system

Abstract

We provide the system. [Solution] Means for collecting learner profile information, A means for generating customized educational content based on the profile information using a generative model, A means for displaying the generated educational content using virtual reality or augmented reality technology, A means of monitoring and recording learner progress data in real time, A means for dynamically adjusting educational content based on the aforementioned progress data, 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] In conventional educational systems, group classes are common, and it is difficult to provide teaching materials according to the understanding and interests of individual learners. Also, in online learning, it is difficult to maintain the motivation of learners. Furthermore, in learning in abstract fields such as mathematics and science, visual support is insufficient. Due to these problems, there is an issue that the learning effect cannot be fully exerted.

Means for Solving the Problems

[0005] This invention provides a system that collects learner profile information and generates individually optimized educational content using a generative model. Furthermore, it provides learners with an immersive learning experience by displaying the generated educational content using virtual reality or augmented reality technology. It also provides a learning environment tailored to each individual learner by monitoring learner progress data in real time and dynamically adjusting the educational content based on that data. This enables learners to learn effectively at their own pace.

[0006] "Learner profile information" refers to information about individual learners, including their past learning history, academic performance, interests, and goals.

[0007] A "generative model" is a machine learning algorithm that automatically generates a specific output based on input data.

[0008] "Educational content" refers to educational materials, including text, images, videos, and interactive simulations, that enable learners to acquire knowledge and skills.

[0009] "Virtual reality" is a technology that uses computer technology to allow users to experience a realistic virtual environment.

[0010] Augmented reality is a technology that overlays digital information onto the real world, displaying an expanded version of the real world.

[0011] "Progress data" refers to data that records the learning status of learners, showing how far they have progressed in which topics and what results they have achieved.

[0012] An "immersive learning experience" is an experience in which learners acquire knowledge and skills in an environment that makes them feel as if they are actually there.

[0013] "Dynamic adjustment" means flexibly changing the system's settings and content in response to changes in circumstances or new data. [Brief explanation of the drawing]

[0014] [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]

[0015] Hereinafter, an example of an embodiment of a system according to the techniques of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (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.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is an educational system that dynamically generates and provides educational content tailored to each learner's level of understanding and interests. The server first collects learner profile information. This profile information includes past learning history, grades, and topics of interest. Based on the collected information, the server uses a generative model to generate educational content tailored to the learner.

[0036] The generated educational content is delivered to learners via devices using virtual reality and augmented reality technologies. Users can experience an immersive learning environment through this content. User progress data is continuously monitored by the device and transmitted to a server. Based on this progress data, the server periodically adjusts the educational content to support and maximize the user's learning effectiveness.

[0037] As a concrete example, consider a case where a user is learning about history. If the user expresses interest in the "Renaissance period," the server generates educational content related to this topic. For example, it might create a virtual museum where the user can view text and video materials covering important events and figures of the Renaissance, as well as works of art from that era. The terminal provides this content to the user in a VR environment, allowing the user to freely deepen their learning within that space. In this way, the present invention can respond to individual learning needs and provide an optimal learning experience tailored to the learner.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server retrieves learner profile information from the database. This information includes past learning history, grades, and topics of interest.

[0041] Step 2:

[0042] Users input their new interests through their devices and send them to the server, ensuring that their latest needs are reflected.

[0043] Step 3:

[0044] The server uses a generative model to create personalized educational content based on collected profile information and the user's latest interests.

[0045] Step 4:

[0046] The server sends the generated educational content to the device. The content consists of text, video, and VR / AR formats.

[0047] Step 5:

[0048] The device processes the received educational content and provides it to the user. This includes creating virtual spaces using VR / AR devices.

[0049] Step 6:

[0050] Users progress through their learning by utilizing educational content provided via their devices. In the virtual space, users can engage in interactive exploration and manipulation.

[0051] Step 7:

[0052] The device monitors the user's learning progress in real time and sends that data to the server. This includes completed sections and access time.

[0053] Step 8:

[0054] The server analyzes progress data and adjusts the learning content as needed. This sets the optimal learning materials for the next session.

[0055] (Example 1)

[0056] 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."

[0057] Traditional education systems have faced challenges in providing appropriate educational content tailored to each learner's level of understanding and interests, resulting in limited learning effectiveness. Furthermore, the inability to dynamically adjust educational content based on learners' progress has made it difficult to improve the actual quality of learning.

[0058] 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.

[0059] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational materials based on the characteristic information using a generation algorithm, and means for displaying the generated educational materials using virtual environment technology. This makes it possible to provide educational materials optimized for each individual learner and improve the quality of learning.

[0060] A "learner" refers to an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0061] "Characteristic information" refers to individual data such as a learner's past activities, academic performance, and interests, and is used to personalize educational content.

[0062] A "generation algorithm" is a computational method used to generate a specific output based on input data, and in this invention, it is used to generate educational materials.

[0063] "Educational materials" refer to information and tools provided to learners to acquire knowledge and skills.

[0064] "Virtual environment technology" refers to technology that uses computer technology to simulate environments that do not exist in reality, providing users with an immersive experience.

[0065] "Activity data" refers to information that indicates the progress and status of a learner's learning, such as their operation history, grades, and usage time.

[0066] An "operational interface" refers to an interface that provides a means for a user to interact with a system, enabling learners to operate in a virtual environment.

[0067] This invention relates to an educational system that dynamically generates and provides personalized educational materials based on learner characteristic information.

[0068] The server utilizes a database system to collect learner characteristic information. For example, learning history and performance information are retrieved through database queries such as SQL. Information about learners' interests is collected through questionnaires or open-ended responses.

[0069] Based on this information, the server uses a generative AI model to generate educational materials. The generative AI model often used is GPT or a similar natural language generation model. For example, a prompt such as "If the learner is interested in the Renaissance period, generate relevant historical content" might be input. Based on this prompt, the AI ​​generates text and scripts for a virtual environment and sends them to the terminal.

[0070] The device utilizes VR and AR technologies to deliver the generated educational materials to the user. The device uses development environments such as Unity and Unreal Engine to construct a virtual space, providing learners with an immersive experience through devices like VR headsets.

[0071] Through this content, users can learn experientially. For example, they can recreate Renaissance artworks and historical events in a VR space and freely explore them.

[0072] In this way, by linking the server and terminals, it is possible to provide a customized learning environment for each learner and improve the quality of the learning experience.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects learner characteristic information, including learning history, grades, and topics of interest. The server connects to a database system and executes SQL queries to retrieve relevant information. The retrieved data is stored in JSON format.

[0076] Step 2:

[0077] The server provides the collected feature information as input to the AI ​​model. Specifically, it generates a prompt message such as, "Generate educational content related to topics that the learner is interested in." Based on this prompt message, the AI ​​model performs data calculations and outputs personalized educational materials in text format.

[0078] Step 3:

[0079] The server sends the generated educational materials to the terminal. The HTTP protocol is used for transmission, and the data is sent in text or script format. This data is used in a virtual environment.

[0080] Step 4:

[0081] The device builds a virtual environment based on the received educational materials. Using a VR or AR development platform, it dynamically places and visualizes 3D objects and text information. An engine such as Unity reads the data, generates the scene, and delivers it to the user through a VR headset.

[0082] Step 5:

[0083] Users access the generated virtual environment and educational materials via a terminal. They can move around the virtual space and explore information using a controller. User interaction data is logged by the terminal.

[0084] Step 6:

[0085] The device sends user activity data to the server in real time. The server analyzes this data as input and evaluates the learner's progress and understanding. Based on the analysis results, the server dynamically adjusts the educational materials and feeds new prompt sentences to the AI ​​model as needed.

[0086] Step 7:

[0087] The server sends the adjusted educational materials back to the terminal, providing new content optimized for the learner. This iterative process supports effective learning while maintaining the learner's motivation.

[0088] (Application Example 1)

[0089] 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."

[0090] The current education system struggles to provide individualized learning experiences tailored to each learner's interests and level of understanding. Furthermore, there is a lack of means to replicate real-world visual and experiential learning in the home environment. Therefore, innovative environments and methods are needed to promote more effective learning.

[0091] 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.

[0092] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational information based on the characteristic information using a model for generation, and means for displaying the generated educational information using virtual reality or augmented reality technology. This makes it possible to provide learners with a personalized visual educational experience.

[0093] "Learner characteristic information" refers to personal information about learners, such as their past learning history, interests, and academic performance, and is data used to personalize educational content.

[0094] A "generation model" refers to algorithms and technologies used to generate educational information based on learner characteristics, and plays a role in dynamically generating appropriate content.

[0095] "Virtual reality" or "augmented reality" refers to virtual environments or display formats created using digital technology that enable learners to engage in visual and experiential learning.

[0096] "Educational information" is a general term for learning materials, including texts, videos, and interactive content, that are provided according to the learner's interests and level of understanding.

[0097] This invention comprises a system for providing learners with personalized educational experiences. The server is responsible for efficiently collecting and managing learner characteristic information. This data is stored in a database using cloud technology.

[0098] The server uses a generative AI model to generate learner-optimized educational information based on collected trait data. The generated educational information is available as various digital content formats (text, images, audio, etc.). The generative AI model combines various existing generative technologies and is built on machine learning frameworks such as TENSORFLOW® and PyTorch.

[0099] The device provides users with generated educational information using virtual reality or augmented reality technology. Development platforms such as Unity or ARKit are used to create an immersive learning environment. For example, when a learner tackles a topic related to space, an AR experience can be provided where outer space realistically appears floating above furniture.

[0100] Users interactively utilize educational information provided through their devices, and progress data is transmitted to the server in real time. The server analyzes this progress data and can dynamically adjust the educational information according to the learner's level of understanding.

[0101] For example, if a user wants to learn about history, they can enter a prompt such as "I would like AR content about the Renaissance," and the server will generate relevant educational information and provide it through the device. In this way, the system allows learners to obtain an optimal learning experience tailored to their interests and level of understanding.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server collects learner profile information and stores it in a cloud database. This information includes learning history, interests, and performance, and is efficiently managed using database technology. The input is profile information provided by the learner, and the output is a database with updated profile information.

[0105] Step 2:

[0106] The user enters a prompt about the topic they want to learn about. For example, they might provide a specific request such as, "I would like AR content about the Renaissance." The input is the user's prompt, and the output is the request data associated with that prompt.

[0107] Step 3:

[0108] The server uses a generative AI model to generate optimized educational information based on characteristic information and user prompts. During this process, the model is executed, creating digital content such as text, images, and audio. The input consists of characteristic information and prompts, while the output is the generated educational information.

[0109] Step 4:

[0110] The device provides the user with generated educational information in a virtual or augmented reality environment. Using a platform such as Unity or ARKit, the user gains an immersive learning experience. The input is the generated educational information, and the output is the user's visual and experiential learning environment.

[0111] Step 5:

[0112] Users learn within the provided environment, and their progress is transmitted to the server in real time via their device. This progress data is used to evaluate learning and adjust content. The input is the user's learning status, and the output is the progress data recorded on the server.

