system

Abstract

We provide the system. [Solution] A means for managing a database that provides hierarchical educational content based on the user's knowledge level, A means for controlling a generative model that automatically generates educational content according to the user's knowledge level and interests, A means for sending and displaying the generated educational content on the user's terminal, A means for collecting user operation data and generating feedback information, A means of adjusting the next content based on feedback information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [There is a problem that in various contents accessible to infants through the Internet, there is a lack of easy-to-understand expressions that match their knowledge level. Also, there is a lack of a mechanism to attract the interest of infants, so it is necessary to solve the problem that the motivation for learning is insufficient.]

Means for Solving the Problems

[0005] [This invention provides a system for managing a database for providing hierarchical educational content based on the user's knowledge level, and for controlling a generative model that automatically generates educational content according to the user's knowledge level and interests. This system transmits the generated educational content to the user's terminal and enables interactive display. Furthermore, it collects user operation data to generate feedback information, adjusts the next content based on the feedback information, and provides an individualized learning path.]

[0006] "Knowledge level" is an indicator that shows the degree of knowledge and understanding a user possesses, and serves as a criterion for adjusting the difficulty level of content.

[0007] "Educational content" is [a collection of information and activities provided for users to use interactively for the purpose of learning and improving their knowledge].

[0008] A "generative model" is a system built using specific algorithms or trained AI to automatically generate content tailored to the user's knowledge level and interests.

[0009] "Feedback information" refers to information generated by analyzing data obtained from user actions and reactions, as an evaluation of the user's level of understanding and interest.

[0010] A "personalized learning path" refers to a unique learning route optimized for each user's knowledge level, interests, and learning progress, supporting efficient learning. [Brief explanation of the drawing]

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

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

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

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

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

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

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

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0028] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] The system of this invention consists of a server, a terminal, and a user, thereby providing educational content that engages young children and promotes learning. The server manages a hierarchical database based on the user's knowledge level and selects appropriate educational content. The generative model automatically generates content according to the user's knowledge level and interests. The generated content is structured as interactive coloring pages and explanations and is sent from the server to the user's terminal.

[0033] The device serves to visually present educational content transmitted from the server to the user. Users can color through the device while simultaneously reading engaging explanations. For example, with the theme of "space," users can color planets and receive related explanations (such as planetary characteristics and location information). This allows learning to progress in a playful way, deepening young children's interest and understanding.

[0034] The results of the coloring activity performed by the user on their device, as well as their level of understanding of the explanations, are sent from the device to the server as feedback. The server analyzes this feedback information and adjusts the user's learning path based on their interests and level of understanding. This adjustment selects the most suitable content for the user to work on next, and this information is then sent back to the device.

[0035] As a concrete example, in the "Animals" theme, beginner users can color a cat-shaped image, read a simple explanation about cats, and learn about their habits and other related information. Data on the user's level of interest and understanding during this process will be used to adjust the content in the future.

[0036] In this way, the present invention realizes a new educational system that attracts the interest of young children while providing effective learning tailored to their knowledge level.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The server receives access requests from users and retrieves the user's identification information and current knowledge level. Based on this information, it selects the appropriate hierarchical syllabus for the relevant topic from the database.

[0040] Step 2:

[0041] The server invokes a generative model to automatically generate educational content based on the selected syllabus. This content includes coloring pages and related explanations tailored to the user's knowledge level and interests.

[0042] Step 3:

[0043] The server sends the generated educational content to the user's device. The data is prepared in an interactive format and configured for easy use by the user.

[0044] Step 4:

[0045] The terminal displays educational content received from the server. The user interface allows users to interact with coloring pages and explanations.

[0046] Step 5:

[0047] The user completes a coloring page provided on their device and begins interacting with the app by following on-screen instructions. Simultaneously, they read explanations and absorb knowledge.

[0048] Step 6:

[0049] The device records the user's actions and responses. This information is then converted into data representing the user's actions and level of understanding of the explanations.

[0050] Step 7:

[0051] The device sends collected operation data and information regarding comprehension to the server. This data is then organized into feedback information.

[0052] Step 8:

[0053] The server analyzes the received feedback information and evaluates the user's interest and level of understanding. Based on these results, it decides how to adjust future educational content.

[0054] Step 9:

[0055] The server incorporates the analysis results and plans the next educational content to be provided. The optimal content is selected and prepared to keep users engaged and enable them to learn effectively.

[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] In children's education, providing appropriate content tailored to each user's knowledge level and interests is challenging, and standardized materials have limited learning effectiveness. Furthermore, static materials make it difficult to maintain young children's interest over extended periods, leading to a decline in the quality of learning.

[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 information and analyzing the user's knowledge level and interests; means for generating prompt statements based on the analysis results and creating educational content using a generative model; and means for transmitting and displaying the created educational content to the user's terminal. This makes it possible to provide personalized and interactive educational content to each user.

[0061] "Means of obtaining user information" refers to methods of collecting data such as the user's age, areas of learning interest, and existing knowledge level.

[0062] "Means of analyzing knowledge level and interests" refers to an analytical process that selects the most suitable educational content for a user based on information obtained from that user.

[0063] "Means for generating prompt sentences" refers to the process by which a generative model creates instruction sentences based on the obtained analysis results, enabling it to create appropriate educational content.

[0064] "Methods for creating educational content using generative models" refer to using AI to generate personalized learning materials based on prompts.

[0065] "Means for sending and displaying content on a user's device" refers to technologies for transferring generated educational content to a user's device via a network and providing it visually.

[0066] This invention provides personalized educational content through a system consisting of a server, a terminal, and user interaction.

[0067] The server receives information from the user. Specifically, it collects information such as the user's age, areas of learning interest, and existing knowledge level. This information is stored in a database on the server. The database, combined with the user's past learning history, forms the basis for analysis.

[0068] A generative AI model runs on the server and generates prompt messages based on user information. This creates personalized educational content. For example, a possible prompt message might be, "The user is at a beginner level, their area of ​​interest is animals, and they have a particular interest in cats." Based on this prompt message, the AI ​​model analyzes the information and creates educational content tailored to the user.

[0069] The created educational content is sent from the server to the terminal. The terminal receives it and can present it visually to the user. The presented content includes learning tasks in the form of interactive coloring pages and accompanying explanatory text. For example, if the user shows interest in animals, a cat coloring page and related explanations about the characteristics and habits of cats will be provided.

[0070] Users access content via their devices and engage in interactive tasks. User behavior data, such as the completion status of coloring pages and the amount of explanations viewed, is sent from the device to the server as feedback. This feedback is important for adjusting the content of the next learning session.

[0071] Through the process described above, this invention provides an individualized learning experience in the early childhood education process based on the user's knowledge level and interests. This helps to deepen children's interest in learning and to help them absorb knowledge more effectively.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user logs into the terminal and enters information about their age, areas of learning interest, and existing knowledge level. This information is sent to the server. The input includes the user's basic information. The output is that this information is stored in the server's database.

[0075] Step 2:

[0076] The server compares the received user information with the database, providing the AI ​​model with the necessary data to generate prompt messages. This process involves analyzing and organizing the user information. The generated prompt messages serve as instructions for the model.

[0077] Step 3:

[0078] The generative AI model receives prompt text from a server as input and generates optimal educational content. Natural language processing and content generation algorithms are used for data computation. The output is interactive educational content.

[0079] Step 4:

[0080] The server sends the generated educational content to the user's device. The input is the educational content from the generating AI model, and the output is that it is displayed on the user's device and made available to the user.

[0081] Step 5:

[0082] Users access educational content on their devices, for example, by coloring or reading explanations. User actions are recorded on the device, and this information is sent to the server as feedback. The input is the user's actions, and the output is the feedback data.

[0083] Step 6:

[0084] The server analyzes the feedback data sent from the terminal and uses it to adjust the next learning content. Feedback information is the input, and preparations for generating the next learning content tailored to the user are made as the output.

[0085] (Application Example 1)

[0086] 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."

[0087] The problem that this invention aims to solve is to enable young children to gain educational learning experiences through products that naturally interest them in physical stores, and to optimize the learning content according to each user's level of understanding. Conventional educational systems have the problem of being limited to the provision of fixed information, making it difficult to dynamically adjust to the user's real-time interests and level of understanding.

[0088] 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.

[0089] In this invention, the server includes means for managing an information infrastructure that provides hierarchical educational information based on the user's knowledge level; means for controlling a generation program that automatically generates educational information according to the user's knowledge level and interests; means for transmitting and displaying the generated educational information on a user's device; and means for identifying objects using information recognition technology and presenting relevant educational information. This makes it possible to provide an interactive learning experience based on the interests of young children in a physical store and to individually tailor the user's learning process.

[0090] "Educational information structured based on the user's knowledge level" refers to learning materials organized in stages according to each user's knowledge and understanding.

[0091] An "information infrastructure" is a collection of databases and systems for managing, storing, and distributing educational information.

[0092] A "generation program" is software that includes an algorithm for automatically generating appropriate educational content based on specific user attributes.

[0093] "User devices" are devices that users use to receive information and enjoy interactive experiences, and include smart glasses and tablets.

[0094] "Information recognition technology" refers to technologies for identifying objects and information from data such as images and audio, and utilizes machine learning and AI models.

[0095] An "interactive learning experience" is a learning method in which users deepen their learning by interacting with educational content and actively participating.

[0096] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server has an information infrastructure that manages hierarchical educational information based on the user's knowledge level, and uses a generation AI model to control the generation program, automatically generating educational information according to the user's knowledge level and interests.

[0097] Specifically, the server collects feedback information sent from the user's device and adjusts the appropriate content for the next session. Through this process, a personalized learning experience is provided. The server also plays a role in identifying objects using information recognition technology, selecting relevant educational information, and sending the generated information to the user's device. The information recognition technologies used include machine learning and AI models (e.g., YOLO and Google® Cloud Vision).

[0098] The terminal uses smart glasses or tablets as user devices to visually present educational information transmitted from the server to the user. Through this, the user enjoys an interactive learning experience and interacts with the information as needed. AR technology (e.g., Lens Studio or ARCore) is used to present the information.

[0099] As a concrete example, when a user picks up a toy in a physical store, the terminal identifies the toy and displays a coloring page and explanation of the associated "animal." Through this process, young children can learn while playing. An example of a prompt message would be, "Identify the code of this product and generate related educational content."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server receives product information transmitted from the user's device. This input data includes images and barcodes of products recognized by smart glasses or tablets. The server uses this information to identify products using machine learning models (e.g., YOLO or Google Cloud Vision) and determine related topics. The output of this process is a theme related to the product (e.g., animals, space, etc.).

[0103] Step 2:

[0104] The server generates educational content tailored to the user's knowledge level and interests, based on a selected theme. A generation AI model is used to select and generate appropriate content from hierarchical educational information in the database. The prompt used is "Generate educational content related to this theme." The generated content consists of interactive coloring pages and explanations. The output is the generated educational content.

[0105] Step 3:

[0106] The server transmits the generated educational content to the user's device. The device visually displays the transmitted content to the user using AR technology. The user interacts with the presented content through smart glasses or a tablet and begins learning. The input is the generated educational content, and the output is the coloring pages and explanations displayed on the user's device.

[0107] Step 4:

[0108] Users color in educational content based on the displayed material and deepen their learning by reading explanations. The data generated during this process includes feedback information on the user's interaction results and level of understanding.

