include the current learning status, achieved goals, and next steps.

[0260] (Example 1)

[0261] 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."

[0262] Traditional learning support systems have had the problem of difficulty in providing flexible educational plans tailored to the individual learning characteristics of each user. Furthermore, the lack of means to manage feedback on learning progress and appropriately adjust educational resources based on that feedback made efficient learning support difficult. In particular, the challenge was to engage users' interest while addressing their individual learning needs.

[0263] 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.

[0264] In this invention, the server includes means for acquiring user learning characteristics information, means for setting goals and generating an educational plan to achieve those goals based on the acquired learning characteristics information, and means for providing appropriate educational resources according to the generated educational plan. This enables efficient and engaging learning support by providing educational resources and plans tailored to each user's individual learning characteristics.

[0265] "User" refers to an individual who uses an educational system to learn.

[0266] "Learning characteristics information" refers to data that includes information such as an individual's age, areas of interest, subjects they excel at and struggle with, and their learning pace.

[0267] "Goals" refer to the specific learning outcomes that learners should achieve.

[0268] An "educational plan" refers to a plan that details the learning steps and activities necessary to achieve a goal.

[0269] "Educational resources" refers to all educational materials, including learning content, teaching materials, and learning support tools.

[0270] "Providing educational resources" refers to presenting learning content and materials in a format that learners can use.

[0271] "Improvement instructions" refer to feedback and guidance for the next steps provided to learners based on their learning progress.

[0272] "Artificial intelligence analysis" refers to the process where a computer system automatically analyzes collected data.

[0273] "Guardian" refers to a parent or equivalent person who is in a position to support the learner.

[0274] To implement this system, the user must first input learner characteristics information using a terminal. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, learning pace, etc., and is collected using an input form on the terminal and transmitted to the server. Smartphones, tablets, and personal computers can be used as terminals.

[0275] The server receives learning characteristics information sent by the user and stores it in a database. The software used here is built with an AI model using a programming language such as Python, and generates individual learning objectives suitable for the learner. This generative AI model uses a pre-trained algorithm to design the optimal learning plan based on the learner's characteristics.

[0276] The server generates educational resources in multiple formats based on the created learning plan. These resources are presented in various forms, such as games and interactive videos, and are designed to engage learners. This allows learners to progress towards achieving their goals while enjoying the learning process.

[0277] The terminal plays a crucial role in providing educational resources. The terminal receives learning plans and educational resources transmitted from the server and presents them to the learner through user interaction. As the learner utilizes the educational resources, their progress is collected in real time by the terminal and fed back to the server.

[0278] As a concrete example, a user might enter a prompt message such as, "I am a 10-year-old learner who is interested in mathematics, and especially wants to learn the basics of arithmetic." Based on this information, the server creates an educational plan to provide the learner with the basics of mathematics in a game format.

[0279] Furthermore, based on progress information, the server performs AI-powered analysis and adjusts the educational plan and resources accordingly. This ensures that feedback and new learning materials are provided in a timely manner, tailored to the learner's understanding and areas of difficulty.

[0280] Therefore, this system can provide an effective learning environment tailored to the individual needs of each learner.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] The user uses a device to input information about the learner's learning characteristics. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, and learning pace. The entered information is sent to the server via a form on the device. Specifically, the user fills out profile data by answering questions on the device screen and completes the information input by pressing the submit button.

[0284] Step 2:

[0285] The server receives the learning characteristic information transmitted from the terminal and stores it in the database. At this time, the server writes the information into the database and organizes the data in association with the learner ID, etc. The input is the learning characteristic information from the terminal, and the output is the information stored in the database. Specifically, it checks the accuracy of the data and registers it in the database using SQL commands.

[0286] Step 3:

[0287] Based on the received data, the server designs a learning achievement goal suitable for the learner using a generative AI model. The model uses a pre-trained algorithm to analyze the learning characteristic information and set an optimal goal. The input is the learning characteristic information, and the output is the set learning achievement goal. As a specific operation, the AI model uses a Python script to analyze the information and generate a goal. [[ID=1:6]]

[0288] Step 4:

[0289] Based on the generated learning achievement goal, the server designs a learning plan. This plan includes educational resources and learning steps. The generative AI model automatically creates the plan and optimizes it using teacher data. The input is the learning achievement goal, and the output is a specific educational plan. The server stores the generated plan in the database for later access.

[0290] Step 5:

[0291] The server transmits the designed learning plan and educational resources to the terminal and provides them to the user. Here, the educational resources are presented in a user-friendly format, such as games or interactive videos. The input is the educational plan, and the output is the data transmission to the terminal. Specifically, the application on the terminal triggers and reflects the reception of data from the server.

[0292] Step 6:

[0293] Users engage with educational resources provided via a device, and their progress is recorded in real time by the device. The device sends this progress data to a server. The input is the user's learning activity, and the output is the progress information recorded by the device. The device automatically collects and transmits user actions and responses using sensors and logging systems.

[0294] Step 7:

[0295] The server analyzes progress information sent from the terminal and uses AI to evaluate the learner's understanding and necessary improvements. Based on the analysis results, it generates specific improvement instructions for the next step. The input is progress information, and the output is improvement instructions and adjusted educational resources. The AI ​​model evaluates the input data through this analysis and generates feedback.

[0296] Step 8:

[0297] The server sends generated improvement instructions and tailored educational resources to the device and provides them to the user. Simultaneously, it regularly notifies parents of learning progress and next steps. The input is improvement instructions and tailored educational resources, and the output is notifications to the device and parents. Notifications to parents are delivered via email and app push notifications.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] An issue is to provide an educational system that can efficiently promote learning while attracting interest by setting optimal learning goals, constantly monitoring progress, and timely providing feedback based on the individual learning profiles of children. In conventional educational systems, it is difficult to dynamically adjust learning content according to the individual progress and interests of children, and there is a lack of a rapid and adaptable mechanism for reflecting the current feedback in the learning plan.

[0301] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0302] In this invention, the server includes means for acquiring the learning profile information of the user, means for setting learning achievement goals based on the acquired information and generating a learning plan, and means for dynamically providing appropriate educational content in various forms according to the generated learning plan. Thereby, it becomes possible to provide a customized learning program for each child and quickly adjust the content according to the progress at that time.

[0303] The "user" is the entity that provides learning profile information through the system and receives educational content.

[0304] The "learning profile information" refers to attribute information such as the user's age, strong and weak fields, learning fields of interest, and desired learning pace.

[0305] The "learning achievement goal" is the ultimate destination or indicator of learning, set based on the learning profile information of the user.

[0306] The "learning plan" is a plan that details the learning content and procedures necessary to reach the learning achievement goal.

[0307] The "educational content" refers to information including teaching materials and learning resources necessary for the execution of the learning plan, and is provided in various forms.

[0308] "Feedback" refers to evaluations and suggestions for the next steps generated based on what the user has learned and their progress.

[0309] A "supervisor" refers to an individual or organization responsible for monitoring a user's learning progress and next steps, and providing appropriate support.

[0310] "Artificial intelligence analysis" is a technology that uses the user's learning progress information to highly analyze and design the next learning step.

[0311] "Diverse formats" refers to educational content being delivered through different media and methods, depending on the learner's interests and level of understanding.

[0312] The system based on this invention acquires the user's learning profile information, sets personalized learning goals based on that information, and generates a learning plan. This information, provided through the terminal, records the user's attributes and interests in detail. The server uses artificial intelligence technology to analyze this information and generate the optimal learning program. Frameworks such as TensorFlow and PyTorch are used for this AI analysis.

[0313] The server sends the generated learning plan and educational content to the device, which the user receives via smartphone or smart glasses to proceed with their learning. These devices have applications built using React Native installed, presenting the content to the user in an interactive and engaging way. The educational content is dynamically adjusted according to progress and delivered in diverse formats. This enhances learning effectiveness and helps maintain sustained interest.

[0314] Furthermore, the server monitors learning progress and generates appropriate feedback. This feedback is also analyzed by AI and used to precisely adjust the next learning process. Supervisors are regularly reported on the user's progress and next steps, allowing them to understand the learning situation in real time.

[0315] For example, if a user expresses interest in history, the server can provide history content tailored to that user in a game format. If the user encounters difficulties understanding ancient civilizations, the learning content can be adjusted by suggesting additional video materials or quizzes.

[0316] Examples of prompts include, "I want to learn about medieval European culture. Please recommend some learning materials," and "Please generate a quiz to learn more about ancient Egyptian civilization." Based on these prompts, the system is structured to prepare for effective learning support for the user.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user inputs learning profile information using a terminal. This information includes age, areas of interest, strengths and weaknesses, and desired learning pace. The terminal stores this information in a database and sends it to the server. Input here is direct input by the user, while output is the transfer of information to the server.

[0320] Step 2:

[0321] The server analyzes the received learning profile information using artificial intelligence technology. Specifically, it uses TensorFlow or PyTorch to set individual learning goals for the AI ​​model. In this step, the input is the user's profile information, and the output is the set learning goals.

[0322] Step 3:

[0323] The server generates a learning plan based on the set learning objectives. This generation process designs step-by-step content while reflecting past learning data and user interests. The generated learning plan is then sent to the terminal as output.

[0324] Step 4:

[0325] The device displays educational content to the user based on a learning plan received from the server. React Native is used here to provide interactive content. The input is the learning plan from the server, and the output is the display of visual content to the user.

[0326] Step 5:

[0327] Users progress through their learning based on the provided educational content. Progress is continuously transmitted from the device to the server as feedback information. Input is the user's response to the learning content, and output is the collection and transmission of progress information.

[0328] Step 6:

[0329] The server receives progress information and generates feedback through AI analysis. It evaluates what the user understands and where they are struggling, and makes necessary adjustments for the next stage. Based on this analysis, data processing is used to dynamically design the next learning plan. As output, the adjusted feedback is provided back to the device.

