system

Abstract

Provide a system. 【Solution means】 Means for inputting information of individual learners and creating a user profile, Means for analyzing learning content based on the user profile and generating learning materials optimized for the learner, Means for transmitting the generated learning materials and providing them in a form accessible to the learner, Means for evaluating the learner's understanding and collecting feedback, Means for updating the user profile and learning materials based on the feedback, Means for inputting health information and generating optimized health and rehabilitation content based on the information, Means for providing health content visually and aurally, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There are many people around the world who have difficulty receiving formal education due to economic circumstances, differences in educational environments due to regions, learning disabilities, etc. Therefore, there is a need for a system that can evenly provide high-quality education according to individual needs. However, since conventional educational systems rely on uniform teaching materials and teaching methods, it is difficult to achieve individualized education corresponding to individual learning styles and levels of understanding. In addition, the lack of specialized knowledge and educational resources in educational institutions is also a serious problem. The purpose is to solve these problems and provide a learning environment in which anyone can learn equally.

Means for Solving the Problems

[0005] This invention provides a generative AI system that creates profiles based on individual learner information and generates optimized learning materials based on those profiles. The server automatically generates learning materials in a format and content suitable for the learner, and the terminal presents these materials to the learner, thereby realizing a learning experience tailored to each individual's learning style. Furthermore, the system evaluates the learner's level of understanding and updates the profile and materials based on that feedback, continuously optimizing learning. This makes it possible to provide an environment where everyone, regardless of their lack of expertise or resources, can receive equally high-quality education.

[0006] A "user profile" is a collection of personalized data generated based on information about each individual learner, reflecting their learning progress, style, and needs.

[0007] "Analysis of learning content" is the process of identifying appropriate learning materials and topics for a given learner based on the information contained in their user profile.

[0008] "Optimized learning materials" are materials that have been tailored in content and format to meet the individual needs of learners and are designed to support effective learning.

[0009] A "generative AI model" is an artificial intelligence algorithm or system used to create educational materials with specific content and format in response to individual requests.

[0010] "Assessing comprehension" is a process of measuring how well learners understand the provided materials and how they have achieved their learning objectives.

[0011] "Feedback" refers to information collected from learners, including their opinions and reactions, which is used to improve the system and optimize learning content. [Brief explanation of the drawing]

[0012] [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]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This system consists of a server, terminals, and user interaction to provide an educational experience tailored to individual learning needs. The server, which plays a central role in the system, dynamically generates learning materials suited to each user's learning style using generative AI technology. Users can access these materials via terminals and proceed with their learning.

[0034] First, users use their devices to input their learning objectives, interests, and skill levels. This allows the server to establish a detailed user profile, creating a foundation for understanding the user's learning tendencies.

[0035] Subsequently, the server utilizes a generative AI model to generate learning materials best suited to the learner based on this profile. For example, if a user wants to strengthen their math fundamentals, the server creates visually clear materials that include a variety of practice problems. Furthermore, by adapting the materials to accommodate multiple information formats, it supports multifaceted understanding through visual and auditory means.

[0036] Once the learning materials are generated, the server sends them to the terminal, making them easily accessible to the user. The terminal supports the user's learning experience by displaying visual materials on the screen and playing audio materials using speech synthesis.

[0037] During learning, the device monitors the user's progress and can send any unclear points or questions that arise during learning to the server. Based on this information, the server assesses the user's understanding and provides additional learning materials or supplementary explanations in different formats as needed. This process allows users to deepen their learning at their own pace.

[0038] As a concrete example, consider a user learning a new language. The server not only generates text materials containing basic words and phrases in that language, but also creates audio materials for pronunciation practice. This allows the user to acquire the language from both written and spoken perspectives, resulting in efficient and effective learning. Thus, the system according to the present invention is an effective means of providing comprehensive and flexible learning support.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses a device to input information such as their learning objectives, skill level, and interests. The device sends this information to the server, and the creation of the user profile begins.

[0042] Step 2:

[0043] The server creates a user profile based on the information it receives. This profile includes learning style, past learning history, and progress toward achieving goals, and serves as the basis for later creation of learning materials.

[0044] Step 3:

[0045] The server uses an AI model to analyze user profiles and automatically generate learning materials tailored to the learner. For example, it generates materials including infographics for visually-oriented learners and audio materials for auditorily-oriented learners.

[0046] Step 4:

[0047] The generated learning materials are sent from the server to the terminal. The terminal displays or plays the received materials in an appropriate format, allowing the user to access them.

[0048] Step 5:

[0049] Users progress through their learning using materials presented via their device. If they encounter any difficulties during their studies, they can send questions to the server from their device.

[0050] Step 6:

[0051] The server analyzes user questions and learning data to assess understanding. Based on the assessment, it generates additional learning materials as needed and provides them to the user via the terminal.

[0052] Step 7:

[0053] Once the learning session is complete, the device collects learning feedback from the user and sends it to the server. The server uses this feedback to update the user profile and the learning material generation algorithm, and utilizes this information for the next learning session.

[0054] (Example 1)

[0055] 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."

[0056] Traditional education systems struggle to provide effective learning support tailored to the individual characteristics and progress of each learner. Furthermore, they lack sufficient resources to create and provide diverse learning materials suited to different learning styles, resulting in inadequate feedback and the provision of supplementary materials according to learners' proficiency levels. This leads to decreased learning efficiency and an inability to adequately meet the needs of individual learners.

[0057] 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.

[0058] In this invention, the server includes means for inputting individual learner data and creating person data; means for monitoring the learner's progress, evaluating their level of understanding, and providing additional educational resources using educational technology; and means for transmitting the generated educational resources and providing them in a format usable by the learner. This makes it possible to provide educational resources optimized for the learner's characteristics and realize effective learning support.

[0059] "Individual learner data" refers to a collection of information about each learner's unique characteristics, learning objectives, interests, and skill levels.

[0060] "Personal data" refers to a data profile built on individual learner data, which provides a detailed description of the learner's characteristics and learning tendencies.

[0061] "Educational technology" refers to technologies that use advanced techniques to monitor learners' progress and understanding, and to provide educational resources tailored to individual needs.

[0062] "Educational resources" is a general term for educational content such as teaching materials, practice problems, and audio materials that are generated to suit the learner's learning style.

[0063] "Feedback data" refers to information obtained from educational technologies based on learners' understanding and progress, and is used to adjust educational resources and update personal data.

[0064] "Practical training resources" refer to educational content, including practice problems and application exercises, that are provided in addition to improve learners' skills.

[0065] "Available formats" refer to educational resources provided in a form that is easily accessible to learners, and can be viewed or listened to on digital devices.

[0066] The system of this invention consists of the interaction of a server, a terminal, and a user. The server forms the core of this system and uses generative AI technology to dynamically generate learning materials based on individual learner data. Specifically, the server receives data entered by the user via the terminal, analyzes it, and creates detailed person data. Using this person data, a generative AI model generates educational resources that are optimal for the learner.

[0067] The terminal is used to provide users with generated educational resources. The terminal receives educational resources sent from the server and displays and plays them in a format easily accessible to the user. This includes the ability to display text-based learning materials on the screen and to play audio materials using speech synthesis.

[0068] When a user learns a new language, the server generates learning materials that include basic words and phrases in that language. It also creates audio materials for pronunciation practice, which the user can view and listen to on their device. This process is initiated by the following prompt: By providing specific instructions to the server, such as "Create basic learning materials for a new language," the generation AI model generates the necessary materials.

[0069] In this way, comprehensive and flexible educational support tailored to individual learning styles is provided, enabling users to learn effectively at their own pace.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] Users input individual learner data, such as learning objectives, interests, and skill levels, using their devices. This input data is sent to the server. Based on this input data, the server generates personalized data that reflects each learner's characteristics. Specifically, the server analyzes the data to determine learning tendencies and the types of educational resources needed.

[0073] Step 2:

[0074] The server uses a generative AI model to create optimal educational resources based on the prompt received from the user. An example of a prompt might be, "Create basic learning materials for learning a new language." Based on this prompt, the server utilizes its generative AI model to generate a variety of learning materials tailored to the learner, including visual and audio materials. This process employs data analysis and generative AI technology to dynamically construct the learning materials.

[0075] Step 3:

[0076] The server sends the generated educational resources to the terminal. The terminal receives these resources and prepares them in a user-friendly format. Specifically, the terminal displays text-based learning materials on its screen and plays audio materials using speech synthesis technology. This process allows users to easily access visual and auditory educational resources through the terminal.

[0077] Step 4:

[0078] Users learn on their devices and input their progress and understanding into the device. The device records this information and sends it to the server. The transmitted data is further analyzed by the server to evaluate the learner's current understanding and the additional educational resources needed. The information obtained in this step is used for feedback in the next step.

[0079] Step 5:

[0080] The server generates additional educational resources and supplementary materials based on the user's progress and understanding assessment, and sends them back to the device. The server responds to learners' needs by providing new practice exercises and explanations in a more easily understandable format as needed. This entire process allows users to continuously receive personalized learning support.

[0081] (Application Example 1)

[0082] 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."

[0083] There is a need for a system that provides individualized training and learning tailored to the health conditions and interests of the elderly and those requiring care. However, current systems struggle to flexibly respond to individual needs and lack the ability to provide interactive experiences through visual and auditory means. Therefore, the challenge lies in effectively and efficiently supporting individual skill development and rehabilitation.

[0084] 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.

[0085] In this invention, the server includes means for inputting information of individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for transmitting the generated learning materials and providing them in a format accessible to the learners. This makes it possible to provide health and rehabilitation content optimized for each individual user and to provide clear instruction through visual and auditory means.

[0086] A "learner" refers to an individual who seeks to learn and understand the content of education or training.

[0087] A "user profile" is a collection of digital data that summarizes information about a learner or user, revealing their individual needs and characteristics.

[0088] "Learning materials" are educational materials used by learners to acquire specific knowledge or skills, and they come in a variety of forms, including text, audio, and visual materials.

[0089] "Feedback" refers to the collection of data and information regarding learners' understanding and progress, which is used to improve and adjust the next steps.

[0090] "Health and rehabilitation content" refers to a collection of programs and exercises aimed at improving the user's physical and mental health.

[0091] "Visual and auditory" refers to the sensory media used by users to receive information, primarily through images and sounds, which facilitate interaction.

[0092] The system of this invention provides health promotion and learning support tailored to individual needs through interaction between a server, a terminal, and a user.

[0093] The server uses a generative AI model to generate personalized user profiles from user input. This profile serves as the foundation for providing appropriate content based on the user's health status and learning objectives. The server also dynamically generates health and rehabilitation content, including visual and audio guidance, based on the profile. The hardware consists of a high-performance data processing server, and the software uses Python and Flask. For generative AI, an AI model from OpenAI® is used, for example.

[0094] The device accepts user input and displays and plays back generated content. This allows users to intuitively understand and act upon the content. The device has diverse user interfaces and is implemented on tablets and smartphones. This allows users to engage with the provided training and learning content at their own pace.

[0095] Users send specific health and learning goals to the server by entering prompts via their terminal. For example, by sending a prompt such as, "I would like suggestions for easy daily stretches and memory-enhancing activities for a user in their 60s," the server generates and provides a personalized program.

[0096] This system makes it possible to provide health promotion and learning experiences tailored to individual users, enabling efficient skill improvement.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The user logs into the application using their device and enters personal profile information. This information includes their health status, current rehabilitation progress, and learning goals. The device collects this information and sends it to the server. The entered information is structured and serves as foundational data for accurately assessing the user's needs.