[0109] Step 5:

[0110] The device sends user interaction results and feedback information to the server. The server collects this information and analyzes it to improve future content generation. The input is user feedback, and the output is the analysis results. This allows for more personalized learning path adjustments in the future.

[0111] 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.

[0112] The present invention provides educational content that maximizes learning effectiveness for young children by combining a server, terminal, user, and emotion engine. The server selects a hierarchical syllabus of appropriate themes from a database based on the user's knowledge level and emotional state. The generative model generates educational content based on this syllabus and sends the content to the user's terminal.

[0113] The device interactively displays received educational content to the user. In addition, the emotion engine analyzes the user's emotions from their facial expressions and voice via the device's camera and microphone, determining their emotional state in real time. For example, if the emotion engine detects "excitement" or "joy" while the user is enjoying a space-themed coloring book, it can maintain the user's motivation to learn by providing new content or difficulty levels appropriate to that state.

[0114] Users can use their devices to not only complete the displayed coloring pages but also read explanatory texts that interest them. The emotion engine understands the user's emotional responses and records emotional data as feedback on the device. This information is then sent to a server, added to normal operation data, and used to adjust the content.

[0115] The server analyzes the received feedback information and adjusts the individual learning path, including the user's emotional data. This adjustment helps select the next educational content and provides a new learning experience that will further engage the user.

[0116] This format allows users to progress through individually optimized educational paths, creating a flexible and effective learning environment that can adapt to changes in motivation based on emotions. For example, based on emotional analysis of animal-themed content, it is possible to suggest dynamic video content to excited users and provide detailed explanations to quiet, focused users.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The server receives requests from users and selects appropriate educational topics by referring to the user's profile and knowledge level. This information is stored in the system's database.

[0120] Step 2:

[0121] The server invokes a generative model to generate educational content based on the user's knowledge level and selected topic. The generated content is structured as coloring pages or explanations.

[0122] Step 3:

[0123] The server sends the generated educational content to the user's device. This data is packaged in a format that allows for immediate display.

[0124] Step 4:

[0125] The device visually displays the received content to the user and activates an emotion engine to monitor the user's emotional state. During this process, the device's camera and microphone are used to analyze emotions in real time.

[0126] Step 5:

[0127] Users color in images provided on their devices and learn by reading related explanations. Emotions are analyzed from the user's facial expressions and voice through an emotion engine.

[0128] Step 6:

[0129] The device records user actions and emotional data and sends it to a server. This information is aggregated as feedback data.

[0130] Step 7:

[0131] The server analyzes the received feedback and sentiment data to evaluate the user's interest and understanding. Based on this evaluation, it optimizes the content delivered next time.

[0132] Step 8:

[0133] Based on the analysis results, the server updates the user's individual learning path and plans the next educational content, taking into account their emotional state.

[0134] Step 9:

[0135] The server generates newly planned educational content and prepares to send it to the device in the next session so that the user can continue learning.

[0136] (Example 2)

[0137] 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".

[0138] In the field of early childhood education, providing customized learning content tailored to individual knowledge levels and emotional states is challenging. Traditional education systems often offer uniform curricula, making it difficult to create educational experiences based on individual learners' interests and emotions. As a result, this can lead to decreased motivation and hinder effective comprehension.

[0139] 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.

[0140] In this invention, the server includes means for managing a collection of information that provides hierarchical educational materials based on the user's knowledge level and emotional state; means for controlling a generation engine that automatically generates educational materials according to the user's knowledge level, emotional state, and interests; and means for transmitting and displaying the generated educational materials on the user's device. This makes it possible to provide a learning experience optimized for each individual user in real time and to effectively improve learning interest and comprehension.

[0141] "User knowledge level" is an indicator that shows the depth and breadth of knowledge that an individual user currently possesses.

[0142] "Emotional state" refers to the state of mind that a user has regarding learning content, representing their psychological reactions and feelings towards it.

[0143] "Hierarchical educational materials" are [a set of educational content organized and structured according to the user's knowledge level and learning progress].

[0144] An "information repository" is a database or information repository used to manage and provide educational materials and learning content.

[0145] A "generative engine" is a system or program that automatically creates appropriate educational materials based on the user's knowledge level and emotional state.

[0146] "User device" refers to [an electronic device on which educational materials are displayed and which the user can operate interactively].

[0147] "Feedback information" refers to data generated based on the user's operation history and emotional responses, which is used to adjust the learning content for the next session.

[0148] A "personalized learning path" is a plan designed to guide users through learning in the optimal order and content, tailored to their interests and level of understanding.

[0149] This invention constitutes a system that efficiently provides educational materials tailored to the individual characteristics of users. Its main components include a server, a terminal, a generative AI model, and an emotion engine.

[0150] The server manages a collection of information for providing hierarchical educational materials based on the user's knowledge level and emotional state. Specifically, it performs SQL query processing to select the most appropriate hierarchical materials by matching the educational materials stored in the database with the user profile.

[0151] The server further controls the generative AI model based on the user's profile information to automatically generate user-specific educational materials. In this process, a well-known natural language processing engine (e.g., GPT-4®) is used as the generative AI model to send prompts and construct optimal learning materials. For example, the prompt "Knowledge level: Beginner, Theme: Space, Emotion: Excitement" might be input to the AI ​​model.

[0152] The generated educational materials are sent from the server to the user's terminal. The terminal has the capability to display the received materials interactively, and the user progresses through learning by interacting with it. Specifically, the terminal is equipped with touchscreen and voice input functions, allowing the user to interact with interactive tasks such as coloring pages and quizzes.

[0153] Furthermore, the device has a built-in camera and microphone, and the emotion engine uses data acquired from these sensors to analyze the user's emotional state in real time. This allows for dynamic adjustments, such as displaying more challenging content when the user is excited, or detailed explanations when the user is focused.

[0154] The feedback information obtained through this process is sent to the server and used to adjust the next set of educational materials. This feedback includes operation logs and data on emotional states, and analysis is used to optimize individual learning paths.

[0155] This system allows users to always receive learning materials tailored to their own learning motivation and level of understanding, enabling them to learn effectively.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server searches the database for relevant, hierarchical educational materials based on user input, including knowledge level and past learning history. Using database queries, it extracts appropriate materials and generates a list of materials that match the user profile. This output is the list of materials to be used in the next step.

[0159] Step 2:

[0160] The server sends prompts to a generative AI model based on the list of materials and the user's emotional state, generating customized educational materials. The generative AI model receives the prompts as input and uses natural language processing techniques to generate the most suitable educational content for the user. The generated educational materials are obtained as output.

[0161] Step 3:

[0162] The server sends the generated learning materials to the user's terminal. Using a communication protocol, it sends the learning material data as packets to the terminal over the internet and maintains the connection until reception is confirmed. The output is the learning material data sent to the terminal.

[0163] Step 4:

[0164] The device displays interactive educational materials to the user based on the received educational data. It accepts touch and voice input through the user interface, visualizing the content to facilitate user interaction with the materials. The output is the interactively displayed educational material.

[0165] Step 5:

[0166] The device uses its camera and microphone to capture the user's facial expressions and voice, inputting them into the emotion engine in real time. The emotion engine analyzes the input data using an emotion analysis algorithm to identify the user's emotional state. The output is the result of the emotional state determination.

[0167] Step 6:

[0168] The device dynamically adjusts the user's learning experience based on the results of its emotional state assessment. It changes the difficulty level and content of the displayed material to enable the user to learn more effectively. The output is the adjusted learning content.

[0169] Step 7:

[0170] The server receives feedback information sent from the terminal and records it in a database. This feedback information includes the user's operation history and sentiment data. Recording it in the database generates foundational data that can be used to adjust the learning content for the next session.

[0171] (Application Example 2)

[0172] 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".

[0173] When providing educational information for young children, it is essential to offer an optimal learning experience tailored to each user's emotional state and interests. However, existing educational systems struggle to provide individualized support. Furthermore, in physical learning environments, the technology to provide interactive content that responds to user reactions in real time is not yet fully established. As a result, users are often limited to one-way information provision, leading to decreased learning efficiency.

[0174] 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.

[0175] In this invention, the server includes means for managing a recording device that provides hierarchical learning information based on user attributes; means for controlling generation rules that automatically generate learning information according to the user's attributes and level of interest; and means for analyzing the user's emotional state through a camera and microphone and recording it as evaluation information. This makes it possible to provide learning information tailored to the user's individual emotional state and interests in real time, thereby improving the quality of the learning experience in physical stores.

[0176] "User attributes" refer to a collection of individually identifiable characteristics, such as the learner's age, knowledge level, and learning history.

[0177] "Hierarchical learning information" refers to educational materials and assignments that are structured in stages according to the learner's level of understanding and progress.

[0178] A "recording device" refers to a machine or program that has functions such as a database for storing and managing information.

[0179] "Generation rules" refer to algorithms or rule sets used to create or refine educational information based on specific conditions.

[0180] "Emotional state" refers to the learner's psychological and emotional state, which can be expressed as specific emotions such as joy, excitement, or concentration.

[0181] "Evaluation information" refers to data obtained based on learners' actions and responses, and is used to measure the progress and effectiveness of learning.

[0182] A "learning path" refers to a specific set of learning steps or processes that learners follow using educational information, providing a customized learning experience.

[0183] The system for carrying out this invention includes a server containing a recording device, generation rules, and an emotion analysis engine, and a terminal equipped with a large display, camera, and microphone that enable interactive display. The server manages hierarchical learning information based on user attribute information and automatically generates appropriate content according to the user's level of interest using a generative AI model. The terminal analyzes the learner's facial expressions and voice in real time and records their emotional state as evaluation information.

[0184] Based on recorded evaluation information, the server adjusts the learner's individual learning path and optimizes the next educational content. Specifically, for example, when a learner shows a happy expression, it provides dynamic activities that evoke joy, and when the learner is observed to be focused, it presents content that encourages deeper learning.

[0185] A concrete example related to the terminal is an interactive touch display installed in the waiting area of ​​a physical store. If a user touches the screen to select animal-themed content and smiles while watching the displayed video of a lion, the emotion engine detects that joy and immediately presents a quiz about lions.

[0186] An example of a prompt might be, "Please share some fun facts about lions, in a simple quiz format, suitable for young children." This would then allow a generative AI model to provide optimal educational information.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The server retrieves user attribute information from a database in the recording device. Based on this input information, it analyzes the user's age, knowledge level, and learning history, and selects appropriate learning information. This selected information is then used as input for subsequent generation rules.

[0190] Step 2:

[0191] The server uses a generative AI model to execute generation rules tailored to the user's interests based on selected training information. The generative AI model uses prompts to automatically generate the most suitable educational content for the user. The generated content becomes output for transmission to the terminal.

[0192] Step 3:

[0193] The device displays the transmitted educational content on a large screen. The device also uses a camera and microphone to capture the user's facial expressions and voice in real time and analyze their emotional state. Based on this input data, the emotion engine records the emotional state as evaluation information and sends it to the server.

[0194] Step 4:

[0195] The server analyzes the received evaluation information and adjusts the user's individual learning path. This includes modifying content and adjusting difficulty levels based on emotional states. The adjusted learning path is used as data to help generate future educational content.