[0330] Step 7:

[0331] The server periodically reports to the supervisor on the user's learning progress and next steps. This information is provided via email and notification functions. The input is accumulated learning data, and the output is notifications to the supervisor.

[0332] 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.

[0333] This invention is a system that incorporates an emotion engine into a learning support system to take into account the user's emotional state and provide an individually tailored learning experience. The system consists of a server, a terminal, the emotion engine, and the user.

[0334] Users enter learning profile information on their device. This includes age, areas of interest, subjects they excel at and struggle with, and their desired learning pace. This data is then sent from the device to the server.

[0335] The server analyzes the transmitted learning profile information and user emotion data obtained from the emotion engine. The emotion engine analyzes micro-expressions, tone of voice, and behavioral patterns while the user is using the learning content, recognizing emotions in real time. Based on this data, the server sets learning objectives optimized for the user and generates a customized learning plan to achieve them.

[0336] The learning plan is dynamically adjusted according to the user's learning progress and emotions. For example, if the user is feeling frustrated, the server will temporarily change the learning content to a game or quiz format to stimulate their motivation. The device provides these changes to the user in real time, encouraging them to continue learning with enthusiasm.

[0337] The user's learning progress and emotional state are constantly transmitted from the device to the server for analysis. Based on this information, the server periodically generates detailed feedback, including the child's learning progress and psychological state, and sends notifications to parents. These notifications include insights into where the user is struggling and what emotional state they are in while learning, allowing parents to adjust their conversations and support with the user based on this information.

[0338] For example, if a child is detected as feeling frustrated while working on math content, the emotion engine will detect this, and the server will provide hints or easier content to simplify the problem. Then, once the user regains their motivation, feedback will be provided to gradually increase the difficulty level. In this way, providing a flexible learning experience that responds to children's emotions creates an effective and enjoyable learning environment.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] Users input learning profile information via their device and send it to the server. This includes age, areas of interest, subjects they excel at and struggle with, and their preferred learning pace.

[0342] Step 2:

[0343] While the user is using the learning content, the device uses an emotion engine to analyze the user's micro-expressions, voice tone, and other characteristics in real time, and acquires emotional data.

[0344] Step 3:

[0345] The device sends the collected emotional data to the server. The server analyzes this data along with the user's learning profile information and sets learning goals tailored to the user.

[0346] Step 4:

[0347] The server generates a customized learning plan based on the learning objectives and sends it to the device. The device then provides the user with learning content in the appropriate format according to this plan.

[0348] Step 5:

[0349] The user engages with learning content. The device continuously monitors the user's progress and emotional state, and sends data to the server as needed.

[0350] Step 6:

[0351] The server analyzes progress information and sentiment data, and adjusts the learning plan as needed. For example, if the user appears confused, the server simplifies the problem and adds voice hints.

[0352] Step 7:

[0353] The server generates notifications for parents and periodically sends reports that include the user's mental state and progress. This allows parents to have a detailed understanding of their child's learning situation.

[0354] Step 8:

[0355] If the user's emotions improve, the device will follow instructions from the server to gradually reduce the difficulty level of the learning content and allow the user to proceed to the next learning step.

[0356] (Example 2)

[0357] 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".

[0358] Modern learning support systems face the challenge of providing flexible learning experiences tailored to the individual needs and emotional states of users. Furthermore, there is a lack of effective means to appropriately inform parents about their child's learning progress and psychological state, and to maintain the user's motivation.

[0359] 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.

[0360] In this invention, the server includes means for acquiring user profile information, means for setting learning objectives and generating an individualized learning plan based on the acquired profile information and emotional data collected in real time, and means for dynamically adjusting the learning plan and content based on the generated feedback. This provides a flexible learning environment that responds to the individual needs and emotional state of the user, enabling effective learning support while maintaining motivation.

[0361] "User profile information" refers to personal learning-related information that users provide to the system, including age, areas of interest, subjects they excel at and struggle with, and their desired learning pace.

[0362] "Emotional data" refers to data about the emotional state a user exhibits during learning, and is collected in real time based on micro-expressions, tone of voice, behavioral patterns, and other factors.

[0363] "Learning objectives" are indicators that show the specific results or states that users are expected to ultimately achieve in their learning.

[0364] "Personalized learning plans" refer to learning schedules and content arrangements designed to meet individual needs, based on user profile information and emotional data.

[0365] "Feedback" refers to information such as evaluations and advice generated based on the user's learning progress and emotional state, with the aim of improving and adjusting their learning.

[0366] An "interactive format" refers to a method of providing learning content that enables two-way interaction with users, and includes games, quizzes, simulations, and other similar methods.

[0367] "Dynamic adjustment" refers to the process by which the system automatically modifies learning plans and content based on the user's latest data and circumstances.

[0368] This invention comprises a learning support system including a server, a terminal, and an emotion engine that complements them. In implementing the system, the user first inputs their profile information using a terminal. The terminal can be a general computer, tablet, or smartphone, and data can be entered via a keyboard or touchscreen. This profile information includes the user's age, areas of learning interest, subjects they excel at and struggle with, and desired learning pace. Subsequently, the information collected from the user is transmitted to the server via the internet.

[0369] The server processes the received profile information and combines it with additional emotional data obtained from the emotion engine. The emotion engine uses the device's camera and microphone to collect and analyze the user's micro-expressions, voice tone, and behavioral patterns in real time. This technology utilizes facial recognition software and voice analysis software. Based on the analyzed data, the server sets user-specific learning goals and learning plans.

[0370] Once a learning plan is generated, it is provided to the user via their device. The learning content is delivered in an interactive format, including gamified learning tools and interactive quizzes. Users can effectively progress through their learning by utilizing this content.

[0371] User progress and behavioral data are constantly transmitted from the device to the server. The server analyzes this information and generates feedback using a generative AI model. This feedback is used to dynamically adjust the user's learning plan. In addition, notifications are sent periodically to parents, providing detailed reports on the user's learning progress and emotional state.

[0372] For example, if the emotion engine detects frustration while a user is solving a math problem, the learning plan is immediately adjusted, and hints and simple tasks are provided to help solve the problem. Furthermore, users can input questions into the system, such as, "How can I see in real time how a child expresses emotions in specific content?" Through this series of technical measures, the optimal learning environment for the user is provided.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The user enters learning profile information on the device. Specifically, the user uses the device's input interface (keyboard, touchscreen, etc.) to enter their age, areas of interest, subjects they excel and struggle with, and desired learning pace. The entered data is sent from the device to the server. The output here is user profile information for processing by the server.

[0376] Step 2:

[0377] The server receives learning profile information transmitted from the terminal and collects user emotion data in real time through the emotion engine. This emotion data is obtained using the terminal's camera and microphone, capturing the user's micro-expressions, voice tone, and behavioral patterns. By analyzing this data, the server determines the user's current emotional state. The output is the analyzed user emotion state information.

[0378] Step 3:

[0379] The server sets optimized learning objectives based on the user's profile information and analyzed sentiment data. It then generates an individualized learning plan. Specifically, the server's algorithm receives profile and sentiment data as input, calculates the learning path, and outputs the result as a learning plan.

[0380] Step 4:

[0381] The device receives a personalized learning plan sent from the server and provides the user with appropriate learning content accordingly. The learning content uses interactive formats to engage the user's interest, including games and quizzes. The output displays the learning content the user should work on.

[0382] Step 5:

[0383] As users utilize learning content and progress, their devices continuously transmit behavioral data and emotional states to a server. The server analyzes the received data and generates feedback using a generative AI model. Based on the input behavioral and emotional data, feedback output is generated and reflected in the learning plan.

[0384] Step 6:

[0385] The server dynamically adjusts the learning plan and content based on the feedback generated. For example, if a user becomes frustrated with a difficult task, the server adjusts the plan by making the task easier or adding hints. As a result, an optimal learning environment is maintained for the user.

[0386] Step 7:

[0387] The server periodically generates detailed reports including the user's learning progress and emotional state, and sends notifications to parents. These notifications include information about where the user is struggling and their emotional state while learning, allowing parents to provide support. The output is a notification that is sent to the parent's device.

[0388] (Application Example 2)

[0389] 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."

[0390] Traditional learning support systems had the problem of providing only a uniform learning experience without considering the emotional state of individual users. As a result, users were prone to losing motivation for the learning material, making it difficult to achieve effective learning. Furthermore, the lack of flexible feedback tailored to learning progress made it difficult to optimize learning.

[0391] 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.

[0392] In this invention, the server includes means for setting learning objectives based on the user's learning profile information and emotional information, and generating a learning plan to reach those objectives; means for monitoring the user's learning progress information and emotional state, and generating feedback based on that data; and means for adjusting the learning plan and content in accordance with the generated feedback and providing them adaptively. This enables the provision of a flexible and personalized learning experience that responds to the user's emotional state, and allows for the optimization of effective learning.

[0393] "User learning profile information" refers to information that indicates the individual learning characteristics of the user, including their age, areas of learning interest, subjects they excel at and struggle with, and their desired learning pace.

[0394] "Emotional information" refers to data that indicates a user's emotional state at a given time, obtained by analyzing micro-expressions, tone of voice, and behavioral patterns exhibited during the learning process.

[0395] "Learning objectives" are standards that indicate the specific level of knowledge and skills that a user should achieve within a particular learning period.

[0396] A "learning plan" is a plan that outlines the steps and resources necessary to achieve learning objectives and designs learning activities optimized for the user.

[0397] "Learning content" refers to educational materials such as text, videos, and interactive quizzes provided to support user learning.

[0398] "Feedback" refers to evaluations and suggestions generated based on the user's learning progress, providing guidance for improving learning and taking the next steps.

[0399] "Emotional state" refers to a user's internal psychological state, and is an emotional state that directly affects their motivation to learn and their ability to concentrate.

[0400] "Stakeholders" refers to individuals who are directly or indirectly involved in the learning progress or outcomes, such as the learner's parents, educators, or supporters.