[0100] Step 2:

[0101] The server creates a user profile using a generative AI model based on user information received from the terminal. Specifically, it analyzes the data using a Python script and builds a profile based on the user's health status and learning objectives. The generated profile serves as the basis for suggesting the most suitable training and learning materials for each individual's goals.

[0102] Step 3:

[0103] The server utilizes a generative AI model to dynamically generate specific health promotion and learning content based on user profiles. The generation process analyzes user-provided prompts and creates content suggesting corresponding stretches and quizzes. The generated content is then formatted into visual and audio materials using technologies such as Text-to-Speech.

[0104] Step 4:

[0105] The server sends the generated content to the terminal. The terminal is responsible for displaying this content in a user-friendly format and playing audio guidance. Specifically, it uses the terminal's graphical user interface to show the stretching method with animations and supports the user with audio guidance.

[0106] Step 5:

[0107] Users engage in the provided health promotion and learning activities. They input their progress and feedback into their device. For example, the number of stretches performed and the results of quizzes answered are collected as input data.

[0108] Step 6:

[0109] The device sends user feedback and progress information to the server. The server analyzes this information and adjusts the user profile and subsequent health and learning content. The algorithms used here make conditional decisions to ensure that the difficulty and type of content are tailored to the user's needs.

[0110] Step 7:

[0111] As users continue to use the system, the data obtained at each step is accumulated and used to create more precise profiles and content. By repeating this process, the system continuously supports the user's unique learning and health improvement needs.

[0112] 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.

[0113] This invention is an educational system that combines a generative AI model and an emotion engine, aiming to provide learners with a more personalized educational experience. This system is based on interaction between a server, a terminal, and a user.

[0114] Users use their devices to input their learning goals, interests, and skill levels, and this data is sent from the device to the server. Based on this information, the server creates a detailed user profile, which forms the basis for generating learning materials.

[0115] The server uses a generative AI model to analyze user profiles and generate optimized learning materials tailored to the individual needs of each learner. These materials are adapted to multiple formats, including visual, audio, and text-based materials, depending on the user's learning style. Furthermore, an emotion engine considers the user's emotional state during the material generation process. For example, if a user is experiencing stress, the server adjusts the difficulty level of the materials to provide a learner-friendly learning environment.

[0116] The device receives the generated learning materials and makes them easily accessible to the user. The user can progress through the learning materials via the device and send questions to the server as needed. The server uses an emotion engine to detect the user's emotions in real time and evaluates their comprehension based on this information. Based on this evaluation, the content and format of the learning materials are dynamically adjusted.

[0117] As a concrete example, if the system detects that the user is feeling anxious, it will provide an emotionally sensitive educational experience by increasing the amount of relaxing visual material or slowing down the learning pace. In this way, the system of the present invention can construct an optimal learning environment while taking into account the learner's mental and physical state.

[0118] Through this process, the present invention provides a system that goes beyond conventional uniform educational methods, offering personalized educational opportunities for each learner and achieving higher educational effectiveness.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The user uses a device to enter their learning objectives, interests, and current skill level. The device sends this information to the server, and the user profile is initially configured.

[0122] Step 2:

[0123] Based on the information received, the server creates a user profile using a generative AI model. This profile includes learning style, progress, and areas of interest, forming the basis for a personalized learning experience.

[0124] Step 3:

[0125] The server analyzes learning content based on user profiles and generates learning materials optimized for the learner. This process utilizes an emotion engine, taking into account user emotional data (e.g., stress levels). Difficulty levels are adjusted, and the materials are adapted to the learner's state.

[0126] Step 4:

[0127] The device receives generated learning materials sent from the server and prepares them to be made available to the user visually or audibly. For example, the colors and tone of the audio materials may be adjusted based on emotions.

[0128] Step 5:

[0129] Users progress through the learning process via their devices. The devices continuously monitor emotional data during learning and send it to the server as needed. The server uses this information to adjust learning materials and learning methods in real time.

[0130] Step 6:

[0131] Once the learning session is complete, the device collects feedback on the user's emotional state and level of understanding, and sends this feedback to the server. The server updates the user profile based on this feedback and uses it as data for the next learning session.

[0132] Step 7:

[0133] The server updates its profile based on feedback, aiming to improve the accuracy of learning materials for the next session. In this process, user emotional changes are given particular importance, and the direction for improving the quality of the learning experience is reviewed.

[0134] (Example 2)

[0135] 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".

[0136] Traditional education systems have struggled to dynamically adjust learning materials according to the individual needs and emotional states of learners, resulting in a decline in learning effectiveness due to the provision of uniform materials. Therefore, there is a need to provide an optimized educational experience for each learner and improve the quality of learning.

[0137] 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.

[0138] In this invention, the server includes means for inputting information on individual learners and creating educational data, means for generating prompt sentences using a generative AI model and generating educational content, and means for evaluating the learner's emotional state using an emotion analysis engine and adjusting the educational content based on that evaluation. This makes it possible to provide an optimal educational environment that is tailored to the individual needs and emotional state of each learner.

[0139] A "learner" is an individual who seeks to acquire knowledge and skills through educational materials using an educational system.

[0140] "Educational data" refers to datasets created based on learner information, reflecting individual needs and skill levels.

[0141] "Educational content" refers to learning materials that are optimized for learners using generative AI models and provided in various formats such as visual, audio, and text.

[0142] A "generative AI model" is an artificial intelligence model that generates text or data tailored to a specific purpose based on the input prompt text.

[0143] A "prompt statement" is an instruction given to a generative AI model, and is text used to request a specific output from the AI.

[0144] A "sentiment analysis engine" is a technology used to evaluate learners' emotional states in real time and to dynamically adjust educational content based on these evaluations.

[0145] Users first access the learning system using a terminal and input their learning goals, interests, and skill level. This input information is sent from the terminal to the server. The server receives the input information and generates detailed educational data. This educational data reflects the learner's needs and forms the basis for optimizing their individual learning experience.

[0146] Next, the server utilizes a generative AI model. The generative AI model generates prompt sentences based on educational data and inputs them into the model. These prompt sentences include specific instructions such as, "Generate intermediate-level English learning materials, taking stress reduction into consideration." Upon receiving this prompt, the AI ​​model generates educational content suitable for the learner. This content takes the form of text, audio, and visual materials, delivered according to the learner's style.

[0147] Furthermore, the system in use incorporates an emotion analysis engine, which the server utilizes to assess the learner's emotional state in real time. For example, if the server detects that a learner is experiencing stress, it adjusts the difficulty level of the educational content to provide a learner-friendly learning environment.

[0148] The device receives educational content from the server and displays it in a user-friendly format. Users can progress through their learning using this content, and if they have any questions, they can send them to the server via the device. The server uses a generative AI model to provide quick and appropriate answers to the received questions.

[0149] In this way, embodiments of the present invention realize a dynamic and personalized educational experience based on the learner's needs and emotional state, thereby improving educational effectiveness.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] Users access the learning system using a device and input information such as their learning goals, interests, and skill levels. This information is sent from the device to the server. The input data forms the basis for creating a user profile, thereby identifying the learner's needs.

[0153] Step 2:

[0154] The server generates educational data based on user information received from the terminal. In this process, the information is stored in a database, and a user profile is created. This profile clarifies the learner's needs and characteristics.

[0155] Step 3:

[0156] The server generates a prompt message for the AI ​​generation model. This prompt message might be in the format of, for example, "Please generate intermediate-level English learning materials, taking stress reduction into consideration." This prompt is sent to the AI ​​generation model, which uses the user profile to generate the most suitable educational content. The model performs data analysis based on the prompt and outputs appropriate learning materials.

[0157] Step 4:

[0158] The generated educational content is sent from the server to the device. The device receives this content and displays it in a format that is easily accessible to the user. At this point, the educational content is converted into text, audio, and visual formats and adapted to the learner's style.

[0159] Step 5:

[0160] The server uses an emotion analysis engine to evaluate the user's real-time emotional state. Based on the evaluation results, the server adjusts the difficulty level and format of the educational content. For example, if the user is feeling stressed, the difficulty level may be lowered and relaxing materials may be added.

[0161] Step 6:

[0162] Users progress through learning using educational content provided via their devices. If questions arise during learning, they can send them to the server via their devices. The server utilizes generative AI models to quickly generate answers and return them to the user. This enhances the user's understanding and provides an effective learning experience.

[0163] (Application Example 2)

[0164] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0165] Modern education demands that it provide instruction tailored to the individual needs and emotional states of each learner. However, traditional education systems fail to adequately consider learners' emotional states and learning styles, and remain limited to providing uniform teaching materials. As a result, learners experience stress and their learning effectiveness suffers.

[0166] 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.

[0167] In this invention, the server includes means for inputting information on individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for evaluating the learners' emotional state in real time and adjusting the learning experience. This makes it possible to provide an individualized educational experience tailored to each learner and maximize learning effectiveness.

[0168] A "learner" is an individual who participates in an educational program with the aim of acquiring knowledge and skills.

[0169] "Information" refers to data about a learner's learning goals, interests, and skill level, used to create a user profile.

[0170] A "user profile" is a set of digital information created based on the individual learner's information, and serves as the foundational data for generating personalized learning materials.

[0171] "Learning content" refers to a collection of educational tasks and themes presented to learners in accordance with specific educational objectives.

[0172] "Learning materials" is a general term for materials and content provided to support learners' learning, and can take the form of digital or physical materials.

[0173] "Emotional state" refers to information that indicates a learner's mental and emotional condition and reactions, and is an important element in the learning process.

[0174] "Real-time" means that processes and operations are carried out within the same timeframe as real-world time.

[0175] "Adjustment" refers to the act of changing or modifying settings or content to conform to specific goals or standards.

[0176] "Generating educational materials" refers to the process of designing and creating necessary educational content based on the individual needs of learners.

[0177] The system that realizes this invention functions through interaction between the server, terminal, and user.

[0178] The server first receives information about each learner and creates a user profile. This information includes learning goals, interests, and skill levels. Next, the server uses a generative AI model to analyze this user profile and generate learning materials optimized for the learner. The materials are delivered in visual, audio, and text formats and are adapted to the learner's style.

[0179] The server further utilizes an emotion engine to assess the learner's real-time emotional state and instantly adjust the learning experience. For example, if it detects frustration, it can take measures to lower the difficulty level.

[0180] The terminal presents the generated learning materials to the learner and functions as an interface for progressing through the learning process. It also collects feedback based on the learner's understanding and sends it to the server. This enables continuous updates of user profiles and learning materials.

[0181] As a concrete example, suppose an elementary school student is learning math at home via a device. In this case, the learning materials generated by the server are used, and feedback is provided at appropriate times according to the student's level of understanding and emotional state.

[0182] Examples of prompts for a generative AI model:

[0183] "The user is showing signs of impatience. Please generate math learning materials with adjusted difficulty levels."

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

[0185] Step 1:

[0186] The server receives the user's initial information.

[0187] The input data consists of the learner's learning goals, interests, and skill level. Based on this data, the server initiates the process of creating a user profile. The data is stored in a database and used for subsequent analysis.

[0188] Step 2:

[0189] The server uses a generated AI model to analyze user profiles and create appropriate learning materials.

[0190] At the start of the process, the server prompts the AI ​​model with the message, "Generate learning content suitable for this profile." Based on this, the AI ​​model designs the content and difficulty level of the learning materials and outputs them as digital learning materials in visual, audio, and text formats.

[0191] Step 3:

[0192] The server uses an emotion engine to evaluate the learner's emotional state in real time.

[0193] The user's facial expressions and voice data are input into an emotion engine, which then identifies emotions such as stress and anxiety. This information is used to adjust the difficulty level of the learning materials and is recorded as output for updating the user profile in real time.