[0113] Step 6:

[0114] The server analyzes the collected progress data and uses a generative AI model to dynamically adjust educational information as needed, thereby maximizing learning effectiveness. The input is progress data, and the output is the adjusted educational information.

[0115] 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.

[0116] This invention provides a system that not only offers personalized educational content based on learner profile information and learning progress, but also detects learners' emotional states in real time and dynamically adjusts the education. This makes it possible to provide a learning environment that adapts not only to learners' interests and understanding, but also to their emotional responses.

[0117] The server collects and registers learner profile data and creates personalized educational content using a generative model. The generated content is delivered to the user through a device using virtual reality or augmented reality technology. As the user progresses through the learning process, the device uses an emotion engine to identify emotions from the user's facial expressions, voice, and input data.

[0118] Based on the detected emotions, the emotion engine suggests alternative content or additional hints if the user is losing interest or not understanding the material. For example, if the server determines that the user is stressed, it may temporarily reduce the learning content or insert relaxing material. Conversely, if the user is curious, it can also present additional challenging tasks.

[0119] As a concrete example, consider a scenario where a user is solving a math problem. If the user shows a thoughtful expression, the emotion engine detects stress. The server receives this information and sends instructions to the terminal, either providing hints to the user or simplifying the practice problems. In this way, it is possible to adapt to the learner's emotional state and provide a more effective and comfortable learning experience.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user enters their learning topics of interest into the device. They also allow the collection of their facial expressions and voice data during learning.

[0123] Step 2:

[0124] The device sends user input data and emotion data captured in real time to the server. The emotion data includes emotional information estimated through facial recognition and voice analysis.

[0125] Step 3:

[0126] The server analyzes user profile data and received sentiment data. Using a generative model, it generates educational content best suited to the user's interests and emotions.

[0127] Step 4:

[0128] The server sends the generated educational content to the device. This content is delivered to the user using virtual reality or augmented reality technology.

[0129] Step 5:

[0130] Users begin learning by utilizing educational content received through their devices. They progress through learning via interactive operations within a virtual space.

[0131] Step 6:

[0132] The device monitors the user's emotional changes in real time during the learning process and analyzes their emotional state using an emotion engine, particularly detecting changes such as stress and excitement.

[0133] Step 7:

[0134] When the emotion engine detects a change in the user's emotions, the device sends that information to the server. The server then dynamically adjusts the learning material based on this information.

[0135] Step 8:

[0136] The server generates and sends educational content tailored to the user's emotional state. For example, if the user is having difficulty, it provides additional hints; if they show interest, it offers more advanced challenges.

[0137] Step 9:

[0138] The device provides users with updated educational content, supporting smooth learning. Users continue learning based on the newly presented content.

[0139] (Example 2)

[0140] 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".

[0141] Traditional education systems have struggled to adapt to the individual needs and emotional states of each learner, particularly in managing the impact of learners' emotions on learning efficiency. Furthermore, the lack of mechanisms to respond to learners' emotional states in real time and dynamically adjust educational materials made it difficult to maintain learners' motivation and concentration.

[0142] 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.

[0143] In this invention, the server includes means for collecting learner attribute information, means for generating personalized educational materials using a generative AI model, and means for presenting the educational materials using virtual reality or augmented reality technology. This makes it possible to detect the learner's emotional state in real time, dynamically adjust the educational materials based on that, and provide a personalized educational experience.

[0144] "Learner attribute information" refers to information necessary to provide an individualized educational experience, such as the learner's basic information, learning history, and interests.

[0145] A "generative AI model" is an algorithm that generates new information and content based on data, and in this invention, it is used to generate personalized educational materials.

[0146] "Personalized educational materials" are created by a generative AI model based on learner attribute information, and are designed to provide an optimal learning experience tailored to the learner's interests and level of understanding.

[0147] "Virtual reality technology" is a technology that allows users to have interactive experiences in a computer-generated virtual environment.

[0148] Augmented reality technology is a technology that overlays computer-generated information onto images of the real world, providing users with a fusion of reality and virtual reality.

[0149] "Emotional state" refers to a learner's feelings and mental responses, and is a psychological state that influences learning effectiveness and motivation.

[0150] A "user interface" is a device or system that enables learners to perform specific operations in a virtual reality or augmented reality environment, and it supports the interaction between the user and the system.

[0151] The present invention aims to effectively provide educational materials that meet the individual needs of learners. Specific embodiments for carrying out the invention are described below.

[0152] At the start of learning, the server collects attribute information such as basic information, past learning history, and interests provided by the learner. This information is aggregated in the learning management platform and stored in a database. Next, the server uses this attribute information to generate educational materials optimized for the learner using a generative AI model. In this process, a computer system with a deep learning algorithm implemented, for example, is used. A common generative AI model used here is one equipped with natural language generation technology, and its prompts might include instructions such as, "Generate math problems appropriate to the user's level calculated from their past math performance."

[0153] Next, the generated educational materials are presented to the user through a device equipped with virtual reality (VR) or augmented reality (AR) technology. The device uses a VR headset or AR-enabled device to provide learners with an interactive learning experience. A specific example is a scenario in which a user wears a VR headset and solves math problems in a virtual classroom environment.

[0154] Furthermore, as learning progresses, the device uses a connected emotion engine to detect and analyze the user's emotional state in real time. This emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and analyzes that data to identify emotions such as stress and excitement.

[0155] The server dynamically adjusts educational materials based on this emotional data. For example, if the emotion engine detects that the user is having difficulty solving a problem, the server sends a command to the device to lower the difficulty level, allowing the user to continue learning while easing their tension. An example of a prompt to the generative AI model would be, "Analyze the user's current emotions based on their facial expression data and suggest appropriate adjustments to the educational content." This operation enables the provision of a flexible and effective educational experience tailored to the individual learner's state.

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

[0157] Step 1:

[0158] The server collects learner attribute information via the learning management platform and registers it in a database. This process involves inputting basic learner information (such as name and grade level), past performance, course history, and interests. This information is output as foundational data for creating personalized educational materials in the generative AI model.

[0159] Step 2:

[0160] The server generates personalized educational materials using a generative AI model based on collected attribute information. In this process, attribute information serves as input, and a generative AI model based on a deep learning algorithm performs data processing and calculations. Specifically, customized learning materials that reflect past learning history and interests are generated and output as educational materials. The prompt used is "Generate questions based on past performance."

[0161] Step 3:

[0162] The terminal presents the learner with educational materials provided by the server. In this step, the terminal uses virtual reality (VR) or augmented reality (AR) technology to visually input the generated educational materials and output them interactively. Specifically, the user can wear a VR headset and explore and answer questions in the displayed virtual space.

[0163] Step 4:

[0164] The device detects the user's emotional state using an emotion engine as the learner uses the content. Here, the learner's facial expressions and voice are input via camera and microphone, and the emotion engine analyzes the data. The output identifies the learner's emotional state (e.g., joy, stress, concentration level).

[0165] Step 5:

[0166] The server dynamically adjusts educational materials based on emotional state data obtained through the terminal. Emotional state data is input, and the server provides appropriate feedback and adjusts learning materials, outputting new educational materials. Specifically, if the user is experiencing stress, materials with adjusted difficulty levels or relaxation content are provided.

[0167] (Application Example 2)

[0168] 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".

[0169] In today's educational environment, there is a need to provide each learner with content optimized for their individual needs and to improve learning efficiency. However, systems equipped with dynamic material adjustment functions that respond to learners' emotions and interests are limited, and there is a particular problem with real-time adaptation based on emotional states. In such a situation, learners find it difficult to maintain their motivation, and effective learning is difficult to achieve.

[0170] 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.

[0171] In this invention, the server includes means for collecting learner characteristic information, means for generating customized educational materials based on the characteristic information using a generative model, and means for displaying the generated educational materials using virtual reality or augmented reality technology. This makes it possible to provide an individualized educational experience based on the learner's emotional state and level of interest.

[0172] "Learner characteristic information" refers to basic information about the learner, including data such as past learning history, areas of interest, and academic performance.

[0173] A "generative model" refers to an algorithm or system used to generate customized content based on collected information, and it commonly utilizes artificial intelligence.

[0174] "Educational materials" refer to learning materials and content used by learners, provided in an individualized format to enhance learning effectiveness.

[0175] "Virtual reality or augmented reality technology" refers to technology that uses digital technology to combine real-world and virtual information, enabling visual and experiential learning.

[0176] "Emotional state" refers to elements related to a learner's emotions, primarily the instantaneous emotional situation analyzed from facial expressions and voice.

[0177] "Interest level" is an indicator that shows how much interest a learner has in a particular piece of content or activity.

[0178] "Personalized learning experiences" mean providing a learning process that is customized to the individual learner's needs and feelings, with the aim of maximizing learning efficiency.

[0179] This invention is implemented as a system that provides an individualized educational experience. This system includes a server, terminals, and a user interface.

[0180] The server's initial role is to collect learner feature information and store it in a database. This collected feature information is then analyzed using a generative AI model to form the basis for generating customized educational materials. The server also utilizes an emotion recognition engine (e.g., Affectiva) to detect emotional states from the user's facial expressions and voice data.

[0181] The generated educational materials are presented to the user via a device using virtual reality or augmented reality technology (e.g., Oculus Rift or Microsoft HoloLens). This device monitors the learner's progress and emotional state in real time and dynamically adjusts the educational materials as needed.

[0182] Users access these educational materials via smartphones or smart glasses. If a user exhibits a specific reaction or interaction with the learning materials, the device sends that information to the server for further adjustments. For example, if a user is experiencing stress, that information instructs the server to change the educational material to relaxing content.

[0183] In this system, an example of a prompt provided to the generative AI model might be an instruction such as, "Generate additional quizzes about historical events that the learner has shown interest in." This provides deeper learning tailored to the learner's interests and contributes to maintaining their motivation to learn.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The server collects learner profile information. It takes data such as learner basic information, past performance, and learning history as input and stores this in a database. The output is organized profile information for later analysis. This process utilizes data collection tools and APIs to ensure data integrity.

[0187] Step 2:

[0188] The server uses a generative AI model based on collected profile information to generate personalized educational materials. The input consists of profile information, the generative AI model, and prompts provided to the model. Specifically, these prompts include instructions such as, "Provide additional information on topics the learner has shown interest in." The output is customized educational material. The model processes large amounts of data and scrutinizes and provides appropriate content.

[0189] Step 3:

[0190] The device presents the generated educational materials to the learner. The input is the material output in step 2, which is presented visually to the user via a virtual reality or augmented reality device. The output is the user's experience of visually receiving the educational materials. The device adjusts the display position and format of the content in real time to aid the learner's understanding.

[0191] Step 4:

[0192] The device analyzes the learner's facial expressions and voice data using an emotion recognition engine to identify their emotional state. Input is real-time data acquired from the camera and microphone. Output is the analyzed data on the learner's emotional state. During this process, the device sets specific triggers to quickly capture changes in emotion.

[0193] Step 5:

[0194] The server dynamically adjusts educational materials using emotional state data received from the terminal. Inputs are emotional state data and the current educational material. Outputs are the adjusted educational materials; for example, if the user indicates stress, simpler exercises or relaxing content may be inserted. The server utilizes adaptive algorithms to provide the most appropriate response for the learner.