[0401] The system implementing this invention consists of a server, a terminal, an emotion engine, and a user. The server plays a central role in providing a personalized learning experience based on the user's input data. The detailed configuration of the system is described below.

[0402] The device provides an interface for users to input learning profile information. This information includes the user's age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is transmitted to the server via an internet connection.

[0403] The server analyzes the user's emotional state using an emotion engine. The emotion engine evaluates the user's micro-expressions and voice tone, captured through the device's camera and microphone, in real time to recognize their emotional state. This analysis utilizes Python's OpenCV library and librosa, among others, to extract emotional information from the data.

[0404] The server sets learning objectives based on acquired learning profile information and emotional information. The generated learning plan is dynamically adjusted according to the user's emotional state, and learning content is delivered to the device at the appropriate time. Learning content is provided in multiple formats, including text, video, and interactive quizzes.

[0405] Furthermore, the server constantly monitors the user's learning progress and emotional state, generating timely feedback. This feedback is generated through artificial intelligence analysis and supports the optimization of learning. It also allows for immediate adjustments to the learning plan and content based on the generated feedback.

[0406] For example, if a user shows frustration while tackling a complex problem, the emotion engine can detect this and the server can switch the learning content to easier problems or game formats. This can help rekindle the user's motivation.

[0407] Examples of prompt messages are as follows:

[0408] "Analyze the user's facial expressions in real time to determine if they are experiencing fatigue or frustration."

[0409] "Based on user sentiment data, select and deliver learning content in the most optimal format."

[0410] In this way, the server can provide a flexible learning experience tailored to the individual needs of the user.

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] Users enter learning profile information on their device. This input process involves entering detailed information such as age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is formatted by the device and sent to the server.

[0414] Step 2:

[0415] The server receives learning profile information sent from the terminal and begins processing it. Based on this information, the server sets initial learning objectives. Here, it uses database queries to compare with past user data and extract the most appropriate learning objectives.

[0416] Step 3:

[0417] The server uses the terminal's camera and microphone to collect emotional information about the user. An emotion engine analyzes this data to identify the user's emotional state from their micro-expressions and voice tone. It uses the Python OpenCV library to analyze facial expressions and librosa to extract voice features. The analysis results are then sent to the server.

[0418] Step 4:

[0419] The server combines received sentiment information and learning profile information to generate a learning plan optimized for the user. This plan determines the list of necessary content and the order in which each piece of content is presented, according to the learning objectives. An AI model is used to fine-tune the plan to achieve the maximum learning effect.

[0420] Step 5:

[0421] The device presents the user with appropriate learning content according to the learning plan received from the server. This content includes text, video, and quiz formats, incorporating features designed to enhance the user's motivation to learn. The device then records how the user responds to this content.

[0422] Step 6:

[0423] The system monitors the user's learning progress and emotional state, periodically sending this data from the device to the server. The server then generates feedback based on this data. This feedback identifies areas of performance that are strong and areas that need improvement, and guides the user through the next steps. This feedback is generated using artificial intelligence analysis, and the analysis results are utilized.

[0424] Step 7:

[0425] The server adjusts the learning plan and content in real time based on the generated feedback, adapting them to create a more effective learning experience. This adjustment is then sent back to the terminal, providing the user with a new learning direction. This process may also involve using prompts to generate new suggestions.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Third Embodiment]

[0430] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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".

[0442] This invention is a system that acquires user learning profile information, sets learning objectives, generates a learning plan based on those objectives, and provides learning content. This system consists of a server, a terminal, and the user.

[0443] First, the user inputs the child's individual learning profile information through the device. This information includes elements such as the child's age, areas of interest, strengths and weaknesses, and desired learning pace. The device then sends this information to the server.

[0444] Based on learning profile information obtained from users, the server uses AI technology to set learning objectives tailored to each child and automatically generates a learning plan to achieve them. This learning plan consists of specific learning content and steps to learn it, and is provided in various formats to engage the user's interest. For example, it incorporates features that make learning fun for children, such as game formats and interactive video content.

[0445] Users engage with the learning content presented on their devices, and learning progress information obtained during this process is continuously transmitted from the device to the server. Importantly, this progress information includes detailed records of what the child understands and in which areas they are struggling.

[0446] The server uses AI to analyze this progress information and evaluate the child's learning status. Based on the results, it generates feedback on the next steps to take and adjusts the learning content accordingly. This feedback and adjusted content are then provided to the user again via the device.

[0447] Furthermore, the server sends regular notifications to parents, reporting on the child's learning progress and next steps. This makes it easier for parents to understand their child's learning situation and provide appropriate support.

[0448] For example, if a child shows interest in history, a game-based learning plan is generated based on their profile information, allowing them to learn the basics of history in a fun way. If progress information reveals that the child lacks understanding of a particular ancient civilization, the plan is adjusted by suggesting more easily understandable video materials. This allows children to continue learning without getting bored, and parents can keep track of their child's progress at each stage. In this way, the present invention provides a means to advance learning in a fun and effective manner.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The user uses a device to enter the child's learning profile information. This includes age, interests, subjects they excel at and struggle with, and their preferred learning pace. The device then sends this information to the server.

[0452] Step 2:

[0453] The server analyzes the received profile information and uses AI to set individual learning goals. Based on these goals, the server generates a customized learning plan for each user and sends it to the device.

[0454] Step 3:

[0455] The device provides the user with appropriate learning content based on the learning plan received from the server. This content is displayed in game or interactive video formats to easily capture the user's interest.

[0456] Step 4:

[0457] Users engage with learning content presented via their device. The device collects progress information during the learning activity and periodically sends it to the server. This progress information includes steps completed and level of understanding.

[0458] Step 5:

[0459] The server uses AI to analyze the received progress information and evaluate the user's current learning status. If problems are detected, the server suggests the next learning steps to take and generates feedback.

[0460] Step 6:

[0461] The server sends analysis results and feedback to the terminal and adjusts the learning content if necessary. The terminal then presents this information to the user to encourage further learning.

[0462] Step 7:

[0463] The server periodically generates learning progress reports for parents and sends them via email or in-app notifications. These reports include the current learning status, achieved goals, and next steps.

[0464] (Example 1)

[0465] 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."

[0466] Traditional learning support systems have had the problem of difficulty in providing flexible educational plans tailored to the individual learning characteristics of each user. Furthermore, the lack of means to manage feedback on learning progress and appropriately adjust educational resources based on that feedback made efficient learning support difficult. In particular, the challenge was to engage users' interest while addressing their individual learning needs.

[0467] 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.

[0468] In this invention, the server includes means for acquiring user learning characteristics information, means for setting goals and generating an educational plan to achieve those goals based on the acquired learning characteristics information, and means for providing appropriate educational resources according to the generated educational plan. This enables efficient and engaging learning support by providing educational resources and plans tailored to each user's individual learning characteristics.

[0469] "User" refers to an individual who uses an educational system to learn.

[0470] "Learning characteristics information" refers to data that includes information such as an individual's age, areas of interest, subjects they excel at and struggle with, and their learning pace.

[0471] "Goals" refer to the specific learning outcomes that learners should achieve.

[0472] An "educational plan" refers to a plan that details the learning steps and activities necessary to achieve a goal.

[0473] "Educational resources" refers to all educational materials, including learning content, teaching materials, and learning support tools.

[0474] "Providing educational resources" refers to presenting learning content and materials in a format that learners can use.

[0475] "Improvement instructions" refer to feedback and guidance for the next steps provided to learners based on their learning progress.

[0476] "Artificial intelligence analysis" refers to the process where a computer system automatically analyzes collected data.

[0477] "Guardian" refers to a parent or equivalent person who is in a position to support the learner.

[0478] To implement this system, the user must first input learner characteristics information using a terminal. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, learning pace, etc., and is collected using an input form on the terminal and transmitted to the server. Smartphones, tablets, and personal computers can be used as terminals.

[0479] The server receives learning characteristics information sent by the user and stores it in a database. The software used here is built with an AI model using a programming language such as Python, and generates individual learning objectives suitable for the learner. This generative AI model uses a pre-trained algorithm to design the optimal learning plan based on the learner's characteristics.

[0480] The server generates educational resources in multiple formats based on the created learning plan. These resources are presented in various forms, such as games and interactive videos, and are designed to engage learners. This allows learners to progress towards achieving their goals while enjoying the learning process.

[0481] The terminal plays a crucial role in providing educational resources. The terminal receives learning plans and educational resources transmitted from the server and presents them to the learner through user interaction. As the learner utilizes the educational resources, their progress is collected in real time by the terminal and fed back to the server.

[0482] As a concrete example, a user might enter a prompt message such as, "I am a 10-year-old learner who is interested in mathematics, and especially wants to learn the basics of arithmetic." Based on this information, the server creates an educational plan to provide the learner with the basics of mathematics in a game format.

[0483] Furthermore, based on progress information, the server performs AI-powered analysis and adjusts the educational plan and resources accordingly. This ensures that feedback and new learning materials are provided in a timely manner, tailored to the learner's understanding and areas of difficulty.

[0484] Therefore, this system can provide an effective learning environment tailored to the individual needs of each learner.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] The user uses a device to input information about the learner's learning characteristics. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, and learning pace. The entered information is sent to the server via a form on the device. Specifically, the user fills out profile data by answering questions on the device screen and completes the information input by pressing the submit button.

[0488] Step 2:

[0489] The server receives learning characteristics information sent from the terminal and stores it in the database. During this process, the server writes the information to the database and organizes the data by associating it with learner IDs and other relevant information. The input is learning characteristics information from the terminal, and the output is the information stored in the database. Specifically, the server verifies the accuracy of the data and registers it in the database using SQL commands.

[0490] Step 3:

[0491] The server uses a generative AI model to design learning objectives appropriate for the learner based on the received data. The model uses a pre-trained algorithm to analyze learning characteristics information and set optimal objectives. The input is learning characteristics information, and the output is the set learning objectives. Specifically, the AI ​​model uses a Python script to analyze the information and generate the objectives.