[0194] Step 4:

[0195] The device displays the generated learning materials to the user and starts a new learning session.

[0196] Upon receiving educational data from the server, the terminal provides it to the learner via a visual display and audio output. While providing the educational materials, the terminal records user input in real time and measures the level of understanding.

[0197] Step 5:

[0198] The device sends user feedback data to the server.

[0199] The device collects user activity history and comprehension assessments, and sends this information to the server. The server receives this feedback and updates the user profile and course materials as needed to prepare for the next session.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] This system consists of a server, terminals, and user interaction to provide an educational experience tailored to individual learning needs. The server, which plays a central role in the system, dynamically generates learning materials suited to each user's learning style using generative AI technology. Users can access these materials via terminals and proceed with their learning.

[0217] First, users use their devices to input their learning objectives, interests, and skill levels. This allows the server to establish a detailed user profile, creating a foundation for understanding the user's learning tendencies.

[0218] Subsequently, the server utilizes a generative AI model to generate learning materials best suited to the learner based on this profile. For example, if a user wants to strengthen their math fundamentals, the server creates visually clear materials that include a variety of practice problems. Furthermore, by adapting the materials to accommodate multiple information formats, it supports multifaceted understanding through visual and auditory means.

[0219] Once the learning materials are generated, the server sends them to the terminal, making them easily accessible to the user. The terminal supports the user's learning experience by displaying visual materials on the screen and playing audio materials using speech synthesis.

[0220] During learning, the device monitors the user's progress and can send any unclear points or questions that arise during learning to the server. Based on this information, the server assesses the user's understanding and provides additional learning materials or supplementary explanations in different formats as needed. This process allows users to deepen their learning at their own pace.

[0221] As a concrete example, consider a user learning a new language. The server not only generates text materials containing basic words and phrases in that language, but also creates audio materials for pronunciation practice. This allows the user to acquire the language from both written and spoken perspectives, resulting in efficient and effective learning. Thus, the system according to the present invention is an effective means of providing comprehensive and flexible learning support.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user uses a device to input information such as their learning objectives, skill level, and interests. The device sends this information to the server, and the creation of the user profile begins.

[0225] Step 2:

[0226] The server creates a user profile based on the information it receives. This profile includes learning style, past learning history, and progress toward achieving goals, and serves as the basis for later creation of learning materials.

[0227] Step 3:

[0228] The server uses an AI model to analyze user profiles and automatically generate learning materials tailored to the learner. For example, it generates materials including infographics for visually-oriented learners and audio materials for auditorily-oriented learners.

[0229] Step 4:

[0230] The generated learning materials are sent from the server to the terminal. The terminal displays or plays the received materials in an appropriate format, allowing the user to access them.

[0231] Step 5:

[0232] Users progress through their learning using materials presented via their device. If they encounter any difficulties during their studies, they can send questions to the server from their device.

[0233] Step 6:

[0234] The server analyzes user questions and learning data to assess understanding. Based on the assessment, it generates additional learning materials as needed and provides them to the user via the terminal.

[0235] Step 7:

[0236] Once the learning session is complete, the device collects learning feedback from the user and sends it to the server. The server uses this feedback to update the user profile and the learning material generation algorithm, and utilizes this information for the next learning session.

[0237] (Example 1)

[0238] 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."

[0239] Traditional education systems struggle to provide effective learning support tailored to the individual characteristics and progress of each learner. Furthermore, they lack sufficient resources to create and provide diverse learning materials suited to different learning styles, resulting in inadequate feedback and the provision of supplementary materials according to learners' proficiency levels. This leads to decreased learning efficiency and an inability to adequately meet the needs of individual learners.

[0240] 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.

[0241] In this invention, the server includes means for inputting individual learner data and creating person data; means for monitoring the learner's progress, evaluating their level of understanding, and providing additional educational resources using educational technology; and means for transmitting the generated educational resources and providing them in a format usable by the learner. This makes it possible to provide educational resources optimized for the learner's characteristics and realize effective learning support.

[0242] "Individual learner data" refers to a collection of information about each learner's unique characteristics, learning objectives, interests, and skill levels.

[0243] "Personal data" refers to a data profile built on individual learner data, which provides a detailed description of the learner's characteristics and learning tendencies.

[0244] "Educational technology" refers to technologies that use advanced techniques to monitor learners' progress and understanding, and to provide educational resources tailored to individual needs.

[0245] "Educational resources" is a general term for educational content such as teaching materials, practice problems, and audio materials that are generated to suit the learner's learning style.

[0246] "Feedback data" refers to information obtained from educational technologies based on learners' understanding and progress, and is used to adjust educational resources and update personal data.

[0247] "Practical training resources" refer to educational content, including practice problems and application exercises, that are provided in addition to improve learners' skills.

[0248] "Available formats" refer to educational resources provided in a form that is easily accessible to learners, and can be viewed or listened to on digital devices.

[0249] The system of this invention consists of the interaction of a server, a terminal, and a user. The server forms the core of this system and uses generative AI technology to dynamically generate learning materials based on individual learner data. Specifically, the server receives data entered by the user via the terminal, analyzes it, and creates detailed person data. Using this person data, a generative AI model generates educational resources that are optimal for the learner.

[0250] The terminal is used to provide users with generated educational resources. The terminal receives educational resources sent from the server and displays and plays them in a format easily accessible to the user. This includes the ability to display text-based learning materials on the screen and to play audio materials using speech synthesis.

[0251] When a user learns a new language, the server generates learning materials that include basic words and phrases in that language. It also creates audio materials for pronunciation practice, which the user can view and listen to on their device. This process is initiated by the following prompt: By providing specific instructions to the server, such as "Create basic learning materials for a new language," the generation AI model generates the necessary materials.

[0252] In this way, comprehensive and flexible educational support tailored to individual learning styles is provided, enabling users to learn effectively at their own pace.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] Users input individual learner data, such as learning objectives, interests, and skill levels, using their devices. This input data is sent to the server. Based on this input data, the server generates personalized data that reflects each learner's characteristics. Specifically, the server analyzes the data to determine learning tendencies and the types of educational resources needed.

[0256] Step 2:

[0257] The server uses a generative AI model to create optimal educational resources based on the prompt received from the user. An example of a prompt might be, "Create basic learning materials for learning a new language." Based on this prompt, the server utilizes its generative AI model to generate a variety of learning materials tailored to the learner, including visual and audio materials. This process employs data analysis and generative AI technology to dynamically construct the learning materials.

[0258] Step 3:

[0259] The server sends the generated educational resources to the terminal. The terminal receives these resources and prepares them in a user-friendly format. Specifically, the terminal displays text-based learning materials on its screen and plays audio materials using speech synthesis technology. This process allows users to easily access visual and auditory educational resources through the terminal.

[0260] Step 4:

[0261] Users learn on their devices and input their progress and understanding into the device. The device records this information and sends it to the server. The transmitted data is further analyzed by the server to evaluate the learner's current understanding and the additional educational resources needed. The information obtained in this step is used for feedback in the next step.

[0262] Step 5:

[0263] The server generates additional educational resources and supplementary materials based on the user's progress and understanding assessment, and sends them back to the device. The server responds to learners' needs by providing new practice exercises and explanations in a more easily understandable format as needed. This entire process allows users to continuously receive personalized learning support.

[0264] (Application Example 1)

[0265] 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."

[0266] There is a need for a system that provides individualized training and learning tailored to the health conditions and interests of the elderly and those requiring care. However, current systems struggle to flexibly respond to individual needs and lack the ability to provide interactive experiences through visual and auditory means. Therefore, the challenge lies in effectively and efficiently supporting individual skill development and rehabilitation.

[0267] 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.

[0268] In this invention, the server includes means for inputting information of individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for transmitting the generated learning materials and providing them in a format accessible to the learners. This makes it possible to provide health and rehabilitation content optimized for each individual user and to provide clear instruction through visual and auditory means.

[0269] A "learner" refers to an individual who seeks to learn and understand the content of education or training.

[0270] A "user profile" is a collection of digital data that summarizes information about a learner or user, revealing their individual needs and characteristics.

[0271] "Learning materials" are educational materials used by learners to acquire specific knowledge or skills, and they come in a variety of forms, including text, audio, and visual materials.

[0272] "Feedback" refers to the collection of data and information regarding learners' understanding and progress, which is used to improve and adjust the next steps.

[0273] "Health and rehabilitation content" refers to a collection of programs and exercises aimed at improving the user's physical and mental health.

[0274] "Visual and auditory" refers to the sensory media used by users to receive information, primarily through images and sounds, which facilitate interaction.

[0275] The system of this invention provides health promotion and learning support tailored to individual needs through interaction between a server, a terminal, and a user.

[0276] The server uses a generative AI model to generate personalized user profiles from user input. This profile serves as the foundation for providing appropriate content based on the user's health status and learning objectives. The server also dynamically generates health and rehabilitation content, including visual and audio guidance, based on the profile. The hardware consists of a high-performance data processing server, and the software uses Python and Flask. For generative AI, an AI model from OpenAI, for example, is used.

[0277] The device accepts user input and displays and plays back generated content. This allows users to intuitively understand and act upon the content. The device has diverse user interfaces and is implemented on tablets and smartphones. This allows users to engage with the provided training and learning content at their own pace.

[0278] Users send specific health and learning goals to the server by entering prompts via their terminal. For example, by sending a prompt such as, "I would like suggestions for easy daily stretches and memory-enhancing activities for a user in their 60s," the server generates and provides a personalized program.

[0279] This system makes it possible to provide health promotion and learning experiences tailored to each individual user, enabling efficient skill improvement.

[0280] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0281] Step 1:

[0282] The user logs in to the application using the terminal and enters personal profile information. This information includes health status, the progress of current rehabilitation, what they want to learn, and their goals. The terminal collects this information and sends it to the server. The input information is structured and becomes the basic data for accurately evaluating the user's needs.

[0283] Step 2:

[0284] The server creates a user profile using the AI model generated based on the user information received from the terminal. Specifically, Python scripts are used to analyze the data and construct a profile based on the user's health status and learning objectives. The generated profile serves as a basis for proposing optimal training and learning materials for individual goals.

[0285] Step 3:

[0286] The server utilizes the generated AI model to dynamically generate specific health improvement and learning content based on the user profile. In the generation process, the prompt text provided by the user is analyzed, and content that proposes corresponding stretches and quizzes is created. The generated content is prepared as visual and audio materials using technologies such as Text-to-Speech technology.

[0287] Step 4:<I

[0288] The server sends the generated content to the terminal. The terminal is responsible for displaying this content in a user-friendly format and playing the voice guidance. Specifically, the method of stretching is shown in animation via the graphical user interface of the terminal, and the user is supported with voice guidance.

[0289] Step 5:

[0290] Users engage in the provided health promotion and learning activities. They input their progress and feedback into their device. For example, the number of stretches performed and the results of quizzes answered are collected as input data.

[0291] Step 6:

[0292] The device sends user feedback and progress information to the server. The server analyzes this information and adjusts the user profile and subsequent health and learning content. The algorithms used here make conditional decisions to ensure that the difficulty and type of content are tailored to the user's needs.

[0293] Step 7:

[0294] As users continue to use the system, the data obtained at each step is accumulated and used to create more precise profiles and content. By repeating this process, the system continuously supports the user's unique learning and health improvement needs.

[0295] 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.

[0296] This invention is an educational system that combines a generative AI model and an emotion engine, aiming to provide learners with a more personalized educational experience. This system is based on interaction between a server, a terminal, and a user.