[0196] Step 5:

[0197] Users can interact with the presented content interactively, including answering on-screen quizzes and selecting materials to deepen their knowledge. This interaction information is collected again and sent to the server as evaluation data.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] The system of this invention consists of a server, a terminal, and a user, thereby providing educational content that engages young children and promotes learning. The server manages a hierarchical database based on the user's knowledge level and selects appropriate educational content. The generative model automatically generates content according to the user's knowledge level and interests. The generated content is structured as interactive coloring pages and explanations and is sent from the server to the user's terminal.

[0215] The device serves to visually present educational content transmitted from the server to the user. Users can color through the device while simultaneously reading engaging explanations. For example, with the theme of "space," users can color planets and receive related explanations (such as planetary characteristics and location information). This allows learning to progress in a playful way, deepening young children's interest and understanding.

[0216] The results of the coloring activity performed by the user on their device, as well as their level of understanding of the explanations, are sent from the device to the server as feedback. The server analyzes this feedback information and adjusts the user's learning path based on their interests and level of understanding. This adjustment selects the most suitable content for the user to work on next, and this information is then sent back to the device.

[0217] As a concrete example, in the "Animals" theme, beginner users can color a cat-shaped image, read a simple explanation about cats, and learn about their habits and other related information. Data on the user's level of interest and understanding during this process will be used to adjust the content in the future.

[0218] In this way, the present invention realizes a new educational system that attracts the interest of young children while providing effective learning tailored to their knowledge level.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server receives access requests from users and retrieves the user's identification information and current knowledge level. Based on this information, it selects the appropriate hierarchical syllabus for the relevant topic from the database.

[0222] Step 2:

[0223] The server invokes a generative model to automatically generate educational content based on the selected syllabus. This content includes coloring pages and related explanations tailored to the user's knowledge level and interests.

[0224] Step 3:

[0225] The server sends the generated educational content to the user's device. The data is prepared in an interactive format and configured for easy use by the user.

[0226] Step 4:

[0227] The terminal displays educational content received from the server. The user interface allows users to interact with coloring pages and explanations.

[0228] Step 5:

[0229] The user completes a coloring page provided on their device and begins interacting with the app by following on-screen instructions. Simultaneously, they read explanations and absorb knowledge.

[0230] Step 6:

[0231] The device records the user's actions and responses. This information is then converted into data representing the user's actions and level of understanding of the explanations.

[0232] Step 7:

[0233] The device sends collected operation data and information regarding comprehension to the server. This data is then organized into feedback information.

[0234] Step 8:

[0235] The server analyzes the received feedback information and evaluates the user's interest and level of understanding. Based on these results, it decides how to adjust future educational content.

[0236] Step 9:

[0237] The server incorporates the analysis results and plans the next educational content to be provided. The optimal content is selected and prepared to keep users engaged and enable them to learn effectively.

[0238] (Example 1)

[0239] 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."

[0240] In children's education, providing appropriate content tailored to each user's knowledge level and interests is challenging, and standardized materials have limited learning effectiveness. Furthermore, static materials make it difficult to maintain young children's interest over extended periods, leading to a decline in the quality of learning.

[0241] 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.

[0242] In this invention, the server includes means for acquiring user information and analyzing the user's knowledge level and interests; means for generating prompt statements based on the analysis results and creating educational content using a generative model; and means for transmitting and displaying the created educational content to the user's terminal. This makes it possible to provide personalized and interactive educational content to each user.

[0243] "Means of obtaining user information" refers to methods of collecting data such as the user's age, areas of learning interest, and existing knowledge level.

[0244] "Means of analyzing knowledge level and interests" refers to an analytical process that selects the most suitable educational content for a user based on information obtained from that user.

[0245] "Means for generating prompt sentences" refers to the process by which a generative model creates instruction sentences based on the obtained analysis results, enabling it to create appropriate educational content.

[0246] "Methods for creating educational content using generative models" refer to using AI to generate personalized learning materials based on prompts.

[0247] "Means for sending and displaying content on a user's device" refers to technologies for transferring generated educational content to a user's device via a network and providing it visually.

[0248] This invention provides personalized educational content through a system consisting of a server, a terminal, and user interaction.

[0249] The server receives information from the user. Specifically, it collects information such as the user's age, areas of learning interest, and existing knowledge level. This information is stored in a database on the server. The database, combined with the user's past learning history, forms the basis for analysis.

[0250] A generative AI model runs on the server and generates prompt messages based on user information. This creates personalized educational content. For example, a possible prompt message might be, "The user is at a beginner level, their area of ​​interest is animals, and they have a particular interest in cats." Based on this prompt message, the AI ​​model analyzes the information and creates educational content tailored to the user.

[0251] The created educational content is sent from the server to the terminal. The terminal receives it and can present it visually to the user. The presented content includes learning tasks in the form of interactive coloring pages and accompanying explanatory text. For example, if the user shows interest in animals, a cat coloring page and related explanations about the characteristics and habits of cats will be provided.

[0252] Users access content via their devices and engage in interactive tasks. User behavior data, such as the completion status of coloring pages and the amount of explanations viewed, is sent from the device to the server as feedback. This feedback is important for adjusting the content of the next learning session.

[0253] Through the process described above, this invention provides an individualized learning experience in the early childhood education process based on the user's knowledge level and interests. This helps to deepen children's interest in learning and to help them absorb knowledge more effectively.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The user logs into the terminal and enters information about their age, areas of learning interest, and existing knowledge level. This information is sent to the server. The input includes the user's basic information. The output is that this information is stored in the server's database.

[0257] Step 2:

[0258] The server compares the received user information with the database, providing the AI ​​model with the necessary data to generate prompt messages. The data processing involved in this process is the analysis and organization of the user information. The generated prompt messages serve as instructions for the model.

[0259] Step 3:

[0260] The generative AI model receives prompt text from a server as input and generates optimal educational content. Natural language processing and content generation algorithms are used for data computation. The output is interactive educational content.

[0261] Step 4:

[0262] The server sends the generated educational content to the user's device. The input is the educational content from the generating AI model, and the output is that it is displayed on the user's device and made available to the user.

[0263] Step 5:

[0264] Users access educational content on their devices, for example, by coloring or reading explanations. User actions are recorded on the device, and this information is sent to the server as feedback. The input is the user's actions, and the output is the feedback data.

[0265] Step 6:

[0266] The server analyzes the feedback data sent from the terminal and uses it to adjust the next learning content. Feedback information is the input, and preparations for generating the next learning content tailored to the user are made as the output.

[0267] (Application Example 1)

[0268] 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."

[0269] The problem that this invention aims to solve is to enable young children to gain educational learning experiences through products that naturally interest them in physical stores, and to optimize the learning content according to each user's level of understanding. Conventional educational systems have the problem of being limited to the provision of fixed information, making it difficult to dynamically adjust to the user's real-time interests and level of understanding.

[0270] 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.

[0271] In this invention, the server includes means for managing an information infrastructure that provides hierarchical educational information based on the user's knowledge level; means for controlling a generation program that automatically generates educational information according to the user's knowledge level and interests; means for transmitting and displaying the generated educational information on a user's device; and means for identifying objects using information recognition technology and presenting relevant educational information. This makes it possible to provide an interactive learning experience based on the interests of young children in a physical store and to individually tailor the user's learning process.

[0272] "Educational information structured based on the user's knowledge level" refers to learning materials organized in stages according to each user's knowledge and understanding.

[0273] An "information infrastructure" is a collection of databases and systems for managing, storing, and distributing educational information.

[0274] A "generation program" is software that includes an algorithm for automatically generating appropriate educational content based on specific user attributes.

[0275] "User devices" are devices that users use to receive information and enjoy interactive experiences, and include smart glasses and tablets.

[0276] "Information recognition technology" refers to technologies for identifying objects and information from data such as images and audio, and utilizes machine learning and AI models.

[0277] An "interactive learning experience" is a learning method in which users deepen their learning by interacting with educational content and actively participating.

[0278] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server has an information infrastructure that manages hierarchical educational information based on the user's knowledge level, and uses a generation AI model to control the generation program, automatically generating educational information according to the user's knowledge level and interests.

[0279] Specifically, the server collects feedback information sent from the user's device and adjusts the content accordingly for the next session. Through this process, a personalized learning experience is provided. The server also uses information recognition technology to identify objects, select relevant educational information, and send the generated information to the user's device. The information recognition technologies used include machine learning and AI models (e.g., YOLO and Google Cloud Vision).

[0280] The terminal uses smart glasses or tablets as user devices to visually present educational information transmitted from the server to the user. Through this, the user enjoys an interactive learning experience and interacts with the information as needed. AR technology (e.g., Lens Studio or ARCore) is used to present the information.

[0281] As a concrete example, when a user picks up a toy in a physical store, the terminal identifies the toy and displays a coloring page and explanation of the associated "animal." Through this process, young children can learn while playing. An example of a prompt message would be, "Identify the code of this product and generate related educational content."

[0282] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0283] Step 1:

[0284] The server receives product information sent from the user's device. This input data includes images of products and barcodes recognized by smart glasses or tablets. The server uses this information to identify the product with a machine learning model (e.g., YOLO or Google Cloud Vision) and determine the relevant topic. The output of this process is the theme related to the product (e.g., animals, space, etc.).

[0285] Step 2:

[0286] Based on the determined theme, the server generates educational content according to the user's knowledge level and interests. A generative AI model is used to select and generate appropriate content from the hierarchical educational information in the database. The prompt sentence "Please generate educational content related to this theme." is used. The generated content is composed of interactive coloring pictures and explanations. The output is the generated educational content.

[0287] Step 3:

[0288] The server sends the generated educational content to the user's device. The terminal visually displays the sent content to the user by leveraging AR technology. The user interacts with the content presented through smart glasses or tablets and starts learning. The input is the generated educational content, and the output is the coloring pictures and explanations displayed on the user terminal.

[0289] Step 4:

[0290] The user colors pictures based on the displayed educational content, reads the explanations, and deepens learning. The data generated in this process is feedback information regarding the user's interaction results and understanding level.

[0291] Step 5:

[0292] The device sends user interaction results and feedback information to the server. The server collects this information and analyzes it to improve future content generation. The input is user feedback, and the output is the analysis results. This allows for more personalized learning path adjustments in the future.

[0293] 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.

[0294] The present invention provides educational content that maximizes learning effectiveness for young children by combining a server, terminal, user, and emotion engine. The server selects a hierarchical syllabus of appropriate themes from a database based on the user's knowledge level and emotional state. The generative model generates educational content based on this syllabus and sends the content to the user's terminal.

[0295] The device interactively displays received educational content to the user. In addition, the emotion engine analyzes the user's emotions from their facial expressions and voice via the device's camera and microphone, determining their emotional state in real time. For example, if the emotion engine detects "excitement" or "joy" while the user is enjoying a space-themed coloring book, it can maintain the user's motivation to learn by providing new content or difficulty levels appropriate to that state.

[0296] Users can use their devices to not only complete the displayed coloring pages but also read explanatory texts that interest them. The emotion engine understands the user's emotional responses and records emotional data as feedback on the device. This information is then sent to a server, added to normal operation data, and used to adjust the content.

[0297] The server analyzes the received feedback information and adjusts the individual learning path, including the user's emotional data. This adjustment helps select the next educational content and provides a new learning experience that will further engage the user.

