system

Abstract

We provide the system. [Solution] A means for collecting and storing user interest data, learning history data, and environmental project planning data, A means of using a generation device that generates relevant educational information based on the user's interest data, A means of providing the generated educational information to an information terminal, A means of collecting input information from users, performing learning evaluations based on said input information, and providing feedback, A means to provide past success stories and information to support users in planning implementation for their environmental projects, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional environmental education system, it is difficult to provide appropriate content according to the interests and learning levels of users, and there is a problem that the efficiency of education and user participation cannot be improved. In addition, for users who want to participate in environmental projects, there is a lack of a function to provide appropriate support information, so the effect of improving environmental awareness through practical learning has not been fully exerted.

Means for Solving the Problems

[0005] This system collects user interest data and learning history data, and generates relevant educational content using a generative model based on this data. It then appropriately delivers the generated content to the user's device and provides learning evaluation and feedback based on user input data. Furthermore, it aims to improve knowledge retention and practical experience by retrieving and providing appropriate support information from the database for environmental projects planned by users.

[0006] "User" refers to an individual or organization that uses this system.

[0007] "Interest data" refers to information about areas and topics that users are particularly interested in.

[0008] "Learning history data" refers to records of the content and learning material that a user has previously accessed.

[0009] "Educational content" refers to interactive learning materials, quizzes, games, and other information provided to users for learning purposes.

[0010] A "generative model" refers to an AI-based model that automatically generates appropriate educational content based on specified conditions and input data.

[0011] "User terminal" refers to an electronic device (such as a smartphone, tablet, or computer) that a user uses to learn using this system.

[0012] "Input data" refers to the answers, feedback, and other information that users provide to the system.

[0013] "Learning assessment" refers to the process of measuring a user's learning progress and achievement level, and then making improvement suggestions based on those results.

[0014] "Feedback" refers to advice and information provided to users based on their learning assessments.

[0015] The "environmental project" refers to activities or undertakings aimed at environmental protection that are participated in or planned by users.

[0016] The "database" refers to a collection of information that systematically collects and stores the information necessary for the operation of the system and makes it searchable as needed.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of the data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of the data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of the data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of the data processing device and the headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of the data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of the data processing device and the robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example No. 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example No. 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a tagged 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), etc.

[0021] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system for effectively conducting environmental education, generating educational content tailored to individual users and performing learning assessments. The operation of the key programs constituting this system will be explained below.

[0039] First, users register their interests, environmental issues they are concerned with, and learning topics they wish to study. This information is transmitted to the system via their device and stored on the server. This creates the user's profile.

[0040] Next, the server analyzes the registered user's profile data and generates appropriate educational content based on their interests. This generation uses an AI-based generative model and includes interactive materials and quizzes designed to maximize the user's learning effectiveness.

[0041] The generated educational content is sent from the server to the terminal and provided to the user. The user uses these materials to progress with their learning. During the learning process, user responses and input data (e.g., quiz answers) are sent to the server in real time.

[0042] Next, the server performs a learning evaluation based on the received input data. This measures learning progress and achievement, and provides feedback on the next learning steps and improvement measures. This feedback is delivered to the user via the device.

[0043] In addition, the system has functions to support environmental projects planned by users. Specifically, when a user plans a project, the server retrieves past success stories and necessary information from the database and provides it to the user.

[0044] For example, if a user plans a "beach cleanup project," the server will provide information on effective implementation procedures and necessary materials based on data from similar projects conducted in the past, supporting the user's execution. In this way, the system integrates education and practical activities, contributing to raising users' environmental awareness.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users access the environmental education platform, select environmental topics of interest and desired learning themes, and register along with their personal information. This information is transmitted to the server via the device.

[0048] Step 2:

[0049] The server stores interest data and learning history data submitted by the user and analyzes this data using AI algorithms. This analysis identifies the information and interests that the user is seeking.

[0050] Step 3:

[0051] The server uses an AI-generated model based on the analysis results to generate user-optimized educational content. This content includes interactive learning materials, quizzes, and games to facilitate learning.

[0052] Step 4:

[0053] The server sends the generated educational content to the user's device. The content is provided in the format best suited to the user's device.

[0054] Step 5:

[0055] The terminal displays educational content received from the server to the user, allowing the user to proceed with their learning. Users can learn in various formats through interactive learning materials.

[0056] Step 6:

[0057] Users input their answers to quizzes and assignments presented during the learning process via their devices, and this data is transmitted to the server in real time.

[0058] Step 7:

[0059] The server analyzes user input data and performs a learning evaluation. Based on this evaluation, it measures the user's learning effectiveness and generates feedback for the next step.

[0060] Step 8:

[0061] Based on the evaluation results, the server creates appropriate feedback for the user and sends it to the user via their device. The feedback includes areas where learning needs improvement and recommended content.

[0062] Step 9:

[0063] If a user wants to plan an environmental project, they propose that plan to the server via their terminal.

[0064] Step 10:

[0065] The server searches the database based on the project plan proposed by the user, collecting similar projects and success stories. It then sends the relevant information to the terminal to support the user.

[0066] (Example 1)

[0067] 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."

[0068] Providing effective environmental education requires delivering appropriate educational content based on users' individual interests and past learning history. However, the current system struggles to generate content tailored to users' interests and to provide timely feedback. Furthermore, it is unable to provide appropriate support information in a timely manner when supporting environmental projects. This project aims to solve these problems.

[0069] 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.

[0070] In this invention, the server includes a device for collecting and storing user interest data and educational history data, a device for using a generation algorithm that generates relevant learning content based on the user interest data, and a device for distributing the generated learning content to the user device. This enables the generation of educational content based on individual user interests, the provision of rapid feedback, and the provision of support information for appropriate environmental projects.

[0071] "Interest data" refers to data that contains information about topics and themes that users are interested in.

[0072] "Educational history data" refers to data that records the education and learning that a user has received to date.

[0073] The term "device" refers to a system or equipment used to achieve a specific function.

[0074] A "generative algorithm" refers to a programmatic method or methodology for producing results based on specific input information.

[0075] A "user device" refers to a device that a user directly operates as an interface to receive information.

[0076] An "information source" refers to a database, system, or other entity that serves as a reference point when collecting data and information.

[0077] To effectively conduct environmental education, it is crucial to provide content tailored to the individual interests and concerns of each user. This system consists of information processing involving three parties: the server, the terminal, and the user. The roles and processes of each are described below.

[0078] Users first use a device to input their interests in environmental issues and topics they wish to learn about. This creates data on the user's interests. This data is sent to a server via the device and stored in a database, creating a detailed user profile.

[0079] The server analyzes stored interest data and educational history data, and uses a generative AI model (e.g., a general generative algorithm) to generate relevant educational content based on the prompt "Generate environmental education content based on the user's interests." This content includes interactive learning materials and quizzes, and is designed to maximize learning effectiveness.

[0080] The generated educational content is sent from the server to the device. The device receives this content and displays it so that the user can learn from it. The user can take quizzes or watch video learning materials.

[0081] Furthermore, the server collects and evaluates the user's learning progress in real time, including quiz answers and interactive operations. Based on the evaluation, suggestions for the next learning steps and improvement measures are created as feedback and sent to the terminal, presented in an easy-to-understand format for the user.

[0082] In addition, when users plan a new environmental project, the server searches the database for relevant past cases and provides effective implementation procedures and necessary information. For example, if a user plans a "community cleanup project," the server will present information including past successful cases and lists of materials. This feature makes it easier for users to create concrete action plans.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users input environmental issues and learning topics that align with their interests through their device. This input data forms a request to the server, representing specific topics that reflect the user's interests. This input data then forms the basis for subsequent profile formation.

[0086] Step 2:

[0087] The terminal sends the entered user data to the server. The server receives this data and stores it in a user-specific profile database. Data processing here includes classifying and tagging user interests, organizing profiles to prepare for future content generation.

[0088] Step 3:

[0089] The server generates prompt text using a generative AI model based on the user profile. Given the prompt "Generate environmental education content based on the user's interests," the generative model outputs appropriate educational content. The data calculations performed in this step include natural language processing and content generation algorithms.

[0090] Step 4:

[0091] The generated educational content is sent from the server to the terminal. The terminal converts the content to an appropriate display format and presents it to the user. Specific actions include adjusting the layout of the content on the terminal's interface.

[0092] Step 5:

[0093] Users learn from the presented content, progressing through quizzes and interactive learning materials. User actions and responses are collected on the device and form input data that is sent to the server.

[0094] Step 6:

[0095] The server analyzes user input data and evaluates learning progress and understanding. Based on this evaluation, it creates the next learning steps and feedback. Specific calculations include statistical analysis and the application of evaluation algorithms. The resulting feedback data indicates the optimal next steps for the user.

[0096] Step 7:

[0097] Evaluations and feedback are sent from the server to the user's device and displayed to the user. The user can then adjust and advance their learning based on this feedback. The feedback specifically outlines areas for improvement and recommendations for their learning strategy.

[0098] (Application Example 1)

[0099] 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."

[0100] Conventional environmental education systems fail to adequately provide educational programs tailored to the individual interests and learning progress of users, and also lack appropriate information regarding support for planning environmental projects. As a result, it has been difficult to stimulate users' motivation to learn and connect it to practical environmental activities. Furthermore, the lack of optimization of educational information according to the display format of user terminals has compromised user convenience.

[0101] 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.

[0102] In this invention, the server includes means for collecting and storing user interest data, learning history data, and environmental project planning data; means for using a generation device that generates relevant educational information based on the user interest data; and means for providing the generated educational information to an information terminal. This enables the provision of personalized, interactive educational programs and effective support for environmental project planning.

[0103] "User interest data" refers to information that specifically represents the interests and concerns that individual users have regarding particular environmental issues or themes.

[0104] "Learning history data" refers to a record of the learning activities a user has undertaken to date, showing the content learned, progress, and level of achievement.

[0105] "Environmental project planning data" refers to information related to environmental activities planned by users, including the project's objectives, plan details, necessary materials, and implementation schedule.

[0106] A "generation device" is a system element that uses AI technology to construct educational information based on user interest data.

[0107] "Information terminals" refer to devices that provide users with generated educational information visually or audibly, and include smartphones, smart glasses, and head-mounted displays.

[0108] "Past success stories" refer to information that demonstrates effective implementation methods and activity results from environmental projects carried out in the past, which current project planners can use as a reference.

[0109] A "data structure" refers to a format for organizing and storing information in an orderly manner, designed to enable efficient searching and extraction.

[0110] This invention is a system for efficiently conducting education on environmental themes of interest to users. Here, we describe an embodiment of a system that utilizes AI technology to provide optimal educational information to individual users.

[0111] First, users register their interests in environmental issues and themes, as well as their learning history, through their device. This information is sent to the server and stored as the user's profile. Based on this stored information, the server utilizes AI-based generative models to generate learning content tailored to each user. Specifically, models such as "OpenAI® GPT" are used in this generation process.

[0112] The generated educational content is sent from the server to the user's device. This device includes smartphones, smart glasses, and head-mounted displays, and the format is optimized for optimal display on any device. Users can then use this to autonomously progress through their learning.

[0113] Furthermore, the user's learning progress is transmitted to the server in real time for evaluation. Based on this evaluation, the server generates the next learning steps and feedback, which are then provided to the user via the device. This allows the user to understand their own learning progress and make necessary adjustments.

[0114] Furthermore, when a user plans a specific environmental project, the server extracts past success stories from its data structure and provides the information necessary for that project. This process allows the user to effectively manage the project.

[0115] For example, if a user registers that they want to learn about a "recycling campaign," the server will provide detailed educational materials on the history, technology, and success stories of recycling, and will also assist in planning local recycling events.

[0116] Examples of prompts for a generative AI model include the following:

[0117] "Please generate educational materials based on the user's registered interest topic, 'Recycling Campaign.' Focus particularly on the history, technology, and success stories of recycling."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server receives interest data and learning history data sent by the user and stores it in the data storage system. In this step, the user uses a terminal to input their areas of interest and past learning history and sends this information to the server. This forms the user's profile, which is then used to generate subsequent content.