[0195] Step 6:

[0196] The user accepts the adjusted educational materials and resumes learning. The input is the content adjusted in Step 5, which forms the basis for continued learning. The output is the progress made and improved understanding. The user can further advance their learning by interacting with the interface.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] This invention is an educational system that dynamically generates and provides educational content tailored to each learner's level of understanding and interests. The server first collects learner profile information. This profile information includes past learning history, grades, and topics of interest. Based on the collected information, the server uses a generative model to generate educational content tailored to the learner.

[0214] The generated educational content is delivered to learners via devices using virtual reality and augmented reality technologies. Users can experience an immersive learning environment through this content. User progress data is continuously monitored by the device and transmitted to a server. Based on this progress data, the server periodically adjusts the educational content to support and maximize the user's learning effectiveness.

[0215] As a concrete example, consider a case where a user is learning about history. If the user expresses interest in the "Renaissance period," the server generates educational content related to this topic. For example, it might create a virtual museum where the user can view text and video materials covering important events and figures of the Renaissance, as well as works of art from that era. The terminal provides this content to the user in a VR environment, allowing the user to freely deepen their learning within that space. In this way, the present invention can respond to individual learning needs and provide an optimal learning experience tailored to the learner.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The server retrieves learner profile information from the database. This information includes past learning history, grades, and topics of interest.

[0219] Step 2:

[0220] Users input their new interests through their devices and send them to the server, ensuring that their latest needs are reflected.

[0221] Step 3:

[0222] The server uses a generative model to create personalized educational content based on collected profile information and the user's latest interests.

[0223] Step 4:

[0224] The server sends the generated educational content to the device. The content consists of text, video, and VR / AR formats.

[0225] Step 5:

[0226] The device processes the received educational content and provides it to the user. This includes creating virtual spaces using VR / AR devices.

[0227] Step 6:

[0228] Users progress through their learning by utilizing educational content provided via their devices. In the virtual space, users can engage in interactive exploration and manipulation.

[0229] Step 7:

[0230] The device monitors the user's learning progress in real time and sends that data to the server. This includes completed sections and access time.

[0231] Step 8:

[0232] The server analyzes progress data and adjusts the learning content as needed. This sets the optimal learning materials for the next session.

[0233] (Example 1)

[0234] 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."

[0235] Traditional education systems have faced challenges in providing appropriate educational content tailored to each learner's level of understanding and interests, resulting in limited learning effectiveness. Furthermore, the inability to dynamically adjust educational content based on learners' progress has made it difficult to improve the actual quality of learning.

[0236] 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.

[0237] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational materials based on the characteristic information using a generation algorithm, and means for displaying the generated educational materials using virtual environment technology. This makes it possible to provide educational materials optimized for each individual learner and improve the quality of learning.

[0238] A "learner" refers to an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0239] "Characteristic information" refers to individual data such as a learner's past activities, academic performance, and interests, and is used to personalize educational content.

[0240] A "generation algorithm" is a computational method used to generate a specific output based on input data, and in this invention, it is used to generate educational materials.

[0241] "Educational materials" refer to information and tools provided to learners to acquire knowledge and skills.

[0242] "Virtual environment technology" refers to technology that uses computer technology to simulate environments that do not exist in reality, providing users with an immersive experience.

[0243] "Activity data" refers to information that indicates the progress and status of a learner's learning, such as their operation history, grades, and usage time.

[0244] An "operational interface" refers to an interface that provides a means for a user to interact with a system, enabling learners to operate in a virtual environment.

[0245] This invention relates to an educational system that dynamically generates and provides personalized educational materials based on learner characteristic information.

[0246] The server utilizes a database system to collect learner characteristic information. For example, learning history and performance information are retrieved through database queries such as SQL. Information about learners' interests is collected through questionnaires or open-ended responses.

[0247] Based on this information, the server uses a generative AI model to generate educational materials. The generative AI model often used is GPT or a similar natural language generation model. For example, a prompt such as "If the learner is interested in the Renaissance period, generate relevant historical content" might be input. Based on this prompt, the AI ​​generates text and scripts for a virtual environment and sends them to the terminal.

[0248] The device utilizes VR and AR technologies to deliver the generated educational materials to the user. The device uses development environments such as Unity and Unreal Engine to construct a virtual space, providing learners with an immersive experience through devices like VR headsets.

[0249] Through this content, users can learn experientially. For example, they can recreate Renaissance artworks and historical events in a VR space and freely explore them.

[0250] In this way, by linking the server and terminals, it is possible to provide a customized learning environment for each learner and improve the quality of the learning experience.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The server collects learner characteristic information, including learning history, grades, and topics of interest. The server connects to a database system and executes SQL queries to retrieve relevant information. The retrieved data is stored in JSON format.

[0254] Step 2:

[0255] The server provides the collected feature information as input to the AI ​​model. Specifically, it generates a prompt message such as, "Generate educational content related to topics that the learner is interested in." Based on this prompt message, the AI ​​model performs data calculations and outputs personalized educational materials in text format.

[0256] Step 3:

[0257] The server sends the generated educational materials to the terminal. The HTTP protocol is used for transmission, and the data is sent in text or script format. This data is used in a virtual environment.

[0258] Step 4:

[0259] The device builds a virtual environment based on the received educational materials. Using a VR or AR development platform, it dynamically places and visualizes 3D objects and text information. An engine such as Unity reads the data, generates the scene, and delivers it to the user through a VR headset.

[0260] Step 5:

[0261] Users access the generated virtual environment and educational materials via a terminal. They can move around the virtual space and explore information using a controller. User interaction data is logged by the terminal.

[0262] Step 6:

[0263] The device sends user activity data to the server in real time. The server analyzes this data as input and evaluates the learner's progress and understanding. Based on the analysis results, the server dynamically adjusts the educational materials and feeds new prompt sentences to the AI ​​model as needed.

[0264] Step 7:

[0265] The server sends the adjusted educational materials back to the terminal, providing new content optimized for the learner. This iterative process supports effective learning while maintaining the learner's motivation.

[0266] (Application Example 1)

[0267] 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."

[0268] The current education system struggles to provide individualized learning experiences tailored to each learner's interests and level of understanding. Furthermore, there is a lack of means to replicate real-world visual and experiential learning in the home environment. Therefore, innovative environments and methods are needed to promote more effective learning.

[0269] 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.

[0270] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational information based on the characteristic information using a model for generation, and means for displaying the generated educational information using virtual reality or augmented reality technology. This makes it possible to provide learners with a personalized visual educational experience.

[0271] "Learner characteristic information" refers to personal information about learners, such as their past learning history, interests, and academic performance, and is data used to personalize educational content.

[0272] A "generation model" refers to algorithms and technologies used to generate educational information based on learner characteristics, and plays a role in dynamically generating appropriate content.

[0273] "Virtual reality" or "augmented reality" refers to virtual environments or display formats created using digital technology that enable learners to engage in visual and experiential learning.

[0274] "Educational information" is a general term for learning materials, including texts, videos, and interactive content, that are provided according to the learner's interests and level of understanding.

[0275] This invention comprises a system for providing learners with personalized educational experiences. The server is responsible for efficiently collecting and managing learner characteristic information. This data is stored in a database using cloud technology.

[0276] The server uses a generative AI model to generate learner-optimized educational information based on collected trait data. The generated educational information is available as various digital content formats (text, images, audio, etc.). The generative AI model combines various existing generative technologies and is built on machine learning frameworks such as TensorFlow and PyTorch.

[0277] The device provides users with generated educational information using virtual reality or augmented reality technology. Development platforms such as Unity or ARKit are used to create an immersive learning environment. For example, when a learner tackles a topic related to space, an AR experience can be provided where outer space realistically appears floating above furniture.

[0278] The user interactively uses the educational information provided through the terminal, and the progress data in the process is transmitted to the server in real time. The server can analyze this progress data and dynamically adjust the educational information according to the learner's understanding level.

[0279] As a specific example, when the user wants to learn about history, by entering the prompt sentence "I hope for AR content about the Renaissance" through the terminal, the server can generate relevant educational information and provide it through the terminal. In this way, the learner can obtain an optimal learning experience according to their interests and understanding level through this system.

[0280] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0281] Step 1:

[0282] The server collects the learner's characteristic information and stores it in the cloud database. This information includes learning history, interests, grades, etc., and is efficiently managed using database technology. The input is the profile information provided by the learner, and the output is the database with updated characteristic information.

[0283] Step 2:

[0284] The user enters a prompt sentence regarding the topic they want to learn. For example, provide a specific request such as "I hope for AR content about the Renaissance". The input is the user's prompt sentence, and the output is the request data related to this prompt sentence.

[0285] Step 3:

[0286] The server uses the generative AI model to generate optimized educational information based on the characteristic information and the user's prompt sentence. At this time, the model is executed, and digital content such as text, images, and audio is created. The input is the characteristic information and the prompt sentence, and the output is the generated educational information.

[0287] Step 4:

[0288] The terminal provides the generated educational information to the user in a virtual reality or augmented reality environment. Through a platform such as Unity or ARKit, the user can obtain an immersive learning experience. The input is the generated educational information, and the output is the visual and experiential learning environment as the user's experience.

[0289] Step 5:

[0290] The user conducts learning in the provided environment, and the progress status is transmitted to the server in real time through the terminal. The progress data is utilized for learning evaluation and content adjustment. The input is the user's learning state, and the output is the progress data recorded on the server.

[0291] Step 6:

[0292] The server analyzes the collected progress data and dynamically adjusts the educational information as needed using the generated AI model to maximize the learning effect. The input is the progress data, and the output is the adjusted educational information.

[0293] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0294] The present invention provides not only individualized educational content based on the learner's profile information and learning situation, but also a system that detects the learner's emotional state in real time and dynamically adjusts the education. Thereby, it is possible to provide a learning environment adapted not only to the learner's interest and understanding level but also to emotional reactions.

[0295] The server collects and registers learner profile data and creates personalized educational content using a generative model. The generated content is delivered to the user through a device using virtual reality or augmented reality technology. As the user progresses through the learning process, the device uses an emotion engine to identify emotions from the user's facial expressions, voice, and input data.

[0296] Based on the detected emotions, the emotion engine suggests alternative content or additional hints if the user is losing interest or not understanding the material. For example, if the server determines that the user is stressed, it may temporarily reduce the learning content or insert relaxing material. Conversely, if the user is curious, it can also present additional challenging tasks.

[0297] As a concrete example, consider a scenario where a user is solving a math problem. If the user shows a thoughtful expression, the emotion engine detects stress. The server receives this information and sends instructions to the terminal, either providing hints to the user or simplifying the practice problems. In this way, it is possible to adapt to the learner's emotional state and provide a more effective and comfortable learning experience.

[0298] The following describes the processing flow.

[0299] Step 1:

[0300] The user enters their learning topics of interest into the device. They also allow the collection of their facial expressions and voice data during learning.

[0301] Step 2:

[0302] The device sends user input data and emotion data captured in real time to the server. The emotion data includes emotional information estimated through facial recognition and voice analysis.