[0492] Step 4:

[0493] The server designs a learning plan based on the generated learning objectives. This plan includes educational resources and learning steps. A generative AI model automatically creates the plan and optimizes it using training data. The input is the learning objectives, and the output is the specific learning plan. The server stores the generated plan in a database for later access.

[0494] Step 5:

[0495] The server sends the designed learning plan and educational resources to the terminal and provides them to the user. Here, the educational resources are presented in a user-friendly format, such as games or interactive videos. The input is the learning plan, and the output is the transmission of data to the terminal. Specifically, the application on the terminal triggers and reflects the reception of data from the server.

[0496] Step 6:

[0497] Users engage with educational resources provided via a device, and their progress is recorded in real time by the device. The device sends this progress data to a server. The input is the user's learning activity, and the output is the progress information recorded by the device. The device automatically collects and transmits user actions and responses using sensors and logging systems.

[0498] Step 7:

[0499] The server analyzes progress information sent from the terminal and uses AI to evaluate the learner's understanding and necessary improvements. Based on the analysis results, it generates specific improvement instructions for the next step. The input is progress information, and the output is improvement instructions and adjusted educational resources. The AI ​​model evaluates the input data through this analysis and generates feedback.

[0500] Step 8:

[0501] The server sends generated improvement instructions and tailored educational resources to the device and provides them to the user. Simultaneously, it regularly notifies parents of learning progress and next steps. The input is improvement instructions and tailored educational resources, and the output is notifications to the device and parents. Notifications to parents are delivered via email and app push notifications.

[0502] (Application Example 1)

[0503] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0504] The challenge lies in providing an educational system that allows students to learn efficiently while stimulating their interest, by setting optimal learning goals based on their individual learning profiles, continuously monitoring their progress, and providing timely feedback. Traditional educational systems struggle to dynamically adjust learning content to match individual students' progress and interests, and lack a rapid and adaptive mechanism for incorporating current feedback into learning plans.

[0505] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0506] In this invention, the server includes means for acquiring user learning profile information, means for setting learning objectives and generating a learning plan based on the acquired information, and means for dynamically providing appropriate educational content in various formats according to the generated learning plan. This makes it possible to provide a learning program customized for each child and to quickly adjust the content according to their progress at any given time.

[0507] A "user" is an entity that provides learning profile information and receives educational content through the system.

[0508] "Learning profile information" refers to attribute information such as the user's age, strengths and weaknesses, areas of interest, and desired learning pace.

[0509] "Learning objectives" are the final destinations or indicators of learning, set based on the user's learning profile information.

[0510] A "learning plan" is a detailed plan that outlines the learning content and procedures necessary to achieve learning objectives.

[0511] "Educational content" refers to information, including teaching materials and learning resources necessary for carrying out a learning plan, and is provided in a variety of formats.

[0512] "Feedback" refers to evaluations and suggestions for the next steps generated based on what the user has learned and their progress.

[0513] A "supervisor" refers to an individual or organization responsible for monitoring a user's learning progress and next steps, and providing appropriate support.

[0514] "Artificial intelligence analysis" is a technology that uses the user's learning progress information to highly analyze and design the next learning step.

[0515] "Diverse formats" refers to educational content being delivered through different media and methods, depending on the learner's interests and level of understanding.

[0516] The system based on this invention acquires the user's learning profile information, sets personalized learning goals based on that information, and generates a learning plan. This information, provided through the terminal, records the user's attributes and interests in detail. The server uses artificial intelligence technology to analyze this information and generate the optimal learning program. Frameworks such as TensorFlow and PyTorch are used for this AI analysis.

[0517] The server sends the generated learning plan and educational content to the device, which the user receives via smartphone or smart glasses to proceed with their learning. These devices have applications built using React Native installed, presenting the content to the user in an interactive and engaging way. The educational content is dynamically adjusted according to progress and delivered in diverse formats. This enhances learning effectiveness and helps maintain sustained interest.

[0518] Furthermore, the server monitors learning progress and generates appropriate feedback. This feedback is also analyzed by AI and used to precisely adjust the next learning process. Supervisors are regularly reported on the user's progress and next steps, allowing them to understand the learning situation in real time.

[0519] For example, if a user expresses interest in history, the server can provide history content tailored to that user in a game format. If the user encounters difficulties understanding ancient civilizations, the learning content can be adjusted by suggesting additional video materials or quizzes.

[0520] Examples of prompts include, "I want to learn about medieval European culture. Please recommend some learning materials," and "Please generate a quiz to learn more about ancient Egyptian civilization." Based on these prompts, the system is structured to prepare for effective learning support for the user.

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

[0522] Step 1:

[0523] The user inputs learning profile information using a terminal. This information includes age, areas of interest, strengths and weaknesses, and desired learning pace. The terminal stores this information in a database and sends it to the server. Input here is direct input by the user, while output is the transfer of information to the server.

[0524] Step 2:

[0525] The server analyzes the received learning profile information using artificial intelligence technology. Specifically, it uses TensorFlow or PyTorch to set individual learning goals for the AI ​​model. In this step, the input is the user's profile information, and the output is the set learning goals.

[0526] Step 3:

[0527] The server generates a learning plan based on the set learning objectives. This generation process designs step-by-step content while reflecting past learning data and user interests. The generated learning plan is then sent to the terminal as output.

[0528] Step 4:

[0529] The device displays educational content to the user based on a learning plan received from the server. React Native is used here to provide interactive content. The input is the learning plan from the server, and the output is the display of visual content to the user.

[0530] Step 5:

[0531] Users progress through their learning based on the provided educational content. Progress is continuously transmitted from the device to the server as feedback information. Input is the user's response to the learning content, and output is the collection and transmission of progress information.

[0532] Step 6:

[0533] The server receives progress information and generates feedback through AI analysis. It evaluates what the user understands and where they are struggling, and makes necessary adjustments for the next stage. Based on this analysis, data processing is used to dynamically design the next learning plan. As output, the adjusted feedback is provided back to the device.

[0534] Step 7:

[0535] The server periodically reports to the supervisor on the user's learning progress and next steps. This information is provided via email and notification functions. The input is accumulated learning data, and the output is notifications to the supervisor.

[0536] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0537] This invention is a system that incorporates an emotion engine into a learning support system to take into account the user's emotional state and provide an individually tailored learning experience. The system consists of a server, a terminal, the emotion engine, and the user.

[0538] Users enter learning profile information on their device. This includes age, areas of interest, subjects they excel at and struggle with, and their desired learning pace. This data is then sent from the device to the server.

[0539] The server analyzes the transmitted learning profile information and user emotion data obtained from the emotion engine. The emotion engine analyzes micro-expressions, tone of voice, and behavioral patterns while the user is using the learning content, recognizing emotions in real time. Based on this data, the server sets learning objectives optimized for the user and generates a customized learning plan to achieve them.

[0540] The learning plan is dynamically adjusted according to the user's learning progress and emotions. For example, if the user is feeling frustrated, the server will temporarily change the learning content to a game or quiz format to stimulate their motivation. The device provides these changes to the user in real time, encouraging them to continue learning with enthusiasm.

[0541] The user's learning progress and emotional state are constantly transmitted from the device to the server for analysis. Based on this information, the server periodically generates detailed feedback, including the child's learning progress and psychological state, and sends notifications to parents. These notifications include insights into where the user is struggling and what emotional state they are in while learning, allowing parents to adjust their conversations and support with the user based on this information.

[0542] For example, if a child is detected as feeling frustrated while working on math content, the emotion engine will detect this, and the server will provide hints or easier content to simplify the problem. Then, once the user regains their motivation, feedback will be provided to gradually increase the difficulty level. In this way, providing a flexible learning experience that responds to children's emotions creates an effective and enjoyable learning environment.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] Users input learning profile information via their device and send it to the server. This includes age, areas of interest, subjects they excel at and struggle with, and their preferred learning pace.

[0546] Step 2:

[0547] While the user is using the learning content, the device uses an emotion engine to analyze the user's micro-expressions, voice tone, and other characteristics in real time, and acquires emotional data.

[0548] Step 3:

[0549] The device sends the collected emotional data to the server. The server analyzes this data along with the user's learning profile information and sets learning goals tailored to the user.

[0550] Step 4:

[0551] The server generates a customized learning plan based on the learning objectives and sends it to the device. The device then provides the user with learning content in the appropriate format according to this plan.

[0552] Step 5:

[0553] The user engages with learning content. The device continuously monitors the user's progress and emotional state, and sends data to the server as needed.

[0554] Step 6:

[0555] The server analyzes progress information and sentiment data, and adjusts the learning plan as needed. For example, if the user appears confused, the server simplifies the problem and adds voice hints.

[0556] Step 7:

[0557] The server generates notifications for parents and periodically sends reports that include the user's mental state and progress. This allows parents to have a detailed understanding of their child's learning situation.

[0558] Step 8:

[0559] If the user's emotions improve, the device will follow instructions from the server to gradually reduce the difficulty level of the learning content and allow the user to proceed to the next learning step.

[0560] (Example 2)

[0561] 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."

[0562] Modern learning support systems face the challenge of providing flexible learning experiences tailored to the individual needs and emotional states of users. Furthermore, there is a lack of effective means to appropriately inform parents about their child's learning progress and psychological state, and to maintain the user's motivation.

[0563] 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.

[0564] In this invention, the server includes means for acquiring user profile information, means for setting learning objectives and generating an individualized learning plan based on the acquired profile information and emotional data collected in real time, and means for dynamically adjusting the learning plan and content based on the generated feedback. This provides a flexible learning environment that responds to the individual needs and emotional state of the user, enabling effective learning support while maintaining motivation.

[0565] "User profile information" refers to personal learning-related information that users provide to the system, including age, areas of interest, subjects they excel at and struggle with, and their desired learning pace.