[0297] Users use their devices to input their learning goals, interests, and skill levels, and this data is sent from the device to the server. Based on this information, the server creates a detailed user profile, which forms the basis for generating learning materials.

[0298] The server uses a generative AI model to analyze user profiles and generate optimized learning materials tailored to the individual needs of each learner. These materials are adapted to multiple formats, including visual, audio, and text-based materials, depending on the user's learning style. Furthermore, an emotion engine considers the user's emotional state during the material generation process. For example, if a user is experiencing stress, the server adjusts the difficulty level of the materials to provide a learner-friendly learning environment.

[0299] The device receives the generated learning materials and makes them easily accessible to the user. The user can progress through the learning materials via the device and send questions to the server as needed. The server uses an emotion engine to detect the user's emotions in real time and evaluates their comprehension based on this information. Based on this evaluation, the content and format of the learning materials are dynamically adjusted.

[0300] As a concrete example, if the system detects that the user is feeling anxious, it will provide an emotionally sensitive educational experience by increasing the amount of relaxing visual material or slowing down the learning pace. In this way, the system of the present invention can construct an optimal learning environment while taking into account the learner's mental and physical state.

[0301] Through this process, the present invention provides a system that goes beyond conventional uniform educational methods, offering personalized educational opportunities for each learner and achieving higher educational effectiveness.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The user uses a device to enter their learning objectives, interests, and current skill level. The device sends this information to the server, and the user profile is initially configured.

[0305] Step 2:

[0306] Based on the received information, the server creates a user profile using a generative AI model. This profile includes learning styles, progress, and areas of interest, serving as the basis for personalized learning experiences.

[0307] Step 3:

[0308] The server analyzes the learning content based on the user profile and generates teaching materials optimized for the corresponding learner. In this process, an emotion engine is utilized, and the user's emotion data (e.g., stress level) is also considered. Adjustments such as difficulty level are made to adapt to the learner's state.

[0309] Step 4:

[0310] The terminal receives the generated teaching materials sent from the server and prepares to provide them for the user to access visually or aurally. For example, the color and voice tone of the teaching materials may be adjusted based on emotions.

[0311] Step 5:

[0312] The user proceeds with learning via the terminal. The terminal continuously monitors the emotion data during learning and transmits it to the server as necessary. The server uses this information to adjust the teaching materials and the way of learning in real time.

[0313] Step 6:

[0314] When learning is completed, the terminal collects feedback regarding the user's emotional state and understanding level and transmits it to the server. The server updates the user profile based on the feedback and utilizes it as data for the next learning session.

[0315] Step 7:

[0316] The server updates its profile based on feedback, aiming to improve the accuracy of learning materials for the next session. In this process, user emotional changes are given particular importance, and the direction for improving the quality of the learning experience is reviewed.

[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 dynamically adjust learning materials according to the individual needs and emotional states of learners, resulting in a decline in learning effectiveness due to the provision of uniform materials. Therefore, there is a need to provide an optimized educational experience for each learner and improve the quality of learning.

[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 inputting information on individual learners and creating educational data, means for generating prompt sentences using a generative AI model and generating educational content, and means for evaluating the learner's emotional state using an emotion analysis engine and adjusting the educational content based on that evaluation. This makes it possible to provide an optimal educational environment that is tailored to the individual needs and emotional state of each learner.

[0322] A "learner" is an individual who seeks to acquire knowledge and skills through educational materials using an educational system.

[0323] "Educational data" refers to datasets created based on learner information, reflecting individual needs and skill levels.

[0324] "Educational content" refers to learning materials that are optimized for learners using generative AI models and provided in various formats such as visual, audio, and text.

[0325] A "generative AI model" is an artificial intelligence model that generates text or data tailored to a specific purpose based on the input prompt text.

[0326] A "prompt statement" is an instruction given to a generative AI model, and is text used to request a specific output from the AI.

[0327] A "sentiment analysis engine" is a technology used to evaluate learners' emotional states in real time and to dynamically adjust educational content based on these evaluations.

[0328] Users first access the learning system using a terminal and input their learning goals, interests, and skill level. This input information is sent from the terminal to the server. The server receives the input information and generates detailed educational data. This educational data reflects the learner's needs and forms the basis for optimizing their individual learning experience.

[0329] Next, the server utilizes a generative AI model. The generative AI model generates prompt sentences based on educational data and inputs them into the model. These prompt sentences include specific instructions such as, "Generate intermediate-level English learning materials, taking stress reduction into consideration." Upon receiving this prompt, the AI ​​model generates educational content suitable for the learner. This content takes the form of text, audio, and visual materials, delivered according to the learner's style.

[0330] Furthermore, the system in use incorporates an emotion analysis engine, which the server utilizes to assess the learner's emotional state in real time. For example, if the server detects that a learner is experiencing stress, it adjusts the difficulty level of the educational content to provide a learner-friendly learning environment.

[0331] The device receives educational content from the server and displays it in a user-friendly format. Users can progress through their learning using this content, and if they have any questions, they can send them to the server via the device. The server uses a generative AI model to provide quick and appropriate answers to the received questions.

[0332] In this way, embodiments of the present invention realize a dynamic and personalized educational experience based on the learner's needs and emotional state, thereby improving educational effectiveness.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] Users access the learning system using a device and input information such as their learning goals, interests, and skill levels. This information is sent from the device to the server. The input data forms the basis for creating a user profile, thereby identifying the learner's needs.

[0336] Step 2:

[0337] The server generates educational data based on user information received from the terminal. In this process, the information is stored in a database, and a user profile is created. This profile clarifies the learner's needs and characteristics.

[0338] Step 3:

[0339] The server generates a prompt message for the AI ​​generation model. This prompt message might be in the format of, for example, "Please generate intermediate-level English learning materials, taking stress reduction into consideration." This prompt is sent to the AI ​​generation model, which uses the user profile to generate the most suitable educational content. The model performs data analysis based on the prompt and outputs appropriate learning materials.

[0340] Step 4:

[0341] The generated educational content is sent from the server to the device. The device receives this content and displays it in a format that is easily accessible to the user. At this point, the educational content is converted into text, audio, and visual formats and adapted to the learner's style.

[0342] Step 5:

[0343] The server uses an emotion analysis engine to evaluate the user's real-time emotional state. Based on the evaluation results, the server adjusts the difficulty level and format of the educational content. For example, if the user is feeling stressed, the difficulty level may be lowered and relaxing materials may be added.

[0344] Step 6:

[0345] Users progress through learning using educational content provided via their devices. If questions arise during learning, they can send them to the server via their devices. The server utilizes generative AI models to quickly generate answers and return them to the user. This enhances the user's understanding and provides an effective learning experience.

[0346] (Application Example 2)

[0347] 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."

[0348] Modern education demands that it provide instruction tailored to the individual needs and emotional states of each learner. However, traditional education systems fail to adequately consider learners' emotional states and learning styles, and remain limited to providing uniform teaching materials. As a result, learners experience stress and their learning effectiveness suffers.

[0349] 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.

[0350] In this invention, the server includes means for inputting information on individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for evaluating the learners' emotional state in real time and adjusting the learning experience. This makes it possible to provide an individualized educational experience tailored to each learner and maximize learning effectiveness.

[0351] A "learner" is an individual who participates in an educational program with the aim of acquiring knowledge and skills.

[0352] "Information" refers to data about a learner's learning goals, interests, and skill level, used to create a user profile.

[0353] A "user profile" is a set of digital information created based on the individual learner's information, and serves as the foundational data for generating personalized learning materials.

[0354] "Learning content" refers to a collection of educational tasks and themes presented to learners in accordance with specific educational objectives.

[0355] "Learning materials" is a general term for materials and content provided to support learners' learning, and can take the form of digital or physical materials.

[0356] "Emotional state" refers to information that indicates a learner's mental and emotional condition and reactions, and is an important element in the learning process.

[0357] "Real-time" means that processes and operations are carried out within the same timeframe as real-world time.

[0358] "Adjustment" refers to the act of changing or modifying settings or content to conform to specific goals or standards.

[0359] "Generating educational materials" refers to the process of designing and creating necessary educational content based on the individual needs of learners.

[0360] The system that realizes this invention functions through interaction between the server, terminal, and user.

[0361] The server first receives information about each learner and creates a user profile. This information includes learning goals, interests, and skill levels. Next, the server uses a generative AI model to analyze this user profile and generate learning materials optimized for the learner. The materials are delivered in visual, audio, and text formats and are adapted to the learner's style.

[0362] The server further utilizes an emotion engine to assess the learner's real-time emotional state and instantly adjust the learning experience. For example, if it detects frustration, it can take measures to lower the difficulty level.

[0363] The terminal presents the generated learning materials to the learner and functions as an interface for progressing through the learning process. It also collects feedback based on the learner's understanding and sends it to the server. This enables continuous updates of user profiles and learning materials.

[0364] As a concrete example, suppose an elementary school student is learning math at home via a device. In this case, the learning materials generated by the server are used, and feedback is provided at appropriate times according to the student's level of understanding and emotional state.

[0365] Examples of prompts for a generative AI model:

[0366] "The user is showing signs of impatience. Please generate math learning materials with adjusted difficulty levels."

[0367] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0368] Step 1:

[0369] The server receives the user's initial information.

[0370] The input data consists of the learner's learning goals, interests, and skill level. Based on this data, the server initiates the process of creating a user profile. The data is stored in a database and used for subsequent analysis.

[0371] Step 2:

[0372] The server uses a generated AI model to analyze user profiles and create appropriate learning materials.

[0373] At the start of the process, the server prompts the AI ​​model with the message, "Generate learning content suitable for this profile." Based on this, the AI ​​model designs the content and difficulty level of the learning materials and outputs them as digital learning materials in visual, audio, and text formats.

[0374] Step 3:

[0375] The server uses an emotion engine to evaluate the learner's emotional state in real time.

[0376] The user's facial expressions and voice data are input into an emotion engine, which then identifies emotions such as stress and anxiety. This information is used to adjust the difficulty level of the learning materials and is recorded as output for updating the user profile in real time.

[0377] Step 4:

[0378] The device displays the generated learning materials to the user and starts a new learning session.

[0379] Upon receiving educational data from the server, the terminal provides it to the learner via a visual display and audio output. While providing the educational materials, the terminal records user input in real time and measures the level of understanding.

[0380] Step 5:

[0381] The device sends user feedback data to the server.

[0382] The device collects user activity history and comprehension assessments, and sends this information to the server. The server receives this feedback and updates the user profile and course materials as needed to prepare for the next session.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] This system consists of a server, terminals, and user interaction to provide an educational experience tailored to individual learning needs. The server, which plays a central role in the system, dynamically generates learning materials suited to each user's learning style using generative AI technology. Users can access these materials via terminals and proceed with their learning.

[0400] First, users use their devices to input their learning objectives, interests, and skill levels. This allows the server to establish a detailed user profile, creating a foundation for understanding the user's learning tendencies.

[0401] Subsequently, the server utilizes a generative AI model to generate learning materials best suited to the learner based on this profile. For example, if a user wants to strengthen their math fundamentals, the server creates visually clear materials that include a variety of practice problems. Furthermore, by adapting the materials to accommodate multiple information formats, it supports multifaceted understanding through visual and auditory means.

[0402] Once the learning materials are generated, the server sends them to the terminal, making them easily accessible to the user. The terminal supports the user's learning experience by displaying visual materials on the screen and playing audio materials using speech synthesis.

[0403] During learning, the device monitors the user's progress and can send any unclear points or questions that arise during learning to the server. Based on this information, the server assesses the user's understanding and provides additional learning materials or supplementary explanations in different formats as needed. This process allows users to deepen their learning at their own pace.