[0303] Step 3:

[0304] The server analyzes the user's profile data and the received emotion data. Using a generation model, it generates educational content that is optimal for the user's interests and emotions.

[0305] Step 4:

[0306] The server sends the generated educational content to the terminal. This content is provided to the user by leveraging virtual reality or augmented reality technology.

[0307] Step 5:

[0308] The user uses the educational content received through the terminal to start learning. Learning progresses through interactive operations within the virtual space.

[0309] Step 6:

[0310] The terminal monitors the user's emotional changes during learning in real time and analyzes the emotional state using an emotion engine. In particular, it captures changes such as stress and excitement.

[0311] Step 7:

[0312] When the emotion engine detects a change in the user's emotions, the terminal sends that information to the server. The server dynamically adjusts the teaching material content based on this information.

[0313] Step 8:

[0314] The server generates the re-adjusted educational content according to the user's emotional state and sends it to the terminal. For example, if the user is feeling difficulty, additional hints are given, and if the user shows interest, more advanced tasks are provided.

[0315] Step 9:

[0316] The device provides users with updated educational content, supporting smooth learning. Users continue learning based on the newly presented content.

[0317] (Example 2)

[0318] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0319] Traditional education systems have struggled to adapt to the individual needs and emotional states of each learner, particularly in managing the impact of learners' emotions on learning efficiency. Furthermore, the lack of mechanisms to respond to learners' emotional states in real time and dynamically adjust educational materials made it difficult to maintain learners' motivation and concentration.

[0320] 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.

[0321] In this invention, the server includes means for collecting learner attribute information, means for generating personalized educational materials using a generative AI model, and means for presenting the educational materials using virtual reality or augmented reality technology. This makes it possible to detect the learner's emotional state in real time, dynamically adjust the educational materials based on that, and provide a personalized educational experience.

[0322] "Learner attribute information" refers to information necessary to provide an individualized educational experience, such as the learner's basic information, learning history, and interests.

[0323] A "generative AI model" is an algorithm that generates new information and content based on data, and in this invention, it is used to generate personalized educational materials.

[0324] "Personalized educational materials" are created by a generative AI model based on learner attribute information, and are designed to provide an optimal learning experience tailored to the learner's interests and level of understanding.

[0325] "Virtual reality technology" is a technology that allows users to have interactive experiences in a computer-generated virtual environment.

[0326] Augmented reality technology is a technology that overlays computer-generated information onto images of the real world, providing users with a fusion of reality and virtual reality.

[0327] "Emotional state" refers to a learner's feelings and mental responses, and is a psychological state that influences learning effectiveness and motivation.

[0328] A "user interface" is a device or system that enables learners to perform specific operations in a virtual reality or augmented reality environment, and it supports the interaction between the user and the system.

[0329] The present invention aims to effectively provide educational materials that meet the individual needs of learners. Specific embodiments for carrying out the invention are described below.

[0330] At the start of learning, the server collects attribute information such as basic information, past learning history, and interests provided by the learner. This information is aggregated in the learning management platform and stored in a database. Next, the server uses this attribute information to generate educational materials optimized for the learner using a generative AI model. In this process, a computer system with a deep learning algorithm implemented, for example, is used. A common generative AI model used here is one equipped with natural language generation technology, and its prompts might include instructions such as, "Generate math problems appropriate to the user's level calculated from their past math performance."

[0331] Next, the generated educational materials are presented to the user through a device equipped with virtual reality (VR) or augmented reality (AR) technology. The device uses a VR headset or AR-enabled device to provide learners with an interactive learning experience. A specific example is a scenario in which a user wears a VR headset and solves math problems in a virtual classroom environment.

[0332] Furthermore, as learning progresses, the device uses a connected emotion engine to detect and analyze the user's emotional state in real time. This emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and analyzes that data to identify emotions such as stress and excitement.

[0333] The server dynamically adjusts educational materials based on this emotional data. For example, if the emotion engine detects that the user is having difficulty solving a problem, the server sends a command to the device to lower the difficulty level, allowing the user to continue learning while easing their tension. An example of a prompt to the generative AI model would be, "Analyze the user's current emotions based on their facial expression data and suggest appropriate adjustments to the educational content." This operation enables the provision of a flexible and effective educational experience tailored to the individual learner's state.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] The server collects learner attribute information via the learning management platform and registers it in a database. This process involves inputting basic learner information (such as name and grade level), past performance, course history, and interests. This information is output as foundational data for creating personalized educational materials in the generative AI model.

[0337] Step 2:

[0338] The server generates personalized educational materials using a generative AI model based on collected attribute information. In this process, attribute information serves as input, and a generative AI model based on a deep learning algorithm performs data processing and calculations. Specifically, customized learning materials that reflect past learning history and interests are generated and output as educational materials. The prompt used is "Generate questions based on past performance."

[0339] Step 3:

[0340] The terminal presents the learner with educational materials provided by the server. In this step, the terminal uses virtual reality (VR) or augmented reality (AR) technology to visually input the generated educational materials and output them interactively. Specifically, the user can wear a VR headset and explore and answer questions in the displayed virtual space.

[0341] Step 4:

[0342] The device detects the user's emotional state using an emotion engine as the learner uses the content. Here, the learner's facial expressions and voice are input via camera and microphone, and the emotion engine analyzes the data. The output identifies the learner's emotional state (e.g., joy, stress, concentration level).

[0343] Step 5:

[0344] The server dynamically adjusts educational materials based on emotional state data obtained through the terminal. Emotional state data is input, and the server provides appropriate feedback and adjusts learning materials, outputting new educational materials. Specifically, if the user is experiencing stress, materials with adjusted difficulty levels or relaxation content are provided.

[0345] (Application Example 2)

[0346] 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."

[0347] In today's educational environment, there is a need to provide each learner with content optimized for their individual needs and to improve learning efficiency. However, systems equipped with dynamic material adjustment functions that respond to learners' emotions and interests are limited, and there is a particular problem with real-time adaptation based on emotional states. In such a situation, learners find it difficult to maintain their motivation, and effective learning is difficult to achieve.

[0348] 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.

[0349] In this invention, the server includes means for collecting learner characteristic information, means for generating customized educational materials based on the characteristic information using a generative model, and means for displaying the generated educational materials using virtual reality or augmented reality technology. This makes it possible to provide an individualized educational experience based on the learner's emotional state and level of interest.

[0350] "Learner characteristic information" refers to basic information about the learner, including data such as past learning history, areas of interest, and academic performance.

[0351] A "generative model" refers to an algorithm or system used to generate customized content based on collected information, and it commonly utilizes artificial intelligence.

[0352] "Educational materials" refer to learning materials and content used by learners, provided in an individualized format to enhance learning effectiveness.

[0353] "Virtual reality or augmented reality technology" refers to technology that uses digital technology to combine real-world and virtual information, enabling visual and experiential learning.

[0354] "Emotional state" refers to elements related to a learner's emotions, primarily the instantaneous emotional situation analyzed from facial expressions and voice.

[0355] "Interest level" is an indicator that shows how much interest a learner has in a particular piece of content or activity.

[0356] "Personalized learning experiences" mean providing a learning process that is customized to the individual learner's needs and feelings, with the aim of maximizing learning efficiency.

[0357] This invention is implemented as a system that provides an individualized educational experience. This system includes a server, terminals, and a user interface.

[0358] The server's initial role is to collect learner feature information and store it in a database. This collected feature information is then analyzed using a generative AI model to form the basis for generating customized educational materials. The server also utilizes an emotion recognition engine (e.g., Affectiva) to detect emotional states from the user's facial expressions and voice data.

[0359] The generated educational materials are presented to the user via a device using virtual reality or augmented reality technology (e.g., Oculus Rift or Microsoft HoloLens). This device monitors the learner's progress and emotional state in real time and dynamically adjusts the educational materials as needed.

[0360] Users access these educational materials via smartphones or smart glasses. If a user exhibits a specific reaction or interaction with the learning materials, the device sends that information to the server for further adjustments. For example, if a user is experiencing stress, that information instructs the server to change the educational material to relaxing content.

[0361] In this system, an example of a prompt provided to the generative AI model might be an instruction such as, "Generate additional quizzes about historical events that the learner has shown interest in." This provides deeper learning tailored to the learner's interests and contributes to maintaining their motivation to learn.

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0363] Step 1:

[0364] The server collects learner profile information. It takes data such as learner basic information, past performance, and learning history as input and stores this in a database. The output is organized profile information for later analysis. This process utilizes data collection tools and APIs to ensure data integrity.

[0365] Step 2:

[0366] The server uses a generative AI model based on collected profile information to generate personalized educational materials. The input consists of profile information, the generative AI model, and prompts provided to the model. Specifically, these prompts include instructions such as, "Provide additional information on topics the learner has shown interest in." The output is customized educational material. The model processes large amounts of data and scrutinizes and provides appropriate content.

[0367] Step 3:

[0368] The device presents the generated educational materials to the learner. The input is the material output in step 2, which is presented visually to the user via a virtual reality or augmented reality device. The output is the user's experience of visually receiving the educational materials. The device adjusts the display position and format of the content in real time to aid the learner's understanding.

[0369] Step 4:

[0370] The device analyzes the learner's facial expressions and voice data using an emotion recognition engine to identify their emotional state. Input is real-time data acquired from the camera and microphone. Output is the analyzed data on the learner's emotional state. During this process, the device sets specific triggers to quickly capture changes in emotion.

[0371] Step 5:

[0372] The server dynamically adjusts educational materials using emotional state data received from the terminal. Inputs are emotional state data and the current educational material. Outputs are the adjusted educational materials; for example, if the user indicates stress, simpler exercises or relaxing content may be inserted. The server utilizes adaptive algorithms to provide the most appropriate response for the learner.

[0373] Step 6:

[0374] The user accepts the adjusted educational materials and resumes learning. The input is the content adjusted in Step 5, which forms the basis for continued learning. The output is the progress made and improved understanding. The user can further advance their learning by interacting with the interface.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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".

[0391] This invention is an educational system that dynamically generates and provides educational content tailored to each learner's level of understanding and interests. The server first collects learner profile information. This profile information includes past learning history, grades, and topics of interest. Based on the collected information, the server uses a generative model to generate educational content tailored to the learner.

[0392] The generated educational content is delivered to learners via devices using virtual reality and augmented reality technologies. Users can experience an immersive learning environment through this content. User progress data is continuously monitored by the device and transmitted to a server. Based on this progress data, the server periodically adjusts the educational content to support and maximize the user's learning effectiveness.

[0393] As a concrete example, consider a case where a user is learning about history. If the user expresses interest in the "Renaissance period," the server generates educational content related to this topic. For example, it might create a virtual museum where the user can view text and video materials covering important events and figures of the Renaissance, as well as works of art from that era. The terminal provides this content to the user in a VR environment, allowing the user to freely deepen their learning within that space. In this way, the present invention can respond to individual learning needs and provide an optimal learning experience tailored to the learner.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The server retrieves learner profile information from the database. This information includes past learning history, grades, and topics of interest.