[0566] "Emotional data" refers to data about the emotional state a user exhibits during learning, and is collected in real time based on micro-expressions, tone of voice, behavioral patterns, and other factors.

[0567] "Learning objectives" are indicators that show the specific results or states that users are expected to ultimately achieve in their learning.

[0568] "Personalized learning plans" refer to learning schedules and content arrangements designed to meet individual needs, based on user profile information and emotional data.

[0569] "Feedback" refers to information such as evaluations and advice generated based on the user's learning progress and emotional state, with the aim of improving and adjusting their learning.

[0570] An "interactive format" refers to a method of providing learning content that enables two-way interaction with users, and includes games, quizzes, simulations, and other similar methods.

[0571] "Dynamic adjustment" refers to the process by which the system automatically modifies learning plans and content based on the user's latest data and circumstances.

[0572] This invention comprises a learning support system including a server, a terminal, and an emotion engine that complements them. In implementing the system, the user first inputs their profile information using a terminal. The terminal can be a general computer, tablet, or smartphone, and data can be entered via a keyboard or touchscreen. This profile information includes the user's age, areas of learning interest, subjects they excel at and struggle with, and desired learning pace. Subsequently, the information collected from the user is transmitted to the server via the internet.

[0573] The server processes the received profile information and combines it with additional emotional data obtained from the emotion engine. The emotion engine uses the device's camera and microphone to collect and analyze the user's micro-expressions, voice tone, and behavioral patterns in real time. This technology utilizes facial recognition software and voice analysis software. Based on the analyzed data, the server sets user-specific learning goals and learning plans.

[0574] Once a learning plan is generated, it is provided to the user via their device. The learning content is delivered in an interactive format, including gamified learning tools and interactive quizzes. Users can effectively progress through their learning by utilizing this content.

[0575] User progress and behavioral data are constantly transmitted from the device to the server. The server analyzes this information and generates feedback using a generative AI model. This feedback is used to dynamically adjust the user's learning plan. In addition, notifications are sent periodically to parents, providing detailed reports on the user's learning progress and emotional state.

[0576] For example, if the emotion engine detects frustration while a user is solving a math problem, the learning plan is immediately adjusted, and hints and simple tasks are provided to help solve the problem. Furthermore, users can input questions into the system, such as, "How can I see in real time how a child expresses emotions in specific content?" Through this series of technical measures, the optimal learning environment for the user is provided.

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] The user enters learning profile information on the device. Specifically, the user uses the device's input interface (keyboard, touchscreen, etc.) to enter their age, areas of interest, subjects they excel and struggle with, and desired learning pace. The entered data is sent from the device to the server. The output here is user profile information for processing by the server.

[0580] Step 2:

[0581] The server receives learning profile information transmitted from the terminal and collects user emotion data in real time through the emotion engine. This emotion data is obtained using the terminal's camera and microphone, capturing the user's micro-expressions, voice tone, and behavioral patterns. By analyzing this data, the server determines the user's current emotional state. The output is the analyzed user emotion state information.

[0582] Step 3:

[0583] The server sets optimized learning objectives based on the user's profile information and analyzed sentiment data. It then generates an individualized learning plan. Specifically, the server's algorithm receives profile and sentiment data as input, calculates the learning path, and outputs the result as a learning plan.

[0584] Step 4:

[0585] The device receives a personalized learning plan sent from the server and provides the user with appropriate learning content accordingly. The learning content uses interactive formats to engage the user's interest, including games and quizzes. The output displays the learning content the user should work on.

[0586] Step 5:

[0587] As users utilize learning content and progress, their devices continuously transmit behavioral data and emotional states to a server. The server analyzes the received data and generates feedback using a generative AI model. Based on the input behavioral and emotional data, feedback output is generated and reflected in the learning plan.

[0588] Step 6:

[0589] The server dynamically adjusts the learning plan and content based on the feedback generated. For example, if a user becomes frustrated with a difficult task, the server adjusts the plan by making the task easier or adding hints. As a result, an optimal learning environment is maintained for the user.

[0590] Step 7:

[0591] The server periodically generates detailed reports including the user's learning progress and emotional state, and sends notifications to parents. These notifications include information about where the user is struggling and their emotional state while learning, allowing parents to provide support. The output is a notification that is sent to the parent's device.

[0592] (Application Example 2)

[0593] 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."

[0594] Traditional learning support systems had the problem of providing only a uniform learning experience without considering the emotional state of individual users. As a result, users were prone to losing motivation for the learning material, making it difficult to achieve effective learning. Furthermore, the lack of flexible feedback tailored to learning progress made it difficult to optimize learning.

[0595] 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.

[0596] In this invention, the server includes means for setting learning objectives based on the user's learning profile information and emotional information, and generating a learning plan to reach those objectives; means for monitoring the user's learning progress information and emotional state, and generating feedback based on that data; and means for adjusting the learning plan and content in accordance with the generated feedback and providing them adaptively. This enables the provision of a flexible and personalized learning experience that responds to the user's emotional state, and allows for the optimization of effective learning.

[0597] "User learning profile information" refers to information that indicates the individual learning characteristics of the user, including their age, areas of learning interest, subjects they excel at and struggle with, and their desired learning pace.

[0598] "Emotional information" refers to data that indicates a user's emotional state at a given time, obtained by analyzing micro-expressions, tone of voice, and behavioral patterns exhibited during the learning process.

[0599] "Learning objectives" are standards that indicate the specific level of knowledge and skills that a user should achieve within a particular learning period.

[0600] A "learning plan" is a plan that outlines the steps and resources necessary to achieve learning objectives and designs learning activities optimized for the user.

[0601] "Learning content" refers to educational materials such as text, videos, and interactive quizzes provided to support user learning.

[0602] "Feedback" refers to evaluations and suggestions generated based on the user's learning progress, providing guidance for improving learning and taking the next steps.

[0603] "Emotional state" refers to a user's internal psychological state, and is an emotional state that directly affects their motivation to learn and their ability to concentrate.

[0604] "Stakeholders" refers to individuals who are directly or indirectly involved in the learning progress or outcomes, such as the learner's parents, educators, or supporters.

[0605] The system implementing this invention consists of a server, a terminal, an emotion engine, and a user. The server plays a central role in providing a personalized learning experience based on the user's input data. The detailed configuration of the system is described below.

[0606] The device provides an interface for users to input learning profile information. This information includes the user's age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is transmitted to the server via an internet connection.

[0607] The server analyzes the user's emotional state using an emotion engine. The emotion engine evaluates the user's micro-expressions and voice tone, captured through the device's camera and microphone, in real time to recognize their emotional state. This analysis utilizes Python's OpenCV library and librosa, among others, to extract emotional information from the data.

[0608] The server sets learning objectives based on acquired learning profile information and emotional information. The generated learning plan is dynamically adjusted according to the user's emotional state, and learning content is delivered to the device at the appropriate time. Learning content is provided in multiple formats, including text, video, and interactive quizzes.

[0609] Furthermore, the server constantly monitors the user's learning progress and emotional state, generating timely feedback. This feedback is generated through artificial intelligence analysis and supports the optimization of learning. It also allows for immediate adjustments to the learning plan and content based on the generated feedback.

[0610] For example, if a user shows frustration while tackling a complex problem, the emotion engine can detect this and the server can switch the learning content to easier problems or game formats. This can help rekindle the user's motivation.

[0611] Examples of prompt messages are as follows:

[0612] "Analyze the user's facial expressions in real time to determine if they are experiencing fatigue or frustration."

[0613] "Based on user sentiment data, select and deliver learning content in the most optimal format."

[0614] In this way, the server can provide a flexible learning experience tailored to the individual needs of the user.

[0615] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0616] Step 1:

[0617] Users enter learning profile information on their device. This input process involves entering detailed information such as age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is formatted by the device and sent to the server.

[0618] Step 2:

[0619] The server receives learning profile information sent from the terminal and begins processing it. Based on this information, the server sets initial learning objectives. Here, it uses database queries to compare with past user data and extract the most appropriate learning objectives.

[0620] Step 3:

[0621] The server uses the terminal's camera and microphone to collect emotional information about the user. An emotion engine analyzes this data to identify the user's emotional state from their micro-expressions and voice tone. It uses the Python OpenCV library to analyze facial expressions and librosa to extract voice features. The analysis results are then sent to the server.

[0622] Step 4:

[0623] The server combines received sentiment information and learning profile information to generate a learning plan optimized for the user. This plan determines the list of necessary content and the order in which each piece of content is presented, according to the learning objectives. An AI model is used to fine-tune the plan to achieve the maximum learning effect.

[0624] Step 5:

[0625] The device presents the user with appropriate learning content according to the learning plan received from the server. This content includes text, video, and quiz formats, incorporating features designed to enhance the user's motivation to learn. The device then records how the user responds to this content.

[0626] Step 6:

[0627] The system monitors the user's learning progress and emotional state, periodically sending this data from the device to the server. The server then generates feedback based on this data. This feedback identifies areas of performance that are strong and areas that need improvement, and guides the user through the next steps. This feedback is generated using artificial intelligence analysis, and the analysis results are utilized.

[0628] Step 7:

[0629] The server adjusts the learning plan and content in real time based on the generated feedback, adapting them to create a more effective learning experience. This adjustment is then sent back to the terminal, providing the user with a new learning direction. This process may also involve using prompts to generate new suggestions.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] [Fourth Embodiment]

[0634] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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".

[0647] This invention is a system that acquires user learning profile information, sets learning objectives, generates a learning plan based on those objectives, and provides learning content. This system consists of a server, a terminal, and the user.

[0648] First, the user inputs the child's individual learning profile information through the device. This information includes elements such as the child's age, areas of interest, strengths and weaknesses, and desired learning pace. The device then sends this information to the server.

[0649] Based on learning profile information obtained from users, the server uses AI technology to set learning objectives tailored to each child and automatically generates a learning plan to achieve them. This learning plan consists of specific learning content and steps to learn it, and is provided in various formats to engage the user's interest. For example, it incorporates features that make learning fun for children, such as game formats and interactive video content.