[0404] As a concrete example, consider a user learning a new language. The server not only generates text materials containing basic words and phrases in that language, but also creates audio materials for pronunciation practice. This allows the user to acquire the language from both written and spoken perspectives, resulting in efficient and effective learning. Thus, the system according to the present invention is an effective means of providing comprehensive and flexible learning support.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The user uses a device to input information such as their learning objectives, skill level, and interests. The device sends this information to the server, and the creation of the user profile begins.

[0408] Step 2:

[0409] The server creates a user profile based on the information it receives. This profile includes learning style, past learning history, and progress toward achieving goals, and serves as the basis for later creation of learning materials.

[0410] Step 3:

[0411] The server uses an AI model to analyze user profiles and automatically generate learning materials tailored to the learner. For example, it generates materials including infographics for visually-oriented learners and audio materials for auditorily-oriented learners.

[0412] Step 4:

[0413] The generated learning materials are sent from the server to the terminal. The terminal displays or plays the received materials in an appropriate format, allowing the user to access them.

[0414] Step 5:

[0415] Users progress through their learning using materials presented via their device. If they encounter any difficulties during their studies, they can send questions to the server from their device.

[0416] Step 6:

[0417] The server analyzes user questions and learning data to assess understanding. Based on the assessment, it generates additional learning materials as needed and provides them to the user via the terminal.

[0418] Step 7:

[0419] Once the learning session is complete, the device collects learning feedback from the user and sends it to the server. The server uses this feedback to update the user profile and the learning material generation algorithm, and utilizes this information for the next learning session.

[0420] (Example 1)

[0421] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0422] Traditional education systems struggle to provide effective learning support tailored to the individual characteristics and progress of each learner. Furthermore, they lack sufficient resources to create and provide diverse learning materials suited to different learning styles, resulting in inadequate feedback and the provision of supplementary materials according to learners' proficiency levels. This leads to decreased learning efficiency and an inability to adequately meet the needs of individual learners.

[0423] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0424] In this invention, the server includes means for inputting individual learner data and creating person data; means for monitoring the learner's progress, evaluating their level of understanding, and providing additional educational resources using educational technology; and means for transmitting the generated educational resources and providing them in a format usable by the learner. This makes it possible to provide educational resources optimized for the learner's characteristics and realize effective learning support.

[0425] "Individual learner data" refers to a collection of information about each learner's unique characteristics, learning objectives, interests, and skill levels.

[0426] "Personal data" refers to a data profile built on individual learner data, which provides a detailed description of the learner's characteristics and learning tendencies.

[0427] "Educational technology" refers to technologies that use advanced techniques to monitor learners' progress and understanding, and to provide educational resources tailored to individual needs.

[0428] "Educational resources" is a general term for educational content such as teaching materials, practice problems, and audio materials that are generated to suit the learner's learning style.

[0429] "Feedback data" refers to information obtained from educational technologies based on learners' understanding and progress, and is used to adjust educational resources and update personal data.

[0430] "Practical training resources" refer to educational content, including practice problems and application exercises, that are provided in addition to improve learners' skills.

[0431] "Available formats" refer to educational resources provided in a form that is easily accessible to learners, and can be viewed or listened to on digital devices.

[0432] The system of this invention consists of the interaction of a server, a terminal, and a user. The server forms the core of this system and uses generative AI technology to dynamically generate learning materials based on individual learner data. Specifically, the server receives data entered by the user via the terminal, analyzes it, and creates detailed person data. Using this person data, a generative AI model generates educational resources that are optimal for the learner.

[0433] The terminal is used to provide users with generated educational resources. The terminal receives educational resources sent from the server and displays and plays them in a format easily accessible to the user. This includes the ability to display text-based learning materials on the screen and to play audio materials using speech synthesis.

[0434] When a user learns a new language, the server generates learning materials that include basic words and phrases in that language. It also creates audio materials for pronunciation practice, which the user can view and listen to on their device. This process is initiated by the following prompt: By providing specific instructions to the server, such as "Create basic learning materials for a new language," the generation AI model generates the necessary materials.

[0435] In this way, comprehensive and flexible educational support tailored to individual learning styles is provided, enabling users to learn effectively at their own pace.

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

[0437] Step 1:

[0438] Users input individual learner data, such as learning objectives, interests, and skill levels, using their devices. This input data is sent to the server. Based on this input data, the server generates personalized data that reflects each learner's characteristics. Specifically, the server analyzes the data to determine learning tendencies and the types of educational resources needed.

[0439] Step 2:

[0440] The server uses a generative AI model to create optimal educational resources based on the prompt received from the user. An example of a prompt might be, "Create basic learning materials for learning a new language." Based on this prompt, the server utilizes its generative AI model to generate a variety of learning materials tailored to the learner, including visual and audio materials. This process employs data analysis and generative AI technology to dynamically construct the learning materials.

[0441] Step 3:

[0442] The server sends the generated educational resources to the terminal. The terminal receives these resources and prepares them in a user-friendly format. Specifically, the terminal displays text-based learning materials on its screen and plays audio materials using speech synthesis technology. This process allows users to easily access visual and auditory educational resources through the terminal.

[0443] Step 4:

[0444] Users learn on their devices and input their progress and understanding into the device. The device records this information and sends it to the server. The transmitted data is further analyzed by the server to evaluate the learner's current understanding and the additional educational resources needed. The information obtained in this step is used for feedback in the next step.

[0445] Step 5:

[0446] The server generates additional educational resources and supplementary materials based on the user's progress and understanding assessment, and sends them back to the device. The server responds to learners' needs by providing new practice exercises and explanations in a more easily understandable format as needed. This entire process allows users to continuously receive personalized learning support.

[0447] (Application Example 1)

[0448] 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."

[0449] There is a need for a system that provides individualized training and learning tailored to the health conditions and interests of the elderly and those requiring care. However, current systems struggle to flexibly respond to individual needs and lack the ability to provide interactive experiences through visual and auditory means. Therefore, the challenge lies in effectively and efficiently supporting individual skill development and rehabilitation.

[0450] 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.

[0451] In this invention, the server includes means for inputting information of individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for transmitting the generated learning materials and providing them in a format accessible to the learners. This makes it possible to provide health and rehabilitation content optimized for each individual user and to provide clear instruction through visual and auditory means.

[0452] A "learner" refers to an individual who seeks to learn and understand the content of education or training.

[0453] A "user profile" is a collection of digital data that summarizes information about a learner or user, revealing their individual needs and characteristics.

[0454] "Learning materials" are educational materials used by learners to acquire specific knowledge or skills, and they come in a variety of forms, including text, audio, and visual materials.

[0455] "Feedback" refers to the collection of data and information regarding learners' understanding and progress, which is used to improve and adjust the next steps.

[0456] "Health and rehabilitation content" refers to a collection of programs and exercises aimed at improving the user's physical and mental health.

[0457] "Visual and auditory" refers to the sensory media used by users to receive information, primarily through images and sounds, which facilitate interaction.

[0458] The system of this invention provides health promotion and learning support tailored to individual needs through interaction between a server, a terminal, and a user.

[0459] The server uses a generative AI model to generate personalized user profiles from user input. This profile serves as the foundation for providing appropriate content based on the user's health status and learning objectives. The server also dynamically generates health and rehabilitation content, including visual and audio guidance, based on the profile. The hardware consists of a high-performance data processing server, and the software uses Python and Flask. For generative AI, an AI model from OpenAI, for example, is used.

[0460] The device accepts user input and displays and plays back generated content. This allows users to intuitively understand and act upon the content. The device has diverse user interfaces and is implemented on tablets and smartphones. This allows users to engage with the provided training and learning content at their own pace.

[0461] Users send specific health and learning goals to the server by entering prompts via their terminal. For example, by sending a prompt such as, "I would like suggestions for easy daily stretches and memory-enhancing activities for a user in their 60s," the server generates and provides a personalized program.

[0462] This system makes it possible to provide health promotion and learning experiences tailored to each individual user, enabling efficient skill improvement.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The user logs into the application using their device and enters personal profile information. This information includes their health status, current rehabilitation progress, and learning goals. The device collects this information and sends it to the server. The entered information is structured and serves as foundational data for accurately assessing the user's needs.

[0466] Step 2:

[0467] The server creates a user profile using a generative AI model based on user information received from the terminal. Specifically, it analyzes the data using a Python script and builds a profile based on the user's health status and learning objectives. The generated profile serves as the basis for suggesting the most suitable training and learning materials for each individual's goals.

[0468] Step 3:

[0469] The server utilizes a generative AI model to dynamically generate specific health promotion and learning content based on user profiles. The generation process analyzes user-provided prompts and creates content suggesting corresponding stretches and quizzes. The generated content is then formatted into visual and audio materials using technologies such as Text-to-Speech.

[0470] Step 4:

[0471] The server sends the generated content to the terminal. The terminal is responsible for displaying this content in a user-friendly format and playing audio guidance. Specifically, it uses the terminal's graphical user interface to show the stretching method with animations and supports the user with audio guidance.

[0472] Step 5:

[0473] Users engage in the provided health promotion and learning activities. They input their progress and feedback into their device. For example, the number of stretches performed and the results of quizzes answered are collected as input data.

[0474] Step 6:

[0475] The device sends user feedback and progress information to the server. The server analyzes this information and adjusts the user profile and subsequent health and learning content. The algorithms used here make conditional decisions to ensure that the difficulty and type of content are tailored to the user's needs.

[0476] Step 7:

[0477] As users continue to use the system, the data obtained at each step is accumulated and used to create more precise profiles and content. By repeating this process, the system continuously supports the user's unique learning and health improvement needs.

[0478] 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.

[0479] This invention is an educational system that combines a generative AI model and an emotion engine, aiming to provide learners with a more personalized educational experience. This system is based on interaction between a server, a terminal, and a user.

[0480] Users use their devices to input their learning goals, interests, and skill levels, and this data is sent from the device to the server. Based on this information, the server creates a detailed user profile, which forms the basis for generating learning materials.

[0481] The server uses a generative AI model to analyze user profiles and generate optimized learning materials tailored to the individual needs of each learner. These materials are adapted to multiple formats, including visual, audio, and text-based materials, depending on the user's learning style. Furthermore, an emotion engine considers the user's emotional state during the material generation process. For example, if a user is experiencing stress, the server adjusts the difficulty level of the materials to provide a learner-friendly learning environment.

[0482] The device receives the generated learning materials and makes them easily accessible to the user. The user can progress through the learning materials via the device and send questions to the server as needed. The server uses an emotion engine to detect the user's emotions in real time and evaluates their comprehension based on this information. Based on this evaluation, the content and format of the learning materials are dynamically adjusted.

[0483] As a concrete example, if the system detects that the user is feeling anxious, it will provide an emotionally sensitive educational experience by increasing the amount of relaxing visual material or slowing down the learning pace. In this way, the system of the present invention can construct an optimal learning environment while taking into account the learner's mental and physical state.

[0484] Through this process, the present invention provides a system that goes beyond conventional uniform educational methods, offering personalized educational opportunities for each learner and achieving higher educational effectiveness.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The user uses a device to enter their learning objectives, interests, and current skill level. The device sends this information to the server, and the user profile is initially configured.

[0488] Step 2:

[0489] Based on the information received, the server creates a user profile using a generative AI model. This profile includes learning style, progress, and areas of interest, forming the basis for a personalized learning experience.

[0490] Step 3:

[0491] The server analyzes learning content based on user profiles and generates learning materials optimized for the learner. This process utilizes an emotion engine, taking into account user emotional data (e.g., stress levels). Difficulty levels are adjusted, and the materials are adapted to the learner's state.