[0397] Step 2:

[0398] Users input their new interests through their devices and send them to the server, ensuring that their latest needs are reflected.

[0399] Step 3:

[0400] The server uses a generative model to create personalized educational content based on collected profile information and the user's latest interests.

[0401] Step 4:

[0402] The server sends the generated educational content to the device. The content consists of text, video, and VR / AR formats.

[0403] Step 5:

[0404] The device processes the received educational content and provides it to the user. This includes creating virtual spaces using VR / AR devices.

[0405] Step 6:

[0406] Users progress through their learning by utilizing educational content provided via their devices. In the virtual space, users can engage in interactive exploration and manipulation.

[0407] Step 7:

[0408] The device monitors the user's learning progress in real time and sends that data to the server. This includes completed sections and access time.

[0409] Step 8:

[0410] The server analyzes progress data and adjusts the learning content as needed. This sets the optimal learning materials for the next session.

[0411] (Example 1)

[0412] 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."

[0413] Traditional education systems have faced challenges in providing appropriate educational content tailored to each learner's level of understanding and interests, resulting in limited learning effectiveness. Furthermore, the inability to dynamically adjust educational content based on learners' progress has made it difficult to improve the actual quality of learning.

[0414] 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.

[0415] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational materials based on the characteristic information using a generation algorithm, and means for displaying the generated educational materials using virtual environment technology. This makes it possible to provide educational materials optimized for each individual learner and improve the quality of learning.

[0416] A "learner" refers to an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0417] "Characteristic information" refers to individual data such as a learner's past activities, academic performance, and interests, and is used to personalize educational content.

[0418] A "generation algorithm" is a computational method used to generate a specific output based on input data, and in this invention, it is used to generate educational materials.

[0419] "Educational materials" refer to information and tools provided to learners to acquire knowledge and skills.

[0420] "Virtual environment technology" refers to technology that uses computer technology to simulate environments that do not exist in reality, providing users with an immersive experience.

[0421] "Activity data" refers to information that indicates the progress and status of a learner's learning, such as their operation history, grades, and usage time.

[0422] An "operational interface" refers to an interface that provides a means for a user to interact with a system, enabling learners to operate in a virtual environment.

[0423] This invention relates to an educational system that dynamically generates and provides personalized educational materials based on learner characteristic information.

[0424] The server utilizes a database system to collect learner characteristic information. For example, learning history and performance information are retrieved through database queries such as SQL. Information about learners' interests is collected through questionnaires or open-ended responses.

[0425] Based on this information, the server uses a generative AI model to generate educational materials. The generative AI model often used is GPT or a similar natural language generation model. For example, a prompt such as "If the learner is interested in the Renaissance period, generate relevant historical content" might be input. Based on this prompt, the AI ​​generates text and scripts for a virtual environment and sends them to the terminal.

[0426] The device utilizes VR and AR technologies to deliver the generated educational materials to the user. The device uses development environments such as Unity and Unreal Engine to construct a virtual space, providing learners with an immersive experience through devices like VR headsets.

[0427] Through this content, users can learn experientially. For example, they can recreate Renaissance artworks and historical events in a VR space and freely explore them.

[0428] In this way, by linking the server and terminals, it is possible to provide a customized learning environment for each learner and improve the quality of the learning experience.

[0429] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0430] Step 1:

[0431] The server collects learner characteristic information, including learning history, grades, and topics of interest. The server connects to a database system and executes SQL queries to retrieve relevant information. The retrieved data is stored in JSON format.

[0432] Step 2:

[0433] The server provides the collected feature information as input to the AI ​​model. Specifically, it generates a prompt message such as, "Generate educational content related to topics that the learner is interested in." Based on this prompt message, the AI ​​model performs data calculations and outputs personalized educational materials in text format.

[0434] Step 3:

[0435] The server sends the generated educational materials to the terminal. The HTTP protocol is used for transmission, and the data is sent in text or script format. This data is used in a virtual environment.

[0436] Step 4:

[0437] The device builds a virtual environment based on the received educational materials. Using a VR or AR development platform, it dynamically places and visualizes 3D objects and text information. An engine such as Unity reads the data, generates the scene, and delivers it to the user through a VR headset.

[0438] Step 5:

[0439] Users access the generated virtual environment and educational materials via a terminal. They can move around the virtual space and explore information using a controller. User interaction data is logged by the terminal.

[0440] Step 6:

[0441] The device sends user activity data to the server in real time. The server analyzes this data as input and evaluates the learner's progress and understanding. Based on the analysis results, the server dynamically adjusts the educational materials and feeds new prompt sentences to the AI ​​model as needed.

[0442] Step 7:

[0443] The server sends the adjusted educational materials back to the terminal, providing new content optimized for the learner. This iterative process supports effective learning while maintaining the learner's motivation.

[0444] (Application Example 1)

[0445] 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."

[0446] The current education system struggles to provide individualized learning experiences tailored to each learner's interests and level of understanding. Furthermore, there is a lack of means to replicate real-world visual and experiential learning in the home environment. Therefore, innovative environments and methods are needed to promote more effective learning.

[0447] 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.

[0448] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational information based on the characteristic information using a model for generation, and means for displaying the generated educational information using virtual reality or augmented reality technology. This makes it possible to provide learners with a personalized visual educational experience.

[0449] "Learner characteristic information" refers to personal information about learners, such as their past learning history, interests, and academic performance, and is data used to personalize educational content.

[0450] A "generation model" refers to algorithms and technologies used to generate educational information based on learner characteristics, and plays a role in dynamically generating appropriate content.

[0451] "Virtual reality" or "augmented reality" refers to virtual environments or display formats created using digital technology that enable learners to engage in visual and experiential learning.

[0452] "Educational information" is a general term for learning materials, including texts, videos, and interactive content, that are provided according to the learner's interests and level of understanding.

[0453] This invention comprises a system for providing learners with personalized educational experiences. The server is responsible for efficiently collecting and managing learner characteristic information. This data is stored in a database using cloud technology.

[0454] The server uses a generative AI model to generate learner-optimized educational information based on collected trait data. The generated educational information is available as various digital content formats (text, images, audio, etc.). The generative AI model combines various existing generative technologies and is built on machine learning frameworks such as TensorFlow and PyTorch.

[0455] The device provides users with generated educational information using virtual reality or augmented reality technology. Development platforms such as Unity or ARKit are used to create an immersive learning environment. For example, when a learner tackles a topic related to space, an AR experience can be provided where outer space realistically appears floating above furniture.

[0456] Users interactively utilize educational information provided through their devices, and progress data is transmitted to the server in real time. The server analyzes this progress data and can dynamically adjust the educational information according to the learner's level of understanding.

[0457] For example, if a user wants to learn about history, they can enter a prompt such as "I would like AR content about the Renaissance," and the server will generate relevant educational information and provide it through the device. In this way, the system allows learners to obtain an optimal learning experience tailored to their interests and level of understanding.

[0458] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0459] Step 1:

[0460] The server collects learner profile information and stores it in a cloud database. This information includes learning history, interests, and performance, and is efficiently managed using database technology. The input is profile information provided by the learner, and the output is a database with updated profile information.

[0461] Step 2:

[0462] The user enters a prompt about the topic they want to learn about. For example, they might provide a specific request such as, "I would like AR content about the Renaissance." The input is the user's prompt, and the output is the request data associated with that prompt.

[0463] Step 3:

[0464] The server uses a generative AI model to generate optimized educational information based on characteristic information and user prompts. During this process, the model is executed, creating digital content such as text, images, and audio. The input consists of characteristic information and prompts, while the output is the generated educational information.

[0465] Step 4:

[0466] The device provides the user with generated educational information in a virtual or augmented reality environment. Using a platform such as Unity or ARKit, the user gains an immersive learning experience. The input is the generated educational information, and the output is the user's visual and experiential learning environment.

[0467] Step 5:

[0468] Users learn within the provided environment, and their progress is transmitted to the server in real time via their device. This progress data is used to evaluate learning and adjust content. The input is the user's learning status, and the output is the progress data recorded on the server.

[0469] Step 6:

[0470] The server analyzes the collected progress data and uses a generative AI model to dynamically adjust educational information as needed, thereby maximizing learning effectiveness. The input is progress data, and the output is the adjusted educational information.

[0471] 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.

[0472] This invention provides a system that not only offers personalized educational content based on learner profile information and learning progress, but also detects learners' emotional states in real time and dynamically adjusts the education. This makes it possible to provide a learning environment that adapts not only to learners' interests and understanding, but also to their emotional responses.

[0473] The server collects and registers learner profile data and creates personalized educational content using a generative model. The generated content is delivered to the user through a device using virtual reality or augmented reality technology. As the user progresses through the learning process, the device uses an emotion engine to identify emotions from the user's facial expressions, voice, and input data.

[0474] Based on the detected emotions, the emotion engine suggests alternative content or additional hints if the user is losing interest or not understanding the material. For example, if the server determines that the user is stressed, it may temporarily reduce the learning content or insert relaxing material. Conversely, if the user is curious, it can also present additional challenging tasks.

[0475] As a concrete example, consider a scenario where a user is solving a math problem. If the user shows a thoughtful expression, the emotion engine detects stress. The server receives this information and sends instructions to the terminal, either providing hints to the user or simplifying the practice problems. In this way, it is possible to adapt to the learner's emotional state and provide a more effective and comfortable learning experience.

[0476] The following describes the processing flow.

[0477] Step 1:

[0478] The user enters their learning topics of interest into the device. They also allow the collection of their facial expressions and voice data during learning.

[0479] Step 2:

[0480] The device sends user input data and emotion data captured in real time to the server. The emotion data includes emotional information estimated through facial recognition and voice analysis.

[0481] Step 3:

[0482] The server analyzes user profile data and received sentiment data. Using a generative model, it generates educational content best suited to the user's interests and emotions.

[0483] Step 4:

[0484] The server sends the generated educational content to the device. This content is delivered to the user using virtual reality or augmented reality technology.

[0485] Step 5:

[0486] Users begin learning by utilizing educational content received through their devices. They progress through learning via interactive operations within a virtual space.

[0487] Step 6:

[0488] The device monitors the user's emotional changes in real time during the learning process and analyzes their emotional state using an emotion engine, particularly detecting changes such as stress and excitement.

[0489] Step 7:

[0490] When the emotion engine detects a change in the user's emotions, the device sends that information to the server. The server then dynamically adjusts the learning material based on this information.

[0491] Step 8:

[0492] The server generates and sends educational content tailored to the user's emotional state. For example, if the user is having difficulty, it provides additional hints; if they show interest, it offers more advanced challenges.

[0493] Step 9:

[0494] The device provides users with updated educational content, supporting smooth learning. Users continue learning based on the newly presented content.

[0495] (Example 2)

[0496] 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."

[0497] Traditional education systems have struggled to adapt to the individual needs and emotional states of each learner, particularly in managing the impact of learners' emotions on learning efficiency. Furthermore, the lack of mechanisms to respond to learners' emotional states in real time and dynamically adjust educational materials made it difficult to maintain learners' motivation and concentration.