[0121] Step 2:

[0122] The server uses stored profile data as input to generate personalized educational content using a generative AI model. In this process, the model constructs relevant educational information based on interest data and outputs it as learning material. The generated content includes interactive elements and is designed to easily capture the user's interest.

[0123] Step 3:

[0124] The generated educational content is sent from the server to the device. The device receives this content as input, optimizes it for the device's format, and displays it to the user. The user then uses this to begin learning and responds to the questions and quizzes included in the content.

[0125] Step 4:

[0126] User responses and input data from the device are sent to the server in real time. The server uses this data as input to perform learning evaluation. Using data analysis techniques, it evaluates the user's learning progress and generates feedback and the next learning steps based on the results.

[0127] Step 5:

[0128] The server sends the learning assessment results to the terminal. The terminal outputs feedback and provides it to the user. This allows the user to understand their learning progress and make adjustments as needed.

[0129] Step 6:

[0130] When a user plans an environmental project, they send a request for necessary information from their terminal to the server. The server refers to a database of past success stories, extracts project-related information as input, and sends it back to the terminal as output. This information helps in creating a concrete project implementation plan.

[0131] 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.

[0132] This invention provides an environmental education system that takes into account the user's emotional state. It uses an emotion engine to recognize emotions in real time and adjusts the presentation of educational content based on those emotions. The embodiments of this system are described in detail below.

[0133] First, the user accesses the environmental education platform and begins learning. The device has a built-in emotion engine that analyzes how the user feels about the content. This emotion engine can determine the user's emotional state based on multiple indicators, such as facial expressions, voice tone, and input speed.

[0134] When providing educational content, the server generates appropriate learning materials based on user interest data and learning history data. The generative model creates content tailored to each user's individual learning needs, but the emotion engine incorporates detected emotion data to dynamically adjust the difficulty level and format of the content.

[0135] For example, if a user expresses frustration with difficult content, the server analyzes the situation and either lowers the difficulty level or re-presents the content in a different format. Conversely, if the user shows excitement or enthusiasm, it can present more challenging tasks.

[0136] Furthermore, emotional data collected using the emotion engine is incorporated into the user's learning assessment. Based on this information, the server provides feedback to the user. This feedback may include suggestions for improving the user's emotional state, along with an assessment of their learning progress.

[0137] Furthermore, during the planning of environmental projects, the user's emotional state is taken into consideration, and support information appropriate for project execution is provided from the server. For example, if a user is feeling anxious, past success stories that are helpful for project implementation are presented to provide reassurance and support.

[0138] In this way, this system provides a personalized learning experience mediated by emotions, contributing to an improvement in users' environmental awareness.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] Users log in to the environmental education platform, select a topic of interest, and begin learning. The device's built-in camera and microphone activate to monitor the user's emotional state in real time.

[0142] Step 2:

[0143] The emotion engine built into the device detects emotions from the user's facial expressions and voice. This data, along with the user's interest data, is sent to the server as learning history data.

[0144] Step 3:

[0145] The server analyzes the transmitted emotion data and evaluates the user's current emotional state. Based on this evaluation, it uses a generative model to generate educational content tailored to the user.

[0146] Step 4:

[0147] The server delivers the generated content to the user's device. The content is adapted based on the user's emotional state, with adjustments made to difficulty level and media format.

[0148] Step 5:

[0149] The device displays the content provided to the user, and the user continues learning. The device continues to monitor using an emotion engine, detecting changes in emotions during learning.

[0150] Step 6:

[0151] The data entered by the user during the learning process (e.g., quiz answers) is sent back to the server. This includes information about emotional responses.

[0152] Step 7:

[0153] The server integrates the collected input data and sentiment data to perform learning evaluation. Based on the evaluation results, it identifies areas for improvement in future learning directions and content.

[0154] Step 8:

[0155] The server generates evaluation-based feedback for the user and sends it to the device. This feedback includes suggestions for improving emotional states and guidelines for the next learning steps.

[0156] Step 9:

[0157] If a user is working on planning an environmental project, they will propose the project details to the server via their terminal.

[0158] Step 10:

[0159] The server considers the user's current emotional state and retrieves project support information from the database, sending it to the terminal. For example, when the user is feeling anxious, it might present encouraging instructions and success stories.

[0160] (Example 2)

[0161] 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".

[0162] The problem that this invention aims to solve is to enhance learning effectiveness and improve the user's learning experience by providing personalized educational information that takes into account the user's emotional state. Furthermore, it aims to improve the success rate of environmental projects by providing support information that reflects the user's emotional state during project planning.

[0163] 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.

[0164] In this invention, the server includes means for analyzing the user's emotional state in real time and determining the emotion using facial expressions, voice tone, and input speed; means for dynamically adjusting the difficulty level and format of the educational content based on the determined emotional state; and means for generating personalized educational information using generative AI technology and providing the generated educational information to the user's terminal. This makes it possible to provide learning information that corresponds to each user's emotional state, thereby improving the quality of the learning experience.

[0165] A "user" refers to an individual who learns through this system, and their interests, preferences, and emotional state influence the system's operation.

[0166] "Emotional state" refers to the psychological condition analyzed in real time from the user's facial expressions, voice tone, input speed, etc., and is an important element in providing educational content.

[0167] "Educational information" refers to learning content generated based on the user's interests, preferences, and emotional state, and is dynamically adjusted to suit the learning objectives.

[0168] "Generative AI technology" refers to a technology that uses artificial intelligence to generate educational information optimized for each individual user, and its complex algorithms enable the provision of highly accurate information.

[0169] A "terminal" refers to an electronic device used by users to receive educational information or provide input information, and it is also a device that collects the user's emotional state via an emotion engine.

[0170] An "information aggregate" refers to a data pool that centrally manages information useful for planning environmental projects and is used to provide appropriate support information.

[0171] This invention is a system that provides personalized educational information that takes into account the user's emotional state. The following describes its specific embodiments.

[0172] Users access the environmental education platform and begin learning. During this process, the device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time. The device then uses facial expression analysis and voice recognition software to analyze the user's emotional state based on this data. Keyboard input speed is also monitored, which is used to determine emotions. This emotional data serves as an important indicator and is used to personalize educational information.

[0173] Next, the server takes on the role of generating educational information based on the user's interests, learning history, and emotional state. Utilizing a generative AI model, it instructs the AI ​​using prompts such as, "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in," thereby creating optimized information. This prompt generates dynamically adapted learning content, which is then provided to the user.

[0174] The generated educational information is then sent to the user's device and presented in an appropriate format. During this process, the difficulty level and format of the information are adjusted based on the user's emotional state. For example, if the emotional state is "frustration," the educational content will be made more concise and visually easier to understand.

[0175] Furthermore, after a learning session ends, the server has the functionality to evaluate and provide feedback based on the user's performance and emotional data. This allows users to understand their own learning process and receive necessary adjustments and advice for the next steps. The process of providing educational information and feedback aims to improve the user's learning experience and is a new learning method that combines dynamic emotional evaluation with information generation.

[0176] Furthermore, the terminal is also useful when planning environmental projects, providing users with supportive information tailored to their needs based on the collected data. In this way, the system of the present invention helps to promote improved environmental awareness and maintain motivation to learn by providing users with an appropriate educational experience based on their emotions.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The user logs into the environmental education platform and launches a learning module. During this process, the user's basic information is entered into the terminal. The terminal uses its camera and microphone to capture emotional data such as facial expressions and voice tone, and transmits this data to the emotion engine in real time. The emotion engine uses facial expression analysis software and voice recognition software to analyze the user's emotional state based on the emotional data. The output is a numerical representation of the user's emotional state.

[0180] Step 2:

[0181] The server receives the user's emotional state, interest information, and learning history information obtained in the previous step. Based on this input data, the generative AI model operates and sets the prompt to "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in." By inputting this prompt into the generative AI model, it generates educational information optimized for each individual user. As output, users receive customized educational information tailored to their needs.

[0182] Step 3:

[0183] The generated educational information is sent from the server to the terminal and presented to the user. Here, the terminal adjusts the information format and visual elements to suit each user's characteristics. For example, if a user expresses anxiety, the information is output in a simpler, more visually appealing format. As output, visually optimized educational information is provided to the user.

[0184] Step 4:

[0185] Once a learning session ends, the server collects the user's learning performance data and emotional state data. This data is then analyzed to comprehensively evaluate the user's learning outcomes and provide feedback. For example, advice such as, "Your progress is good. Let's delve deeper into this topic next," might be generated. Detailed learning feedback is then provided to the user as output.

[0186] Step 5:

[0187] When a user is planning an environmental project, the device retrieves support information from the server based on past emotional data and learning history to help the project succeed. The prompt message used is "Please provide examples of successful projects that the user has been interested in in the past." Using this information, if the user is feeling anxious, specific examples that provide reassurance are presented. Appropriate support information is then provided to the user as output.

[0188] (Application Example 2)

[0189] 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."

[0190] Traditional education systems typically provided uniform content regardless of the user's emotional state. This made it difficult to achieve efficient education tailored to individual users' emotional states and learning paces, resulting in limited learning effectiveness. Furthermore, the lack of adequate feedback and support information that considered users' emotions left improving user motivation a challenge.

[0191] 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.

[0192] In this invention, the server includes an emotion analysis engine for detecting the user's emotional state, the emotion analysis engine includes means for identifying the emotional state using facial expression data and voice data, means for dynamically adjusting the presentation of educational content based on the user's emotional state, and means for using a generative model that generates relevant educational content based on the user's interest data and learning history data. This makes it possible to provide personalized educational content that responds to the user's emotions, thereby improving learning efficiency and maintaining motivation.

[0193] An "emotion analysis engine" is a technology used to analyze a user's facial expression and voice data in real time to identify their current emotional state.

[0194] "Educational content" refers to all information and learning materials provided to users for learning purposes, and includes a variety of formats such as text, videos, and quizzes.

[0195] "User interest data" refers to information that shows what fields and topics users are interested in, based on their past behavior and preferences.

[0196] "Learning history data" refers to data that records what educational content and assignments a user has worked on in the past.

[0197] A "generative model" is an algorithm or framework for generating new content based on input data.

[0198] The system for implementing this invention mainly consists of a server, a user terminal, and a communication network. The server is equipped with an emotion analysis engine, which analyzes facial expression data and voice data sent from the user terminal. Specifically, it identifies the user's emotional state in real time based on this data acquired by the camera and microphone. Based on the results, the server uses a generative AI model to generate appropriate educational content and dynamically creates content that best matches the user's interest data and learning history data.

[0199] On the user's device, generated educational content is provided, with the difficulty level and format adjusted according to their emotional state. This ensures an optimal learning experience for the user. The server also collects user input data and provides appropriate feedback. For example, if a user shows little interest or concentration on a particular topic, the content is modified and re-presented.

[0200] For example, if a user wants to relax, the system can provide relaxing content, and conversely, if they want to be active, it can present active learning materials. Examples of prompts include, "If the user's facial expression and voice indicate relaxation, recommend relaxing content," or "Suggest a video that would be effective for a user who is feeling stressed." Through such prompts, the system can provide education that flexibly responds to the user's emotions.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The user's device captures facial and voice data through its camera and microphone. This data is sent to the server in real time. Based on this input, the server prepares its emotion analysis engine to identify the user's emotions based on their current facial expressions and voice tone.

[0204] Step 2:

[0205] The server uses an emotion analysis engine to analyze facial and voice data sent by the user. Based on the input data, it analyzes facial features and voice tone, performs data calculations, and identifies the user's emotional state. As a result of the analysis, an emotion label such as "relaxed" or "stressed" is output.

[0206] Step 3:

[0207] The server references user interest data and learning history data, and uses a generative AI model to generate educational content best suited to the user. Using emotion labels such as "relax" and "stress" as input, the generative AI model dynamically generates content; specifically, relaxation videos are selected for relaxation, and relaxation music for stress.

[0208] Step 4:

[0209] The server optimizes and sends the generated educational content to the user's device. In this step, the content format is adjusted to suit the device, for example, the resolution and sound quality of videos are adapted. This allows the user's device to display the received content smoothly.

