system

Abstract

We provide the system. [Solution] An information processing device for knowledge management, A display means for displaying the simulation provided by the information processing device, A recording means for recording the user's actions during the aforementioned simulation, Information generation means that generates feedback based on the data obtained by the recording means, A disaster prevention education system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In regions where natural disasters occur frequently, there is a problem that effective disaster prevention education has not penetrated, so appropriate actions cannot be taken during disasters and the damage expands. In addition, conventional disaster prevention education methods are not attractive and have the problem that the educational effect on the young generation is not sufficient. Furthermore, since an educational method suitable for individual users has not been provided, the learning effect is limited.

Means for Solving the Problems

[0005] To address this challenge, the present invention proposes a disaster prevention education system centered on an information processing device for knowledge management, equipped with a simulation display means and a recording means for recording user actions. Furthermore, by using an information generation means that generates feedback based on the recorded data, users can reflect on their own actions. This system enhances learning effectiveness by providing quiz-style evaluations through a user interface, and enables appropriate feedback according to the user's level of understanding by using natural language processing technology.

[0006] "Knowledge management" is the process of organizing, storing, and retrieving information so that users can access the appropriate information when they need it.

[0007] An "information processing device" is a computer system or a part thereof that inputs, processes, stores, and outputs data.

[0008] "Simulation" is a technique that imitates real-world events, allowing users to experience specific situations in a virtual environment.

[0009] "Recording means" refers to functions or devices for saving user actions and events as data.

[0010] An "information generation means" is a system or device that generates new information or feedback based on input data.

[0011] A "disaster prevention education system" is a system that provides educational programs to enhance knowledge and response capabilities regarding natural disasters.

[0012] "User interface" refers to the screens and operating methods that users use to interact with a system.

[0013] "Quiz-based evaluation" is a method of measuring a user's knowledge level and understanding through multiple-choice questions and answers.

[0014] "Natural language processing technology" is a technology that enables computers to understand, generate, and interact with the language that humans use on a daily basis. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The disaster prevention education system of this invention aims to enable users to effectively learn about disasters and acquire practical response skills. This system is centered around an information processing device for knowledge management and provides users with an interactive learning experience.

[0037] Server operation

[0038] The server maintains a database of extensive disaster prevention content and delivers the most relevant learning materials in response to user requests. The server tracks users' learning history and progress, generating personalized simulations and quizzes. Using natural language processing technology, it generates feedback tailored to each user, aiming to improve their understanding.

[0039] Terminal operation

[0040] The terminal provides an interface for the user to interact with the system. Based on the content selected by the user, it visually reproduces various disaster simulations, such as earthquakes and typhoons. User knowledge is evaluated through quiz-style questions, and actions and selections are recorded. The terminal also records user behavior, which serves as basic data for the server to generate feedback based on that data.

[0041] User interaction

[0042] Users can access disaster prevention content through their devices and engage in an interactive learning experience. They can check their understanding by answering quizzes and deepen their learning by receiving feedback from the server. Furthermore, they can develop practical response skills through simulations.

[0043] Specific example

[0044] For example, if an earthquake scenario is selected, the server provides information on earthquake mechanisms and evacuation methods. The terminal presents the user with a virtual space during the earthquake and prompts them to choose a response. The user answers a quiz based on their chosen action, and the results are evaluated by the server, which provides feedback to guide them toward appropriate action. Based on this feedback, the user can review their response strategy.

[0045] These specific embodiments enable the present invention to provide effective learning support for users to develop a high level of understanding of and response capabilities to disasters.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user logs into the system via their device. The device sends the user's authentication information to the server, and if authentication is successful, the server retrieves the user's past learning history.

[0049] Step 2:

[0050] The server analyzes the user's learning history and selects the most suitable learning content based on their current level of understanding. It then sends the selected content information to the user's device.

[0051] Step 3:

[0052] The device displays learning content in its user interface. The user reviews the displayed information and selects simulations or quizzes that interest them.

[0053] Step 4:

[0054] When the user selects a simulation, the terminal launches the simulation operation interface. The server sends the simulation data, and the terminal displays the virtual disaster scenario.

[0055] Step 5:

[0056] The user selects a specified action within the simulation and performs that action. The terminal records the user's selection and sends it to the server.