[0498] 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.

[0499] In this invention, the server includes means for collecting learner attribute information, means for generating personalized educational materials using a generative AI model, and means for presenting the educational materials using virtual reality or augmented reality technology. This makes it possible to detect the learner's emotional state in real time, dynamically adjust the educational materials based on that, and provide a personalized educational experience.

[0500] "Learner attribute information" refers to information necessary to provide an individualized educational experience, such as the learner's basic information, learning history, and interests.

[0501] A "generative AI model" is an algorithm that generates new information and content based on data, and in this invention, it is used to generate personalized educational materials.

[0502] "Personalized educational materials" are created by a generative AI model based on learner attribute information, and are designed to provide an optimal learning experience tailored to the learner's interests and level of understanding.

[0503] "Virtual reality technology" is a technology that allows users to have interactive experiences in a computer-generated virtual environment.

[0504] Augmented reality technology is a technology that overlays computer-generated information onto images of the real world, providing users with a fusion of reality and virtual reality.

[0505] "Emotional state" refers to a learner's feelings and mental responses, and is a psychological state that influences learning effectiveness and motivation.

[0506] A "user interface" is a device or system that enables learners to perform specific operations in a virtual reality or augmented reality environment, and it supports the interaction between the user and the system.

[0507] The present invention aims to effectively provide educational materials that meet the individual needs of learners. Specific embodiments for carrying out the invention are described below.

[0508] At the start of learning, the server collects attribute information such as basic information, past learning history, and interests provided by the learner. This information is aggregated in the learning management platform and stored in a database. Next, the server uses this attribute information to generate educational materials optimized for the learner using a generative AI model. In this process, a computer system with a deep learning algorithm implemented, for example, is used. A common generative AI model used here is one equipped with natural language generation technology, and its prompts might include instructions such as, "Generate math problems appropriate to the user's level calculated from their past math performance."

[0509] Next, the generated educational materials are presented to the user through a device equipped with virtual reality (VR) or augmented reality (AR) technology. The device uses a VR headset or AR-enabled device to provide learners with an interactive learning experience. A specific example is a scenario in which a user wears a VR headset and solves math problems in a virtual classroom environment.

[0510] Furthermore, as learning progresses, the device uses a connected emotion engine to detect and analyze the user's emotional state in real time. This emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and analyzes that data to identify emotions such as stress and excitement.

[0511] The server dynamically adjusts educational materials based on this emotional data. For example, if the emotion engine detects that the user is having difficulty solving a problem, the server sends a command to the device to lower the difficulty level, allowing the user to continue learning while easing their tension. An example of a prompt to the generative AI model would be, "Analyze the user's current emotions based on their facial expression data and suggest appropriate adjustments to the educational content." This operation enables the provision of a flexible and effective educational experience tailored to the individual learner's state.

[0512] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0513] Step 1:

[0514] The server collects learner attribute information via the learning management platform and registers it in a database. This process involves inputting basic learner information (such as name and grade level), past performance, course history, and interests. This information is output as foundational data for creating personalized educational materials in the generative AI model.

[0515] Step 2:

[0516] The server generates personalized educational materials using a generative AI model based on collected attribute information. In this process, attribute information serves as input, and a generative AI model based on a deep learning algorithm performs data processing and calculations. Specifically, customized learning materials that reflect past learning history and interests are generated and output as educational materials. The prompt used is "Generate questions based on past performance."

[0517] Step 3:

[0518] The terminal presents the learner with educational materials provided by the server. In this step, the terminal uses virtual reality (VR) or augmented reality (AR) technology to visually input the generated educational materials and output them interactively. Specifically, the user can wear a VR headset and explore and answer questions in the displayed virtual space.

[0519] Step 4:

[0520] The device detects the user's emotional state using an emotion engine as the learner uses the content. Here, the learner's facial expressions and voice are input via camera and microphone, and the emotion engine analyzes the data. The output identifies the learner's emotional state (e.g., joy, stress, concentration level).

[0521] Step 5:

[0522] The server dynamically adjusts educational materials based on emotional state data obtained through the terminal. Emotional state data is input, and the server provides appropriate feedback and adjusts learning materials, outputting new educational materials. Specifically, if the user is experiencing stress, materials with adjusted difficulty levels or relaxation content are provided.

[0523] (Application Example 2)

[0524] 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."

[0525] In today's educational environment, there is a need to provide each learner with content optimized for their individual needs and to improve learning efficiency. However, systems equipped with dynamic material adjustment functions that respond to learners' emotions and interests are limited, and there is a particular problem with real-time adaptation based on emotional states. In such a situation, learners find it difficult to maintain their motivation, and effective learning is difficult to achieve.

[0526] 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.

[0527] In this invention, the server includes means for collecting learner characteristic information, means for generating customized educational materials based on the characteristic information using a generative model, and means for displaying the generated educational materials using virtual reality or augmented reality technology. This makes it possible to provide an individualized educational experience based on the learner's emotional state and level of interest.

[0528] "Learner characteristic information" refers to basic information about the learner, including data such as past learning history, areas of interest, and academic performance.

[0529] A "generative model" refers to an algorithm or system used to generate customized content based on collected information, and it commonly utilizes artificial intelligence.

[0530] "Educational materials" refer to learning materials and content used by learners, provided in an individualized format to enhance learning effectiveness.

[0531] "Virtual reality or augmented reality technology" refers to technology that uses digital technology to combine real-world and virtual information, enabling visual and experiential learning.

[0532] "Emotional state" refers to elements related to a learner's emotions, primarily the instantaneous emotional situation analyzed from facial expressions and voice.

[0533] "Interest level" is an indicator that shows how much interest a learner has in a particular piece of content or activity.

[0534] "Personalized learning experiences" mean providing a learning process that is customized to the individual learner's needs and feelings, with the aim of maximizing learning efficiency.

[0535] This invention is implemented as a system that provides an individualized educational experience. This system includes a server, terminals, and a user interface.

[0536] The server's initial role is to collect learner feature information and store it in a database. This collected feature information is then analyzed using a generative AI model to form the basis for generating customized educational materials. The server also utilizes an emotion recognition engine (e.g., Affectiva) to detect emotional states from the user's facial expressions and voice data.

[0537] The generated educational materials are presented to the user via a device using virtual reality or augmented reality technology (e.g., Oculus Rift or Microsoft HoloLens). This device monitors the learner's progress and emotional state in real time and dynamically adjusts the educational materials as needed.

[0538] Users access these educational materials via smartphones or smart glasses. If a user exhibits a specific reaction or interaction with the learning materials, the device sends that information to the server for further adjustments. For example, if a user is experiencing stress, that information instructs the server to change the educational material to relaxing content.

[0539] In this system, an example of a prompt provided to the generative AI model might be an instruction such as, "Generate additional quizzes about historical events that the learner has shown interest in." This provides deeper learning tailored to the learner's interests and contributes to maintaining their motivation to learn.

[0540] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0541] Step 1:

[0542] The server collects learner profile information. It takes data such as learner basic information, past performance, and learning history as input and stores this in a database. The output is organized profile information for later analysis. This process utilizes data collection tools and APIs to ensure data integrity.

[0543] Step 2:

[0544] The server uses a generative AI model based on collected profile information to generate personalized educational materials. The input consists of profile information, the generative AI model, and prompts provided to the model. Specifically, these prompts include instructions such as, "Provide additional information on topics the learner has shown interest in." The output is customized educational material. The model processes large amounts of data and scrutinizes and provides appropriate content.

[0545] Step 3:

[0546] The device presents the generated educational materials to the learner. The input is the material output in step 2, which is presented visually to the user via a virtual reality or augmented reality device. The output is the user's experience of visually receiving the educational materials. The device adjusts the display position and format of the content in real time to aid the learner's understanding.

[0547] Step 4:

[0548] The device analyzes the learner's facial expressions and voice data using an emotion recognition engine to identify their emotional state. Input is real-time data acquired from the camera and microphone. Output is the analyzed data on the learner's emotional state. During this process, the device sets specific triggers to quickly capture changes in emotion.

[0549] Step 5:

[0550] The server dynamically adjusts educational materials using emotional state data received from the terminal. Inputs are emotional state data and the current educational material. Outputs are the adjusted educational materials; for example, if the user indicates stress, simpler exercises or relaxing content may be inserted. The server utilizes adaptive algorithms to provide the most appropriate response for the learner.

[0551] Step 6:

[0552] The user accepts the adjusted educational materials and resumes learning. The input is the content adjusted in Step 5, which forms the basis for continued learning. The output is the progress made and improved understanding. The user can further advance their learning by interacting with the interface.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] [Fourth Embodiment]

[0557] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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".

[0570] This invention is an educational system that dynamically generates and provides educational content tailored to each learner's level of understanding and interests. The server first collects learner profile information. This profile information includes past learning history, grades, and topics of interest. Based on the collected information, the server uses a generative model to generate educational content tailored to the learner.

[0571] The generated educational content is delivered to learners via devices using virtual reality and augmented reality technologies. Users can experience an immersive learning environment through this content. User progress data is continuously monitored by the device and transmitted to a server. Based on this progress data, the server periodically adjusts the educational content to support and maximize the user's learning effectiveness.

[0572] As a concrete example, consider a case where a user is learning about history. If the user expresses interest in the "Renaissance period," the server generates educational content related to this topic. For example, it might create a virtual museum where the user can view text and video materials covering important events and figures of the Renaissance, as well as works of art from that era. The terminal provides this content to the user in a VR environment, allowing the user to freely deepen their learning within that space. In this way, the present invention can respond to individual learning needs and provide an optimal learning experience tailored to the learner.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] The server retrieves learner profile information from the database. This information includes past learning history, grades, and topics of interest.

[0576] Step 2:

[0577] Users input their new interests through their devices and send them to the server, ensuring that their latest needs are reflected.

[0578] Step 3:

[0579] The server uses a generative model to create personalized educational content based on collected profile information and the user's latest interests.

[0580] Step 4:

[0581] The server sends the generated educational content to the device. The content consists of text, video, and VR / AR formats.

[0582] Step 5:

[0583] The device processes the received educational content and provides it to the user. This includes creating virtual spaces using VR / AR devices.

[0584] Step 6:

[0585] Users progress through their learning by utilizing educational content provided via their devices. In the virtual space, users can engage in interactive exploration and manipulation.

[0586] Step 7:

[0587] The device monitors the user's learning progress in real time and sends that data to the server. This includes completed sections and access time.

[0588] Step 8:

[0589] The server analyzes progress data and adjusts the learning content as needed. This sets the optimal learning materials for the next session.

[0590] (Example 1)

[0591] 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".

[0592] Traditional education systems have faced challenges in providing appropriate educational content tailored to each learner's level of understanding and interests, resulting in limited learning effectiveness. Furthermore, the inability to dynamically adjust educational content based on learners' progress has made it difficult to improve the actual quality of learning.

[0593] 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.

[0594] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational materials based on the characteristic information using a generation algorithm, and means for displaying the generated educational materials using virtual environment technology. This makes it possible to provide educational materials optimized for each individual learner and improve the quality of learning.