[0298] This format allows users to progress through individually optimized educational paths, creating a flexible and effective learning environment that can adapt to changes in motivation based on emotions. For example, based on emotional analysis of animal-themed content, it is possible to suggest dynamic video content to excited users and provide detailed explanations to quiet, focused users.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server receives requests from users and selects appropriate educational topics by referring to the user's profile and knowledge level. This information is stored in the system's database.

[0302] Step 2:

[0303] The server invokes a generative model to generate educational content based on the user's knowledge level and selected topic. The generated content is structured as coloring pages or explanations.

[0304] Step 3:

[0305] The server sends the generated educational content to the user's device. This data is packaged in a format that allows for immediate display.

[0306] Step 4:

[0307] The terminal visually displays the received content to the user and activates the emotion engine to monitor the user's emotional state. At this time, the camera and microphone on the terminal are utilized to analyze emotions in real time.

[0308] Step 5:

[0309] The user performs a coloring book provided on the terminal, reads the related explanations, and progresses in learning. Through the emotion engine, emotions are analyzed from the user's expressions and voice.

[0310] Step 6:

[0311] The terminal records the user's operations and emotion data and transmits it to the server. This information is aggregated as feedback data.

[0312] Step 7:

[0313] The server analyzes the received feedback and emotion data and evaluates the user's interests and understanding. Based on this evaluation, the next content to be provided is optimized.

[0314] Step 8:

[0315] The server updates the user's individual learning path based on the analysis results and plans the next educational content considering the emotional state.

[0316] Step 9:

[0317] The server generates the newly planned educational content and prepares to transmit it to the terminal in the next session so that the user can continue further learning.

[0318] (Example 2)

[0319] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0320] In the field of early childhood education, providing customized learning content tailored to individual knowledge levels and emotional states is challenging. Traditional education systems often offer uniform curricula, making it difficult to create educational experiences based on individual learners' interests and emotions. As a result, this can lead to decreased motivation and hinder effective comprehension.

[0321] 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.

[0322] In this invention, the server includes means for managing a collection of information that provides hierarchical educational materials based on the user's knowledge level and emotional state; means for controlling a generation engine that automatically generates educational materials according to the user's knowledge level, emotional state, and interests; and means for transmitting and displaying the generated educational materials on the user's device. This makes it possible to provide a learning experience optimized for each individual user in real time and to effectively improve learning interest and comprehension.

[0323] "User knowledge level" is an indicator that shows the depth and breadth of knowledge that an individual user currently possesses.

[0324] "Emotional state" refers to the state of mind that a user has regarding learning content, representing their psychological reactions and feelings towards it.

[0325] "Hierarchical educational materials" are [a set of educational content organized and structured according to the user's knowledge level and learning progress].

[0326] An "information repository" is a database or information repository used to manage and provide educational materials and learning content.

[0327] A "generative engine" is a system or program that automatically creates appropriate educational materials based on the user's knowledge level and emotional state.

[0328] "User device" refers to [an electronic device on which educational materials are displayed and which the user can operate interactively].

[0329] "Feedback information" refers to data generated based on the user's operation history and emotional responses, which is used to adjust the learning content for the next session.

[0330] A "personalized learning path" is a plan designed to guide users through learning in the optimal order and content, tailored to their interests and level of understanding.

[0331] This invention constitutes a system that efficiently provides educational materials tailored to the individual characteristics of users. Its main components include a server, a terminal, a generative AI model, and an emotion engine.

[0332] The server manages a collection of information for providing hierarchical educational materials based on the user's knowledge level and emotional state. Specifically, it performs SQL query processing to select the most appropriate hierarchical materials by matching the educational materials stored in the database with the user profile.

[0333] The server further controls the generative AI model based on the user's profile information to automatically generate user-specific educational materials. In this process, a well-known natural language processing engine (e.g., GPT-4) is used as the generative AI model to receive prompts and construct optimal learning materials. For example, the prompt "Knowledge level: Beginner, Theme: Space, Emotion: Excitement" might be input to the AI ​​model.

[0334] The generated educational materials are sent from the server to the user's terminal. The terminal has the capability to display the received materials interactively, and the user progresses through learning by interacting with it. Specifically, the terminal is equipped with touchscreen and voice input functions, allowing the user to interact with interactive tasks such as coloring pages and quizzes.

[0335] Furthermore, the device has a built-in camera and microphone, and the emotion engine uses data acquired from these sensors to analyze the user's emotional state in real time. This allows for dynamic adjustments, such as displaying more challenging content when the user is excited, or detailed explanations when the user is focused.

[0336] The feedback information obtained through this process is sent to the server and used to adjust the next set of educational materials. This feedback includes operation logs and data on emotional states, and analysis is used to optimize individual learning paths.

[0337] This system allows users to always receive learning materials tailored to their own learning motivation and level of understanding, enabling them to learn effectively.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The server searches the database for relevant, hierarchical educational materials based on user input, including knowledge level and past learning history. Using database queries, it extracts appropriate materials and generates a list of materials that match the user profile. This output is the list of materials to be used in the next step.

[0341] Step 2:

[0342] The server sends prompts to a generative AI model based on the list of materials and the user's emotional state, generating customized educational materials. The generative AI model receives the prompts as input and uses natural language processing techniques to generate the most suitable educational content for the user. The generated educational materials are obtained as output.

[0343] Step 3:

[0344] The server sends the generated learning materials to the user's terminal. Using a communication protocol, it sends the learning material data as packets to the terminal over the internet and maintains the connection until reception is confirmed. The output is the learning material data sent to the terminal.

[0345] Step 4:

[0346] The device displays interactive educational materials to the user based on the received educational data. It accepts touch and voice input through the user interface, visualizing the content to facilitate user interaction with the materials. The output is the interactively displayed educational material.

[0347] Step 5:

[0348] The device uses its camera and microphone to capture the user's facial expressions and voice, inputting them into the emotion engine in real time. The emotion engine analyzes the input data using an emotion analysis algorithm to identify the user's emotional state. The output is the result of the emotional state determination.

[0349] Step 6:

[0350] The device dynamically adjusts the user's learning experience based on the results of its emotional state assessment. It changes the difficulty level and content of the displayed material to enable the user to learn more effectively. The output is the adjusted learning content.

[0351] Step 7:

[0352] The server receives feedback information sent from the terminal and records it in a database. This feedback information includes the user's operation history and sentiment data. Recording it in the database generates foundational data that can be used to adjust the learning content for the next session.

[0353] (Application Example 2)

[0354] 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."

[0355] When providing educational information for young children, it is essential to offer an optimal learning experience tailored to each user's emotional state and interests. However, existing educational systems struggle to provide individualized support. Furthermore, in physical learning environments, the technology to provide interactive content that responds to user reactions in real time is not yet fully established. As a result, users are often limited to one-way information provision, leading to decreased learning efficiency.

[0356] 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.

[0357] In this invention, the server includes means for managing a recording device that provides hierarchical learning information based on user attributes; means for controlling generation rules that automatically generate learning information according to the user's attributes and level of interest; and means for analyzing the user's emotional state through a camera and microphone and recording it as evaluation information. This makes it possible to provide learning information tailored to the user's individual emotional state and interests in real time, thereby improving the quality of the learning experience in physical stores.

[0358] "User attributes" refer to a collection of individually identifiable characteristics, such as the learner's age, knowledge level, and learning history.

[0359] "Hierarchical learning information" refers to educational materials and assignments that are structured in stages according to the learner's level of understanding and progress.

[0360] A "recording device" refers to a machine or program that has functions such as a database for storing and managing information.

[0361] "Generation rules" refer to algorithms or rule sets used to create or refine educational information based on specific conditions.

[0362] "Emotional state" refers to the learner's psychological and emotional state, which can be expressed as specific emotions such as joy, excitement, or concentration.

[0363] "Evaluation information" refers to data obtained based on learners' actions and responses, and is used to measure the progress and effectiveness of learning.

[0364] A "learning path" refers to a specific set of learning steps or processes that learners follow using educational information, providing a customized learning experience.

[0365] The system for carrying out this invention includes a server containing a recording device, generation rules, and an emotion analysis engine, and a terminal equipped with a large display, camera, and microphone that enable interactive display. The server manages hierarchical learning information based on user attribute information and automatically generates appropriate content according to the user's level of interest using a generative AI model. The terminal analyzes the learner's facial expressions and voice in real time and records their emotional state as evaluation information.

[0366] Based on recorded evaluation information, the server adjusts the learner's individual learning path and optimizes the next educational content. Specifically, for example, when a learner shows a happy expression, it provides dynamic activities that evoke joy, and when the learner is observed to be focused, it presents content that encourages deeper learning.

[0367] A concrete example related to the terminal is an interactive touch display installed in the waiting area of ​​a physical store. If a user touches the screen to select animal-themed content and smiles while watching the displayed video of a lion, the emotion engine detects that joy and immediately presents a quiz about lions.

[0368] An example of a prompt might be, "Please share some fun facts about lions, in a simple quiz format, suitable for young children." This would then allow a generative AI model to provide optimal educational information.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server retrieves user attribute information from a database in the recording device. Based on this input information, it analyzes the user's age, knowledge level, and learning history, and selects appropriate learning information. This selected information is then used as input for subsequent generation rules.

[0372] Step 2:

[0373] The server uses a generative AI model to execute generation rules tailored to the user's interests based on selected training information. The generative AI model uses prompts to automatically generate the most suitable educational content for the user. The generated content becomes output for transmission to the terminal.

[0374] Step 3:

[0375] The device displays the transmitted educational content on a large screen. The device also uses a camera and microphone to capture the user's facial expressions and voice in real time and analyze their emotional state. Based on this input data, the emotion engine records the emotional state as evaluation information and sends it to the server.

[0376] Step 4:

[0377] The server analyzes the received evaluation information and adjusts the user's individual learning path. This includes modifying content and adjusting difficulty levels based on emotional states. The adjusted learning path is used as data to help generate future educational content.

[0378] Step 5:

[0379] Users can interact with the presented content interactively, including answering on-screen quizzes and selecting materials to deepen their knowledge. This interaction information is collected again and sent to the server as evaluation data.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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".

[0396] The system of this invention consists of a server, a terminal, and a user, thereby providing educational content that engages young children and promotes learning. The server manages a hierarchical database based on the user's knowledge level and selects appropriate educational content. The generative model automatically generates content according to the user's knowledge level and interests. The generated content is structured as interactive coloring pages and explanations and is transmitted from the server to the user's terminal.

[0397] The device serves to visually present educational content transmitted from the server to the user. Users can color through the device while simultaneously reading engaging explanations. For example, with the theme of "space," users can color planets and receive related explanations (such as planetary characteristics and location information). This allows learning to progress in a playful way, deepening young children's interest and understanding.

[0398] The results of the coloring activity performed by the user on their device, as well as their level of understanding of the explanations, are sent from the device to the server as feedback. The server analyzes this feedback information and adjusts the user's learning path based on their interests and level of understanding. This adjustment selects the most suitable content for the user to work on next, and this information is then sent back to the device.

[0399] As a concrete example, in the "Animals" theme, beginner users can color a cat-shaped image, read a simple explanation about cats, and learn about their habits and other related information. Data on the user's level of interest and understanding during this process will be used to adjust the content in the future.