[0057] Step 6:

[0058] The server analyzes the user's behavior record and generates appropriate feedback. The generated feedback is sent to the device and displayed to the user.

[0059] Step 7:

[0060] The device provides feedback to the user, who then evaluates their own learning based on that feedback. If necessary, they can select additional learning content or simulations to continue learning.

[0061] Step 8:

[0062] When a user ends a learning session, the device sends the latest progress data to the server, which then stores it in a database. This allows the user to continue learning the next time they log in.

[0063] (Example 1)

[0064] 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."

[0065] In disaster prevention education, traditional methods of delivery are limited to passive learning, making it difficult for users to effectively acquire practical response skills. Furthermore, there is a need for a system that provides personalized learning experiences tailored to individual users, rather than simply providing one-way information.

[0066] 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.

[0067] In this invention, the server includes means for performing information processing for knowledge management, means for visually displaying a virtual environment provided by the device performing the information processing, and means for accumulating user selections in the virtual environment. This enables interactive and personalized disaster prevention education and learning for users.

[0068] A "device that performs information processing for knowledge management" is a device that organizes and stores information related to disaster prevention and has the function of selecting the most suitable content according to the user's learning status.

[0069] "Means of visually representing a virtual environment" refers to a means of simulating and visually representing natural disasters such as earthquakes and typhoons, so that users can safely learn about disaster response.

[0070] A "means for accumulating user choices" refers to a means that records user behavior and choices within a virtual environment and stores them for later analysis and feedback.

[0071] A "means for generating corrective information" refers to a means that analyzes accumulated user data and generates feedback, including suggestions for improvement and advice, regarding user behavior.

[0072] The system of this invention provides disaster prevention education aimed at improving disaster response skills. This system functions through the interaction of a server, terminals, and users.

[0073] The server manages a database of disaster prevention information using an information processing device. Web server software such as Apache® or Nginx is used here. Furthermore, it selects the most appropriate disaster prevention content based on the user's learning progress and generates personalized feedback and quizzes using natural language processing technologies such as TENSORFLOW®.

[0074] The terminal provides an interface for users to interact with the system. This could be an iOS or Android® mobile device, or a PC running Windows or macOS®. The terminal receives content delivered from the server and visually recreates a virtual disaster environment. Here, the terminal records the user's choices and actions, and sends this data to the server for evaluation.

[0075] Users acquire practical disaster preparedness skills through simulations and quizzes provided via their devices. For example, they can simulate scenarios of how to act in the event of an earthquake and experience appropriate responses.

[0076] As a concrete example, if an earthquake is simulated, the server delivers educational materials on the mechanism of shaking and evacuation routes. The terminal provides a visual representation of the virtual space during the earthquake, and the user selects an evacuation action. The user's answers to quizzes presented after this action selection are recorded by the server, and feedback is generated regarding the appropriate action selection.

[0077] An example of a prompt might be, "Please explain what preparations are necessary in the event of a disaster." This prompt provides users with clues to gain deeper insights related to disaster response.

[0078] This invention provides a personalized learning experience, enabling users to efficiently acquire disaster preparedness skills.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user sends a request to access disaster prevention content using their device. This input involves the user selecting a scenario, such as an earthquake or a typhoon, and choosing from different disaster simulations. The user's selection is transmitted to the server as string information.

[0082] Step 2:

[0083] The server receives a user request and searches the database for relevant disaster prevention content. The input includes the user's selection information and past learning history. The server uses this data to filter and select information, and utilizes a generative AI model to generate optimal learning content. As a result, a personalized set of learning materials is output to the user.

[0084] Step 3:

[0085] The server sends the selected learning content to the device. The output includes visual simulations and quiz-style questions. In this step, data is transferred via network communication, and the device prepares to visually present the received content to the user.

[0086] Step 4:

[0087] The terminal displays a simulation and provides a quiz to the user. It uses content received from the server as input and executes it under its control. The terminal also records user input, such as selections and quiz answers. During this process, animations of the virtual environment are executed, and a user-interactive interface is provided.

[0088] Step 5:

[0089] Users experience a simulation on their device and answer quizzes. Their input, including selections and answers, is recorded. This data is collected for subsequent evaluation and sent to a server.