[0650] Users engage with the learning content presented on their devices, and learning progress information obtained during this process is continuously transmitted from the device to the server. Importantly, this progress information includes detailed records of what the child understands and in which areas they are struggling.

[0651] The server uses AI to analyze this progress information and evaluate the child's learning status. Based on the results, it generates feedback on the next steps to take and adjusts the learning content accordingly. This feedback and adjusted content are then provided to the user again via the device.

[0652] Furthermore, the server sends regular notifications to parents, reporting on the child's learning progress and next steps. This makes it easier for parents to understand their child's learning situation and provide appropriate support.

[0653] For example, if a child shows interest in history, a game-based learning plan is generated based on their profile information, allowing them to learn the basics of history in a fun way. If progress information reveals that the child lacks understanding of a particular ancient civilization, the plan is adjusted by suggesting more easily understandable video materials. This allows children to continue learning without getting bored, and parents can keep track of their child's progress at each stage. In this way, the present invention provides a means to advance learning in a fun and effective manner.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The user uses a device to enter the child's learning profile information. This includes age, interests, subjects they excel at and struggle with, and their preferred learning pace. The device then sends this information to the server.

[0657] Step 2:

[0658] The server analyzes the received profile information and uses AI to set individual learning goals. Based on these goals, the server generates a customized learning plan for each user and sends it to the device.

[0659] Step 3:

[0660] The device provides the user with appropriate learning content based on the learning plan received from the server. This content is displayed in game or interactive video formats to easily capture the user's interest.

[0661] Step 4:

[0662] Users engage with learning content presented via their device. The device collects progress information during the learning activity and periodically sends it to the server. This progress information includes steps completed and level of understanding.

[0663] Step 5:

[0664] The server uses AI to analyze the received progress information and evaluate the user's current learning status. If problems are detected, the server suggests the next learning steps to take and generates feedback.

[0665] Step 6:

[0666] The server sends analysis results and feedback to the terminal and adjusts the learning content if necessary. The terminal then presents this information to the user to encourage further learning.

[0667] Step 7:

[0668] The server periodically generates learning progress reports for parents and sends them via email or in-app notifications. These reports include the current learning status, achieved goals, and next steps.

[0669] (Example 1)

[0670] 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".

[0671] Traditional learning support systems have had the problem of difficulty in providing flexible educational plans tailored to the individual learning characteristics of each user. Furthermore, the lack of means to manage feedback on learning progress and appropriately adjust educational resources based on that feedback made efficient learning support difficult. In particular, the challenge was to engage users' interest while addressing their individual learning needs.

[0672] 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.

[0673] In this invention, the server includes means for acquiring user learning characteristics information, means for setting goals and generating an educational plan to achieve those goals based on the acquired learning characteristics information, and means for providing appropriate educational resources according to the generated educational plan. This enables efficient and engaging learning support by providing educational resources and plans tailored to each user's individual learning characteristics.

[0674] "User" refers to an individual who uses an educational system to learn.

[0675] "Learning characteristics information" refers to data that includes information such as an individual's age, areas of interest, subjects they excel at and struggle with, and their learning pace.

[0676] "Goals" refer to the specific learning outcomes that learners should achieve.

[0677] An "educational plan" refers to a plan that details the learning steps and activities necessary to achieve a goal.

[0678] "Educational resources" refers to all educational materials, including learning content, teaching materials, and learning support tools.

[0679] "Providing educational resources" refers to presenting learning content and materials in a format that learners can use.

[0680] "Improvement instructions" refer to feedback and guidance for the next steps provided to learners based on their learning progress.

[0681] "Artificial intelligence analysis" refers to the process where a computer system automatically analyzes collected data.

[0682] "Guardian" refers to a parent or equivalent person who is in a position to support the learner.

[0683] To implement this system, the user must first input learner characteristics information using a terminal. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, learning pace, etc., and is collected using an input form on the terminal and transmitted to the server. Smartphones, tablets, and personal computers can be used as terminals.

[0684] The server receives learning characteristics information sent by the user and stores it in a database. The software used here is built with an AI model using a programming language such as Python, and generates individual learning objectives suitable for the learner. This generative AI model uses a pre-trained algorithm to design the optimal learning plan based on the learner's characteristics.

[0685] The server generates educational resources in multiple formats based on the created learning plan. These resources are presented in various forms, such as games and interactive videos, and are designed to engage learners. This allows learners to progress towards achieving their goals while enjoying the learning process.

[0686] The terminal plays a crucial role in providing educational resources. The terminal receives learning plans and educational resources transmitted from the server and presents them to the learner through user interaction. As the learner utilizes the educational resources, their progress is collected in real time by the terminal and fed back to the server.

[0687] As a concrete example, a user might enter a prompt message such as, "I am a 10-year-old learner who is interested in mathematics, and especially wants to learn the basics of arithmetic." Based on this information, the server creates an educational plan to provide the learner with the basics of mathematics in a game format.

[0688] Furthermore, based on progress information, the server performs AI-powered analysis and adjusts the educational plan and resources accordingly. This ensures that feedback and new learning materials are provided in a timely manner, tailored to the learner's understanding and areas of difficulty.

[0689] Therefore, this system can provide an effective learning environment tailored to the individual needs of each learner.

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1:

[0692] The user uses a device to input information about the learner's learning characteristics. This information includes the learner's age, areas of interest, strengths and weaknesses in subjects, and learning pace. The entered information is sent to the server via a form on the device. Specifically, the user fills out profile data by answering questions on the device screen and completes the information input by pressing the submit button.

[0693] Step 2:

[0694] The server receives learning characteristics information sent from the terminal and stores it in the database. During this process, the server writes the information to the database and organizes the data by associating it with learner IDs and other relevant information. The input is learning characteristics information from the terminal, and the output is the information stored in the database. Specifically, the server verifies the accuracy of the data and registers it in the database using SQL commands.

[0695] Step 3:

[0696] The server uses a generative AI model to design learning objectives appropriate for the learner based on the received data. The model uses a pre-trained algorithm to analyze learning characteristics information and set optimal objectives. The input is learning characteristics information, and the output is the set learning objectives. Specifically, the AI ​​model uses a Python script to analyze the information and generate the objectives.

[0697] Step 4:

[0698] The server designs a learning plan based on the generated learning objectives. This plan includes educational resources and learning steps. A generative AI model automatically creates the plan and optimizes it using training data. The input is the learning objectives, and the output is the specific learning plan. The server stores the generated plan in a database for later access.

[0699] Step 5:

[0700] The server sends the designed learning plan and educational resources to the terminal and provides them to the user. Here, the educational resources are presented in a user-friendly format, such as games or interactive videos. The input is the learning plan, and the output is the transmission of data to the terminal. Specifically, the application on the terminal triggers and reflects the reception of data from the server.

[0701] Step 6:

[0702] Users engage with educational resources provided via a device, and their progress is recorded in real time by the device. The device sends this progress data to a server. The input is the user's learning activity, and the output is the progress information recorded by the device. The device automatically collects and transmits user actions and responses using sensors and logging systems.

[0703] Step 7:

[0704] The server analyzes progress information sent from the terminal and uses AI to evaluate the learner's understanding and necessary improvements. Based on the analysis results, it generates specific improvement instructions for the next step. The input is progress information, and the output is improvement instructions and adjusted educational resources. The AI ​​model evaluates the input data through this analysis and generates feedback.

[0705] Step 8:

[0706] The server sends generated improvement instructions and tailored educational resources to the device and provides them to the user. Simultaneously, it regularly notifies parents of learning progress and next steps. The input is improvement instructions and tailored educational resources, and the output is notifications to the device and parents. Notifications to parents are delivered via email and app push notifications.

[0707] (Application Example 1)

[0708] 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".

[0709] The challenge lies in providing an educational system that allows students to learn efficiently while stimulating their interest, by setting optimal learning goals based on their individual learning profiles, continuously monitoring their progress, and providing timely feedback. Traditional educational systems struggle to dynamically adjust learning content to match individual students' progress and interests, and lack a rapid and adaptive mechanism for incorporating current feedback into learning plans.

[0710] 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.

[0711] In this invention, the server includes means for acquiring user learning profile information, means for setting learning objectives and generating a learning plan based on the acquired information, and means for dynamically providing appropriate educational content in various formats according to the generated learning plan. This makes it possible to provide a learning program customized for each child and to quickly adjust the content according to their progress at any given time.

[0712] A "user" is an entity that provides learning profile information and receives educational content through the system.

[0713] "Learning profile information" refers to attribute information such as the user's age, strengths and weaknesses, areas of interest, and desired learning pace.

[0714] "Learning objectives" are the final destinations or indicators of learning, set based on the user's learning profile information.

[0715] A "learning plan" is a detailed plan that outlines the learning content and procedures necessary to achieve learning objectives.

[0716] "Educational content" refers to information, including teaching materials and learning resources necessary for carrying out a learning plan, and is provided in a variety of formats.

[0717] "Feedback" refers to evaluations and suggestions for the next steps generated based on what the user has learned and their progress.

[0718] A "supervisor" refers to an individual or organization responsible for monitoring a user's learning progress and next steps, and providing appropriate support.

[0719] "Artificial intelligence analysis" is a technology that uses the user's learning progress information to highly analyze and design the next learning step.

[0720] "Diverse formats" refers to educational content being delivered through different media and methods, depending on the learner's interests and level of understanding.

[0721] The system based on this invention acquires the user's learning profile information, sets personalized learning goals based on that information, and generates a learning plan. This information, provided through the terminal, records the user's attributes and interests in detail. The server uses artificial intelligence technology to analyze this information and generate the optimal learning program. Frameworks such as TensorFlow and PyTorch are used for this AI analysis.