[0400] In this way, the present invention realizes a new educational system that attracts the interest of young children while providing effective learning tailored to their knowledge level.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The server receives access requests from users and retrieves the user's identification information and current knowledge level. Based on this information, it selects the appropriate hierarchical syllabus for the relevant topic from the database.

[0404] Step 2:

[0405] The server invokes a generative model to automatically generate educational content based on the selected syllabus. This content includes coloring pages and related explanations tailored to the user's knowledge level and interests.

[0406] Step 3:

[0407] The server sends the generated educational content to the user's device. The data is prepared in an interactive format and configured for easy use by the user.

[0408] Step 4:

[0409] The terminal displays educational content received from the server. The user interface allows users to interact with coloring pages and explanations.

[0410] Step 5:

[0411] The user completes a coloring page provided on their device and begins interacting with the app by following on-screen instructions. Simultaneously, they read explanations and absorb knowledge.

[0412] Step 6:

[0413] The device records the user's actions and responses. This information is then converted into data representing the user's actions and level of understanding of the explanations.

[0414] Step 7:

[0415] The device sends collected operation data and information regarding comprehension to the server. This data is then organized into feedback information.

[0416] Step 8:

[0417] The server analyzes the received feedback information and evaluates the user's interest and level of understanding. Based on these results, it decides how to adjust future educational content.

[0418] Step 9:

[0419] The server incorporates the analysis results and plans the next educational content to be provided. The optimal content is selected and prepared to keep users engaged and enable them to learn effectively.

[0420] (Example 1)

[0421] 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."

[0422] In children's education, providing appropriate content tailored to each user's knowledge level and interests is challenging, and standardized materials have limited learning effectiveness. Furthermore, static materials make it difficult to maintain young children's interest over extended periods, leading to a decline in the quality of learning.

[0423] 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.

[0424] In this invention, the server includes means for acquiring user information and analyzing the user's knowledge level and interests; means for generating prompt statements based on the analysis results and creating educational content using a generative model; and means for transmitting and displaying the created educational content to the user's terminal. This makes it possible to provide personalized and interactive educational content to each user.

[0425] "Means of obtaining user information" refers to methods of collecting data such as the user's age, areas of learning interest, and existing knowledge level.

[0426] "Means of analyzing knowledge level and interests" refers to an analytical process that selects the most suitable educational content for a user based on information obtained from that user.

[0427] "Means for generating prompt sentences" refers to the process by which a generative model creates instruction sentences based on the obtained analysis results, enabling it to create appropriate educational content.

[0428] "Methods for creating educational content using generative models" refer to using AI to generate personalized learning materials based on prompts.

[0429] "Means for sending and displaying content on a user's device" refers to technologies for transferring generated educational content to a user's device via a network and providing it visually.

[0430] This invention provides personalized educational content through a system consisting of a server, a terminal, and user interaction.

[0431] The server receives information from the user. Specifically, it collects information such as the user's age, areas of learning interest, and existing knowledge level. This information is stored in a database on the server. The database, combined with the user's past learning history, forms the basis for analysis.

[0432] A generative AI model runs on the server and generates prompt messages based on user information. This creates personalized educational content. For example, a possible prompt message might be, "The user is at a beginner level, their area of ​​interest is animals, and they have a particular interest in cats." Based on this prompt message, the AI ​​model analyzes the information and creates educational content tailored to the user.

[0433] The created educational content is sent from the server to the terminal. The terminal receives it and can present it visually to the user. The presented content includes learning tasks in the form of interactive coloring pages and accompanying explanatory text. For example, if the user shows interest in animals, a cat coloring page and related explanations about the characteristics and habits of cats will be provided.

[0434] Users access content via their devices and engage in interactive tasks. User behavior data, such as the completion status of coloring pages and the amount of explanations viewed, is sent from the device to the server as feedback. This feedback is important for adjusting the content of the next learning session.

[0435] Through the process described above, this invention provides an individualized learning experience in the early childhood education process based on the user's knowledge level and interests. This helps to deepen children's interest in learning and to help them absorb knowledge more effectively.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The user logs into the terminal and enters information about their age, areas of learning interest, and existing knowledge level. This information is sent to the server. The input includes the user's basic information. The output is that this information is stored in the server's database.

[0439] Step 2:

[0440] The server compares the received user information with the database, providing the AI ​​model with the necessary data to generate prompt messages. The data processing involved in this process is the analysis and organization of the user information. The generated prompt messages serve as instructions for the model.

[0441] Step 3:

[0442] The generative AI model receives prompt text from a server as input and generates optimal educational content. Natural language processing and content generation algorithms are used for data computation. The output is interactive educational content.

[0443] Step 4:

[0444] The server sends the generated educational content to the user's device. The input is the educational content from the generating AI model, and the output is that it is displayed on the user's device and made available to the user.

[0445] Step 5:

[0446] Users access educational content on their devices, for example, by coloring or reading explanations. User actions are recorded on the device, and this information is sent to the server as feedback. The input is the user's actions, and the output is the feedback data.

[0447] Step 6:

[0448] The server analyzes the feedback data sent from the terminal and uses it to adjust the next learning content. Feedback information is the input, and preparations for generating the next learning content tailored to the user are made as the output.

[0449] (Application Example 1)

[0450] 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."

[0451] The problem that this invention aims to solve is to enable young children to gain educational learning experiences through products that naturally interest them in physical stores, and to optimize the learning content according to each user's level of understanding. Conventional educational systems have the problem of being limited to the provision of fixed information, making it difficult to dynamically adjust to the user's real-time interests and level of understanding.

[0452] 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.

[0453] In this invention, the server includes means for managing an information infrastructure that provides hierarchical educational information based on the user's knowledge level; means for controlling a generation program that automatically generates educational information according to the user's knowledge level and interests; means for transmitting and displaying the generated educational information on a user's device; and means for identifying objects using information recognition technology and presenting relevant educational information. This makes it possible to provide an interactive learning experience based on the interests of young children in a physical store and to individually tailor the user's learning process.

[0454] "Educational information structured based on the user's knowledge level" refers to learning materials organized in stages according to each user's knowledge and understanding.

[0455] An "information infrastructure" is a collection of databases and systems for managing, storing, and distributing educational information.

[0456] A "generation program" is software that includes an algorithm for automatically generating appropriate educational content based on specific user attributes.

[0457] "User devices" are devices that users use to receive information and enjoy interactive experiences, and include smart glasses and tablets.

[0458] "Information recognition technology" refers to technologies for identifying objects and information from data such as images and audio, and utilizes machine learning and AI models.

[0459] An "interactive learning experience" is a learning method in which users deepen their learning by interacting with educational content and actively participating.

[0460] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server has an information infrastructure that manages hierarchical educational information based on the user's knowledge level, and uses a generation AI model to control the generation program, automatically generating educational information according to the user's knowledge level and interests.

[0461] Specifically, the server collects feedback information sent from the user's device and adjusts the content accordingly for the next session. Through this process, a personalized learning experience is provided. The server also uses information recognition technology to identify objects, select relevant educational information, and send the generated information to the user's device. The information recognition technologies used include machine learning and AI models (e.g., YOLO and Google Cloud Vision).

[0462] The terminal uses smart glasses or tablets as user devices to visually present educational information transmitted from the server to the user. Through this, the user enjoys an interactive learning experience and interacts with the information as needed. AR technology (e.g., Lens Studio or ARCore) is used to present the information.

[0463] As a concrete example, when a user picks up a toy in a physical store, the terminal identifies the toy and displays a coloring page and explanation of the associated "animal." Through this process, young children can learn while playing. An example of a prompt message would be, "Identify the code of this product and generate related educational content."

[0464] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0465] Step 1:

[0466] The server receives product information transmitted from the user's device. This input data includes images and barcodes of products recognized by smart glasses or tablets. The server uses this information to identify products using machine learning models (e.g., YOLO or Google Cloud Vision) and determine related topics. The output of this process is a theme related to the product (e.g., animals, space, etc.).

[0467] Step 2:

[0468] The server generates educational content tailored to the user's knowledge level and interests, based on a selected theme. A generation AI model is used to select and generate appropriate content from hierarchical educational information in the database. The prompt used is "Generate educational content related to this theme." The generated content consists of interactive coloring pages and explanations. The output is the generated educational content.

[0469] Step 3:

[0470] The server transmits the generated educational content to the user's device. The device visually displays the transmitted content to the user using AR technology. The user interacts with the presented content through smart glasses or a tablet and begins learning. The input is the generated educational content, and the output is the coloring pages and explanations displayed on the user's device.

[0471] Step 4:

[0472] Users color in educational content based on the displayed material and deepen their learning by reading explanations. The data generated during this process includes feedback information on the user's interaction results and level of understanding.

[0473] Step 5:

[0474] The device sends user interaction results and feedback information to the server. The server collects this information and analyzes it to improve future content generation. The input is user feedback, and the output is the analysis results. This allows for more personalized learning path adjustments in the future.

[0475] 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.

[0476] The present invention provides educational content that maximizes learning effectiveness for young children by combining a server, terminal, user, and emotion engine. The server selects a hierarchical syllabus of appropriate themes from a database based on the user's knowledge level and emotional state. The generative model generates educational content based on this syllabus and sends the content to the user's terminal.

[0477] The device interactively displays received educational content to the user. In addition, the emotion engine analyzes the user's emotions from their facial expressions and voice via the device's camera and microphone, determining their emotional state in real time. For example, if the emotion engine detects "excitement" or "joy" while the user is enjoying a space-themed coloring book, it can maintain the user's motivation to learn by providing new content or difficulty levels appropriate to that state.

[0478] Users can use their devices to not only complete the displayed coloring pages but also read explanatory texts that interest them. The emotion engine understands the user's emotional responses and records emotional data as feedback on the device. This information is then sent to a server, added to normal operation data, and used to adjust the content.

[0479] The server analyzes the received feedback information and adjusts the individual learning path, including the user's emotional data. This adjustment helps select the next educational content and provides a new learning experience that will further engage the user.

[0480] This format allows users to progress through individually optimized educational paths, creating a flexible and effective learning environment that can adapt to changes in motivation based on emotions. For example, based on emotional analysis of animal-themed content, it is possible to suggest dynamic video content to excited users and provide detailed explanations to quiet, focused users.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The server receives requests from users and selects appropriate educational topics by referring to the user's profile and knowledge level. This information is stored in the system's database.

[0484] Step 2:

[0485] The server invokes a generative model to generate educational content based on the user's knowledge level and selected topic. The generated content is structured as coloring pages or explanations.

[0486] Step 3:

[0487] The server sends the generated educational content to the user's device. This data is packaged in a format that allows for immediate display.

[0488] Step 4:

[0489] The device visually displays the received content to the user and activates an emotion engine to monitor the user's emotional state. During this process, the device's camera and microphone are used to analyze emotions in real time.

[0490] Step 5:

[0491] Users color in images provided on their devices and learn by reading related explanations. Emotions are analyzed from the user's facial expressions and voice through an emotion engine.

[0492] Step 6:

[0493] The device records user actions and emotional data and sends it to a server. This information is aggregated as feedback data.

[0494] Step 7:

[0495] The server analyzes the received feedback and sentiment data to evaluate the user's interest and understanding. Based on this evaluation, it optimizes the content delivered next time.

[0496] Step 8:

[0497] Based on the analysis results, the server updates the user's individual learning path and plans the next educational content, taking into account their emotional state.