[0090] Step 6:

[0091] The server receives user selection and response data and generates feedback using a generative AI model. It processes user data as input and creates specific feedback messages as corresponding output. This feedback is based on an analysis of the user's actions and supports learning improvement.

[0092] Step 7:

[0093] The terminal receives feedback from the server and presents it to the user. This includes a specific process of receiving output from the server, visualizing it, and displaying it to the user. The user can review this feedback and reassess their disaster preparedness skills.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] For urban residents to respond quickly and effectively during disasters, it is crucial to raise disaster preparedness awareness during peacetime and acquire specific knowledge and skills regarding region-specific disaster risks. However, traditional disaster prevention education has been formal and has limitations in acquiring practical response skills. To improve this, interactive and personalized learning experiences are necessary.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes an information processing device for knowledge management, a visualization means for displaying a simulation based on the urban environment, and a recording means for recording the user's actions during the simulation displayed through the visualization means. This makes it possible for urban residents to acquire practical disaster prevention skills tailored to the characteristics of their region.

[0099] A "knowledge management information processing device" is an electronic device that organizes and stores disaster prevention data and provides it to users in the most optimal format.

[0100] A "visualization means for displaying simulations based on the urban environment" is a device that uses real-world urban geographic information to virtually recreate disaster situations and presents them in a format that users can visually understand.

[0101] A "recording system for documenting user behavior" is a system that saves the choices and responses of users during the learning process as digital data, and uses this data for later analysis and feedback generation.

[0102] An "information generation means" is a processing mechanism that uses collected data to generate user-specific feedback and learning advice.

[0103] A "navigation system" is a device or system that uses urban geographical information to instruct users on appropriate evacuation routes or directions to their destinations.

[0104] This disaster preparedness education system is designed to help urban residents acquire practical response skills to local disasters. The system consists of multiple components, including a server and user terminals.

[0105] The server acts as an information processing device for knowledge management, managing content in various data formats related to disaster prevention education. The data is stored via a cloud service, and personalized simulations and quizzes are generated based on the user's learning history. Specific technologies used include an artificial intelligence-based natural language processing engine built in Python and TensorFlow.

[0106] The terminal is a device that enables interactive communication with the user. For example, an application on a smartphone uses Unity or ARCore to visually recreate a simulated urban environment based on real-world geographical data using augmented reality (AR). This allows users to participate in disaster prevention training in a virtual space in real time. In addition, by recording the user's actions, the server generates and provides feedback to the user based on that data.

[0107] Users access learning content through their devices and assess their abilities through quiz-based evaluations. Furthermore, they learn about real-world evacuation routes and countermeasures using simulations. For example, when heavy rain is expected, the app displays the optimal route to the nearest evacuation shelter and provides interactive instructions.

[0108] Specific examples of prompt statements are as follows:

[0109] "Please select a route from your current location to the nearest safe shelter. Pay attention to your surroundings and confirm the safe route using AR display."

[0110] This will enable urban residents to raise their awareness of disaster prevention and respond quickly and effectively to disasters.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server uses the user's current location and individual learning history as input when they access the system to search for relevant disaster prevention content from a database in the cloud. This data processing outputs optimal learning materials that take into account the user's geographical location and past learning content.

[0114] Step 2:

[0115] The server generates scenario data tailored to the city based on the selected disaster prevention simulation. It utilizes a natural language processing engine built in Python to automatically construct user-specific quizzes and feedback. The input is the content data selected in the previous step, and the output is a personalized simulation scenario and quiz questions.

[0116] Step 3:

[0117] The device receives simulation data sent from the server and displays it as an AR environment using Unity and ARCore. User input (action selections in the simulation) is recorded, and its impact on subsequent actions is shown. The input is the user's selected action, and the output is the resulting visual change.

[0118] Step 4:

[0119] The user answers a quiz presented on their device, and the results are sent to the server. Here, the user's thought process and choices are accumulated, generating data that enables personalized learning feedback. The input is the user's quiz answers, and the output is the answer results and their analysis data.

[0120] Step 5:

[0121] The server aggregates user behavior data and quiz results, and generates feedback using natural language processing technology. This feedback is sent to the user's email address or as an in-app message, providing insights for continuous learning. The input consists of past learning history and newly acquired data, and the output is a personalized feedback message.