[0722] The server sends the generated learning plan and educational content to the device, which the user receives via smartphone or smart glasses to proceed with their learning. These devices have applications built using React Native installed, presenting the content to the user in an interactive and engaging way. The educational content is dynamically adjusted according to progress and delivered in diverse formats. This enhances learning effectiveness and helps maintain sustained interest.

[0723] Furthermore, the server monitors learning progress and generates appropriate feedback. This feedback is also analyzed by AI and used to precisely adjust the next learning process. Supervisors are regularly reported on the user's progress and next steps, allowing them to understand the learning situation in real time.

[0724] For example, if a user expresses interest in history, the server can provide history content tailored to that user in a game format. If the user encounters difficulties understanding ancient civilizations, the learning content can be adjusted by suggesting additional video materials or quizzes.

[0725] Examples of prompts include, "I want to learn about medieval European culture. Please recommend some learning materials," and "Please generate a quiz to learn more about ancient Egyptian civilization." Based on these prompts, the system is structured to prepare for effective learning support for the user.

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The user inputs learning profile information using a terminal. This information includes age, areas of interest, strengths and weaknesses, and desired learning pace. The terminal stores this information in a database and sends it to the server. Input here is direct input by the user, while output is the transfer of information to the server.

[0729] Step 2:

[0730] The server analyzes the received learning profile information using artificial intelligence technology. Specifically, it uses TensorFlow or PyTorch to set individual learning goals for the AI ​​model. In this step, the input is the user's profile information, and the output is the set learning goals.

[0731] Step 3:

[0732] The server generates a learning plan based on the set learning objectives. This generation process designs step-by-step content while reflecting past learning data and user interests. The generated learning plan is then sent to the terminal as output.

[0733] Step 4:

[0734] The device displays educational content to the user based on a learning plan received from the server. React Native is used here to provide interactive content. The input is the learning plan from the server, and the output is the display of visual content to the user.

[0735] Step 5:

[0736] Users progress through their learning based on the provided educational content. Progress is continuously transmitted from the device to the server as feedback information. Input is the user's response to the learning content, and output is the collection and transmission of progress information.

[0737] Step 6:

[0738] The server receives progress information and generates feedback through AI analysis. It evaluates what the user understands and where they are struggling, and makes necessary adjustments for the next stage. Based on this analysis, data processing is used to dynamically design the next learning plan. As output, the adjusted feedback is provided back to the device.

[0739] Step 7:

[0740] The server periodically reports to the supervisor on the user's learning progress and next steps. This information is provided via email and notification functions. The input is accumulated learning data, and the output is notifications to the supervisor.

[0741] 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.

[0742] This invention is a system that incorporates an emotion engine into a learning support system to take into account the user's emotional state and provide an individually tailored learning experience. The system consists of a server, a terminal, the emotion engine, and the user.

[0743] Users enter learning profile information on their device. This includes age, areas of interest, subjects they excel at and struggle with, and their desired learning pace. This data is then sent from the device to the server.

[0744] The server analyzes the transmitted learning profile information and user emotion data obtained from the emotion engine. The emotion engine analyzes micro-expressions, tone of voice, and behavioral patterns while the user is using the learning content, recognizing emotions in real time. Based on this data, the server sets learning objectives optimized for the user and generates a customized learning plan to achieve them.

[0745] The learning plan is dynamically adjusted according to the user's learning progress and emotions. For example, if the user is feeling frustrated, the server will temporarily change the learning content to a game or quiz format to stimulate their motivation. The device provides these changes to the user in real time, encouraging them to continue learning with enthusiasm.

[0746] The user's learning progress and emotional state are constantly transmitted from the device to the server for analysis. Based on this information, the server periodically generates detailed feedback, including the child's learning progress and psychological state, and sends notifications to parents. These notifications include insights into where the user is struggling and what emotional state they are in while learning, allowing parents to adjust their conversations and support with the user based on this information.

[0747] For example, if a child is detected as feeling frustrated while working on math content, the emotion engine will detect this, and the server will provide hints or easier content to simplify the problem. Then, once the user regains their motivation, feedback will be provided to gradually increase the difficulty level. In this way, providing a flexible learning experience that responds to children's emotions creates an effective and enjoyable learning environment.

[0748] The following describes the processing flow.

[0749] Step 1:

[0750] Users input learning profile information via their device and send it to the server. This includes age, areas of interest, subjects they excel at and struggle with, and their preferred learning pace.

[0751] Step 2:

[0752] While the user is using the learning content, the device uses an emotion engine to analyze the user's micro-expressions, voice tone, and other characteristics in real time, and acquires emotional data.

[0753] Step 3:

[0754] The device sends the collected emotional data to the server. The server analyzes this data along with the user's learning profile information and sets learning goals tailored to the user.

[0755] Step 4:

[0756] The server generates a customized learning plan based on the learning objectives and sends it to the device. The device then provides the user with learning content in the appropriate format according to this plan.

[0757] Step 5:

[0758] The user engages with learning content. The device continuously monitors the user's progress and emotional state, and sends data to the server as needed.

[0759] Step 6:

[0760] The server analyzes progress information and sentiment data, and adjusts the learning plan as needed. For example, if the user appears confused, the server simplifies the problem and adds voice hints.

[0761] Step 7:

[0762] The server generates notifications for parents and periodically sends reports that include the user's mental state and progress. This allows parents to have a detailed understanding of their child's learning situation.

[0763] Step 8:

[0764] If the user's emotions improve, the device will follow instructions from the server to gradually reduce the difficulty level of the learning content and allow the user to proceed to the next learning step.

[0765] (Example 2)

[0766] 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".

[0767] Modern learning support systems face the challenge of providing flexible learning experiences tailored to the individual needs and emotional states of users. Furthermore, there is a lack of effective means to appropriately inform parents about their child's learning progress and psychological state, and to maintain the user's motivation.

[0768] 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.

[0769] In this invention, the server includes means for acquiring user profile information, means for setting learning objectives and generating an individualized learning plan based on the acquired profile information and emotional data collected in real time, and means for dynamically adjusting the learning plan and content based on the generated feedback. This provides a flexible learning environment that responds to the individual needs and emotional state of the user, enabling effective learning support while maintaining motivation.

[0770] "User profile information" refers to personal learning-related information that users provide to the system, including age, areas of interest, subjects they excel at and struggle with, and their desired learning pace.

[0771] "Emotional data" refers to data about the emotional state a user exhibits during learning, and is collected in real time based on micro-expressions, tone of voice, behavioral patterns, and other factors.

[0772] "Learning objectives" are indicators that show the specific results or states that users are expected to ultimately achieve in their learning.

[0773] "Personalized learning plans" refer to learning schedules and content arrangements designed to meet individual needs, based on user profile information and emotional data.

[0774] "Feedback" refers to information such as evaluations and advice generated based on the user's learning progress and emotional state, with the aim of improving and adjusting their learning.

[0775] An "interactive format" refers to a method of providing learning content that enables two-way interaction with users, and includes games, quizzes, simulations, and other similar methods.

[0776] "Dynamic adjustment" refers to the process by which the system automatically modifies learning plans and content based on the user's latest data and circumstances.

[0777] This invention comprises a learning support system including a server, a terminal, and an emotion engine that complements them. In implementing the system, the user first inputs their profile information using a terminal. The terminal can be a general computer, tablet, or smartphone, and data can be entered via a keyboard or touchscreen. This profile information includes the user's age, areas of learning interest, subjects they excel at and struggle with, and desired learning pace. Subsequently, the information collected from the user is transmitted to the server via the internet.

[0778] The server processes the received profile information and combines it with additional emotional data obtained from the emotion engine. The emotion engine uses the device's camera and microphone to collect and analyze the user's micro-expressions, voice tone, and behavioral patterns in real time. This technology utilizes facial recognition software and voice analysis software. Based on the analyzed data, the server sets user-specific learning goals and learning plans.

[0779] Once a learning plan is generated, it is provided to the user via their device. The learning content is delivered in an interactive format, including gamified learning tools and interactive quizzes. Users can effectively progress through their learning by utilizing this content.

[0780] User progress and behavioral data are constantly transmitted from the device to the server. The server analyzes this information and generates feedback using a generative AI model. This feedback is used to dynamically adjust the user's learning plan. In addition, notifications are sent periodically to parents, providing detailed reports on the user's learning progress and emotional state.

[0781] For example, if the emotion engine detects frustration while a user is solving a math problem, the learning plan is immediately adjusted, and hints and simple tasks are provided to help solve the problem. Furthermore, users can input questions into the system, such as, "How can I see in real time how a child expresses emotions in specific content?" Through this series of technical measures, the optimal learning environment for the user is provided.

[0782] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0783] Step 1:

[0784] The user enters learning profile information on the device. Specifically, the user uses the device's input interface (keyboard, touchscreen, etc.) to enter their age, areas of interest, subjects they excel and struggle with, and desired learning pace. The entered data is sent from the device to the server. The output here is user profile information for processing by the server.

[0785] Step 2:

[0786] The server receives learning profile information transmitted from the terminal and collects user emotion data in real time through the emotion engine. This emotion data is obtained using the terminal's camera and microphone, capturing the user's micro-expressions, voice tone, and behavioral patterns. By analyzing this data, the server determines the user's current emotional state. The output is the analyzed user emotion state information.

[0787] Step 3:

[0788] The server sets optimized learning objectives based on the user's profile information and analyzed sentiment data. It then generates an individualized learning plan. Specifically, the server's algorithm receives profile and sentiment data as input, calculates the learning path, and outputs the result as a learning plan.

[0789] Step 4:

[0790] The device receives a personalized learning plan sent from the server and provides the user with appropriate learning content accordingly. The learning content uses interactive formats to engage the user's interest, including games and quizzes. The output displays the learning content the user should work on.

[0791] Step 5:

[0792] As users utilize learning content and progress, their devices continuously transmit behavioral data and emotional states to a server. The server analyzes the received data and generates feedback using a generative AI model. Based on the input behavioral and emotional data, feedback output is generated and reflected in the learning plan.