[0498] Step 9:

[0499] The server generates newly planned educational content and prepares to send it to the device in the next session so that the user can continue learning.

[0500] (Example 2)

[0501] 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."

[0502] In the field of early childhood education, providing customized learning content tailored to individual knowledge levels and emotional states is challenging. Traditional education systems often offer uniform curricula, making it difficult to create educational experiences based on individual learners' interests and emotions. As a result, this can lead to decreased motivation and hinder effective comprehension.

[0503] 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.

[0504] In this invention, the server includes means for managing a collection of information that provides hierarchical educational materials based on the user's knowledge level and emotional state; means for controlling a generation engine that automatically generates educational materials according to the user's knowledge level, emotional state, and interests; and means for transmitting and displaying the generated educational materials on the user's device. This makes it possible to provide a learning experience optimized for each individual user in real time and to effectively improve learning interest and comprehension.

[0505] "User knowledge level" is an indicator that shows the depth and breadth of knowledge that an individual user currently possesses.

[0506] "Emotional state" refers to the state of mind that a user has regarding learning content, representing their psychological reactions and feelings towards it.

[0507] "Hierarchical educational materials" are [a set of educational content organized and structured according to the user's knowledge level and learning progress].

[0508] An "information repository" is a database or information repository used to manage and provide educational materials and learning content.

[0509] A "generative engine" is a system or program that automatically creates appropriate educational materials based on the user's knowledge level and emotional state.

[0510] "User device" refers to [an electronic device on which educational materials are displayed and which the user can operate interactively].

[0511] "Feedback information" refers to data generated based on the user's operation history and emotional responses, which is used to adjust the learning content for the next session.

[0512] A "personalized learning path" is a plan designed to guide users through learning in the optimal order and content, tailored to their interests and level of understanding.

[0513] This invention constitutes a system that efficiently provides educational materials tailored to the individual characteristics of users. Its main components include a server, a terminal, a generative AI model, and an emotion engine.

[0514] The server manages a collection of information for providing hierarchical educational materials based on the user's knowledge level and emotional state. Specifically, it performs SQL query processing to select the most appropriate hierarchical materials by matching the educational materials stored in the database with the user profile.

[0515] The server further controls the generative AI model based on the user's profile information to automatically generate user-specific educational materials. In this process, a well-known natural language processing engine (e.g., GPT-4) is used as the generative AI model to receive prompts and construct optimal learning materials. For example, the prompt "Knowledge level: Beginner, Theme: Space, Emotion: Excitement" might be input to the AI ​​model.

[0516] The generated educational materials are sent from the server to the user's terminal. The terminal has the capability to display the received materials interactively, and the user progresses through learning by interacting with it. Specifically, the terminal is equipped with touchscreen and voice input functions, allowing the user to interact with interactive tasks such as coloring pages and quizzes.

[0517] Furthermore, the device has a built-in camera and microphone, and the emotion engine uses data acquired from these sensors to analyze the user's emotional state in real time. This allows for dynamic adjustments, such as displaying more challenging content when the user is excited, or detailed explanations when the user is focused.

[0518] The feedback information obtained through this process is sent to the server and used to adjust the next set of educational materials. This feedback includes operation logs and data on emotional states, and analysis is used to optimize individual learning paths.

[0519] This system allows users to always receive learning materials tailored to their own learning motivation and level of understanding, enabling them to learn effectively.

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] The server searches the database for relevant, hierarchical educational materials based on user input, including knowledge level and past learning history. Using database queries, it extracts appropriate materials and generates a list of materials that match the user profile. This output is the list of materials to be used in the next step.

[0523] Step 2:

[0524] The server sends prompts to a generative AI model based on the list of materials and the user's emotional state, generating customized educational materials. The generative AI model receives the prompts as input and uses natural language processing techniques to generate the most suitable educational content for the user. The generated educational materials are obtained as output.

[0525] Step 3:

[0526] The server sends the generated learning materials to the user's terminal. Using a communication protocol, it sends the learning material data as packets to the terminal over the internet and maintains the connection until reception is confirmed. The output is the learning material data sent to the terminal.

[0527] Step 4:

[0528] The device displays interactive educational materials to the user based on the received educational data. It accepts touch and voice input through the user interface, visualizing the content to facilitate user interaction with the materials. The output is the interactively displayed educational material.

[0529] Step 5:

[0530] The device uses its camera and microphone to capture the user's facial expressions and voice, inputting them into the emotion engine in real time. The emotion engine analyzes the input data using an emotion analysis algorithm to identify the user's emotional state. The output is the result of the emotional state determination.

[0531] Step 6:

[0532] The device dynamically adjusts the user's learning experience based on the results of its emotional state assessment. It changes the difficulty level and content of the displayed material to enable the user to learn more effectively. The output is the adjusted learning content.

[0533] Step 7:

[0534] The server receives feedback information sent from the terminal and records it in a database. This feedback information includes the user's operation history and sentiment data. Recording it in the database generates foundational data that can be used to adjust the learning content for the next session.

[0535] (Application Example 2)

[0536] 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."

[0537] When providing educational information for young children, it is essential to offer an optimal learning experience tailored to each user's emotional state and interests. However, existing educational systems struggle to provide individualized support. Furthermore, in physical learning environments, the technology to provide interactive content that responds to user reactions in real time is not yet fully established. As a result, users are often limited to one-way information provision, leading to decreased learning efficiency.

[0538] 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.

[0539] In this invention, the server includes means for managing a recording device that provides hierarchical learning information based on user attributes; means for controlling generation rules that automatically generate learning information according to the user's attributes and level of interest; and means for analyzing the user's emotional state through a camera and microphone and recording it as evaluation information. This makes it possible to provide learning information tailored to the user's individual emotional state and interests in real time, thereby improving the quality of the learning experience in physical stores.

[0540] "User attributes" refer to a collection of individually identifiable characteristics, such as the learner's age, knowledge level, and learning history.

[0541] "Hierarchical learning information" refers to educational materials and assignments that are structured in stages according to the learner's level of understanding and progress.

[0542] A "recording device" refers to a machine or program that has functions such as a database for storing and managing information.

[0543] "Generation rules" refer to algorithms or rule sets used to create or refine educational information based on specific conditions.

[0544] "Emotional state" refers to the learner's psychological and emotional state, which can be expressed as specific emotions such as joy, excitement, or concentration.

[0545] "Evaluation information" refers to data obtained based on learners' actions and responses, and is used to measure the progress and effectiveness of learning.

[0546] A "learning path" refers to a specific set of learning steps or processes that learners follow using educational information, providing a customized learning experience.

[0547] The system for carrying out this invention includes a server containing a recording device, generation rules, and an emotion analysis engine, and a terminal equipped with a large display, camera, and microphone that enable interactive display. The server manages hierarchical learning information based on user attribute information and automatically generates appropriate content according to the user's level of interest using a generative AI model. The terminal analyzes the learner's facial expressions and voice in real time and records their emotional state as evaluation information.

[0548] Based on recorded evaluation information, the server adjusts the learner's individual learning path and optimizes the next educational content. Specifically, for example, when a learner shows a happy expression, it provides dynamic activities that evoke joy, and when the learner is observed to be focused, it presents content that encourages deeper learning.

[0549] A concrete example related to the terminal is an interactive touch display installed in the waiting area of ​​a physical store. If a user touches the screen to select animal-themed content and smiles while watching the displayed video of a lion, the emotion engine detects that joy and immediately presents a quiz about lions.

[0550] An example of a prompt might be, "Please share some fun facts about lions, in a simple quiz format, suitable for young children." This would then allow a generative AI model to provide optimal educational information.

[0551] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0552] Step 1:

[0553] The server retrieves user attribute information from a database in the recording device. Based on this input information, it analyzes the user's age, knowledge level, and learning history, and selects appropriate learning information. This selected information is then used as input for subsequent generation rules.

[0554] Step 2:

[0555] The server uses a generative AI model to execute generation rules tailored to the user's interests based on selected training information. The generative AI model uses prompts to automatically generate the most suitable educational content for the user. The generated content becomes output for transmission to the terminal.

[0556] Step 3:

[0557] The device displays the transmitted educational content on a large screen. The device also uses a camera and microphone to capture the user's facial expressions and voice in real time and analyze their emotional state. Based on this input data, the emotion engine records the emotional state as evaluation information and sends it to the server.

[0558] Step 4:

[0559] The server analyzes the received evaluation information and adjusts the user's individual learning path. This includes modifying content and adjusting difficulty levels based on emotional states. The adjusted learning path is used as data to help generate future educational content.

[0560] Step 5:

[0561] Users can interact with the presented content interactively, including answering on-screen quizzes and selecting materials to deepen their knowledge. This interaction information is collected again and sent to the server as evaluation data.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] [Fourth Embodiment]

[0566] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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".

[0579] The system of this invention consists of a server, a terminal, and a user, thereby providing educational content that engages young children and promotes learning. The server manages a hierarchical database based on the user's knowledge level and selects appropriate educational content. The generative model automatically generates content according to the user's knowledge level and interests. The generated content is structured as interactive coloring pages and explanations and is sent from the server to the user's terminal.

[0580] The device serves to visually present educational content transmitted from the server to the user. Users can color through the device while simultaneously reading engaging explanations. For example, with the theme of "space," users can color planets and receive related explanations (such as planetary characteristics and location information). This allows learning to progress in a playful way, deepening young children's interest and understanding.

[0581] The results of the coloring activity performed by the user on their device, as well as their level of understanding of the explanations, are sent from the device to the server as feedback. The server analyzes this feedback information and adjusts the user's learning path based on their interests and level of understanding. This adjustment selects the most suitable content for the user to work on next, and this information is then sent back to the device.

[0582] As a concrete example, in the "Animals" theme, beginner users can color a cat-shaped image, read a simple explanation about cats, and learn about their habits and other related information. Data on the user's level of interest and understanding during this process will be used to adjust the content in the future.

[0583] In this way, the present invention realizes a new educational system that attracts the interest of young children while providing effective learning tailored to their knowledge level.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The server receives access requests from users and retrieves the user's identification information and current knowledge level. Based on this information, it selects the appropriate hierarchical syllabus for the relevant topic from the database.

[0587] Step 2:

[0588] The server invokes a generative model to automatically generate educational content based on the selected syllabus. This content includes coloring pages and related explanations tailored to the user's knowledge level and interests.

[0589] Step 3:

[0590] The server sends the generated educational content to the user's device. The data is prepared in an interactive format and configured for easy use by the user.

[0591] Step 4:

[0592] The terminal displays educational content received from the server. The user interface allows users to interact with coloring pages and explanations.

[0593] Step 5:

[0594] The user completes a coloring page provided on their device and begins interacting with the app by following on-screen instructions. Simultaneously, they read explanations and absorb knowledge.

[0595] Step 6:

[0596] The device records the user's actions and responses. This information is then converted into data representing the user's actions and level of understanding of the explanations.

[0597] Step 7:

[0598] The device sends collected operation data and information regarding comprehension to the server. This data is then organized into feedback information.

[0599] Step 8:

[0600] The server analyzes the received feedback information and evaluates the user's interest and level of understanding. Based on these results, it decides how to adjust future educational content.

[0601] Step 9:

[0602] The server incorporates the analysis results and plans the next educational content to be provided. The optimal content is selected and prepared to keep users engaged and enable them to learn effectively.