[0492] Step 4:

[0493] The device receives generated learning materials sent from the server and prepares them to be made available to the user visually or audibly. For example, the colors and tone of the audio materials may be adjusted based on emotions.

[0494] Step 5:

[0495] Users progress through the learning process via their devices. The devices continuously monitor emotional data during learning and send it to the server as needed. The server uses this information to adjust learning materials and learning methods in real time.

[0496] Step 6:

[0497] Once the learning session is complete, the device collects feedback on the user's emotional state and level of understanding, and sends this feedback to the server. The server updates the user profile based on this feedback and uses it as data for the next learning session.

[0498] Step 7:

[0499] The server updates its profile based on feedback, aiming to improve the accuracy of learning materials for the next session. In this process, user emotional changes are given particular importance, and the direction for improving the quality of the learning experience is reviewed.

[0500] (Example 2)

[0501] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0502] Traditional education systems have struggled to dynamically adjust learning materials according to the individual needs and emotional states of learners, resulting in a decline in learning effectiveness due to the provision of uniform materials. Therefore, there is a need to provide an optimized educational experience for each learner and improve the quality of learning.

[0503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0504] In this invention, the server includes means for inputting information on individual learners and creating educational data, means for generating prompt sentences using a generative AI model and generating educational content, and means for evaluating the learner's emotional state using an emotion analysis engine and adjusting the educational content based on that evaluation. This makes it possible to provide an optimal educational environment that is tailored to the individual needs and emotional state of each learner.

[0505] A "learner" is an individual who seeks to acquire knowledge and skills through educational materials using an educational system.

[0506] "Educational data" refers to datasets created based on learner information, reflecting individual needs and skill levels.

[0507] "Educational content" refers to learning materials that are optimized for learners using generative AI models and provided in various formats such as visual, audio, and text.

[0508] A "generative AI model" is an artificial intelligence model that generates text or data tailored to a specific purpose based on the input prompt text.

[0509] A "prompt statement" is an instruction given to a generative AI model, and is text used to request a specific output from the AI.

[0510] A "sentiment analysis engine" is a technology used to evaluate learners' emotional states in real time and to dynamically adjust educational content based on these evaluations.

[0511] Users first access the learning system using a terminal and input their learning goals, interests, and skill level. This input information is sent from the terminal to the server. The server receives the input information and generates detailed educational data. This educational data reflects the learner's needs and forms the basis for optimizing their individual learning experience.

[0512] Next, the server utilizes a generative AI model. The generative AI model generates prompt sentences based on educational data and inputs them into the model. These prompt sentences include specific instructions such as, "Generate intermediate-level English learning materials, taking stress reduction into consideration." Upon receiving this prompt, the AI ​​model generates educational content suitable for the learner. This content takes the form of text, audio, and visual materials, delivered according to the learner's style.

[0513] Furthermore, the system in use incorporates an emotion analysis engine, which the server utilizes to assess the learner's emotional state in real time. For example, if the server detects that a learner is experiencing stress, it adjusts the difficulty level of the educational content to provide a learner-friendly learning environment.

[0514] The device receives educational content from the server and displays it in a user-friendly format. Users can progress through their learning using this content, and if they have any questions, they can send them to the server via the device. The server uses a generative AI model to provide quick and appropriate answers to the received questions.

[0515] In this way, embodiments of the present invention realize a dynamic and personalized educational experience based on the learner's needs and emotional state, thereby improving educational effectiveness.

[0516] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0517] Step 1:

[0518] Users access the learning system using a device and input information such as their learning goals, interests, and skill levels. This information is sent from the device to the server. The input data forms the basis for creating a user profile, thereby identifying the learner's needs.

[0519] Step 2:

[0520] The server generates educational data based on user information received from the terminal. In this process, the information is stored in a database, and a user profile is created. This profile clarifies the learner's needs and characteristics.

[0521] Step 3:

[0522] The server generates a prompt message for the AI ​​generation model. This prompt message might be in the format of, for example, "Please generate intermediate-level English learning materials, taking stress reduction into consideration." This prompt is sent to the AI ​​generation model, which uses the user profile to generate the most suitable educational content. The model performs data analysis based on the prompt and outputs appropriate learning materials.

[0523] Step 4:

[0524] The generated educational content is sent from the server to the device. The device receives this content and displays it in a format that is easily accessible to the user. At this point, the educational content is converted into text, audio, and visual formats and adapted to the learner's style.

[0525] Step 5:

[0526] The server uses an emotion analysis engine to evaluate the user's real-time emotional state. Based on the evaluation results, the server adjusts the difficulty level and format of the educational content. For example, if the user is feeling stressed, the difficulty level may be lowered and relaxing materials may be added.

[0527] Step 6:

[0528] Users progress through learning using educational content provided via their devices. If questions arise during learning, they can send them to the server via their devices. The server utilizes generative AI models to quickly generate answers and return them to the user. This enhances the user's understanding and provides an effective learning experience.

[0529] (Application Example 2)

[0530] 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."

[0531] Modern education demands that it provide instruction tailored to the individual needs and emotional states of each learner. However, traditional education systems fail to adequately consider learners' emotional states and learning styles, and remain limited to providing uniform teaching materials. As a result, learners experience stress and their learning effectiveness suffers.

[0532] 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.

[0533] In this invention, the server includes means for inputting information on individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for evaluating the learners' emotional state in real time and adjusting the learning experience. This makes it possible to provide an individualized educational experience tailored to each learner and maximize learning effectiveness.

[0534] A "learner" is an individual who participates in an educational program with the aim of acquiring knowledge and skills.

[0535] "Information" refers to data about a learner's learning goals, interests, and skill level, used to create a user profile.

[0536] A "user profile" is a set of digital information created based on the individual learner's information, and serves as the foundational data for generating personalized learning materials.

[0537] "Learning content" refers to a collection of educational tasks and themes presented to learners in accordance with specific educational objectives.

[0538] "Learning materials" is a general term for materials and content provided to support learners' learning, and can take the form of digital or physical materials.

[0539] "Emotional state" refers to information that indicates a learner's mental and emotional condition and reactions, and is an important element in the learning process.

[0540] "Real-time" means that processes and operations are carried out within the same timeframe as real-world time.

[0541] "Adjustment" refers to the act of changing or modifying settings or content to conform to specific goals or standards.

[0542] "Generating educational materials" refers to the process of designing and creating necessary educational content based on the individual needs of learners.

[0543] The system that realizes this invention functions through interaction between the server, terminal, and user.

[0544] The server first receives information about each learner and creates a user profile. This information includes learning goals, interests, and skill levels. Next, the server uses a generative AI model to analyze this user profile and generate learning materials optimized for the learner. The materials are delivered in visual, audio, and text formats and are adapted to the learner's style.

[0545] The server further utilizes an emotion engine to assess the learner's real-time emotional state and instantly adjust the learning experience. For example, if it detects frustration, it can take measures to lower the difficulty level.

[0546] The terminal presents the generated learning materials to the learner and functions as an interface for progressing through the learning process. It also collects feedback based on the learner's understanding and sends it to the server. This enables continuous updates of user profiles and learning materials.

[0547] As a concrete example, suppose an elementary school student is learning math at home via a device. In this case, the learning materials generated by the server are used, and feedback is provided at appropriate times according to the student's level of understanding and emotional state.

[0548] Examples of prompts for a generative AI model:

[0549] "The user is showing signs of impatience. Please generate math learning materials with adjusted difficulty levels."

[0550] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0551] Step 1:

[0552] The server receives the user's initial information.

[0553] The input data consists of the learner's learning goals, interests, and skill level. Based on this data, the server initiates the process of creating a user profile. The data is stored in a database and used for subsequent analysis.

[0554] Step 2:

[0555] The server uses a generated AI model to analyze user profiles and create appropriate learning materials.

[0556] At the start of the process, the server prompts the AI ​​model with the message, "Generate learning content suitable for this profile." Based on this, the AI ​​model designs the content and difficulty level of the learning materials and outputs them as digital learning materials in visual, audio, and text formats.

[0557] Step 3:

[0558] The server uses an emotion engine to evaluate the learner's emotional state in real time.

[0559] The user's facial expressions and voice data are input into an emotion engine, which then identifies emotions such as stress and anxiety. This information is used to adjust the difficulty level of the learning materials and is recorded as output for updating the user profile in real time.

[0560] Step 4:

[0561] The device displays the generated learning materials to the user and starts a new learning session.

[0562] Upon receiving educational data from the server, the terminal provides it to the learner via a visual display and audio output. While providing the educational materials, the terminal records user input in real time and measures the level of understanding.

[0563] Step 5:

[0564] The device sends user feedback data to the server.

[0565] The device collects user activity history and comprehension assessments, and sends this information to the server. The server receives this feedback and updates the user profile and course materials as needed to prepare for the next session.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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".

[0583] This system consists of a server, terminals, and user interaction to provide an educational experience tailored to individual learning needs. The server, which plays a central role in the system, dynamically generates learning materials suited to each user's learning style using generative AI technology. Users can access these materials via terminals and proceed with their learning.

[0584] First, users use their devices to input their learning objectives, interests, and skill levels. This allows the server to establish a detailed user profile, creating a foundation for understanding the user's learning tendencies.

[0585] Subsequently, the server utilizes a generative AI model to generate learning materials best suited to the learner based on this profile. For example, if a user wants to strengthen their math fundamentals, the server creates visually clear materials that include a variety of practice problems. Furthermore, by adapting the materials to accommodate multiple information formats, it supports multifaceted understanding through visual and auditory means.

[0586] Once the learning materials are generated, the server sends them to the terminal, making them easily accessible to the user. The terminal supports the user's learning experience by displaying visual materials on the screen and playing audio materials using speech synthesis.

[0587] During learning, the device monitors the user's progress and can send any unclear points or questions that arise during learning to the server. Based on this information, the server assesses the user's understanding and provides additional learning materials or supplementary explanations in different formats as needed. This process allows users to deepen their learning at their own pace.

[0588] As a concrete example, consider a user learning a new language. The server not only generates text materials containing basic words and phrases in that language, but also creates audio materials for pronunciation practice. This allows the user to acquire the language from both written and spoken perspectives, resulting in efficient and effective learning. Thus, the system according to the present invention is an effective means of providing comprehensive and flexible learning support.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The user uses a device to input information such as their learning objectives, skill level, and interests. The device sends this information to the server, and the creation of the user profile begins.

[0592] Step 2:

[0593] The server creates a user profile based on the information it receives. This profile includes learning style, past learning history, and progress toward achieving goals, and serves as the basis for later creation of learning materials.

[0594] Step 3:

[0595] The server uses an AI model to analyze user profiles and automatically generate learning materials tailored to the learner. For example, it generates materials including infographics for visually-oriented learners and audio materials for auditorily-oriented learners.

[0596] Step 4:

[0597] The generated learning materials are sent from the server to the terminal. The terminal displays or plays the received materials in an appropriate format, allowing the user to access them.

[0598] Step 5:

[0599] Users progress through their learning using materials presented via their device. If they encounter any difficulties during their studies, they can send questions to the server from their device.

[0600] Step 6:

[0601] The server analyzes user questions and learning data to assess understanding. Based on the assessment, it generates additional learning materials as needed and provides them to the user via the terminal.

[0602] Step 7:

[0603] Once the learning session is complete, the device collects learning feedback from the user and sends it to the server. The server uses this feedback to update the user profile and the learning material generation algorithm, and utilizes this information for the next learning session.

[0604] (Example 1)

[0605] 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".