[0210] Step 5:

[0211] Users view the content they receive and provide feedback. This feedback data is sent back to the server and used to update the user's emotional state and interests. Specifically, content to which users respond positively is recorded.

[0212] Step 6:

[0213] The server uses the feedback data to generate the next educational content. This information is added as learning history, improving the accuracy of future content generation and providing users with a more personalized experience.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention is a system for effectively conducting environmental education, generating educational content tailored to individual users and performing learning assessments. The operation of the key programs constituting this system will be explained below.

[0231] First, users register their interests, environmental issues they are concerned with, and learning topics they wish to study. This information is transmitted to the system via their device and stored on the server. This creates the user's profile.

[0232] Next, the server analyzes the registered user's profile data and generates appropriate educational content based on their interests. This generation uses an AI-based generative model and includes interactive materials and quizzes designed to maximize the user's learning effectiveness.

[0233] The generated educational content is sent from the server to the terminal and provided to the user. The user uses these materials to progress with their learning. During the learning process, user responses and input data (e.g., quiz answers) are sent to the server in real time.

[0234] Next, the server performs a learning evaluation based on the received input data. This measures learning progress and achievement, and provides feedback on the next learning steps and improvement measures. This feedback is delivered to the user via the device.

[0235] In addition, the system has functions to support environmental projects planned by users. Specifically, when a user plans a project, the server retrieves past success stories and necessary information from the database and provides it to the user.

[0236] For example, if a user plans a "beach cleanup project," the server will provide information on effective implementation procedures and necessary materials based on data from similar projects conducted in the past, supporting the user's execution. In this way, the system integrates education and practical activities, contributing to raising users' environmental awareness.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] Users access the environmental education platform, select environmental topics of interest and desired learning themes, and register along with their personal information. This information is transmitted to the server via the device.

[0240] Step 2:

[0241] The server stores interest data and learning history data submitted by the user and analyzes this data using AI algorithms. This analysis identifies the information and interests that the user is seeking.

[0242] Step 3:

[0243] The server uses an AI-generated model based on the analysis results to generate user-optimized educational content. This content includes interactive learning materials, quizzes, and games to facilitate learning.

[0244] Step 4:

[0245] The server sends the generated educational content to the user's device. The content is provided in the format best suited to the user's device.

[0246] Step 5:

[0247] The terminal displays educational content received from the server to the user, allowing the user to proceed with their learning. Users can learn in various formats through interactive learning materials.

[0248] Step 6:

[0249] Users input their answers to quizzes and assignments presented during the learning process via their devices, and this data is transmitted to the server in real time.

[0250] Step 7:

[0251] The server analyzes user input data and performs a learning evaluation. Based on this evaluation, it measures the user's learning effectiveness and generates feedback for the next step.

[0252] Step 8:

[0253] Based on the evaluation results, the server creates appropriate feedback for the user and sends it to the user via their device. The feedback includes areas where learning needs improvement and recommended content.

[0254] Step 9:

[0255] If a user wants to plan an environmental project, they propose that plan to the server via their terminal.

[0256] Step 10:

[0257] The server searches the database based on the project plan proposed by the user, collecting similar projects and success stories. It then sends the relevant information to the terminal to support the user.

[0258] (Example 1)

[0259] 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."

[0260] Providing effective environmental education requires delivering appropriate educational content based on users' individual interests and past learning history. However, the current system struggles to generate content tailored to users' interests and to provide timely feedback. Furthermore, it is unable to provide appropriate support information in a timely manner when supporting environmental projects. This project aims to solve these problems.

[0261] 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.

[0262] In this invention, the server includes a device for collecting and storing user interest data and educational history data, a device for using a generation algorithm that generates relevant learning content based on the user interest data, and a device for distributing the generated learning content to the user device. This enables the generation of educational content based on individual user interests, the provision of rapid feedback, and the provision of support information for appropriate environmental projects.

[0263] "Interest data" refers to data that contains information about topics and themes that users are interested in.

[0264] "Educational history data" refers to data that records the education and learning that a user has received to date.

[0265] The term "device" refers to a system or equipment used to achieve a specific function.

[0266] A "generative algorithm" refers to a programmatic method or methodology for producing results based on specific input information.

[0267] A "user device" refers to a device that a user directly operates as an interface to receive information.

[0268] An "information source" refers to a database, system, or other entity that serves as a reference point when collecting data and information.

[0269] To effectively conduct environmental education, it is crucial to provide content tailored to the individual interests and concerns of each user. This system consists of information processing involving three parties: the server, the terminal, and the user. The roles and processes of each are described below.

[0270] Users first use a device to input their interests in environmental issues and topics they wish to learn about. This creates data on the user's interests. This data is sent to a server via the device and stored in a database, creating a detailed user profile.

[0271] The server analyzes stored interest data and educational history data, and uses a generative AI model (e.g., a general generative algorithm) to generate relevant educational content based on the prompt "Generate environmental education content based on the user's interests." This content includes interactive learning materials and quizzes, and is designed to maximize learning effectiveness.

[0272] The generated educational content is sent from the server to the device. The device receives this content and displays it so that the user can learn from it. The user can take quizzes or watch video learning materials.

[0273] Furthermore, the server collects and evaluates the user's learning progress in real time, including quiz answers and interactive operations. Based on the evaluation, suggestions for the next learning steps and improvement measures are created as feedback and sent to the terminal, presented in an easy-to-understand format for the user.

[0274] In addition, when users plan a new environmental project, the server searches the database for relevant past cases and provides effective implementation procedures and necessary information. For example, if a user plans a "community cleanup project," the server will present information including past successful cases and lists of materials. This feature makes it easier for users to create concrete action plans.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user inputs environmental problems or themes to be learned according to their interests through the terminal. The input data constitutes a request to the server as a specific topic indicating the user's interests. This input data serves as the basis for subsequent profile formation.

[0278] Step 2:

[0279] The terminal sends the input user data to the server. The server receives this data and stores it in the user-specific profile database. The data processing here includes the classification and tagging of the user's interests, and by organizing the profile, it prepares for future content generation.

[0280] Step 3:

[0281] The server uses the AI model generated based on the user profile to generate a prompt sentence. With a prompt such as "Please generate environmental education content based on the user's interests" as the input, the generation model outputs appropriate educational content. The data operations performed in this step include natural language processing and content generation algorithms.

[0282] Step 4:

[0283] The generated educational content is sent from the server to the terminal. The terminal appropriately converts the content into a display format and presents it to the user. Specific operations include adjusting the layout of the content in the interface on the terminal.

[0284] Step 5:

[0285] The user learns the presented content and advances the learning through answering quizzes and using interactive teaching materials. The user's operations and answers are collected on the terminal and form the input data sent to the server.

[0286] Step 6:

[0287] The server analyzes the input data from the user and evaluates the learning progress and understanding level. Based on this evaluation, the next learning steps and feedback are created. Specific operations include statistical analysis and the application of evaluation algorithms. The resulting feedback data indicates the optimal next steps for the user.

[0288] Step 7:

[0289] The evaluation and feedback are sent from the server to the terminal and displayed to the user. The user can further adjust and proceed with learning based on this feedback. The feedback specifically shows areas for improvement and recommendations for the learning strategy.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In a conventional environmental education system, the provision of educational programs according to the individual interests and learning progress of users is insufficient, and appropriate information provision regarding the planning support of environmental projects is not carried out. As a result, it has been difficult to stimulate the learning motivation of users and connect it to actual environmental activities. Furthermore, due to the lack of optimization of educational information according to the display format of information on the user terminal, the convenience for users has been impaired.

[0293] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0294] In this invention, the server includes means for collecting and storing user interest data, learning history data, and environmental project planning data; means for using a generation device that generates relevant educational information based on the user interest data; and means for providing the generated educational information to an information terminal. This enables the provision of personalized, interactive educational programs and effective support for environmental project planning.

[0295] "User interest data" refers to information that specifically represents the interests and concerns that individual users have regarding particular environmental issues or themes.

[0296] "Learning history data" refers to a record of the learning activities a user has undertaken to date, showing the content learned, progress, and level of achievement.

[0297] "Environmental project planning data" refers to information related to environmental activities planned by users, including the project's objectives, plan details, necessary materials, and implementation schedule.

[0298] A "generation device" is a system element that uses AI technology to construct educational information based on user interest data.

[0299] "Information terminals" refer to devices that provide users with generated educational information visually or audibly, and include smartphones, smart glasses, and head-mounted displays.

[0300] "Past success stories" refer to information that demonstrates effective implementation methods and activity results from environmental projects carried out in the past, which current project planners can use as a reference.

[0301] A "data structure" refers to a format for organizing and storing information in an orderly manner, designed to enable efficient searching and extraction.

[0302] This invention is a system for efficiently implementing education in environmental themes that users are interested in. Here, an embodiment of a system for utilizing AI technology to provide optimal educational information to individual users is shown.

[0303] First, the user registers their environmental problems, themes of interest, and learning history with the system through a terminal. This information is sent to the server and stored as the user's profile. Based on the stored information, the server utilizes an AI-based generation model to generate learning content suitable for each user. Specifically, models such as "OpenAI GPT" are used in this generation process.

[0304] The generated educational content is sent from the server to the user's terminal. The terminal includes smartphones, smart glasses, and head-mounted displays, and the format is adjusted so that the information is optimally displayed on any device. The user can use this to autonomously proceed with learning.

[0305] Furthermore, the user's learning status is sent to the server in real-time for learning evaluation. Based on this evaluation, the server generates the next learning steps and feedback and provides them to the user through the terminal. This enables the user to grasp their learning progress and make necessary adjustments.

[0306] Also, when the user plans a specific environmental project, the server extracts past success cases from the data structure and provides the information necessary for the project. Through this process, the user can effectively proceed with the project.

[0307] For example, when the user registers an interest in learning about a "recycling campaign", the server provides detailed teaching materials on the history, technology, and success cases of recycling and also provides support when planning recycling events in the region.

[0308] Examples of prompts for a generative AI model include the following:

[0309] "Please generate educational materials based on the user's registered interest topic, 'Recycling Campaign.' Focus particularly on the history, technology, and success stories of recycling."

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] The server receives interest data and learning history data sent by the user and stores it in the data storage system. In this step, the user uses a terminal to input their areas of interest and past learning history and sends this information to the server. This forms the user's profile, which is then used to generate subsequent content.

[0313] Step 2:

[0314] The server uses stored profile data as input to generate personalized educational content using a generative AI model. In this process, the model constructs relevant educational information based on interest data and outputs it as learning material. The generated content includes interactive elements and is designed to easily capture the user's interest.

[0315] Step 3:

[0316] The generated educational content is sent from the server to the device. The device receives this content as input, optimizes it for the device's format, and displays it to the user. The user then uses this to begin learning and responds to the questions and quizzes included in the content.

[0317] Step 4:

[0318] User responses and input data from the device are sent to the server in real time. The server uses this data as input to perform learning evaluation. Using data analysis techniques, it evaluates the user's learning progress and generates feedback and the next learning steps based on the results.

[0319] Step 5:

[0320] The server sends the learning assessment results to the terminal. The terminal outputs feedback and provides it to the user. This allows the user to understand their learning progress and make adjustments as needed.

[0321] Step 6:

[0322] When a user plans an environmental project, they send a request for necessary information from their terminal to the server. The server refers to a database of past success stories, extracts project-related information as input, and sends it back to the terminal as output. This information helps in creating a concrete project implementation plan.

[0323] 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.

[0324] This invention provides an environmental education system that takes into account the user's emotional state. It uses an emotion engine to recognize emotions in real time and adjusts the presentation of educational content based on those emotions. The embodiments of this system are described in detail below.

[0325] First, the user accesses the environmental education platform and begins learning. The device has a built-in emotion engine that analyzes how the user feels about the content. This emotion engine can determine the user's emotional state based on multiple indicators, such as facial expressions, voice tone, and input speed.