[0603] (Example 1)

[0604] 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".

[0605] In children's education, providing appropriate content tailored to each user's knowledge level and interests is challenging, and standardized materials have limited learning effectiveness. Furthermore, static materials make it difficult to maintain young children's interest over extended periods, leading to a decline in the quality of learning.

[0606] 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.

[0607] In this invention, the server includes means for acquiring user information and analyzing the user's knowledge level and interests; means for generating prompt statements based on the analysis results and creating educational content using a generative model; and means for transmitting and displaying the created educational content to the user's terminal. This makes it possible to provide personalized and interactive educational content to each user.

[0608] "Means of obtaining user information" refers to methods of collecting data such as the user's age, areas of learning interest, and existing knowledge level.

[0609] "Means of analyzing knowledge level and interests" refers to an analytical process that selects the most suitable educational content for a user based on information obtained from that user.

[0610] "Means for generating prompt sentences" refers to the process by which a generative model creates instruction sentences based on the obtained analysis results, enabling it to create appropriate educational content.

[0611] "Methods for creating educational content using generative models" refer to using AI to generate personalized learning materials based on prompts.

[0612] "Means for sending and displaying content on a user's device" refers to technologies for transferring generated educational content to a user's device via a network and providing it visually.

[0613] This invention provides personalized educational content through a system consisting of a server, a terminal, and user interaction.

[0614] The server receives information from the user. Specifically, it collects information such as the user's age, areas of learning interest, and existing knowledge level. This information is stored in a database on the server. The database, combined with the user's past learning history, forms the basis for analysis.

[0615] A generative AI model runs on the server and generates prompt messages based on user information. This creates personalized educational content. For example, a possible prompt message might be, "The user is at a beginner level, their area of ​​interest is animals, and they have a particular interest in cats." Based on this prompt message, the AI ​​model analyzes the information and creates educational content tailored to the user.

[0616] The created educational content is sent from the server to the terminal. The terminal receives it and can present it visually to the user. The presented content includes learning tasks in the form of interactive coloring pages and accompanying explanatory text. For example, if the user shows interest in animals, a cat coloring page and related explanations about the characteristics and habits of cats will be provided.

[0617] Users access content via their devices and engage in interactive tasks. User behavior data, such as the completion status of coloring pages and the amount of explanations viewed, is sent from the device to the server as feedback. This feedback is important for adjusting the content of the next learning session.

[0618] Through the process described above, this invention provides an individualized learning experience in the early childhood education process based on the user's knowledge level and interests. This helps to deepen children's interest in learning and to help them absorb knowledge more effectively.

[0619] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0620] Step 1:

[0621] The user logs into the terminal and enters information about their age, areas of learning interest, and existing knowledge level. This information is sent to the server. The input includes the user's basic information. The output is that this information is stored in the server's database.

[0622] Step 2:

[0623] The server compares the received user information with the database, providing the AI ​​model with the necessary data to generate prompt messages. This process involves analyzing and organizing the user information. The generated prompt messages serve as instructions for the model.

[0624] Step 3:

[0625] The generative AI model receives prompt text from a server as input and generates optimal educational content. Natural language processing and content generation algorithms are used for data computation. The output is interactive educational content.

[0626] Step 4:

[0627] The server sends the generated educational content to the user's device. The input is the educational content from the generating AI model, and the output is that it is displayed on the user's device and made available to the user.

[0628] Step 5:

[0629] Users access educational content on their devices, for example, by coloring or reading explanations. User actions are recorded on the device, and this information is sent to the server as feedback. The input is the user's actions, and the output is the feedback data.

[0630] Step 6:

[0631] The server analyzes the feedback data sent from the terminal and uses it to adjust the next learning content. Feedback information is the input, and preparations for generating the next learning content tailored to the user are made as the output.

[0632] (Application Example 1)

[0633] 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".

[0634] The problem that this invention aims to solve is to enable young children to gain educational learning experiences through products that naturally interest them in physical stores, and to optimize the learning content according to each user's level of understanding. Conventional educational systems have the problem of being limited to the provision of fixed information, making it difficult to dynamically adjust to the user's real-time interests and level of understanding.

[0635] 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.

[0636] In this invention, the server includes means for managing an information infrastructure that provides hierarchical educational information based on the user's knowledge level; means for controlling a generation program that automatically generates educational information according to the user's knowledge level and interests; means for transmitting and displaying the generated educational information on a user's device; and means for identifying objects using information recognition technology and presenting relevant educational information. This makes it possible to provide an interactive learning experience based on the interests of young children in a physical store and to individually tailor the user's learning process.

[0637] "Educational information structured based on the user's knowledge level" refers to learning materials organized in stages according to each user's knowledge and understanding.

[0638] An "information infrastructure" is a collection of databases and systems for managing, storing, and distributing educational information.

[0639] A "generation program" is software that includes an algorithm for automatically generating appropriate educational content based on specific user attributes.

[0640] "User devices" are devices that users use to receive information and enjoy interactive experiences, and include smart glasses and tablets.

[0641] "Information recognition technology" refers to technologies for identifying objects and information from data such as images and audio, and utilizes machine learning and AI models.

[0642] An "interactive learning experience" is a learning method in which users deepen their learning by interacting with educational content and actively participating.

[0643] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server has an information infrastructure that manages hierarchical educational information based on the user's knowledge level, and uses a generation AI model to control the generation program, automatically generating educational information according to the user's knowledge level and interests.

[0644] Specifically, the server collects feedback information sent from the user's device and adjusts the content accordingly for the next session. Through this process, a personalized learning experience is provided. The server also uses information recognition technology to identify objects, select relevant educational information, and send the generated information to the user's device. The information recognition technologies used include machine learning and AI models (e.g., YOLO and Google Cloud Vision).

[0645] The terminal uses smart glasses or tablets as user devices to visually present educational information transmitted from the server to the user. Through this, the user enjoys an interactive learning experience and interacts with the information as needed. AR technology (e.g., Lens Studio or ARCore) is used to present the information.

[0646] As a concrete example, when a user picks up a toy in a physical store, the terminal identifies the toy and displays a coloring page and explanation of the associated "animal." Through this process, young children can learn while playing. An example of a prompt message would be, "Identify the code of this product and generate related educational content."

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] The server receives product information transmitted from the user's device. This input data includes images and barcodes of products recognized by smart glasses or tablets. The server uses this information to identify products using machine learning models (e.g., YOLO or Google Cloud Vision) and determine related topics. The output of this process is a theme related to the product (e.g., animals, space, etc.).

[0650] Step 2:

[0651] The server generates educational content tailored to the user's knowledge level and interests, based on a selected theme. A generation AI model is used to select and generate appropriate content from hierarchical educational information in the database. The prompt used is "Generate educational content related to this theme." The generated content consists of interactive coloring pages and explanations. The output is the generated educational content.

[0652] Step 3:

[0653] The server transmits the generated educational content to the user's device. The device visually displays the transmitted content to the user using AR technology. The user interacts with the presented content through smart glasses or a tablet and begins learning. The input is the generated educational content, and the output is the coloring pages and explanations displayed on the user's device.

[0654] Step 4:

[0655] Users color in educational content based on the displayed material and deepen their learning by reading explanations. The data generated during this process includes feedback information on the user's interaction results and level of understanding.

[0656] Step 5:

[0657] The device sends user interaction results and feedback information to the server. The server collects this information and analyzes it to improve future content generation. The input is user feedback, and the output is the analysis results. This allows for more personalized learning path adjustments in the future.

[0658] 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.

[0659] The present invention provides educational content that maximizes learning effectiveness for young children by combining a server, terminal, user, and emotion engine. The server selects a hierarchical syllabus of appropriate themes from a database based on the user's knowledge level and emotional state. The generative model generates educational content based on this syllabus and sends the content to the user's terminal.

[0660] The device interactively displays received educational content to the user. In addition, the emotion engine analyzes the user's emotions from their facial expressions and voice via the device's camera and microphone, determining their emotional state in real time. For example, if the emotion engine detects "excitement" or "joy" while the user is enjoying a space-themed coloring book, it can maintain the user's motivation to learn by providing new content or difficulty levels appropriate to that state.

[0661] Users can use their devices to not only complete the displayed coloring pages but also read explanatory texts that interest them. The emotion engine understands the user's emotional responses and records emotional data as feedback on the device. This information is then sent to a server, added to normal operation data, and used to adjust the content.

[0662] The server analyzes the received feedback information and adjusts the individual learning path, including the user's emotional data. This adjustment helps select the next educational content and provides a new learning experience that will further engage the user.

[0663] This format allows users to progress through individually optimized educational paths, creating a flexible and effective learning environment that can adapt to changes in motivation based on emotions. For example, based on emotional analysis of animal-themed content, it is possible to suggest dynamic video content to excited users and provide detailed explanations to quiet, focused users.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The server receives requests from users and selects appropriate educational topics by referring to the user's profile and knowledge level. This information is stored in the system's database.

[0667] Step 2:

[0668] The server invokes a generative model to generate educational content based on the user's knowledge level and selected topic. The generated content is structured as coloring pages or explanations.

[0669] Step 3:

[0670] The server sends the generated educational content to the user's device. This data is packaged in a format that allows for immediate display.

[0671] Step 4:

[0672] The device visually displays the received content to the user and activates an emotion engine to monitor the user's emotional state. During this process, the device's camera and microphone are used to analyze emotions in real time.

[0673] Step 5:

[0674] Users color in images provided on their devices and learn by reading related explanations. Emotions are analyzed from the user's facial expressions and voice through an emotion engine.

[0675] Step 6:

[0676] The device records user actions and emotional data and sends it to a server. This information is aggregated as feedback data.

[0677] Step 7:

[0678] The server analyzes the received feedback and sentiment data to evaluate the user's interest and understanding. Based on this evaluation, it optimizes the content delivered next time.

[0679] Step 8:

[0680] Based on the analysis results, the server updates the user's individual learning path and plans the next educational content, taking into account their emotional state.

[0681] Step 9:

[0682] The server generates newly planned educational content and prepares to send it to the device in the next session so that the user can continue learning.

[0683] (Example 2)

[0684] 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".

[0685] In the field of early childhood education, providing customized learning content tailored to individual knowledge levels and emotional states is challenging. Traditional education systems often offer uniform curricula, making it difficult to create educational experiences based on individual learners' interests and emotions. As a result, this can lead to decreased motivation and hinder effective comprehension.

[0686] 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.

[0687] In this invention, the server includes means for managing a collection of information that provides hierarchical educational materials based on the user's knowledge level and emotional state; means for controlling a generation engine that automatically generates educational materials according to the user's knowledge level, emotional state, and interests; and means for transmitting and displaying the generated educational materials on the user's device. This makes it possible to provide a learning experience optimized for each individual user in real time and to effectively improve learning interest and comprehension.

[0688] "User knowledge level" is an indicator that shows the depth and breadth of knowledge that an individual user currently possesses.

[0689] "Emotional state" refers to the state of mind that a user has regarding learning content, representing their psychological reactions and feelings towards it.

[0690] "Hierarchical educational materials" are [a set of educational content organized and structured according to the user's knowledge level and learning progress].

[0691] An "information repository" is a database or information repository used to manage and provide educational materials and learning content.