[0122] 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.

[0123] This disaster prevention education system is designed to enable users to effectively learn the knowledge and skills necessary to prepare for disasters. The system incorporates an emotion engine that recognizes and responds to the user's emotional state.

[0124] Server operation

[0125] The server stores disaster prevention education-related content in a database and delivers appropriate learning materials in response to user requests. In addition, it uses an emotion engine to process user emotion data and personalize learning content and feedback. Emotional information is combined with the user's learning history to generate optimized feedback. Furthermore, natural language processing technology is used to provide feedback that is easy for users to understand.

[0126] Terminal operation

[0127] The terminal visually displays disaster prevention content through a user interface, providing an environment where users can interact with the system. It displays simulation and quiz-style content and records user selections in real time. In addition, it uses a camera and microphone to input the user's facial expressions and voice tone into an emotion engine to sense the user's emotional state.

[0128] User interaction

[0129] Users access the disaster prevention system and learn through presented simulations and quizzes. During learning, the emotion engine analyzes the user's facial expressions and voice to recognize changes in stress levels and concentration. Based on this information, the system adjusts the learning pace and content to suit the user and provides appropriate support. For example, if the user is feeling stressed, the system may simplify explanations or display encouraging messages to help them relax.

[0130] Specific example

[0131] For example, when a user participates in an earthquake evacuation simulation, the device displays the simulation environment and prompts the user to act. When the user is acting calmly, the system gradually increases the difficulty of the tasks and provides more positive feedback. On the other hand, if the emotion engine detects the user's anxiety, it emphasizes positive feedback more when actions are successful, working to boost the user's confidence.

[0132] This invention makes it possible to improve the efficiency and effectiveness of learning by accurately recognizing the user's emotional state and providing flexible educational content accordingly.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user logs into the system using their device. The device sends the user's input information to the server, which verifies the user's authentication and then retrieves their past learning history.

[0136] Step 2:

[0137] The server utilizes an emotion engine to analyze user emotion data (e.g., facial expressions and tone of voice) transmitted in real time from the device. This allows it to determine the user's current emotional state.

[0138] Step 3:

[0139] The server generates optimal learning content based on the user's past learning history and current emotional state. For example, if the user is relaxed, it may suggest a more challenging simulation.

[0140] Step 4:

[0141] The device visually displays learning content sent from the server to the user. The user views the content (simulations and quizzes) and selects or performs specific actions.

[0142] Step 5:

[0143] When a user starts a simulation, the device records the user's actions and choices, and simultaneously sends data to the server in real time to track changes in the user's facial expressions and voice using an emotion engine.

[0144] Step 6:

[0145] The server generates appropriate feedback based on recorded user behavior and emotional data. It also adjusts the content of the feedback according to the user's state and offers suggestions for stress reduction as needed.

[0146] Step 7:

[0147] The device presents the generated feedback to the user. Based on the feedback, the user can reflect on their learning and choose what to learn next.

[0148] Step 8:

[0149] When a user finishes a learning session, the device sends data on their latest learning progress and emotional responses to the server. The server stores this data in a database and uses it to optimize the learning experience during their next login.

[0150] (Example 2)

[0151] 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".

[0152] Traditional disaster prevention education systems often provided uniform content without considering users' learning progress or emotional states. This made it difficult to provide personalized learning experiences tailored to individual users, resulting in decreased learning efficiency. Furthermore, they lacked features to provide appropriate support when users felt stressed or anxious.

[0153] 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.

[0154] In this invention, the server includes information processing means for processing knowledge information, display means for displaying a virtual experience, recording means for recording the user's activities, emotion analysis means for detecting and analyzing emotional states, and content adjustment means for adjusting learning content. This makes it possible to provide optimal learning materials and feedback tailored to each user's emotional state and learning history.

[0155] "Information processing means for processing knowledge information" refers to means that have the function of analyzing the user's learning data and past history to select the most suitable educational content.

[0156] A "display means for displaying virtual experiences" is a device that provides users with visual learning simulations and quizzes, thereby realizing an interactive learning environment.

[0157] "Means for recording user activity" refers to means that have the function of tracking the choices and actions taken by the user during a virtual experience and saving them as data.