[0326] When providing educational content, the server generates appropriate learning materials based on user interest data and learning history data. The generative model creates content tailored to each user's individual learning needs, but the emotion engine incorporates detected emotion data to dynamically adjust the difficulty level and format of the content.

[0327] For example, if a user expresses frustration with difficult content, the server analyzes the situation and either lowers the difficulty level or re-presents the content in a different format. Conversely, if the user shows excitement or enthusiasm, it can present more challenging tasks.

[0328] Furthermore, emotional data collected using the emotion engine is incorporated into the user's learning assessment. Based on this information, the server provides feedback to the user. This feedback may include suggestions for improving the user's emotional state, along with an assessment of their learning progress.

[0329] Furthermore, during the planning of environmental projects, the user's emotional state is taken into consideration, and support information appropriate for project execution is provided from the server. For example, if a user is feeling anxious, past success stories that are helpful for project implementation are presented to provide reassurance and support.

[0330] In this way, this system provides a personalized learning experience mediated by emotions, contributing to an improvement in users' environmental awareness.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] Users log in to the environmental education platform, select a topic of interest, and begin learning. The device's built-in camera and microphone activate to monitor the user's emotional state in real time.

[0334] Step 2:

[0335] The emotion engine built into the device detects emotions from the user's facial expressions and voice. This data, along with the user's interest data, is sent to the server as learning history data.

[0336] Step 3:

[0337] The server analyzes the transmitted emotion data and evaluates the user's current emotional state. Based on this evaluation, it uses a generative model to generate educational content tailored to the user.

[0338] Step 4:

[0339] The server delivers the generated content to the user's device. The content is adapted based on the user's emotional state, with adjustments made to difficulty level and media format.

[0340] Step 5:

[0341] The device displays the content provided to the user, and the user continues learning. The device continues to monitor using an emotion engine, detecting changes in emotions during learning.

[0342] Step 6:

[0343] The data entered by the user during the learning process (e.g., quiz answers) is sent back to the server. This includes information about emotional responses.

[0344] Step 7:

[0345] The server integrates the collected input data and sentiment data to perform learning evaluation. Based on the evaluation results, it identifies areas for improvement in future learning directions and content.

[0346] Step 8:

[0347] The server generates evaluation-based feedback for the user and sends it to the device. This feedback includes suggestions for improving emotional states and guidelines for the next learning steps.

[0348] Step 9:

[0349] If a user is working on planning an environmental project, they will propose the project details to the server via their terminal.

[0350] Step 10:

[0351] The server considers the user's current emotional state and retrieves project support information from the database, sending it to the terminal. For example, when the user is feeling anxious, it might present encouraging instructions and success stories.

[0352] (Example 2)

[0353] 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".

[0354] The problem that this invention aims to solve is to enhance learning effectiveness and improve the user's learning experience by providing personalized educational information that takes into account the user's emotional state. Furthermore, it aims to improve the success rate of environmental projects by providing support information that reflects the user's emotional state during project planning.

[0355] 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.

[0356] In this invention, the server includes means for analyzing the user's emotional state in real time and determining the emotion using facial expressions, voice tone, and input speed; means for dynamically adjusting the difficulty level and format of the educational content based on the determined emotional state; and means for generating personalized educational information using generative AI technology and providing the generated educational information to the user's terminal. This makes it possible to provide learning information that corresponds to each user's emotional state, thereby improving the quality of the learning experience.

[0357] A "user" refers to an individual who learns through this system, and their interests, preferences, and emotional state influence the system's operation.

[0358] "Emotional state" refers to the psychological condition analyzed in real time from the user's facial expressions, voice tone, input speed, etc., and is an important element in providing educational content.

[0359] "Educational information" refers to learning content generated based on the user's interests, preferences, and emotional state, and is dynamically adjusted to suit the learning objectives.

[0360] "Generative AI technology" refers to a technology that uses artificial intelligence to generate educational information optimized for each individual user, and its complex algorithms enable the provision of highly accurate information.

[0361] A "terminal" refers to an electronic device used by users to receive educational information or provide input information, and it is also a device that collects the user's emotional state via an emotion engine.

[0362] An "information aggregate" refers to a data pool that centrally manages information useful for planning environmental projects and is used to provide appropriate support information.

[0363] This invention is a system that provides personalized educational information that takes into account the user's emotional state. The following describes its specific embodiments.

[0364] Users access the environmental education platform and begin learning. During this process, the device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time. The device then uses facial expression analysis and voice recognition software to analyze the user's emotional state based on this data. Keyboard input speed is also monitored, which is used to determine emotions. This emotional data serves as an important indicator and is used to personalize educational information.

[0365] Next, the server takes on the role of generating educational information based on the user's interests, learning history, and emotional state. Utilizing a generative AI model, it instructs the AI ​​using prompts such as, "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in," thereby creating optimized information. This prompt generates dynamically adapted learning content, which is then provided to the user.

[0366] The generated educational information is then sent to the user's device and presented in an appropriate format. During this process, the difficulty level and format of the information are adjusted based on the user's emotional state. For example, if the emotional state is "frustration," the educational content will be made more concise and visually easier to understand.

[0367] Furthermore, after a learning session ends, the server has the functionality to evaluate and provide feedback based on the user's performance and emotional data. This allows users to understand their own learning process and receive necessary adjustments and advice for the next steps. The process of providing educational information and feedback aims to improve the user's learning experience and is a new learning method that combines dynamic emotional evaluation with information generation.

[0368] Furthermore, the terminal is also useful when planning environmental projects, providing users with supportive information tailored to their needs based on the collected data. In this way, the system of the present invention helps to promote improved environmental awareness and maintain motivation to learn by providing users with an appropriate educational experience based on their emotions.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The user logs into the environmental education platform and launches a learning module. During this process, the user's basic information is entered into the terminal. The terminal uses its camera and microphone to capture emotional data such as facial expressions and voice tone, and transmits this data to the emotion engine in real time. The emotion engine uses facial expression analysis software and voice recognition software to analyze the user's emotional state based on the emotional data. The output is a numerical representation of the user's emotional state.

[0372] Step 2:

[0373] The server receives the user's emotional state, interest information, and learning history information obtained in the previous step. Based on this input data, the generative AI model operates and sets the prompt to "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in." By inputting this prompt into the generative AI model, it generates educational information optimized for each individual user. As output, users receive customized educational information tailored to their needs.

[0374] Step 3:

[0375] The generated educational information is sent from the server to the terminal and presented to the user. Here, the terminal adjusts the information format and visual elements to suit each user's characteristics. For example, if a user expresses anxiety, the information is output in a simpler, more visually appealing format. As output, visually optimized educational information is provided to the user.

[0376] Step 4:

[0377] Once a learning session ends, the server collects the user's learning performance data and emotional state data. This data is then analyzed to comprehensively evaluate the user's learning outcomes and provide feedback. For example, advice such as, "Your progress is good. Let's delve deeper into this topic next," might be generated. Detailed learning feedback is then provided to the user as output.

[0378] Step 5:

[0379] When a user is planning an environmental project, the device retrieves support information from the server based on past emotional data and learning history to help the project succeed. The prompt message used is "Please provide examples of successful projects that the user has been interested in in the past." Using this information, if the user is feeling anxious, specific examples that provide reassurance are presented. Appropriate support information is then provided to the user as output.

[0380] (Application Example 2)

[0381] 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 as the "terminal".

[0382] Traditional education systems typically provided uniform content regardless of the user's emotional state. This made it difficult to achieve efficient education tailored to individual users' emotional states and learning paces, resulting in limited learning effectiveness. Furthermore, the lack of adequate feedback and support information that considered users' emotions left improving user motivation a challenge.

[0383] 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.

[0384] In this invention, the server includes an emotion analysis engine for detecting the user's emotional state, the emotion analysis engine includes means for identifying the emotional state using facial expression data and voice data, means for dynamically adjusting the presentation of educational content based on the user's emotional state, and means for using a generative model that generates relevant educational content based on the user's interest data and learning history data. This makes it possible to provide personalized educational content that responds to the user's emotions, thereby improving learning efficiency and maintaining motivation.

[0385] An "emotion analysis engine" is a technology used to analyze a user's facial expression and voice data in real time to identify their current emotional state.

[0386] "Educational content" refers to all information and learning materials provided to users for learning purposes, and includes a variety of formats such as text, videos, and quizzes.

[0387] "User interest data" refers to information that shows what fields and topics users are interested in, based on their past behavior and preferences.

[0388] "Learning history data" refers to data that records what educational content and assignments a user has worked on in the past.

[0389] A "generative model" is an algorithm or framework for generating new content based on input data.

[0390] The system for implementing this invention mainly consists of a server, a user terminal, and a communication network. The server is equipped with an emotion analysis engine, which analyzes facial expression data and voice data sent from the user terminal. Specifically, it identifies the user's emotional state in real time based on this data acquired by the camera and microphone. Based on the results, the server uses a generative AI model to generate appropriate educational content and dynamically creates content that best matches the user's interest data and learning history data.

[0391] On the user's device, generated educational content is provided, with the difficulty level and format adjusted according to their emotional state. This ensures an optimal learning experience for the user. The server also collects user input data and provides appropriate feedback. For example, if a user shows little interest or concentration on a particular topic, the content is modified and re-presented.

[0392] For example, if a user wants to relax, the system can provide relaxing content, and conversely, if they want to be active, it can present active learning materials. Examples of prompts include, "If the user's facial expression and voice indicate relaxation, recommend relaxing content," or "Suggest a video that would be effective for a user who is feeling stressed." Through such prompts, the system can provide education that flexibly responds to the user's emotions.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The user's device captures facial and voice data through its camera and microphone. This data is sent to the server in real time. Based on this input, the server prepares its emotion analysis engine to identify the user's emotions based on their current facial expressions and voice tone.

[0396] Step 2:

[0397] The server uses an emotion analysis engine to analyze facial and voice data sent by the user. Based on the input data, it analyzes facial features and voice tone, performs data calculations, and identifies the user's emotional state. As a result of the analysis, an emotion label such as "relaxed" or "stressed" is output.

[0398] Step 3:

[0399] The server references user interest data and learning history data, and uses a generative AI model to generate educational content best suited to the user. Using emotion labels such as "relax" and "stress" as input, the generative AI model dynamically generates content; specifically, relaxation videos are selected for relaxation, and relaxation music for stress.

[0400] Step 4:

[0401] The server optimizes and sends the generated educational content to the user's device. In this step, the content format is adjusted to suit the device, for example, the resolution and sound quality of videos are adapted. This allows the user's device to display the received content smoothly.

[0402] Step 5:

[0403] Users view the content they receive and provide feedback. This feedback data is sent back to the server and used to update the user's emotional state and interests. Specifically, content to which users respond positively is recorded.

[0404] Step 6:

[0405] The server uses the feedback data to generate the next educational content. This information is added as learning history, improving the accuracy of future content generation and providing users with a more personalized experience.

[0406] 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.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] This invention is a system for effectively conducting environmental education, generating educational content tailored to individual users and performing learning assessments. The operation of the key programs constituting this system will be explained below.

[0423] First, users register their interests, environmental issues they are concerned with, and learning topics they wish to study. This information is transmitted to the system via their device and stored on the server. This creates the user's profile.

[0424] Next, the server analyzes the registered user's profile data and generates appropriate educational content based on their interests. This generation uses an AI-based generative model and includes interactive materials and quizzes designed to maximize the user's learning effectiveness.

[0425] The generated educational content is sent from the server to the terminal and provided to the user. The user uses these materials to progress with their learning. During the learning process, user responses and input data (e.g., quiz answers) are sent to the server in real time.

[0426] Next, the server performs a learning evaluation based on the received input data. This measures learning progress and achievement, and provides feedback on the next learning steps and improvement measures. This feedback is delivered to the user via the device.

[0427] In addition, the system has functions to support environmental projects planned by users. Specifically, when a user plans a project, the server retrieves past success stories and necessary information from the database and provides it to the user.

[0428] For example, if a user plans a "beach cleanup project," the server will provide information on effective implementation procedures and necessary materials based on data from similar projects conducted in the past, supporting the user's execution. In this way, the system integrates education and practical activities, contributing to raising users' environmental awareness.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] Users access the environmental education platform, select environmental topics of interest and desired learning themes, and register along with their personal information. This information is transmitted to the server via the device.