[0606] Traditional education systems struggle to provide effective learning support tailored to the individual characteristics and progress of each learner. Furthermore, they lack sufficient resources to create and provide diverse learning materials suited to different learning styles, resulting in inadequate feedback and the provision of supplementary materials according to learners' proficiency levels. This leads to decreased learning efficiency and an inability to adequately meet the needs of individual learners.

[0607] 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.

[0608] In this invention, the server includes means for inputting individual learner data and creating person data; means for monitoring the learner's progress, evaluating their level of understanding, and providing additional educational resources using educational technology; and means for transmitting the generated educational resources and providing them in a format usable by the learner. This makes it possible to provide educational resources optimized for the learner's characteristics and realize effective learning support.

[0609] "Individual learner data" refers to a collection of information about each learner's unique characteristics, learning objectives, interests, and skill levels.

[0610] "Personal data" refers to a data profile built on individual learner data, which provides a detailed description of the learner's characteristics and learning tendencies.

[0611] "Educational technology" refers to technologies that use advanced techniques to monitor learners' progress and understanding, and to provide educational resources tailored to individual needs.

[0612] "Educational resources" is a general term for educational content such as teaching materials, practice problems, and audio materials that are generated to suit the learner's learning style.

[0613] "Feedback data" refers to information obtained from educational technologies based on learners' understanding and progress, and is used to adjust educational resources and update personal data.

[0614] "Practical training resources" refer to educational content, including practice problems and application exercises, that are provided in addition to improve learners' skills.

[0615] "Available formats" refer to educational resources provided in a form that is easily accessible to learners, and can be viewed or listened to on digital devices.

[0616] The system of this invention consists of the interaction of a server, a terminal, and a user. The server forms the core of this system and uses generative AI technology to dynamically generate learning materials based on individual learner data. Specifically, the server receives data entered by the user via the terminal, analyzes it, and creates detailed person data. Using this person data, a generative AI model generates educational resources that are optimal for the learner.

[0617] The terminal is used to provide users with generated educational resources. The terminal receives educational resources sent from the server and displays and plays them in a format easily accessible to the user. This includes the ability to display text-based learning materials on the screen and to play audio materials using speech synthesis.

[0618] When a user learns a new language, the server generates learning materials that include basic words and phrases in that language. It also creates audio materials for pronunciation practice, which the user can view and listen to on their device. This process is initiated by the following prompt: By providing specific instructions to the server, such as "Create basic learning materials for a new language," the generation AI model generates the necessary materials.

[0619] In this way, comprehensive and flexible educational support tailored to individual learning styles is provided, enabling users to learn effectively at their own pace.

[0620] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0621] Step 1:

[0622] Users input individual learner data, such as learning objectives, interests, and skill levels, using their devices. This input data is sent to the server. Based on this input data, the server generates personalized data that reflects each learner's characteristics. Specifically, the server analyzes the data to determine learning tendencies and the types of educational resources needed.

[0623] Step 2:

[0624] The server uses a generative AI model to create optimal educational resources based on the prompt received from the user. An example of a prompt might be, "Create basic learning materials for learning a new language." Based on this prompt, the server utilizes its generative AI model to generate a variety of learning materials tailored to the learner, including visual and audio materials. This process employs data analysis and generative AI technology to dynamically construct the learning materials.

[0625] Step 3:

[0626] The server sends the generated educational resources to the terminal. The terminal receives these resources and prepares them in a user-friendly format. Specifically, the terminal displays text-based learning materials on its screen and plays audio materials using speech synthesis technology. This process allows users to easily access visual and auditory educational resources through the terminal.

[0627] Step 4:

[0628] Users learn on their devices and input their progress and understanding into the device. The device records this information and sends it to the server. The transmitted data is further analyzed by the server to evaluate the learner's current understanding and the additional educational resources needed. The information obtained in this step is used for feedback in the next step.

[0629] Step 5:

[0630] The server generates additional educational resources and supplementary materials based on the user's progress and understanding assessment, and sends them back to the device. The server responds to learners' needs by providing new practice exercises and explanations in a more easily understandable format as needed. This entire process allows users to continuously receive personalized learning support.

[0631] (Application Example 1)

[0632] 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".

[0633] There is a need for a system that provides individualized training and learning tailored to the health conditions and interests of the elderly and those requiring care. However, current systems struggle to flexibly respond to individual needs and lack the ability to provide interactive experiences through visual and auditory means. Therefore, the challenge lies in effectively and efficiently supporting individual skill development and rehabilitation.

[0634] 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.

[0635] In this invention, the server includes means for inputting information of individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for transmitting the generated learning materials and providing them in a format accessible to the learners. This makes it possible to provide health and rehabilitation content optimized for each individual user and to provide clear instruction through visual and auditory means.

[0636] A "learner" refers to an individual who seeks to learn and understand the content of education or training.

[0637] A "user profile" is a collection of digital data that summarizes information about a learner or user, revealing their individual needs and characteristics.

[0638] "Learning materials" are educational materials used by learners to acquire specific knowledge or skills, and they come in a variety of forms, including text, audio, and visual materials.

[0639] "Feedback" refers to the collection of data and information regarding learners' understanding and progress, which is used to improve and adjust the next steps.

[0640] "Health and rehabilitation content" refers to a collection of programs and exercises aimed at improving the user's physical and mental health.

[0641] "Visual and auditory" refers to the sensory media used by users to receive information, primarily through images and sounds, which facilitate interaction.

[0642] The system of this invention provides health promotion and learning support tailored to individual needs through interaction between a server, a terminal, and a user.

[0643] The server uses a generative AI model to generate personalized user profiles from user input. This profile serves as the foundation for providing appropriate content based on the user's health status and learning objectives. The server also dynamically generates health and rehabilitation content, including visual and audio guidance, based on the profile. The hardware consists of a high-performance data processing server, and the software uses Python and Flask. For generative AI, an AI model from OpenAI, for example, is used.

[0644] The device accepts user input and displays and plays back generated content. This allows users to intuitively understand and act upon the content. The device has diverse user interfaces and is implemented on tablets and smartphones. This allows users to engage with the provided training and learning content at their own pace.

[0645] Users send specific health and learning goals to the server by entering prompts via their terminal. For example, by sending a prompt such as, "I would like suggestions for easy daily stretches and memory-enhancing activities for a user in their 60s," the server generates and provides a personalized program.

[0646] This system makes it possible to provide health promotion and learning experiences tailored to each individual user, enabling efficient skill improvement.

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

[0648] Step 1:

[0649] The user logs into the application using their device and enters personal profile information. This information includes their health status, current rehabilitation progress, and learning goals. The device collects this information and sends it to the server. The entered information is structured and serves as foundational data for accurately assessing the user's needs.

[0650] Step 2:

[0651] The server creates a user profile using a generative AI model based on user information received from the terminal. Specifically, it analyzes the data using a Python script and builds a profile based on the user's health status and learning objectives. The generated profile serves as the basis for suggesting the most suitable training and learning materials for each individual's goals.

[0652] Step 3:

[0653] The server utilizes a generative AI model to dynamically generate specific health promotion and learning content based on user profiles. The generation process analyzes user-provided prompts and creates content suggesting corresponding stretches and quizzes. The generated content is then formatted into visual and audio materials using technologies such as Text-to-Speech.

[0654] Step 4:

[0655] The server sends the generated content to the terminal. The terminal is responsible for displaying this content in a user-friendly format and playing audio guidance. Specifically, it uses the terminal's graphical user interface to show the stretching method with animations and supports the user with audio guidance.

[0656] Step 5:

[0657] Users engage in the provided health promotion and learning activities. They input their progress and feedback into their device. For example, the number of stretches performed and the results of quizzes answered are collected as input data.

[0658] Step 6:

[0659] The device sends user feedback and progress information to the server. The server analyzes this information and adjusts the user profile and subsequent health and learning content. The algorithms used here make conditional decisions to ensure that the difficulty and type of content are tailored to the user's needs.

[0660] Step 7:

[0661] As users continue to use the system, the data obtained at each step is accumulated and used to create more precise profiles and content. By repeating this process, the system continuously supports the user's unique learning and health improvement needs.

[0662] 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.

[0663] This invention is an educational system that combines a generative AI model and an emotion engine, aiming to provide learners with a more personalized educational experience. This system is based on interaction between a server, a terminal, and a user.

[0664] Users use their devices to input their learning goals, interests, and skill levels, and this data is sent from the device to the server. Based on this information, the server creates a detailed user profile, which forms the basis for generating learning materials.

[0665] The server uses a generative AI model to analyze user profiles and generate optimized learning materials tailored to the individual needs of each learner. These materials are adapted to multiple formats, including visual, audio, and text-based materials, depending on the user's learning style. Furthermore, an emotion engine considers the user's emotional state during the material generation process. For example, if a user is experiencing stress, the server adjusts the difficulty level of the materials to provide a learner-friendly learning environment.

[0666] The device receives the generated learning materials and makes them easily accessible to the user. The user can progress through the learning materials via the device and send questions to the server as needed. The server uses an emotion engine to detect the user's emotions in real time and evaluates their comprehension based on this information. Based on this evaluation, the content and format of the learning materials are dynamically adjusted.

[0667] As a concrete example, if the system detects that the user is feeling anxious, it will provide an emotionally sensitive educational experience by increasing the amount of relaxing visual material or slowing down the learning pace. In this way, the system of the present invention can construct an optimal learning environment while taking into account the learner's mental and physical state.

[0668] Through this process, the present invention provides a system that goes beyond conventional uniform educational methods, offering personalized educational opportunities for each learner and achieving higher educational effectiveness.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The user uses a device to enter their learning objectives, interests, and current skill level. The device sends this information to the server, and the user profile is initially configured.

[0672] Step 2:

[0673] Based on the information received, the server creates a user profile using a generative AI model. This profile includes learning style, progress, and areas of interest, forming the basis for a personalized learning experience.

[0674] Step 3:

[0675] The server analyzes learning content based on user profiles and generates learning materials optimized for the learner. This process utilizes an emotion engine, taking into account user emotional data (e.g., stress levels). Difficulty levels are adjusted, and the materials are adapted to the learner's state.

[0676] Step 4:

[0677] The device receives generated learning materials sent from the server and prepares them to be made available to the user visually or audibly. For example, the colors and tone of the audio materials may be adjusted based on emotions.

[0678] Step 5:

[0679] Users progress through the learning process via their devices. The devices continuously monitor emotional data during learning and send it to the server as needed. The server uses this information to adjust learning materials and learning methods in real time.

[0680] Step 6:

[0681] Once the learning session is complete, the device collects feedback on the user's emotional state and level of understanding, and sends this feedback to the server. The server updates the user profile based on this feedback and uses it as data for the next learning session.

[0682] Step 7:

[0683] The server updates its profile based on feedback, aiming to improve the accuracy of learning materials for the next session. In this process, user emotional changes are given particular importance, and the direction for improving the quality of the learning experience is reviewed.

[0684] (Example 2)

[0685] 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".

[0686] Traditional education systems have struggled to dynamically adjust learning materials according to the individual needs and emotional states of learners, resulting in a decline in learning effectiveness due to the provision of uniform materials. Therefore, there is a need to provide an optimized educational experience for each learner and improve the quality of learning.

[0687] 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.

[0688] In this invention, the server includes means for inputting information on individual learners and creating educational data, means for generating prompt sentences using a generative AI model and generating educational content, and means for evaluating the learner's emotional state using an emotion analysis engine and adjusting the educational content based on that evaluation. This makes it possible to provide an optimal educational environment that is tailored to the individual needs and emotional state of each learner.