[0595] A "learner" refers to an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0596] "Characteristic information" refers to individual data such as a learner's past activities, academic performance, and interests, and is used to personalize educational content.

[0597] A "generation algorithm" is a computational method used to generate a specific output based on input data, and in this invention, it is used to generate educational materials.

[0598] "Educational materials" refer to information and tools provided to learners to acquire knowledge and skills.

[0599] "Virtual environment technology" refers to technology that uses computer technology to simulate environments that do not exist in reality, providing users with an immersive experience.

[0600] "Activity data" refers to information that indicates the progress and status of a learner's learning, such as their operation history, grades, and usage time.

[0601] An "operational interface" refers to an interface that provides a means for a user to interact with a system, enabling learners to operate in a virtual environment.

[0602] This invention relates to an educational system that dynamically generates and provides personalized educational materials based on learner characteristic information.

[0603] The server utilizes a database system to collect learner characteristic information. For example, learning history and performance information are retrieved through database queries such as SQL. Information about learners' interests is collected through questionnaires or open-ended responses.

[0604] Based on this information, the server uses a generative AI model to generate educational materials. The generative AI model often used is GPT or a similar natural language generation model. For example, a prompt such as "If the learner is interested in the Renaissance period, generate relevant historical content" might be input. Based on this prompt, the AI ​​generates text and scripts for a virtual environment and sends them to the terminal.

[0605] The device utilizes VR and AR technologies to deliver the generated educational materials to the user. The device uses development environments such as Unity and Unreal Engine to construct a virtual space, providing learners with an immersive experience through devices like VR headsets.

[0606] Through this content, users can learn experientially. For example, they can recreate Renaissance artworks and historical events in a VR space and freely explore them.

[0607] In this way, by linking the server and terminals, it is possible to provide a customized learning environment for each learner and improve the quality of the learning experience.

[0608] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0609] Step 1:

[0610] The server collects learner characteristic information, including learning history, grades, and topics of interest. The server connects to a database system and executes SQL queries to retrieve relevant information. The retrieved data is stored in JSON format.

[0611] Step 2:

[0612] The server provides the collected feature information as input to the AI ​​model. Specifically, it generates a prompt message such as, "Generate educational content related to topics that the learner is interested in." Based on this prompt message, the AI ​​model performs data calculations and outputs personalized educational materials in text format.

[0613] Step 3:

[0614] The server sends the generated educational materials to the terminal. The HTTP protocol is used for transmission, and the data is sent in text or script format. This data is used in a virtual environment.

[0615] Step 4:

[0616] The device builds a virtual environment based on the received educational materials. Using a VR or AR development platform, it dynamically places and visualizes 3D objects and text information. An engine such as Unity reads the data, generates the scene, and delivers it to the user through a VR headset.

[0617] Step 5:

[0618] Users access the generated virtual environment and educational materials via a terminal. They can move around the virtual space and explore information using a controller. User interaction data is logged by the terminal.

[0619] Step 6:

[0620] The device sends user activity data to the server in real time. The server analyzes this data as input and evaluates the learner's progress and understanding. Based on the analysis results, the server dynamically adjusts the educational materials and feeds new prompt sentences to the AI ​​model as needed.

[0621] Step 7:

[0622] The server sends the adjusted educational materials back to the terminal, providing new content optimized for the learner. This iterative process supports effective learning while maintaining the learner's motivation.

[0623] (Application Example 1)

[0624] 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".

[0625] The current education system struggles to provide individualized learning experiences tailored to each learner's interests and level of understanding. Furthermore, there is a lack of means to replicate real-world visual and experiential learning in the home environment. Therefore, innovative environments and methods are needed to promote more effective learning.

[0626] 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.

[0627] In this invention, the server includes means for collecting learner characteristic information, means for generating personalized educational information based on the characteristic information using a model for generation, and means for displaying the generated educational information using virtual reality or augmented reality technology. This makes it possible to provide learners with a personalized visual educational experience.

[0628] "Learner characteristic information" refers to personal information about learners, such as their past learning history, interests, and academic performance, and is data used to personalize educational content.

[0629] A "generation model" refers to algorithms and technologies used to generate educational information based on learner characteristics, and plays a role in dynamically generating appropriate content.

[0630] "Virtual reality" or "augmented reality" refers to virtual environments or display formats created using digital technology that enable learners to engage in visual and experiential learning.

[0631] "Educational information" is a general term for learning materials, including texts, videos, and interactive content, that are provided according to the learner's interests and level of understanding.

[0632] This invention comprises a system for providing learners with personalized educational experiences. The server is responsible for efficiently collecting and managing learner characteristic information. This data is stored in a database using cloud technology.

[0633] The server uses a generative AI model to generate learner-optimized educational information based on collected trait data. The generated educational information is available as various digital content formats (text, images, audio, etc.). The generative AI model combines various existing generative technologies and is built on machine learning frameworks such as TensorFlow and PyTorch.

[0634] The device provides users with generated educational information using virtual reality or augmented reality technology. Development platforms such as Unity or ARKit are used to create an immersive learning environment. For example, when a learner tackles a topic related to space, an AR experience can be provided where outer space realistically appears floating above furniture.

[0635] Users interactively utilize educational information provided through their devices, and progress data is transmitted to the server in real time. The server analyzes this progress data and can dynamically adjust the educational information according to the learner's level of understanding.

[0636] For example, if a user wants to learn about history, they can enter a prompt such as "I would like AR content about the Renaissance," and the server will generate relevant educational information and provide it through the device. In this way, the system allows learners to obtain an optimal learning experience tailored to their interests and level of understanding.

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The server collects learner profile information and stores it in a cloud database. This information includes learning history, interests, and performance, and is efficiently managed using database technology. The input is profile information provided by the learner, and the output is a database with updated profile information.

[0640] Step 2:

[0641] The user enters a prompt about the topic they want to learn about. For example, they might provide a specific request such as, "I would like AR content about the Renaissance." The input is the user's prompt, and the output is the request data associated with that prompt.

[0642] Step 3:

[0643] The server uses a generative AI model to generate optimized educational information based on characteristic information and user prompts. During this process, the model is executed, creating digital content such as text, images, and audio. The input consists of characteristic information and prompts, while the output is the generated educational information.

[0644] Step 4:

[0645] The device provides the user with generated educational information in a virtual or augmented reality environment. Using a platform such as Unity or ARKit, the user gains an immersive learning experience. The input is the generated educational information, and the output is the user's visual and experiential learning environment.

[0646] Step 5:

[0647] Users learn within the provided environment, and their progress is transmitted to the server in real time via their device. This progress data is used to evaluate learning and adjust content. The input is the user's learning status, and the output is the progress data recorded on the server.

[0648] Step 6:

[0649] The server analyzes the collected progress data and uses a generative AI model to dynamically adjust educational information as needed, thereby maximizing learning effectiveness. The input is progress data, and the output is the adjusted educational information.

[0650] 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.

[0651] This invention provides a system that not only offers personalized educational content based on learner profile information and learning progress, but also detects learners' emotional states in real time and dynamically adjusts the education. This makes it possible to provide a learning environment that adapts not only to learners' interests and understanding, but also to their emotional responses.

[0652] The server collects and registers learner profile data and creates personalized educational content using a generative model. The generated content is delivered to the user through a device using virtual reality or augmented reality technology. As the user progresses through the learning process, the device uses an emotion engine to identify emotions from the user's facial expressions, voice, and input data.

[0653] Based on the detected emotions, the emotion engine suggests alternative content or additional hints if the user is losing interest or not understanding the material. For example, if the server determines that the user is stressed, it may temporarily reduce the learning content or insert relaxing material. Conversely, if the user is curious, it can also present additional challenging tasks.

[0654] As a concrete example, consider a scenario where a user is solving a math problem. If the user shows a thoughtful expression, the emotion engine detects stress. The server receives this information and sends instructions to the terminal, either providing hints to the user or simplifying the practice problems. In this way, it is possible to adapt to the learner's emotional state and provide a more effective and comfortable learning experience.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] The user enters their learning topics of interest into the device. They also allow the collection of their facial expressions and voice data during learning.

[0658] Step 2:

[0659] The device sends user input data and emotion data captured in real time to the server. The emotion data includes emotional information estimated through facial recognition and voice analysis.

[0660] Step 3:

[0661] The server analyzes user profile data and received sentiment data. Using a generative model, it generates educational content best suited to the user's interests and emotions.

[0662] Step 4:

[0663] The server sends the generated educational content to the device. This content is delivered to the user using virtual reality or augmented reality technology.

[0664] Step 5:

[0665] Users begin learning by utilizing educational content received through their devices. They progress through learning via interactive operations within a virtual space.

[0666] Step 6:

[0667] The device monitors the user's emotional changes in real time during the learning process and analyzes their emotional state using an emotion engine, particularly detecting changes such as stress and excitement.

[0668] Step 7:

[0669] When the emotion engine detects a change in the user's emotions, the device sends that information to the server. The server then dynamically adjusts the learning material based on this information.

[0670] Step 8:

[0671] The server generates and sends educational content tailored to the user's emotional state. For example, if the user is having difficulty, it provides additional hints; if they show interest, it offers more advanced challenges.

[0672] Step 9:

[0673] The device provides users with updated educational content, supporting smooth learning. Users continue learning based on the newly presented content.

[0674] (Example 2)

[0675] 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".

[0676] Traditional education systems have struggled to adapt to the individual needs and emotional states of each learner, particularly in managing the impact of learners' emotions on learning efficiency. Furthermore, the lack of mechanisms to respond to learners' emotional states in real time and dynamically adjust educational materials made it difficult to maintain learners' motivation and concentration.

[0677] 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.

[0678] In this invention, the server includes means for collecting learner attribute information, means for generating personalized educational materials using a generative AI model, and means for presenting the educational materials using virtual reality or augmented reality technology. This makes it possible to detect the learner's emotional state in real time, dynamically adjust the educational materials based on that, and provide a personalized educational experience.

[0679] "Learner attribute information" refers to information necessary to provide an individualized educational experience, such as the learner's basic information, learning history, and interests.

[0680] A "generative AI model" is an algorithm that generates new information and content based on data, and in this invention, it is used to generate personalized educational materials.

[0681] "Personalized educational materials" are created by a generative AI model based on learner attribute information, and are designed to provide an optimal learning experience tailored to the learner's interests and level of understanding.

[0682] "Virtual reality technology" is a technology that allows users to have interactive experiences in a computer-generated virtual environment.

[0683] Augmented reality technology is a technology that overlays computer-generated information onto images of the real world, providing users with a fusion of reality and virtual reality.

[0684] "Emotional state" refers to a learner's feelings and mental responses, and is a psychological state that influences learning effectiveness and motivation.

[0685] A "user interface" is a device or system that enables learners to perform specific operations in a virtual reality or augmented reality environment, and it supports the interaction between the user and the system.

[0686] The present invention aims to effectively provide educational materials that meet the individual needs of learners. Specific embodiments for carrying out the invention are described below.