[0432] Step 2:

[0433] The server stores interest data and learning history data submitted by the user and analyzes this data using AI algorithms. This analysis identifies the information and interests that the user is seeking.

[0434] Step 3:

[0435] The server uses an AI-generated model based on the analysis results to generate user-optimized educational content. This content includes interactive learning materials, quizzes, and games to facilitate learning.

[0436] Step 4:

[0437] The server sends the generated educational content to the user's device. The content is provided in the format best suited to the user's device.

[0438] Step 5:

[0439] The terminal displays educational content received from the server to the user, allowing the user to proceed with their learning. Users can learn in various formats through interactive learning materials.

[0440] Step 6:

[0441] Users input their answers to quizzes and assignments presented during the learning process via their devices, and this data is transmitted to the server in real time.

[0442] Step 7:

[0443] The server analyzes user input data and performs a learning evaluation. Based on this evaluation, it measures the user's learning effectiveness and generates feedback for the next step.

[0444] Step 8:

[0445] Based on the evaluation results, the server creates appropriate feedback for the user and sends it to the user via their device. The feedback includes areas where learning needs improvement and recommended content.

[0446] Step 9:

[0447] If a user wants to plan an environmental project, they propose that plan to the server via their terminal.

[0448] Step 10:

[0449] The server searches the database based on the project plan proposed by the user, collecting similar projects and success stories. It then sends the relevant information to the terminal to support the user.

[0450] (Example 1)

[0451] 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."

[0452] Providing effective environmental education requires delivering appropriate educational content based on users' individual interests and past learning history. However, the current system struggles to generate content tailored to users' interests and to provide timely feedback. Furthermore, it is unable to provide appropriate support information in a timely manner when supporting environmental projects. This project aims to solve these problems.

[0453] 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.

[0454] In this invention, the server includes a device for collecting and storing user interest data and educational history data, a device for using a generation algorithm that generates relevant learning content based on the user interest data, and a device for distributing the generated learning content to the user device. This enables the generation of educational content based on individual user interests, the provision of rapid feedback, and the provision of support information for appropriate environmental projects.

[0455] "Interest data" refers to data that contains information about topics and themes that users are interested in.

[0456] "Educational history data" refers to data that records the education and learning that a user has received to date.

[0457] The term "device" refers to a system or equipment used to achieve a specific function.

[0458] A "generative algorithm" refers to a programmatic method or methodology for producing results based on specific input information.

[0459] A "user device" refers to a device that a user directly operates as an interface to receive information.

[0460] An "information source" refers to a database, system, or other entity that serves as a reference point when collecting data and information.

[0461] To effectively conduct environmental education, it is crucial to provide content tailored to the individual interests and concerns of each user. This system consists of information processing involving three parties: the server, the terminal, and the user. The roles and processes of each are described below.

[0462] Users first use a device to input their interests in environmental issues and topics they wish to learn about. This creates data on the user's interests. This data is sent to a server via the device and stored in a database, creating a detailed user profile.

[0463] The server analyzes stored interest data and educational history data, and uses a generative AI model (e.g., a general generative algorithm) to generate relevant educational content based on the prompt "Generate environmental education content based on the user's interests." This content includes interactive learning materials and quizzes, and is designed to maximize learning effectiveness.

[0464] The generated educational content is sent from the server to the device. The device receives this content and displays it so that the user can learn from it. The user can take quizzes or watch video learning materials.

[0465] Furthermore, the server collects and evaluates the user's learning progress in real time, including quiz answers and interactive operations. Based on the evaluation, suggestions for the next learning steps and improvement measures are created as feedback and sent to the terminal, presented in an easy-to-understand format for the user.

[0466] In addition, when users plan a new environmental project, the server searches the database for relevant past cases and provides effective implementation procedures and necessary information. For example, if a user plans a "community cleanup project," the server will present information including past successful cases and lists of materials. This feature makes it easier for users to create concrete action plans.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] Users input environmental issues and learning topics that align with their interests through their device. This input data forms a request to the server, representing specific topics that reflect the user's interests. This input data then forms the basis for subsequent profile formation.

[0470] Step 2:

[0471] The terminal sends the entered user data to the server. The server receives this data and stores it in a user-specific profile database. Data processing here includes classifying and tagging user interests, organizing profiles to prepare for future content generation.

[0472] Step 3:

[0473] The server generates prompt text using a generative AI model based on the user profile. Given the prompt "Generate environmental education content based on the user's interests," the generative model outputs appropriate educational content. The data calculations performed in this step include natural language processing and content generation algorithms.

[0474] Step 4:

[0475] The generated educational content is sent from the server to the terminal. The terminal converts the content to an appropriate display format and presents it to the user. Specific actions include adjusting the layout of the content on the terminal's interface.

[0476] Step 5:

[0477] Users learn from the presented content, progressing through quizzes and interactive learning materials. User actions and responses are collected on the device and form input data that is sent to the server.

[0478] Step 6:

[0479] The server analyzes user input data and evaluates learning progress and understanding. Based on this evaluation, it creates the next learning steps and feedback. Specific calculations include statistical analysis and the application of evaluation algorithms. The resulting feedback data indicates the optimal next steps for the user.

[0480] Step 7:

[0481] Evaluations and feedback are sent from the server to the user's device and displayed to the user. The user can then adjust and advance their learning based on this feedback. The feedback specifically outlines areas for improvement and recommendations for their learning strategy.

[0482] (Application Example 1)

[0483] 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."

[0484] Conventional environmental education systems fail to adequately provide educational programs tailored to the individual interests and learning progress of users, and also lack appropriate information regarding support for planning environmental projects. As a result, it has been difficult to stimulate users' motivation to learn and connect it to practical environmental activities. Furthermore, the lack of optimization of educational information according to the display format of user terminals has compromised user convenience.

[0485] 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.

[0486] In this invention, the server includes means for collecting and storing user interest data, learning history data, and environmental project planning data; means for using a generation device that generates relevant educational information based on the user interest data; and means for providing the generated educational information to an information terminal. This enables the provision of personalized, interactive educational programs and effective support for environmental project planning.

[0487] "User interest data" refers to information that specifically represents the interests and concerns that individual users have regarding particular environmental issues or themes.

[0488] "Learning history data" refers to a record of the learning activities a user has undertaken to date, showing the content learned, progress, and level of achievement.

[0489] "Environmental project planning data" refers to information related to environmental activities planned by users, including the project's objectives, plan details, necessary materials, and implementation schedule.

[0490] A "generation device" is a system element that uses AI technology to construct educational information based on user interest data.

[0491] "Information terminals" refer to devices that provide users with generated educational information visually or audibly, and include smartphones, smart glasses, and head-mounted displays.

[0492] "Past success stories" refer to information that demonstrates effective implementation methods and activity results from environmental projects carried out in the past, which current project planners can use as a reference.

[0493] A "data structure" refers to a format for organizing and storing information in an orderly manner, designed to enable efficient searching and extraction.

[0494] This invention is a system for efficiently conducting education on environmental themes of interest to users. Here, we describe an embodiment of a system that utilizes AI technology to provide optimal educational information to individual users.

[0495] First, users register their interests in environmental issues and themes, as well as their learning history, through their device. This information is sent to the server and stored as the user's profile. Based on this stored information, the server utilizes AI-based generative models to generate learning content tailored to each user. Specifically, models such as "OpenAI GPT" are used in this generation process.

[0496] The generated educational content is sent from the server to the user's device. This device includes smartphones, smart glasses, and head-mounted displays, and the format is optimized for optimal display on any device. Users can then use this to autonomously progress through their learning.

[0497] Furthermore, the user's learning progress is transmitted to the server in real time for evaluation. Based on this evaluation, the server generates the next learning steps and feedback, which are then provided to the user via the device. This allows the user to understand their own learning progress and make necessary adjustments.

[0498] Furthermore, when a user plans a specific environmental project, the server extracts past success stories from its data structure and provides the information necessary for that project. This process allows the user to effectively manage the project.

[0499] For example, if a user registers that they want to learn about a "recycling campaign," the server will provide detailed educational materials on the history, technology, and success stories of recycling, and will also assist in planning local recycling events.

[0500] Examples of prompts for a generative AI model include the following:

[0501] "Please generate educational materials based on the user's registered interest topic, 'Recycling Campaign.' Focus particularly on the history, technology, and success stories of recycling."

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server receives interest data and learning history data sent by the user and stores it in the data storage system. In this step, the user uses a terminal to input their areas of interest and past learning history and sends this information to the server. This forms the user's profile, which is then used to generate subsequent content.

[0505] Step 2:

[0506] The server uses stored profile data as input to generate personalized educational content using a generative AI model. In this process, the model constructs relevant educational information based on interest data and outputs it as learning material. The generated content includes interactive elements and is designed to easily capture the user's interest.

[0507] Step 3:

[0508] The generated educational content is sent from the server to the device. The device receives this content as input, optimizes it for the device's format, and displays it to the user. The user then uses this to begin learning and responds to the questions and quizzes included in the content.

[0509] Step 4:

[0510] User responses and input data from the device are sent to the server in real time. The server uses this data as input to perform learning evaluation. Using data analysis techniques, it evaluates the user's learning progress and generates feedback and the next learning steps based on the results.

[0511] Step 5:

[0512] The server sends the learning assessment results to the terminal. The terminal outputs feedback and provides it to the user. This allows the user to understand their learning progress and make adjustments as needed.

[0513] Step 6:

[0514] When a user plans an environmental project, they send a request for necessary information from their terminal to the server. The server refers to a database of past success stories, extracts project-related information as input, and sends it back to the terminal as output. This information helps in creating a concrete project implementation plan.

[0515] 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.

[0516] This invention provides an environmental education system that takes into account the user's emotional state. It uses an emotion engine to recognize emotions in real time and adjusts the presentation of educational content based on those emotions. The embodiments of this system are described in detail below.

[0517] First, the user accesses the environmental education platform and begins learning. The device has a built-in emotion engine that analyzes how the user feels about the content. This emotion engine can determine the user's emotional state based on multiple indicators, such as facial expressions, voice tone, and input speed.

[0518] When providing educational content, the server generates appropriate learning materials based on user interest data and learning history data. The generative model creates content tailored to each user's individual learning needs, but the emotion engine incorporates detected emotion data to dynamically adjust the difficulty level and format of the content.

[0519] For example, if a user expresses frustration with difficult content, the server analyzes the situation and either lowers the difficulty level or re-presents the content in a different format. Conversely, if the user shows excitement or enthusiasm, it can present more challenging tasks.

[0520] Furthermore, emotional data collected using the emotion engine is incorporated into the user's learning assessment. Based on this information, the server provides feedback to the user. This feedback may include suggestions for improving the user's emotional state, along with an assessment of their learning progress.

[0521] Furthermore, during the planning of environmental projects, the user's emotional state is taken into consideration, and support information appropriate for project execution is provided from the server. For example, if a user is feeling anxious, past success stories that are helpful for project implementation are presented to provide reassurance and support.

[0522] In this way, this system provides a personalized learning experience mediated by emotions, contributing to an improvement in users' environmental awareness.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] Users log in to the environmental education platform, select a topic of interest, and begin learning. The device's built-in camera and microphone activate to monitor the user's emotional state in real time.

[0526] Step 2:

[0527] The emotion engine built into the device detects emotions from the user's facial expressions and voice. This data, along with the user's interest data, is sent to the server as learning history data.

[0528] Step 3:

[0529] The server analyzes the transmitted emotion data and evaluates the user's current emotional state. Based on this evaluation, it uses a generative model to generate educational content tailored to the user.

[0530] Step 4:

[0531] The server delivers the generated content to the user's device. The content is adapted based on the user's emotional state, with adjustments made to difficulty level and media format.

[0532] Step 5:

[0533] The device displays the content provided to the user, and the user continues learning. The device continues to monitor using an emotion engine, detecting changes in emotions during learning.