[0689] A "learner" is an individual who seeks to acquire knowledge and skills through educational materials using an educational system.

[0690] "Educational data" refers to datasets created based on learner information, reflecting individual needs and skill levels.

[0691] "Educational content" refers to learning materials that are optimized for learners using generative AI models and provided in various formats such as visual, audio, and text.

[0692] A "generative AI model" is an artificial intelligence model that generates text or data tailored to a specific purpose based on the input prompt text.

[0693] A "prompt statement" is an instruction given to a generative AI model, and is text used to request a specific output from the AI.

[0694] A "sentiment analysis engine" is a technology used to evaluate learners' emotional states in real time and to dynamically adjust educational content based on these evaluations.

[0695] Users first access the learning system using a terminal and input their learning goals, interests, and skill level. This input information is sent from the terminal to the server. The server receives the input information and generates detailed educational data. This educational data reflects the learner's needs and forms the basis for optimizing their individual learning experience.

[0696] Next, the server utilizes a generative AI model. The generative AI model generates prompt sentences based on educational data and inputs them into the model. These prompt sentences include specific instructions such as, "Generate intermediate-level English learning materials, taking stress reduction into consideration." Upon receiving this prompt, the AI ​​model generates educational content suitable for the learner. This content takes the form of text, audio, and visual materials, delivered according to the learner's style.

[0697] Furthermore, the system in use incorporates an emotion analysis engine, which the server utilizes to assess the learner's emotional state in real time. For example, if the server detects that a learner is experiencing stress, it adjusts the difficulty level of the educational content to provide a learner-friendly learning environment.

[0698] The device receives educational content from the server and displays it in a user-friendly format. Users can progress through their learning using this content, and if they have any questions, they can send them to the server via the device. The server uses a generative AI model to provide quick and appropriate answers to the received questions.

[0699] In this way, embodiments of the present invention realize a dynamic and personalized educational experience based on the learner's needs and emotional state, thereby improving educational effectiveness.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] Users access the learning system using a device and input information such as their learning goals, interests, and skill levels. This information is sent from the device to the server. The input data forms the basis for creating a user profile, thereby identifying the learner's needs.

[0703] Step 2:

[0704] The server generates educational data based on user information received from the terminal. In this process, the information is stored in a database, and a user profile is created. This profile clarifies the learner's needs and characteristics.

[0705] Step 3:

[0706] The server generates a prompt message for the AI ​​generation model. This prompt message might be in the format of, for example, "Please generate intermediate-level English learning materials, taking stress reduction into consideration." This prompt is sent to the AI ​​generation model, which uses the user profile to generate the most suitable educational content. The model performs data analysis based on the prompt and outputs appropriate learning materials.

[0707] Step 4:

[0708] The generated educational content is sent from the server to the device. The device receives this content and displays it in a format that is easily accessible to the user. At this point, the educational content is converted into text, audio, and visual formats and adapted to the learner's style.

[0709] Step 5:

[0710] The server uses an emotion analysis engine to evaluate the user's real-time emotional state. Based on the evaluation results, the server adjusts the difficulty level and format of the educational content. For example, if the user is feeling stressed, the difficulty level may be lowered and relaxing materials may be added.

[0711] Step 6:

[0712] Users progress through learning using educational content provided via their devices. If questions arise during learning, they can send them to the server via their devices. The server utilizes generative AI models to quickly generate answers and return them to the user. This enhances the user's understanding and provides an effective learning experience.

[0713] (Application Example 2)

[0714] 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".

[0715] Modern education demands that it provide instruction tailored to the individual needs and emotional states of each learner. However, traditional education systems fail to adequately consider learners' emotional states and learning styles, and remain limited to providing uniform teaching materials. As a result, learners experience stress and their learning effectiveness suffers.

[0716] 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.

[0717] In this invention, the server includes means for inputting information on individual learners and creating user profiles, means for analyzing learning content based on the user profiles and generating learning materials optimized for the learners, and means for evaluating the learners' emotional state in real time and adjusting the learning experience. This makes it possible to provide an individualized educational experience tailored to each learner and maximize learning effectiveness.

[0718] A "learner" is an individual who participates in an educational program with the aim of acquiring knowledge and skills.

[0719] "Information" refers to data about a learner's learning goals, interests, and skill level, used to create a user profile.

[0720] A "user profile" is a set of digital information created based on the individual learner's information, and serves as the foundational data for generating personalized learning materials.

[0721] "Learning content" refers to a collection of educational tasks and themes presented to learners in accordance with specific educational objectives.

[0722] "Learning materials" is a general term for materials and content provided to support learners' learning, and can take the form of digital or physical materials.

[0723] "Emotional state" refers to information that indicates a learner's mental and emotional condition and reactions, and is an important element in the learning process.

[0724] "Real-time" means that processes and operations are carried out within the same timeframe as real-world time.

[0725] "Adjustment" refers to the act of changing or modifying settings or content to conform to specific goals or standards.

[0726] "Generating educational materials" refers to the process of designing and creating necessary educational content based on the individual needs of learners.

[0727] The system that realizes this invention functions through interaction between the server, terminal, and user.

[0728] The server first receives information about each learner and creates a user profile. This information includes learning goals, interests, and skill levels. Next, the server uses a generative AI model to analyze this user profile and generate learning materials optimized for the learner. The materials are delivered in visual, audio, and text formats and are adapted to the learner's style.

[0729] The server further utilizes an emotion engine to assess the learner's real-time emotional state and instantly adjust the learning experience. For example, if it detects frustration, it can take measures to lower the difficulty level.

[0730] The terminal presents the generated learning materials to the learner and functions as an interface for progressing through the learning process. It also collects feedback based on the learner's understanding and sends it to the server. This enables continuous updates of user profiles and learning materials.

[0731] As a concrete example, suppose an elementary school student is learning math at home via a device. In this case, the learning materials generated by the server are used, and feedback is provided at appropriate times according to the student's level of understanding and emotional state.

[0732] Examples of prompts for a generative AI model:

[0733] "The user is showing signs of impatience. Please generate math learning materials with adjusted difficulty levels."

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

[0735] Step 1:

[0736] The server receives the user's initial information.

[0737] The input data consists of the learner's learning goals, interests, and skill level. Based on this data, the server initiates the process of creating a user profile. The data is stored in a database and used for subsequent analysis.

[0738] Step 2:

[0739] The server uses a generated AI model to analyze user profiles and create appropriate learning materials.

[0740] At the start of the process, the server prompts the AI ​​model with the message, "Generate learning content suitable for this profile." Based on this, the AI ​​model designs the content and difficulty level of the learning materials and outputs them as digital learning materials in visual, audio, and text formats.

[0741] Step 3:

[0742] The server uses an emotion engine to evaluate the learner's emotional state in real time.

[0743] The user's facial expressions and voice data are input into an emotion engine, which then identifies emotions such as stress and anxiety. This information is used to adjust the difficulty level of the learning materials and is recorded as output for updating the user profile in real time.

[0744] Step 4:

[0745] The device displays the generated learning materials to the user and starts a new learning session.

[0746] Upon receiving educational data from the server, the terminal provides it to the learner via a visual display and audio output. While providing the educational materials, the terminal records user input in real time and measures the level of understanding.

[0747] Step 5:

[0748] The device sends user feedback data to the server.

[0749] The device collects user activity history and comprehension assessments, and sends this information to the server. The server receives this feedback and updates the user profile and course materials as needed to prepare for the next session.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] A means of inputting information about individual learners and creating user profiles,

[0774] A means for analyzing learning content based on the user profile and generating learning materials optimized for the learner,

[0775] A means of sending generated learning materials and providing them in a format accessible to learners,

[0776] A means of evaluating learners' understanding and collecting feedback,

[0777] A means for updating user profiles and learning materials based on the aforementioned feedback,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, wherein the generated learning materials include multiple formats and are adjusted to suit the learner's learning style.

[0781] (Claim 3)

[0782] The system according to claim 1, comprising means for evaluating a learner's learning progress in real time and providing additional practice problems.

[0783] "Example 1"

[0784] (Claim 1)

[0785] A means of inputting individual learner data and creating person data,

[0786] A means for analyzing educational information based on the aforementioned person data and generating educational resources optimized for the learner,

[0787] Means for transmitting generated educational resources and providing them in a format usable by learners,

[0788] A means of using educational technology to monitor learners' progress, assess their understanding, and provide additional educational resources.

[0789] A means for updating person data and educational resources based on feedback data generated by the aforementioned educational technology,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, wherein the generated educational resources include multiple forms and are adjusted to suit the learner's learning style.

[0793] (Claim 3)

[0794] The system according to claim 1, comprising means for evaluating learners' educational progress in real time and providing additional practical training resources.

[0795] "Application Example 1"

[0796] (Claim 1)

[0797] A means of inputting information about individual learners and creating user profiles,

[0798] A means for analyzing learning content based on the user profile and generating learning materials optimized for the learner,

[0799] A means of sending generated learning materials and providing them in a format accessible to learners,

[0800] A means of evaluating learners' understanding and collecting feedback,

[0801] A means for updating user profiles and learning materials based on the aforementioned feedback,

[0802] A means for inputting health information and generating optimized health and rehabilitation content based on said information,

[0803] Means of providing health information through visual and auditory means,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the generated learning materials and health content include multiple formats and are adapted to the learner's and user's style.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for evaluating the progress of learners and users in real time and providing additional practice problems and health content.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] A means of inputting information on individual learners and creating educational data,

[0812] A means for analyzing learning content based on the aforementioned educational data and generating educational content optimized for the learner,

[0813] A means for generating prompt sentences using a generative AI model and generating the aforementioned educational content,

[0814] A means of transmitting generated educational content and providing it in a format accessible to learners,

[0815] A means of evaluating learners' emotional states using an emotion analysis engine and adjusting educational content based on that evaluation,

[0816] A means of evaluating learners' understanding and collecting feedback,

[0817] A means for updating educational data and educational content based on the aforementioned feedback,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the generated educational content includes multiple formats and is adjusted to suit the learner's learning style.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising means for evaluating a learner's learning progress in real time and providing additional practice problems, and means for an emotion analysis engine to dynamically adjust educational content in response to changes in the learner's emotions.

[0823] "Application example 2 when combining with an emotional engine"

[0824] (Claim 1)

[0825] A means of inputting information about individual learners and creating user profiles,

[0826] A means for analyzing learning content based on the user profile and generating learning materials optimized for the learner,

[0827] A means of sending generated learning materials and providing them in a format accessible to learners,

[0828] A means of evaluating learners' understanding and collecting feedback,

[0829] A means for updating user profiles and learning materials based on the aforementioned feedback,

[0830] A means of evaluating learners' emotional states in real time and adjusting their learning experience,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the generated learning materials include multiple formats and are adjusted to suit the learner's learning style.

[0834] (Claim 3)

[0835] The system according to claim 1, comprising means for evaluating a learner's learning progress in real time and providing additional practice problems. [Explanation of Symbols]

[0836] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting information about individual learners and creating user profiles, A means for analyzing learning content based on the user profile and generating learning materials optimized for the learner, A means of sending generated learning materials and providing them in a format accessible to learners, A means of evaluating learners' understanding and collecting feedback, A means for updating user profiles and learning materials based on the aforementioned feedback, A means for inputting health information and generating optimized health and rehabilitation content based on said information, Means of providing health information through visual and auditory means, A system that includes this.

2. The system according to claim 1, wherein the generated learning materials and health content include multiple formats and are adjusted to suit the learner's and user's style.

3. The system according to claim 1, comprising means for evaluating the progress of learners and users in real time and providing additional practice problems and health content.

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