[0687] At the start of learning, the server collects attribute information such as basic information, past learning history, and interests provided by the learner. This information is aggregated in the learning management platform and stored in a database. Next, the server uses this attribute information to generate educational materials optimized for the learner using a generative AI model. In this process, a computer system with a deep learning algorithm implemented, for example, is used. A common generative AI model used here is one equipped with natural language generation technology, and its prompts might include instructions such as, "Generate math problems appropriate to the user's level calculated from their past math performance."

[0688] Next, the generated educational materials are presented to the user through a device equipped with virtual reality (VR) or augmented reality (AR) technology. The device uses a VR headset or AR-enabled device to provide learners with an interactive learning experience. A specific example is a scenario in which a user wears a VR headset and solves math problems in a virtual classroom environment.

[0689] Furthermore, as learning progresses, the device uses a connected emotion engine to detect and analyze the user's emotional state in real time. This emotion engine uses a camera and microphone to capture the user's facial expressions and voice, and analyzes that data to identify emotions such as stress and excitement.

[0690] The server dynamically adjusts educational materials based on this emotional data. For example, if the emotion engine detects that the user is having difficulty solving a problem, the server sends a command to the device to lower the difficulty level, allowing the user to continue learning while easing their tension. An example of a prompt to the generative AI model would be, "Analyze the user's current emotions based on their facial expression data and suggest appropriate adjustments to the educational content." This operation enables the provision of a flexible and effective educational experience tailored to the individual learner's state.

[0691] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0692] Step 1:

[0693] The server collects learner attribute information via the learning management platform and registers it in a database. This process involves inputting basic learner information (such as name and grade level), past performance, course history, and interests. This information is output as foundational data for creating personalized educational materials in the generative AI model.

[0694] Step 2:

[0695] The server generates personalized educational materials using a generative AI model based on collected attribute information. In this process, attribute information serves as input, and a generative AI model based on a deep learning algorithm performs data processing and calculations. Specifically, customized learning materials that reflect past learning history and interests are generated and output as educational materials. The prompt used is "Generate questions based on past performance."

[0696] Step 3:

[0697] The terminal presents the learner with educational materials provided by the server. In this step, the terminal uses virtual reality (VR) or augmented reality (AR) technology to visually input the generated educational materials and output them interactively. Specifically, the user can wear a VR headset and explore and answer questions in the displayed virtual space.

[0698] Step 4:

[0699] The device detects the user's emotional state using an emotion engine as the learner uses the content. Here, the learner's facial expressions and voice are input via camera and microphone, and the emotion engine analyzes the data. The output identifies the learner's emotional state (e.g., joy, stress, concentration level).

[0700] Step 5:

[0701] The server dynamically adjusts educational materials based on emotional state data obtained through the terminal. Emotional state data is input, and the server provides appropriate feedback and adjusts learning materials, outputting new educational materials. Specifically, if the user is experiencing stress, materials with adjusted difficulty levels or relaxation content are provided.

[0702] (Application Example 2)

[0703] 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".

[0704] In today's educational environment, there is a need to provide each learner with content optimized for their individual needs and to improve learning efficiency. However, systems equipped with dynamic material adjustment functions that respond to learners' emotions and interests are limited, and there is a particular problem with real-time adaptation based on emotional states. In such a situation, learners find it difficult to maintain their motivation, and effective learning is difficult to achieve.

[0705] 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.

[0706] In this invention, the server includes means for collecting learner characteristic information, means for generating customized educational materials based on the characteristic information using a generative model, and means for displaying the generated educational materials using virtual reality or augmented reality technology. This makes it possible to provide an individualized educational experience based on the learner's emotional state and level of interest.

[0707] "Learner characteristic information" refers to basic information about the learner, including data such as past learning history, areas of interest, and academic performance.

[0708] A "generative model" refers to an algorithm or system used to generate customized content based on collected information, and it commonly utilizes artificial intelligence.

[0709] "Educational materials" refer to learning materials and content used by learners, provided in an individualized format to enhance learning effectiveness.

[0710] "Virtual reality or augmented reality technology" refers to technology that uses digital technology to combine real-world and virtual information, enabling visual and experiential learning.

[0711] "Emotional state" refers to elements related to a learner's emotions, primarily the instantaneous emotional situation analyzed from facial expressions and voice.

[0712] "Interest level" is an indicator that shows how much interest a learner has in a particular piece of content or activity.

[0713] "Personalized learning experiences" mean providing a learning process that is customized to the individual learner's needs and feelings, with the aim of maximizing learning efficiency.

[0714] This invention is implemented as a system that provides an individualized educational experience. This system includes a server, terminals, and a user interface.

[0715] The server's initial role is to collect learner feature information and store it in a database. This collected feature information is then analyzed using a generative AI model to form the basis for generating customized educational materials. The server also utilizes an emotion recognition engine (e.g., Affectiva) to detect emotional states from the user's facial expressions and voice data.

[0716] The generated educational materials are presented to the user via a device using virtual reality or augmented reality technology (e.g., Oculus Rift or Microsoft HoloLens). This device monitors the learner's progress and emotional state in real time and dynamically adjusts the educational materials as needed.

[0717] Users access these educational materials via smartphones or smart glasses. If a user exhibits a specific reaction or interaction with the learning materials, the device sends that information to the server for further adjustments. For example, if a user is experiencing stress, that information instructs the server to change the educational material to relaxing content.

[0718] In this system, an example of a prompt provided to the generative AI model might be an instruction such as, "Generate additional quizzes about historical events that the learner has shown interest in." This provides deeper learning tailored to the learner's interests and contributes to maintaining their motivation to learn.

[0719] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0720] Step 1:

[0721] The server collects learner profile information. It takes data such as learner basic information, past performance, and learning history as input and stores this in a database. The output is organized profile information for later analysis. This process utilizes data collection tools and APIs to ensure data integrity.

[0722] Step 2:

[0723] The server uses a generative AI model based on collected profile information to generate personalized educational materials. The input consists of profile information, the generative AI model, and prompts provided to the model. Specifically, these prompts include instructions such as, "Provide additional information on topics the learner has shown interest in." The output is customized educational material. The model processes large amounts of data and scrutinizes and provides appropriate content.

[0724] Step 3:

[0725] The device presents the generated educational materials to the learner. The input is the material output in step 2, which is presented visually to the user via a virtual reality or augmented reality device. The output is the user's experience of visually receiving the educational materials. The device adjusts the display position and format of the content in real time to aid the learner's understanding.

[0726] Step 4:

[0727] The device analyzes the learner's facial expressions and voice data using an emotion recognition engine to identify their emotional state. Input is real-time data acquired from the camera and microphone. Output is the analyzed data on the learner's emotional state. During this process, the device sets specific triggers to quickly capture changes in emotion.

[0728] Step 5:

[0729] The server dynamically adjusts educational materials using emotional state data received from the terminal. Inputs are emotional state data and the current educational material. Outputs are the adjusted educational materials; for example, if the user indicates stress, simpler exercises or relaxing content may be inserted. The server utilizes adaptive algorithms to provide the most appropriate response for the learner.

[0730] Step 6:

[0731] The user accepts the adjusted educational materials and resumes learning. The input is the content adjusted in Step 5, which forms the basis for continued learning. The output is the progress made and improved understanding. The user can further advance their learning by interacting with the interface.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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."

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0753] The following is further disclosed regarding the embodiments described above.

[0754] (Claim 1)

[0755] Means for collecting learner profile information,

[0756] A means for generating customized educational content based on the profile information using a generative model,

[0757] A means for displaying the generated educational content using virtual reality or augmented reality technology,

[0758] A means of monitoring and recording learner progress data in real time,

[0759] A means for dynamically adjusting educational content based on the aforementioned progress data,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, further comprising means for detecting the learner's level of concentration and providing feedback.

[0763] (Claim 3)

[0764] The system according to claim 1, further comprising means for providing an interface that enables a learner to perform a specific operation in a virtual reality or augmented reality environment.

[0765] "Example 1"

[0766] (Claim 1)

[0767] Means for collecting learner characteristic information,

[0768] A means for generating personalized educational materials based on the aforementioned feature information using a generation algorithm,

[0769] A means for displaying the generated educational materials using virtual environment technology,

[0770] A means of instantly monitoring and recording learner activity data,

[0771] A means for dynamically adjusting educational materials based on the aforementioned activity data,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, further comprising means for detecting the learner's level of concentration and providing feedback.

[0775] (Claim 3)

[0776] The system according to claim 1, further comprising means for providing an operation interface that enables a learner to perform specific operations in a virtual environment.

[0777] "Application Example 1"

[0778] (Claim 1)

[0779] Means for collecting learner characteristics information,

[0780] A means for generating personalized educational information based on the characteristic information using a model for generation,

[0781] Means for displaying the generated educational information using virtual reality or augmented reality technology,

[0782] A means of instantly monitoring and recording learners' progress,

[0783] A means for dynamically adjusting educational information based on the aforementioned progress information,

[0784] Means for providing an interface that enables an object within the environment to perform a specific operation,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, further comprising means for detecting the learner's level of concentration and providing a response.

[0788] (Claim 3)

[0789] The system according to claim 1, further comprising means for providing learners with a visual educational experience through learning aids.

[0790] "Example 2 of combining an emotion engine"

[0791] (Claim 1)

[0792] Means for collecting learner attribute information,

[0793] A means for generating personalized educational materials based on attribute information using a generative AI model,

[0794] A means for presenting the generated educational material using virtual reality or augmented reality technology,

[0795] A means of detecting and analyzing learners' emotional states in real time,

[0796] Means for dynamically adjusting educational materials based on the aforementioned emotional state and learning progress,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, further comprising means for detecting the learner's level of concentration and emotional response and providing corresponding feedback.

[0800] (Claim 3)

[0801] The system according to claim 1, further comprising means for providing a user interface that enables a learner to perform specific operations in a virtual reality or augmented reality environment.

[0802] "Application example 2 when combining with an emotional engine"

[0803] (Claim 1)

[0804] Means for collecting learner characteristic information,

[0805] A means for generating customized educational materials based on the aforementioned feature information using a generative model,

[0806] A means for displaying the generated educational material using virtual reality or augmented reality technology,

[0807] A means of monitoring and recording learners' progress in real time,

[0808] Means for dynamically adjusting educational materials based on the aforementioned progress information and emotional information,

[0809] A means for detecting the learner's emotional state and adapting educational materials based on the said emotional state,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for detecting the learner's level of concentration and interest and providing feedback.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for providing an interface that enables a learner to perform a specific operation in a virtual reality or augmented reality environment, and for guiding the operation in accordance with the learner's emotions. [Explanation of symbols]

[0815] 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. Means for collecting learner profile information, A means for generating customized educational content based on the profile information using a generative model, A means for displaying the generated educational content using virtual reality or augmented reality technology, A means of monitoring and recording learner progress data in real time, A means for dynamically adjusting educational content based on the aforementioned progress data, A system that includes this.

2. The system according to claim 1, further comprising means for detecting the learner's level of concentration and providing feedback.

3. The system according to claim 1, further comprising means for providing an interface that enables a learner to perform a specific operation in a virtual reality or augmented reality environment.

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