[0534] Step 6:

[0535] The data entered by the user during the learning process (e.g., quiz answers) is sent back to the server. This includes information about emotional responses.

[0536] Step 7:

[0537] The server integrates the collected input data and sentiment data to perform learning evaluation. Based on the evaluation results, it identifies areas for improvement in future learning directions and content.

[0538] Step 8:

[0539] The server generates evaluation-based feedback for the user and sends it to the device. This feedback includes suggestions for improving emotional states and guidelines for the next learning steps.

[0540] Step 9:

[0541] If a user is working on planning an environmental project, they will propose the project details to the server via their terminal.

[0542] Step 10:

[0543] The server considers the user's current emotional state and retrieves project support information from the database, sending it to the terminal. For example, when the user is feeling anxious, it might present encouraging instructions and success stories.

[0544] (Example 2)

[0545] 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."

[0546] The problem that this invention aims to solve is to enhance learning effectiveness and improve the user's learning experience by providing personalized educational information that takes into account the user's emotional state. Furthermore, it aims to improve the success rate of environmental projects by providing support information that reflects the user's emotional state during project planning.

[0547] 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.

[0548] In this invention, the server includes means for analyzing the user's emotional state in real time and determining the emotion using facial expressions, voice tone, and input speed; means for dynamically adjusting the difficulty level and format of the educational content based on the determined emotional state; and means for generating personalized educational information using generative AI technology and providing the generated educational information to the user's terminal. This makes it possible to provide learning information that corresponds to each user's emotional state, thereby improving the quality of the learning experience.

[0549] A "user" refers to an individual who learns through this system, and their interests, preferences, and emotional state influence the system's operation.

[0550] "Emotional state" refers to the psychological condition analyzed in real time from the user's facial expressions, voice tone, input speed, etc., and is an important element in providing educational content.

[0551] "Educational information" refers to learning content generated based on the user's interests, preferences, and emotional state, and is dynamically adjusted to suit the learning objectives.

[0552] "Generative AI technology" refers to a technology that uses artificial intelligence to generate educational information optimized for each individual user, and its complex algorithms enable the provision of highly accurate information.

[0553] A "terminal" refers to an electronic device used by users to receive educational information or provide input information, and it is also a device that collects the user's emotional state via an emotion engine.

[0554] An "information aggregate" refers to a data pool that centrally manages information useful for planning environmental projects and is used to provide appropriate support information.

[0555] This invention is a system that provides personalized educational information that takes into account the user's emotional state. The following describes its specific embodiments.

[0556] Users access the environmental education platform and begin learning. During this process, the device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time. The device then uses facial expression analysis and voice recognition software to analyze the user's emotional state based on this data. Keyboard input speed is also monitored, which is used to determine emotions. This emotional data serves as an important indicator and is used to personalize educational information.

[0557] Next, the server takes on the role of generating educational information based on the user's interests, learning history, and emotional state. Utilizing a generative AI model, it instructs the AI ​​using prompts such as, "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in," thereby creating optimized information. This prompt generates dynamically adapted learning content, which is then provided to the user.

[0558] The generated educational information is then sent to the user's device and presented in an appropriate format. During this process, the difficulty level and format of the information are adjusted based on the user's emotional state. For example, if the emotional state is "frustration," the educational content will be made more concise and visually easier to understand.

[0559] Furthermore, after a learning session ends, the server has the functionality to evaluate and provide feedback based on the user's performance and emotional data. This allows users to understand their own learning process and receive necessary adjustments and advice for the next steps. The process of providing educational information and feedback aims to improve the user's learning experience and is a new learning method that combines dynamic emotional evaluation with information generation.

[0560] Furthermore, the terminal is also useful when planning environmental projects, providing users with supportive information tailored to their needs based on the collected data. In this way, the system of the present invention helps to promote improved environmental awareness and maintain motivation to learn by providing users with an appropriate educational experience based on their emotions.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The user logs into the environmental education platform and launches a learning module. During this process, the user's basic information is entered into the terminal. The terminal uses its camera and microphone to capture emotional data such as facial expressions and voice tone, and transmits this data to the emotion engine in real time. The emotion engine uses facial expression analysis software and voice recognition software to analyze the user's emotional state based on the emotional data. The output is a numerical representation of the user's emotional state.

[0564] Step 2:

[0565] The server receives the user's emotional state, interest information, and learning history information obtained in the previous step. Based on this input data, the generative AI model operates and sets the prompt to "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in." By inputting this prompt into the generative AI model, it generates educational information optimized for each individual user. As output, users receive customized educational information tailored to their needs.

[0566] Step 3:

[0567] The generated educational information is sent from the server to the terminal and presented to the user. Here, the terminal adjusts the information format and visual elements to suit each user's characteristics. For example, if a user expresses anxiety, the information is output in a simpler, more visually appealing format. As output, visually optimized educational information is provided to the user.

[0568] Step 4:

[0569] Once a learning session ends, the server collects the user's learning performance data and emotional state data. This data is then analyzed to comprehensively evaluate the user's learning outcomes and provide feedback. For example, advice such as, "Your progress is good. Let's delve deeper into this topic next," might be generated. Detailed learning feedback is then provided to the user as output.

[0570] Step 5:

[0571] When a user is planning an environmental project, the device retrieves support information from the server based on past emotional data and learning history to help the project succeed. The prompt message used is "Please provide examples of successful projects that the user has been interested in in the past." Using this information, if the user is feeling anxious, specific examples that provide reassurance are presented. Appropriate support information is then provided to the user as output.

[0572] (Application Example 2)

[0573] 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."

[0574] Traditional education systems typically provided uniform content regardless of the user's emotional state. This made it difficult to achieve efficient education tailored to individual users' emotional states and learning paces, resulting in limited learning effectiveness. Furthermore, the lack of adequate feedback and support information that considered users' emotions left improving user motivation a challenge.

[0575] 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.

[0576] In this invention, the server includes an emotion analysis engine for detecting the user's emotional state, the emotion analysis engine includes means for identifying the emotional state using facial expression data and voice data, means for dynamically adjusting the presentation of educational content based on the user's emotional state, and means for using a generative model that generates relevant educational content based on the user's interest data and learning history data. This makes it possible to provide personalized educational content that responds to the user's emotions, thereby improving learning efficiency and maintaining motivation.

[0577] An "emotion analysis engine" is a technology used to analyze a user's facial expression and voice data in real time to identify their current emotional state.

[0578] "Educational content" refers to all information and learning materials provided to users for learning purposes, and includes a variety of formats such as text, videos, and quizzes.

[0579] "User interest data" refers to information that shows what fields and topics users are interested in, based on their past behavior and preferences.

[0580] "Learning history data" refers to data that records what educational content and assignments a user has worked on in the past.

[0581] A "generative model" is an algorithm or framework for generating new content based on input data.

[0582] The system for implementing this invention mainly consists of a server, a user terminal, and a communication network. The server is equipped with an emotion analysis engine, which analyzes facial expression data and voice data sent from the user terminal. Specifically, it identifies the user's emotional state in real time based on this data acquired by the camera and microphone. Based on the results, the server uses a generative AI model to generate appropriate educational content and dynamically creates content that best matches the user's interest data and learning history data.

[0583] On the user's device, generated educational content is provided, with the difficulty level and format adjusted according to their emotional state. This ensures an optimal learning experience for the user. The server also collects user input data and provides appropriate feedback. For example, if a user shows little interest or concentration on a particular topic, the content is modified and re-presented.

[0584] For example, if a user wants to relax, the system can provide relaxing content, and conversely, if they want to be active, it can present active learning materials. Examples of prompts include, "If the user's facial expression and voice indicate relaxation, recommend relaxing content," or "Suggest a video that would be effective for a user who is feeling stressed." Through such prompts, the system can provide education that flexibly responds to the user's emotions.

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The user's device captures facial and voice data through its camera and microphone. This data is sent to the server in real time. Based on this input, the server prepares its emotion analysis engine to identify the user's emotions based on their current facial expressions and voice tone.

[0588] Step 2:

[0589] The server uses an emotion analysis engine to analyze facial and voice data sent by the user. Based on the input data, it analyzes facial features and voice tone, performs data calculations, and identifies the user's emotional state. As a result of the analysis, an emotion label such as "relaxed" or "stressed" is output.

[0590] Step 3:

[0591] The server references user interest data and learning history data, and uses a generative AI model to generate educational content best suited to the user. Using emotion labels such as "relax" and "stress" as input, the generative AI model dynamically generates content; specifically, relaxation videos are selected for relaxation, and relaxation music for stress.

[0592] Step 4:

[0593] The server optimizes and sends the generated educational content to the user's device. In this step, the content format is adjusted to suit the device, for example, the resolution and sound quality of videos are adapted. This allows the user's device to display the received content smoothly.

[0594] Step 5:

[0595] Users view the content they receive and provide feedback. This feedback data is sent back to the server and used to update the user's emotional state and interests. Specifically, content to which users respond positively is recorded.

[0596] Step 6:

[0597] The server uses the feedback data to generate the next educational content. This information is added as learning history, improving the accuracy of future content generation and providing users with a more personalized experience.

[0598] 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.

[0599] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] This invention is a system for effectively conducting environmental education, generating educational content tailored to individual users and performing learning assessments. The operation of the key programs constituting this system will be explained below.

[0616] First, users register their interests, environmental issues they are concerned with, and learning topics they wish to study. This information is transmitted to the system via their device and stored on the server. This creates the user's profile.

[0617] Next, the server analyzes the registered user's profile data and generates appropriate educational content based on their interests. This generation uses an AI-based generative model and includes interactive materials and quizzes designed to maximize the user's learning effectiveness.

[0618] The generated educational content is sent from the server to the terminal and provided to the user. The user uses these materials to progress with their learning. During the learning process, user responses and input data (e.g., quiz answers) are sent to the server in real time.

[0619] Next, the server performs a learning evaluation based on the received input data. This measures learning progress and achievement, and provides feedback on the next learning steps and improvement measures. This feedback is delivered to the user via the device.

[0620] In addition, the system has functions to support environmental projects planned by users. Specifically, when a user plans a project, the server retrieves past success stories and necessary information from the database and provides it to the user.

[0621] For example, if a user plans a "beach cleanup project," the server will provide information on effective implementation procedures and necessary materials based on data from similar projects conducted in the past, supporting the user's execution. In this way, the system integrates education and practical activities, contributing to raising users' environmental awareness.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] Users access the environmental education platform, select environmental topics of interest and desired learning themes, and register along with their personal information. This information is transmitted to the server via the device.

[0625] Step 2:

[0626] The server stores interest data and learning history data submitted by the user and analyzes this data using AI algorithms. This analysis identifies the information and interests that the user is seeking.

[0627] Step 3:

[0628] The server uses an AI-generated model based on the analysis results to generate user-optimized educational content. This content includes interactive learning materials, quizzes, and games to facilitate learning.

[0629] Step 4:

[0630] The server sends the generated educational content to the user's device. The content is provided in the format best suited to the user's device.

[0631] Step 5:

[0632] The terminal displays educational content received from the server to the user, allowing the user to proceed with their learning. Users can learn in various formats through interactive learning materials.

[0633] Step 6:

[0634] Users input their answers to quizzes and assignments presented during the learning process via their devices, and this data is transmitted to the server in real time.

[0635] Step 7:

[0636] The server analyzes user input data and performs a learning evaluation. Based on this evaluation, it measures the user's learning effectiveness and generates feedback for the next step.

[0637] Step 8:

[0638] Based on the evaluation results, the server creates appropriate feedback for the user and sends it to the user via their device. The feedback includes areas where learning needs improvement and recommended content.

[0639] Step 9:

[0640] If a user wants to plan an environmental project, they propose that plan to the server via their terminal.

[0641] Step 10:

[0642] The server searches the database based on the project plan proposed by the user, collecting similar projects and success stories. It then sends the relevant information to the terminal to support the user.

[0643] (Example 1)

[0644] 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".

[0645] Providing effective environmental education requires delivering appropriate educational content based on users' individual interests and past learning history. However, the current system struggles to generate content tailored to users' interests and to provide timely feedback. Furthermore, it is unable to provide appropriate support information in a timely manner when supporting environmental projects. This project aims to solve these problems.