[0692] A "generative engine" is a system or program that automatically creates appropriate educational materials based on the user's knowledge level and emotional state.

[0693] "User device" refers to [an electronic device on which educational materials are displayed and which the user can operate interactively].

[0694] "Feedback information" refers to data generated based on the user's operation history and emotional responses, which is used to adjust the learning content for the next session.

[0695] A "personalized learning path" is a plan designed to guide users through learning in the optimal order and content, tailored to their interests and level of understanding.

[0696] This invention constitutes a system that efficiently provides educational materials tailored to the individual characteristics of users. Its main components include a server, a terminal, a generative AI model, and an emotion engine.

[0697] The server manages a collection of information for providing hierarchical educational materials based on the user's knowledge level and emotional state. Specifically, it performs SQL query processing to select the most appropriate hierarchical materials by matching the educational materials stored in the database with the user profile.

[0698] The server further controls the generative AI model based on the user's profile information to automatically generate user-specific educational materials. In this process, a well-known natural language processing engine (e.g., GPT-4) is used as the generative AI model to receive prompts and construct optimal learning materials. For example, the prompt "Knowledge level: Beginner, Theme: Space, Emotion: Excitement" might be input to the AI ​​model.

[0699] The generated educational materials are sent from the server to the user's terminal. The terminal has the capability to display the received materials interactively, and the user progresses through learning by interacting with it. Specifically, the terminal is equipped with touchscreen and voice input functions, allowing the user to interact with interactive tasks such as coloring pages and quizzes.

[0700] Furthermore, the device has a built-in camera and microphone, and the emotion engine uses data acquired from these sensors to analyze the user's emotional state in real time. This allows for dynamic adjustments, such as displaying more challenging content when the user is excited, or detailed explanations when the user is focused.

[0701] The feedback information obtained through this process is sent to the server and used to adjust the next set of educational materials. This feedback includes operation logs and data on emotional states, and analysis is used to optimize individual learning paths.

[0702] This system allows users to always receive learning materials tailored to their own learning motivation and level of understanding, enabling them to learn effectively.

[0703] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0704] Step 1:

[0705] The server searches the database for relevant, hierarchical educational materials based on user input, including knowledge level and past learning history. Using database queries, it extracts appropriate materials and generates a list of materials that match the user profile. This output is the list of materials to be used in the next step.

[0706] Step 2:

[0707] The server sends prompts to a generative AI model based on the list of materials and the user's emotional state, generating customized educational materials. The generative AI model receives the prompts as input and uses natural language processing techniques to generate the most suitable educational content for the user. The generated educational materials are obtained as output.

[0708] Step 3:

[0709] The server sends the generated learning materials to the user's terminal. Using a communication protocol, it sends the learning material data as packets to the terminal over the internet and maintains the connection until reception is confirmed. The output is the learning material data sent to the terminal.

[0710] Step 4:

[0711] The device displays interactive educational materials to the user based on the received educational data. It accepts touch and voice input through the user interface, visualizing the content to facilitate user interaction with the materials. The output is the interactively displayed educational material.

[0712] Step 5:

[0713] The device uses its camera and microphone to capture the user's facial expressions and voice, inputting them into the emotion engine in real time. The emotion engine analyzes the input data using an emotion analysis algorithm to identify the user's emotional state. The output is the result of the emotional state determination.

[0714] Step 6:

[0715] The device dynamically adjusts the user's learning experience based on the results of its emotional state assessment. It changes the difficulty level and content of the displayed material to enable the user to learn more effectively. The output is the adjusted learning content.

[0716] Step 7:

[0717] The server receives feedback information sent from the terminal and records it in a database. This feedback information includes the user's operation history and sentiment data. Recording it in the database generates foundational data that can be used to adjust the learning content for the next session.

[0718] (Application Example 2)

[0719] 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".

[0720] When providing educational information for young children, it is essential to offer an optimal learning experience tailored to each user's emotional state and interests. However, existing educational systems struggle to provide individualized support. Furthermore, in physical learning environments, the technology to provide interactive content that responds to user reactions in real time is not yet fully established. As a result, users are often limited to one-way information provision, leading to decreased learning efficiency.

[0721] 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.

[0722] In this invention, the server includes means for managing a recording device that provides hierarchical learning information based on user attributes; means for controlling generation rules that automatically generate learning information according to the user's attributes and level of interest; and means for analyzing the user's emotional state through a camera and microphone and recording it as evaluation information. This makes it possible to provide learning information tailored to the user's individual emotional state and interests in real time, thereby improving the quality of the learning experience in physical stores.

[0723] "User attributes" refer to a collection of individually identifiable characteristics, such as the learner's age, knowledge level, and learning history.

[0724] "Hierarchical learning information" refers to educational materials and assignments that are structured in stages according to the learner's level of understanding and progress.

[0725] A "recording device" refers to a machine or program that has functions such as a database for storing and managing information.

[0726] "Generation rules" refer to algorithms or rule sets used to create or refine educational information based on specific conditions.

[0727] "Emotional state" refers to the learner's psychological and emotional state, which can be expressed as specific emotions such as joy, excitement, or concentration.

[0728] "Evaluation information" refers to data obtained based on learners' actions and responses, and is used to measure the progress and effectiveness of learning.

[0729] A "learning path" refers to a specific set of learning steps or processes that learners follow using educational information, providing a customized learning experience.

[0730] The system for carrying out this invention includes a server containing a recording device, generation rules, and an emotion analysis engine, and a terminal equipped with a large display, camera, and microphone that enable interactive display. The server manages hierarchical learning information based on user attribute information and automatically generates appropriate content according to the user's level of interest using a generative AI model. The terminal analyzes the learner's facial expressions and voice in real time and records their emotional state as evaluation information.

[0731] Based on recorded evaluation information, the server adjusts the learner's individual learning path and optimizes the next educational content. Specifically, for example, when a learner shows a happy expression, it provides dynamic activities that evoke joy, and when the learner is observed to be focused, it presents content that encourages deeper learning.

[0732] A concrete example related to the terminal is an interactive touch display installed in the waiting area of ​​a physical store. If a user touches the screen to select animal-themed content and smiles while watching the displayed video of a lion, the emotion engine detects that joy and immediately presents a quiz about lions.

[0733] An example of a prompt might be, "Please share some fun facts about lions, in a simple quiz format, suitable for young children." This would then allow a generative AI model to provide optimal educational information.

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The server retrieves user attribute information from a database in the recording device. Based on this input information, it analyzes the user's age, knowledge level, and learning history, and selects appropriate learning information. This selected information is then used as input for subsequent generation rules.

[0737] Step 2:

[0738] The server uses a generative AI model to execute generation rules tailored to the user's interests based on selected training information. The generative AI model uses prompts to automatically generate the most suitable educational content for the user. The generated content becomes output for transmission to the terminal.

[0739] Step 3:

[0740] The device displays the transmitted educational content on a large screen. The device also uses a camera and microphone to capture the user's facial expressions and voice in real time and analyze their emotional state. Based on this input data, the emotion engine records the emotional state as evaluation information and sends it to the server.

[0741] Step 4:

[0742] The server analyzes the received evaluation information and adjusts the user's individual learning path. This includes modifying content and adjusting difficulty levels based on emotional states. The adjusted learning path is used as data to help generate future educational content.

[0743] Step 5:

[0744] Users can interact with the presented content interactively, including answering on-screen quizzes and selecting materials to deepen their knowledge. This interaction information is collected again and sent to the server as evaluation data.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] [Means for managing a database that provides hierarchical educational content based on the user's knowledge level,

[0769] [Means for controlling a generative model that automatically generates educational content according to the user's knowledge level and interests,

[0770] [Means for sending and displaying generated educational content on a user's terminal,

[0771] [Means for collecting user operation data and generating feedback information,

[0772] [Means for adjusting the next content based on feedback information,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] [The system according to claim 1, which provides an interactive task in the form of a coloring book as educational content.

[0776] (Claim 3)

[0777] [The system according to claim 1, which analyzes user feedback information and adjusts individual learning paths based on the user's interests and level of understanding.

[0778] "Example 1"

[0779] (Claim 1)

[0780] [Means for acquiring user information and analyzing knowledge level and interests,

[0781] [A means for generating prompt sentences based on analysis results and creating educational content using a generative model,

[0782] [Means for sending and displaying the created educational content on the user's device,

[0783] [Means for recording user actions and sending feedback information to a server,

[0784] [Methods for optimizing the next educational content provided based on feedback information,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein the educational content includes a coloring book format as an interactive task.

[0788] (Claim 3)

[0789] [The system according to claim 1, which analyzes user feedback information and adjusts individual learning paths based on the user's interests and level of understanding.

[0790] "Application Example 1"

[0791] (Claim 1)

[0792] [Means for managing an information infrastructure that provides hierarchical educational information based on the user's knowledge level,

[0793] [Means for controlling a generation program that automatically generates educational information according to the user's knowledge level and interests,

[0794] [Means for transmitting and displaying generated educational information on user devices,

[0795] [Means for identifying objects using information recognition technology and presenting related educational information,

[0796] [Means for collecting user operation information and generating feedback information,

[0797] [Means for adjusting the next information based on feedback information,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] [The system according to claim 1, which provides an interactive activity in the form of coloring as educational information.

[0801] (Claim 3)

[0802] [The system according to claim 1, which analyzes user feedback information and adjusts individual learning paths based on the user's interests and level of understanding.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] [Means for managing a collection of information that provides hierarchical educational materials based on the user's knowledge level and emotional state,

[0806] [Means for controlling a generation engine that automatically generates educational materials according to the user's knowledge level, emotional state, and interests,

[0807] [Means for transmitting and displaying generated educational materials on the user's device,

[0808] [Means for collecting user operation data and emotional data, and generating feedback information,

[0809] [Means for adjusting the next materials based on feedback information,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] [The system according to claim 1, which provides an interactive task in the form of a coloring book as educational material.

[0813] (Claim 3)

[0814] [The system according to claim 1, which analyzes user feedback information and emotional data and adjusts individual learning paths based on the user's interests and level of understanding.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] [Means for managing a recording device that provides hierarchical learning information based on user attributes,

[0818] [Means for controlling generation rules that automatically generate learning information according to the user's attributes and level of interest,

[0819] [Means for transmitting and displaying the generated learning information to the user device,

[0820] [Means for collecting user operation information and generating evaluation information,

[0821] [Means of analyzing the user's emotional state through cameras and microphones and recording it as evaluation information,

[0822] [Means for adjusting the next information based on evaluation information,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] [The system according to claim 1, which provides adaptive, interactive activities as learning information and operates on a large information display device in a physical store.

[0826] (Claim 3)

[0827] [The system according to claim 1, which analyzes evaluation information from users and adjusts individual learning paths based on the users' interests and awareness. [Explanation of Symbols]

[0828] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for managing a database that provides hierarchical educational content based on the user's knowledge level, A means for controlling a generative model that automatically generates educational content according to the user's knowledge level and interests, A means for sending and displaying the generated educational content on the user's terminal, A means for collecting user operation data and generating feedback information, A means of adjusting the next content based on feedback information, A system that includes this.

2. The system according to claim 1, which provides an interactive task in the form of a coloring book as educational content.

3. The system according to claim 1, which analyzes user feedback information and adjusts individual learning paths based on the user's interests and level of understanding.

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