[0793] Step 6:

[0794] The server dynamically adjusts the learning plan and content based on the feedback generated. For example, if a user becomes frustrated with a difficult task, the server adjusts the plan by making the task easier or adding hints. As a result, an optimal learning environment is maintained for the user.

[0795] Step 7:

[0796] The server periodically generates detailed reports including the user's learning progress and emotional state, and sends notifications to parents. These notifications include information about where the user is struggling and their emotional state while learning, allowing parents to provide support. The output is a notification that is sent to the parent's device.

[0797] (Application Example 2)

[0798] 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".

[0799] Traditional learning support systems had the problem of providing only a uniform learning experience without considering the emotional state of individual users. As a result, users were prone to losing motivation for the learning material, making it difficult to achieve effective learning. Furthermore, the lack of flexible feedback tailored to learning progress made it difficult to optimize learning.

[0800] 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.

[0801] In this invention, the server includes means for setting learning objectives based on the user's learning profile information and emotional information, and generating a learning plan to reach those objectives; means for monitoring the user's learning progress information and emotional state, and generating feedback based on that data; and means for adjusting the learning plan and content in accordance with the generated feedback and providing them adaptively. This enables the provision of a flexible and personalized learning experience that responds to the user's emotional state, and allows for the optimization of effective learning.

[0802] "User learning profile information" refers to information that indicates the individual learning characteristics of the user, including their age, areas of learning interest, subjects they excel at and struggle with, and their desired learning pace.

[0803] "Emotional information" refers to data that indicates a user's emotional state at a given time, obtained by analyzing micro-expressions, tone of voice, and behavioral patterns exhibited during the learning process.

[0804] "Learning objectives" are standards that indicate the specific level of knowledge and skills that a user should achieve within a particular learning period.

[0805] A "learning plan" is a plan that outlines the steps and resources necessary to achieve learning objectives and designs learning activities optimized for the user.

[0806] "Learning content" refers to educational materials such as text, videos, and interactive quizzes provided to support user learning.

[0807] "Feedback" refers to evaluations and suggestions generated based on the user's learning progress, providing guidance for improving learning and taking the next steps.

[0808] "Emotional state" refers to a user's internal psychological state, and is an emotional state that directly affects their motivation to learn and their ability to concentrate.

[0809] "Stakeholders" refers to individuals who are directly or indirectly involved in the learning progress or outcomes, such as the learner's parents, educators, or supporters.

[0810] The system implementing this invention consists of a server, a terminal, an emotion engine, and a user. The server plays a central role in providing a personalized learning experience based on the user's input data. The detailed configuration of the system is described below.

[0811] The device provides an interface for users to input learning profile information. This information includes the user's age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is transmitted to the server via an internet connection.

[0812] The server analyzes the user's emotional state using an emotion engine. The emotion engine evaluates the user's micro-expressions and voice tone, captured through the device's camera and microphone, in real time to recognize their emotional state. This analysis utilizes Python's OpenCV library and librosa, among others, to extract emotional information from the data.

[0813] The server sets learning objectives based on acquired learning profile information and emotional information. The generated learning plan is dynamically adjusted according to the user's emotional state, and learning content is delivered to the device at the appropriate time. Learning content is provided in multiple formats, including text, video, and interactive quizzes.

[0814] Furthermore, the server constantly monitors the user's learning progress and emotional state, generating timely feedback. This feedback is generated through artificial intelligence analysis and supports the optimization of learning. It also allows for immediate adjustments to the learning plan and content based on the generated feedback.

[0815] For example, if a user shows frustration while tackling a complex problem, the emotion engine can detect this and the server can switch the learning content to easier problems or game formats. This can help rekindle the user's motivation.

[0816] Examples of prompt messages are as follows:

[0817] "Analyze the user's facial expressions in real time to determine if they are experiencing fatigue or frustration."

[0818] "Based on user sentiment data, select and deliver learning content in the most optimal format."

[0819] In this way, the server can provide a flexible learning experience tailored to the individual needs of the user.

[0820] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0821] Step 1:

[0822] Users enter learning profile information on their device. This input process involves entering detailed information such as age, areas of interest, strengths and weaknesses in subjects, and desired learning pace. The entered data is formatted by the device and sent to the server.

[0823] Step 2:

[0824] The server receives learning profile information sent from the terminal and begins processing it. Based on this information, the server sets initial learning objectives. Here, it uses database queries to compare with past user data and extract the most appropriate learning objectives.

[0825] Step 3:

[0826] The server uses the terminal's camera and microphone to collect emotional information about the user. An emotion engine analyzes this data to identify the user's emotional state from their micro-expressions and voice tone. It uses the Python OpenCV library to analyze facial expressions and librosa to extract voice features. The analysis results are then sent to the server.

[0827] Step 4:

[0828] The server combines received sentiment information and learning profile information to generate a learning plan optimized for the user. This plan determines the list of necessary content and the order in which each piece of content is presented, according to the learning objectives. An AI model is used to fine-tune the plan to achieve the maximum learning effect.

[0829] Step 5:

[0830] The device presents the user with appropriate learning content according to the learning plan received from the server. This content includes text, video, and quiz formats, incorporating features designed to enhance the user's motivation to learn. The device then records how the user responds to this content.

[0831] Step 6:

[0832] The system monitors the user's learning progress and emotional state, periodically sending this data from the device to the server. The server then generates feedback based on this data. This feedback identifies areas of performance that are strong and areas that need improvement, and guides the user through the next steps. This feedback is generated using artificial intelligence analysis, and the analysis results are utilized.

[0833] Step 7:

[0834] The server adjusts the learning plan and content in real time based on the generated feedback, adapting them to create a more effective learning experience. This adjustment is then sent back to the terminal, providing the user with a new learning direction. This process may also involve using prompts to generate new suggestions.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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."

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] The following is further disclosed regarding the embodiments described above.

[0857] (Claim 1)

[0858] Methods for obtaining user learning profile information,

[0859] A means for setting learning objectives and generating a learning plan to achieve them, based on acquired learning profile information.

[0860] A means of providing appropriate learning content according to the generated learning plan,

[0861] A means for monitoring the user's learning progress and generating feedback based on that progress,

[0862] Means for adjusting learning plans and content in response to generated feedback,

[0863] A means of regularly notifying parents and reporting on progress and the next steps,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, wherein learning content is provided in multiple formats that attract the user's interest.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein feedback generation is performed by AI analysis of learning progress information.

[0869] "Example 1"

[0870] (Claim 1)

[0871] A means of acquiring user learning characteristics information,

[0872] A means for setting goals and generating an educational plan to achieve those goals based on acquired learning characteristics information,

[0873] Means of providing appropriate educational resources in accordance with the generated educational plan,

[0874] A means for monitoring the user's learning progress and generating improvement instructions based on that progress,

[0875] Means for adjusting educational plans and resources in response to the generated improvement directives,

[0876] A means of regularly notifying parents of information and reporting on progress and the next steps,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein educational resources are provided in a variety of formats that engage the user's interest.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein improvement instructions are generated by artificial intelligence analysis of learning progress information.

[0882] "Application Example 1"

[0883] (Claim 1)

[0884] The means of obtaining the user's learning profile information,

[0885] A means for setting learning objectives and generating a learning plan to achieve them, based on acquired learning profile information.

[0886] A means of providing appropriate educational content according to the generated learning plan and making it dynamically adjustable in various formats,

[0887] A means for monitoring users' learning progress and generating feedback based on that progress,

[0888] A means of adjusting learning plans and educational content in response to generated feedback,

[0889] A means of regularly notifying supervisors and reporting on progress and next steps,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, in which educational content is provided through multiple media that capture the user's interest and is adaptively modified.

[0893] (Claim 3)

[0894] The system according to claim 1, wherein feedback generation is performed by artificial intelligence analysis of learning progress information and provides data for adjusting the next learning plan.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] Means for obtaining user profile information,

[0898] A means for setting learning objectives and generating an individualized learning plan based on acquired profile information and emotion data collected in real time,

[0899] A means of providing diverse learning content according to the generated learning plan,

[0900] A means for monitoring user behavior data and learning progress, and generating feedback including emotional state,

[0901] Means for dynamically adjusting learning plans and content based on generated feedback,

[0902] A means of sending regular notifications to parents and guardians and reporting on progress and psychological state,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, wherein the learning content is provided in an interactive format to engage the user's interest.

[0906] (Claim 3)

[0907] The system according to claim 1, in which feedback generation is performed by artificial intelligence analysis of user behavior data and emotional data.

[0908] "Application example 2 when combining with an emotional engine"

[0909] (Claim 1)

[0910] Methods for obtaining user learning profile information,

[0911] A means for setting learning objectives and generating a learning plan to achieve those objectives based on acquired learning profile information and emotional information,

[0912] A means for dynamically providing appropriate learning content according to the generated learning plan,

[0913] A means for monitoring the user's learning progress and emotional state, and generating feedback based on that data,

[0914] A means of adjusting and adaptively providing learning plans and content in response to generated feedback,

[0915] A means of regularly notifying stakeholders and reporting on progress and next steps,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, wherein learning content is provided in multiple formats that elicit the user's interest and emotional state.

[0919] (Claim 3)

[0920] The system according to claim 1, wherein feedback generation is performed by artificial intelligence analysis of learning progress information and emotional data. [Explanation of Symbols]

[0921] 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. Methods for obtaining user learning profile information, A means for setting learning objectives and generating a learning plan to achieve them, based on acquired learning profile information. A means of providing appropriate learning content according to the generated learning plan, A means for monitoring the user's learning progress and generating feedback based on that progress, Means for adjusting learning plans and content in response to generated feedback, A means of regularly notifying parents and reporting on progress and the next steps, A system that includes this.

2. The system according to claim 1, wherein learning content is provided in multiple formats that attract the user's interest.

3. The system according to claim 1, wherein feedback generation is performed by AI analysis of learning progress information.

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