[0646] 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.

[0647] In this invention, the server includes a device for collecting and storing user interest data and educational history data, a device for using a generation algorithm that generates relevant learning content based on the user interest data, and a device for distributing the generated learning content to the user device. This enables the generation of educational content based on individual user interests, the provision of rapid feedback, and the provision of support information for appropriate environmental projects.

[0648] "Interest data" refers to data that contains information about topics and themes that users are interested in.

[0649] "Educational history data" refers to data that records the education and learning that a user has received to date.

[0650] The term "device" refers to a system or equipment used to achieve a specific function.

[0651] A "generative algorithm" refers to a programmatic method or methodology for producing results based on specific input information.

[0652] A "user device" refers to a device that a user directly operates as an interface to receive information.

[0653] An "information source" refers to a database, system, or other entity that serves as a reference point when collecting data and information.

[0654] To effectively conduct environmental education, it is crucial to provide content tailored to the individual interests and concerns of each user. This system consists of information processing involving three parties: the server, the terminal, and the user. The roles and processes of each are described below.

[0655] Users first use a device to input their interests in environmental issues and topics they wish to learn about. This creates data on the user's interests. This data is sent to a server via the device and stored in a database, creating a detailed user profile.

[0656] The server analyzes stored interest data and educational history data, and uses a generative AI model (e.g., a general generative algorithm) to generate relevant educational content based on the prompt "Generate environmental education content based on the user's interests." This content includes interactive learning materials and quizzes, and is designed to maximize learning effectiveness.

[0657] The generated educational content is sent from the server to the device. The device receives this content and displays it so that the user can learn from it. The user can take quizzes or watch video learning materials.

[0658] Furthermore, the server collects and evaluates the user's learning progress in real time, including quiz answers and interactive operations. Based on the evaluation, suggestions for the next learning steps and improvement measures are created as feedback and sent to the terminal, presented in an easy-to-understand format for the user.

[0659] In addition, when users plan a new environmental project, the server searches the database for relevant past cases and provides effective implementation procedures and necessary information. For example, if a user plans a "community cleanup project," the server will present information including past successful cases and lists of materials. This feature makes it easier for users to create concrete action plans.

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] Users input environmental issues and learning topics that align with their interests through their device. This input data forms a request to the server, representing specific topics that reflect the user's interests. This input data then forms the basis for subsequent profile formation.

[0663] Step 2:

[0664] The terminal sends the entered user data to the server. The server receives this data and stores it in a user-specific profile database. Data processing here includes classifying and tagging user interests, organizing profiles to prepare for future content generation.

[0665] Step 3:

[0666] The server generates prompt text using a generative AI model based on the user profile. Given the prompt "Generate environmental education content based on the user's interests," the generative model outputs appropriate educational content. The data calculations performed in this step include natural language processing and content generation algorithms.

[0667] Step 4:

[0668] The generated educational content is sent from the server to the terminal. The terminal converts the content to an appropriate display format and presents it to the user. Specific actions include adjusting the layout of the content on the terminal's interface.

[0669] Step 5:

[0670] Users learn from the presented content, progressing through quizzes and interactive learning materials. User actions and responses are collected on the device and form input data that is sent to the server.

[0671] Step 6:

[0672] The server analyzes user input data and evaluates learning progress and understanding. Based on this evaluation, it creates the next learning steps and feedback. Specific calculations include statistical analysis and the application of evaluation algorithms. The resulting feedback data indicates the optimal next steps for the user.

[0673] Step 7:

[0674] Evaluations and feedback are sent from the server to the user's device and displayed to the user. The user can then adjust and advance their learning based on this feedback. The feedback specifically outlines areas for improvement and recommendations for their learning strategy.

[0675] (Application Example 1)

[0676] 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".

[0677] Conventional environmental education systems fail to adequately provide educational programs tailored to the individual interests and learning progress of users, and also lack appropriate information regarding support for planning environmental projects. As a result, it has been difficult to stimulate users' motivation to learn and connect it to practical environmental activities. Furthermore, the lack of optimization of educational information according to the display format of user terminals has compromised user convenience.

[0678] 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.

[0679] In this invention, the server includes means for collecting and storing user interest data, learning history data, and environmental project planning data; means for using a generation device that generates relevant educational information based on the user interest data; and means for providing the generated educational information to an information terminal. This enables the provision of personalized, interactive educational programs and effective support for environmental project planning.

[0680] "User interest data" refers to information that specifically represents the interests and concerns that individual users have regarding particular environmental issues or themes.

[0681] "Learning history data" refers to a record of the learning activities a user has undertaken to date, showing the content learned, progress, and level of achievement.

[0682] "Environmental project planning data" refers to information related to environmental activities planned by users, including the project's objectives, plan details, necessary materials, and implementation schedule.

[0683] A "generation device" is a system element that uses AI technology to construct educational information based on user interest data.

[0684] "Information terminals" refer to devices that provide users with generated educational information visually or audibly, and include smartphones, smart glasses, and head-mounted displays.

[0685] "Past success stories" refer to information that demonstrates effective implementation methods and activity results from environmental projects carried out in the past, which current project planners can use as a reference.

[0686] A "data structure" refers to a format for organizing and storing information in an orderly manner, designed to enable efficient searching and extraction.

[0687] This invention is a system for efficiently conducting education on environmental themes of interest to users. Here, we describe an embodiment of a system that utilizes AI technology to provide optimal educational information to individual users.

[0688] First, users register their interests in environmental issues and themes, as well as their learning history, through their device. This information is sent to the server and stored as the user's profile. Based on this stored information, the server utilizes AI-based generative models to generate learning content tailored to each user. Specifically, models such as "OpenAI GPT" are used in this generation process.

[0689] The generated educational content is sent from the server to the user's device. This device includes smartphones, smart glasses, and head-mounted displays, and the format is optimized for optimal display on any device. Users can then use this to autonomously progress through their learning.

[0690] Furthermore, the user's learning progress is transmitted to the server in real time for evaluation. Based on this evaluation, the server generates the next learning steps and feedback, which are then provided to the user via the device. This allows the user to understand their own learning progress and make necessary adjustments.

[0691] Furthermore, when a user plans a specific environmental project, the server extracts past success stories from its data structure and provides the information necessary for that project. This process allows the user to effectively manage the project.

[0692] For example, if a user registers that they want to learn about a "recycling campaign," the server will provide detailed educational materials on the history, technology, and success stories of recycling, and will also assist in planning local recycling events.

[0693] Examples of prompts for a generative AI model include the following:

[0694] "Please generate educational materials based on the user's registered interest topic, 'Recycling Campaign.' Focus particularly on the history, technology, and success stories of recycling."

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The server receives interest data and learning history data sent by the user and stores it in the data storage system. In this step, the user uses a terminal to input their areas of interest and past learning history and sends this information to the server. This forms the user's profile, which is then used to generate subsequent content.

[0698] Step 2:

[0699] The server uses stored profile data as input to generate personalized educational content using a generative AI model. In this process, the model constructs relevant educational information based on interest data and outputs it as learning material. The generated content includes interactive elements and is designed to easily capture the user's interest.

[0700] Step 3:

[0701] The generated educational content is sent from the server to the device. The device receives this content as input, optimizes it for the device's format, and displays it to the user. The user then uses this to begin learning and responds to the questions and quizzes included in the content.

[0702] Step 4:

[0703] User responses and input data from the device are sent to the server in real time. The server uses this data as input to perform learning evaluation. Using data analysis techniques, it evaluates the user's learning progress and generates feedback and the next learning steps based on the results.

[0704] Step 5:

[0705] The server sends the learning assessment results to the terminal. The terminal outputs feedback and provides it to the user. This allows the user to understand their learning progress and make adjustments as needed.

[0706] Step 6:

[0707] When a user plans an environmental project, they send a request for necessary information from their terminal to the server. The server refers to a database of past success stories, extracts project-related information as input, and sends it back to the terminal as output. This information helps in creating a concrete project implementation plan.

[0708] 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.

[0709] This invention provides an environmental education system that takes into account the user's emotional state. It uses an emotion engine to recognize emotions in real time and adjusts the presentation of educational content based on those emotions. The embodiments of this system are described in detail below.

[0710] First, the user accesses the environmental education platform and begins learning. The device has a built-in emotion engine that analyzes how the user feels about the content. This emotion engine can determine the user's emotional state based on multiple indicators, such as facial expressions, voice tone, and input speed.

[0711] When providing educational content, the server generates appropriate learning materials based on user interest data and learning history data. The generative model creates content tailored to each user's individual learning needs, but the emotion engine incorporates detected emotion data to dynamically adjust the difficulty level and format of the content.

[0712] For example, if a user expresses frustration with difficult content, the server analyzes the situation and either lowers the difficulty level or re-presents the content in a different format. Conversely, if the user shows excitement or enthusiasm, it can present more challenging tasks.

[0713] Furthermore, emotional data collected using the emotion engine is incorporated into the user's learning assessment. Based on this information, the server provides feedback to the user. This feedback may include suggestions for improving the user's emotional state, along with an assessment of their learning progress.

[0714] Furthermore, during the planning of environmental projects, the user's emotional state is taken into consideration, and support information appropriate for project execution is provided from the server. For example, if a user is feeling anxious, past success stories that are helpful for project implementation are presented to provide reassurance and support.

[0715] In this way, this system provides a personalized learning experience mediated by emotions, contributing to an improvement in users' environmental awareness.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] Users log in to the environmental education platform, select a topic of interest, and begin learning. The device's built-in camera and microphone activate to monitor the user's emotional state in real time.

[0719] Step 2:

[0720] The emotion engine built into the device detects emotions from the user's facial expressions and voice. This data, along with the user's interest data, is sent to the server as learning history data.

[0721] Step 3:

[0722] The server analyzes the transmitted emotion data and evaluates the user's current emotional state. Based on this evaluation, it uses a generative model to generate educational content tailored to the user.

[0723] Step 4:

[0724] The server delivers the generated content to the user's device. The content is adapted based on the user's emotional state, with adjustments made to difficulty level and media format.

[0725] Step 5:

[0726] The device displays the content provided to the user, and the user continues learning. The device continues to monitor using an emotion engine, detecting changes in emotions during learning.

[0727] Step 6:

[0728] The data entered by the user during the learning process (e.g., quiz answers) is sent back to the server. This includes information about emotional responses.

[0729] Step 7:

[0730] The server integrates the collected input data and sentiment data to perform learning evaluation. Based on the evaluation results, it identifies areas for improvement in future learning directions and content.

[0731] Step 8:

[0732] The server generates evaluation-based feedback for the user and sends it to the device. This feedback includes suggestions for improving emotional states and guidelines for the next learning steps.

[0733] Step 9:

[0734] If a user is working on planning an environmental project, they will propose the project details to the server via their terminal.

[0735] Step 10:

[0736] The server considers the user's current emotional state and retrieves project support information from the database, sending it to the terminal. For example, when the user is feeling anxious, it might present encouraging instructions and success stories.

[0737] (Example 2)

[0738] 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".

[0739] The problem that this invention aims to solve is to enhance learning effectiveness and improve the user's learning experience by providing personalized educational information that takes into account the user's emotional state. Furthermore, it aims to improve the success rate of environmental projects by providing support information that reflects the user's emotional state during project planning.

[0740] 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.

[0741] In this invention, the server includes means for analyzing the user's emotional state in real time and determining the emotion using facial expressions, voice tone, and input speed; means for dynamically adjusting the difficulty level and format of the educational content based on the determined emotional state; and means for generating personalized educational information using generative AI technology and providing the generated educational information to the user's terminal. This makes it possible to provide learning information that corresponds to each user's emotional state, thereby improving the quality of the learning experience.

[0742] A "user" refers to an individual who learns through this system, and their interests, preferences, and emotional state influence the system's operation.

[0743] "Emotional state" refers to the psychological condition analyzed in real time from the user's facial expressions, voice tone, input speed, etc., and is an important element in providing educational content.

[0744] "Educational information" refers to learning content generated based on the user's interests, preferences, and emotional state, and is dynamically adjusted to suit the learning objectives.

[0745] "Generative AI technology" refers to a technology that uses artificial intelligence to generate educational information optimized for each individual user, and its complex algorithms enable the provision of highly accurate information.

[0746] A "terminal" refers to an electronic device used by users to receive educational information or provide input information, and it is also a device that collects the user's emotional state via an emotion engine.

[0747] An "information aggregate" refers to a data pool that centrally manages information useful for planning environmental projects and is used to provide appropriate support information.

[0748] This invention is a system that provides personalized educational information that takes into account the user's emotional state. The following describes its specific embodiments.

[0749] Users access the environmental education platform and begin learning. During this process, the device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time. The device then uses facial expression analysis and voice recognition software to analyze the user's emotional state based on this data. Keyboard input speed is also monitored, which is used to determine emotions. This emotional data serves as an important indicator and is used to personalize educational information.

[0750] Next, the server takes on the role of generating educational information based on the user's interests, learning history, and emotional state. Utilizing a generative AI model, it instructs the AI ​​using prompts such as, "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in," thereby creating optimized information. This prompt generates dynamically adapted learning content, which is then provided to the user.

[0751] The generated educational information is then sent to the user's device and presented in an appropriate format. During this process, the difficulty level and format of the information are adjusted based on the user's emotional state. For example, if the emotional state is "frustration," the educational content will be made more concise and visually easier to understand.

[0752] Furthermore, after a learning session ends, the server has the functionality to evaluate and provide feedback based on the user's performance and emotional data. This allows users to understand their own learning process and receive necessary adjustments and advice for the next steps. The process of providing educational information and feedback aims to improve the user's learning experience and is a new learning method that combines dynamic emotional evaluation with information generation.

[0753] Furthermore, the terminal is also useful when planning environmental projects, providing users with supportive information tailored to their needs based on the collected data. In this way, the system of the present invention helps to promote improved environmental awareness and maintain motivation to learn by providing users with an appropriate educational experience based on their emotions.

[0754] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0755] Step 1:

[0756] The user logs into the environmental education platform and launches a learning module. During this process, the user's basic information is entered into the terminal. The terminal uses its camera and microphone to capture emotional data such as facial expressions and voice tone, and transmits this data to the emotion engine in real time. The emotion engine uses facial expression analysis software and voice recognition software to analyze the user's emotional state based on the emotional data. The output is a numerical representation of the user's emotional state.

[0757] Step 2:

[0758] The server receives the user's emotional state, interest information, and learning history information obtained in the previous step. Based on this input data, the generative AI model operates and sets the prompt to "Consider the user's current emotional state and suggest learning materials of appropriate difficulty regarding the environmental issues the user has shown interest in." By inputting this prompt into the generative AI model, it generates educational information optimized for each individual user. As output, users receive customized educational information tailored to their needs.

[0759] Step 3:

[0760] The generated educational information is sent from the server to the terminal and presented to the user. Here, the terminal adjusts the information format and visual elements to suit each user's characteristics. For example, if a user expresses anxiety, the information is output in a simpler, more visually appealing format. As output, visually optimized educational information is provided to the user.

[0761] Step 4:

[0762] Once a learning session ends, the server collects the user's learning performance data and emotional state data. This data is then analyzed to comprehensively evaluate the user's learning outcomes and provide feedback. For example, advice such as, "Your progress is good. Let's delve deeper into this topic next," might be generated. Detailed learning feedback is then provided to the user as output.

[0763] Step 5:

[0764] When a user is planning an environmental project, the device retrieves support information from the server based on past emotional data and learning history to help the project succeed. The prompt message used is "Please provide examples of successful projects that the user has been interested in in the past." Using this information, if the user is feeling anxious, specific examples that provide reassurance are presented. Appropriate support information is then provided to the user as output.

[0765] (Application Example 2)

[0766] 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".

[0767] Traditional education systems typically provided uniform content regardless of the user's emotional state. This made it difficult to achieve efficient education tailored to individual users' emotional states and learning paces, resulting in limited learning effectiveness. Furthermore, the lack of adequate feedback and support information that considered users' emotions left improving user motivation a challenge.

[0768] 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.

[0769] In this invention, the server includes an emotion analysis engine for detecting the user's emotional state, the emotion analysis engine includes means for identifying the emotional state using facial expression data and voice data, means for dynamically adjusting the presentation of educational content based on the user's emotional state, and means for using a generative model that generates relevant educational content based on the user's interest data and learning history data. This makes it possible to provide personalized educational content that responds to the user's emotions, thereby improving learning efficiency and maintaining motivation.

[0770] An "emotion analysis engine" is a technology used to analyze a user's facial expression and voice data in real time to identify their current emotional state.

[0771] "Educational content" refers to all information and learning materials provided to users for learning purposes, and includes a variety of formats such as text, videos, and quizzes.

[0772] "User interest data" refers to information that shows what fields and topics users are interested in, based on their past behavior and preferences.

[0773] "Learning history data" refers to data that records what educational content and assignments a user has worked on in the past.

[0774] A "generative model" is an algorithm or framework for generating new content based on input data.

[0775] The system for implementing this invention mainly consists of a server, a user terminal, and a communication network. The server is equipped with an emotion analysis engine, which analyzes facial expression data and voice data sent from the user terminal. Specifically, it identifies the user's emotional state in real time based on this data acquired by the camera and microphone. Based on the results, the server uses a generative AI model to generate appropriate educational content and dynamically creates content that best matches the user's interest data and learning history data.

[0776] On the user's device, generated educational content is provided, with the difficulty level and format adjusted according to their emotional state. This ensures an optimal learning experience for the user. The server also collects user input data and provides appropriate feedback. For example, if a user shows little interest or concentration on a particular topic, the content is modified and re-presented.

[0777] For example, if a user wants to relax, the system can provide relaxing content, and conversely, if they want to be active, it can present active learning materials. Examples of prompts include, "If the user's facial expression and voice indicate relaxation, recommend relaxing content," or "Suggest a video that would be effective for a user who is feeling stressed." Through such prompts, the system can provide education that flexibly responds to the user's emotions.

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The user's device captures facial and voice data through its camera and microphone. This data is sent to the server in real time. Based on this input, the server prepares its emotion analysis engine to identify the user's emotions based on their current facial expressions and voice tone.

[0781] Step 2:

[0782] The server uses an emotion analysis engine to analyze facial and voice data sent by the user. Based on the input data, it analyzes facial features and voice tone, performs data calculations, and identifies the user's emotional state. As a result of the analysis, an emotion label such as "relaxed" or "stressed" is output.

[0783] Step 3:

[0784] The server references user interest data and learning history data, and uses a generative AI model to generate educational content best suited to the user. Using emotion labels such as "relax" and "stress" as input, the generative AI model dynamically generates content; specifically, relaxation videos are selected for relaxation, and relaxation music for stress.

[0785] Step 4:

[0786] The server optimizes and sends the generated educational content to the user's device. In this step, the content format is adjusted to suit the device, for example, the resolution and sound quality of videos are adapted. This allows the user's device to display the received content smoothly.

[0787] Step 5:

[0788] Users view the content they receive and provide feedback. This feedback data is sent back to the server and used to update the user's emotional state and interests. Specifically, content to which users respond positively is recorded.

[0789] Step 6:

[0790] The server uses the feedback data to generate the next educational content. This information is added as learning history, improving the accuracy of future content generation and providing users with a more personalized experience.

[0791] 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.

[0792] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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."

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] The following is further disclosed regarding the embodiments described above.

[0813] (Claim 1)

[0814] A means of collecting and storing user interest data and learning history data,

[0815] A means of using a generative model that generates relevant educational content based on the user's interest data,

[0816] A means of providing the generated educational content to the user's terminal,

[0817] A means of collecting input data from users, performing learning evaluations based on said input data, and providing feedback,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] A means for users to plan environmental projects and retrieve information from a database that provides support information for those projects,

[0821] The system according to claim 1, including the following:

[0822] (Claim 3)

[0823] A means of providing generated educational content optimized for the format of the user's device,

[0824] The system according to claim 1, including the following:

[0825] "Example 1"

[0826] (Claim 1)

[0827] A device that collects and stores user interest data and educational history data,

[0828] A device that uses a generation algorithm to create relevant learning content based on the user's interest data,

[0829] A device that distributes the generated learning content to the user's device,

[0830] A device that collects input information from users, performs educational evaluations based on that input information, and provides responses,

[0831] A device that generates learning content using prompt statements in the aforementioned generation algorithm,

[0832] Information processing device including

[0833] (Claim 2)

[0834] A device that extracts data from information sources to provide support information for users to formulate environmental plans,

[0835] The information processing apparatus according to claim 1, including the following:

[0836] (Claim 3)

[0837] A device that optimizes and distributes the generated learning content in the format of the user's device,

[0838] The information processing apparatus according to claim 1, including the following:

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A means for collecting and storing user interest data, learning history data, and environmental project planning data,

[0842] A means of using a generation device that generates relevant educational information based on the user's interest data,

[0843] A means of providing the generated educational information to an information terminal,

[0844] A means of collecting input information from users, performing learning evaluations based on said input information, and providing feedback,

[0845] A means to provide past success stories and information to support users in planning implementation for their environmental projects,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] A means of providing generated educational information optimized for the information display format of an information terminal,

[0849] The system according to claim 1, including the following:

[0850] (Claim 3)

[0851] A means of extracting and providing the required information from the data structure when users formulate an environmental action plan,

[0852] The system according to claim 1, including the following:

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] A means of collecting and storing user interest information and learning history information,

[0856] A means for analyzing the user's emotional state in real time and determining their emotions using facial expressions, voice tone, and input speed,

[0857] A means of dynamically adjusting the difficulty level and format of educational content based on the judged emotional state,

[0858] A means for generating personalized educational information using generation AI technology and providing the generated educational information to the user's terminal,

[0859] A means for collecting input information from the user's terminal, evaluating learning outcomes based on said input information and emotional state, and providing feedback.

[0860] A system that includes this.

[0861] (Claim 2)

[0862] A means for users to plan environmental projects and obtain information from an information aggregate that provides support information for said plans,

[0863] A means of providing past success stories while taking into account the emotional state of the user,

[0864] The system according to claim 1, including the following:

[0865] (Claim 3)

[0866] A means of providing generated educational information optimized for the format of the user's terminal,

[0867] A means of dynamically adjusting the content of information based on the user's emotional state,

[0868] The system according to claim 1, including the following:

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] It includes an emotion analysis engine for detecting the user's emotional state, and the emotion analysis engine includes means for identifying the emotional state using facial expression data and voice data,

[0872] A means of dynamically adjusting the presentation of educational content based on the user's emotional state,

[0873] A means of collecting and storing user interest data and learning history data,

[0874] A means of using a generative model that generates relevant educational content based on the user's interest data,

[0875] A means of providing the generated educational content to the user's terminal,

[0876] A means of collecting input data from users, performing learning evaluations based on said input data, and providing feedback,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] A means for users to plan environmental projects and retrieve information from a database that provides support information for those projects,

[0880] A means of providing support appropriate for project execution, taking into account the emotional state of the users,

[0881] The system according to claim 1, including the following:

[0882] (Claim 3)

[0883] A means of providing generated educational content optimized for the format of the user's device,

[0884] A means of adjusting the difficulty level and format of content while taking user sentiment data into consideration,

[0885] The system according to claim 1, including the following: [Explanation of Symbols]

[0886] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting and storing user interest data, learning history data, and environmental project planning data, A means of using a generation device that generates relevant educational information based on the user's interest data, A means of providing the generated educational information to an information terminal, A means of collecting input information from users, performing learning evaluations based on said input information, and providing feedback, A means to provide past success stories and information to support users in planning implementation for their environmental projects, A system that includes this.

2. A means of providing generated educational information optimized for the information display format of an information terminal, The system according to claim 1, including the following:

3. A means of extracting and providing the required information from the data structure when users formulate an environmental action plan, The system according to claim 1, including the following:

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