[0158] An "emotional analysis means for detecting and analyzing emotional states" is a means that has the function of grasping the user's emotional state in real time through facial expressions and voice, and analyzing that information.

[0159] "Content adjustment means for adjusting learning content" refers to a means for flexibly changing the difficulty level and content of learning materials according to the user's emotional state and learning progress, thereby providing an individually optimized learning experience.

[0160] The disaster prevention education system of this invention is designed to enable users to effectively learn the knowledge and skills necessary in the event of a disaster. Embodiments of this system are described below.

[0161] First, the server uses software to process knowledge information and provides personalized educational content based on the user's learning history and emotional data. This software stores content using a database management system and selects appropriate learning materials in response to user requests. In addition, computer vision and speech analysis technologies are used as emotional analysis tools to analyze the user's facial expressions and tone of voice in real time. This allows for the acquisition of data to adjust the learning pace and content of the learning materials.

[0162] The terminal includes an interface device for visually providing a virtual experience. Specific examples include a device equipped with a display, a touchscreen, a camera, and a microphone. The terminal displays simulation and quiz-style content received from the server and interactively records the user's choices and responses. It also uses its built-in camera and microphone to continuously transmit the user's emotional state to an emotion analysis system.

[0163] Users participate in simulations and quizzes presented through their devices. For example, in an earthquake evacuation simulation, users select actions based on the displayed scenario. Through emotion analysis, it is determined whether the user is relaxed or tense, and the results are reflected in the learning process.

[0164] As a concrete example, when a user answers a quiz asking "What should you do first during an earthquake?", the server can consider the user's past accuracy rate and current emotional state to generate appropriate feedback. The generative AI model used generates feedback and encouragement based on a variety of prompts from the user.

[0165] An example of a prompt might be, "In a disaster simulation, please tell us what to do if the user is stressed. Also, please give an example of how to alleviate the user's stress." Through such prompts, the system implements techniques to provide appropriate support.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user logs into the system using a terminal. Login information is sent to the terminal as input, and the terminal forwards this information to the server. The server authenticates the user's login information and performs data processing by retrieving relevant data from the learning history database. The authentication result and the user's learning history are generated as output.

[0169] Step 2:

[0170] The server receives the user's learning history as input and selects appropriate disaster prevention education content based on past learning. The selected content is generated as output and sent to the user's terminal. This data processing determines personalized initial learning materials for the user.

[0171] Step 3:

[0172] The terminal receives educational content sent from the server and displays it through a user interface. Specifically, it presents the user with visual simulations and text-based quiz content. The input is content data from the server, and the output is interface information displayed on the screen.

[0173] Step 4:

[0174] The user progresses through the presented simulations and quizzes in sequence. The user's selected actions are recorded as input on the terminal. The terminal prepares to send this action data to the server, and the selected data is returned as output. For example, if the user selects "evacuate," that selection is recorded and transmitted to the server.

[0175] Step 5:

[0176] The device's camera and microphone capture the user's facial expressions and voice tone as input. The device sends this data to an emotion analysis system, which then performs specific actions to analyze the user's emotional state. The analysis results are generated as output and sent to the server.

[0177] Step 6:

[0178] The server receives the results of sentiment analysis and user selection data as input, and uses a generative AI model to generate feedback for the user. The feedback includes advice and encouraging messages to adjust the learning pace. The output feedback takes into account the user's emotional state.

[0179] Step 7:

[0180] The terminal receives feedback from the server and presents it to the user. The input to this process is feedback data sent from the server, and the output is messages and guidance displayed on the user's screen. Specifically, it displays evaluations based on the user's progress and advice on the next steps.

[0181] (Application Example 2)

[0182] 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."

[0183] In modern society, with the proliferation of autonomous vehicles, disaster preparedness education for drivers and passengers is crucial. However, conventional disaster preparedness education systems have struggled to provide personalized feedback tailored to the individual user's emotions and circumstances. Furthermore, these systems lacked the real-time interaction and emotional recognition necessary to maximize the effectiveness of education for users.

[0184] 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.

[0185] In this invention, the server includes information processing means for managing knowledge, visual display means for displaying simulations provided by the information processing means, recording means for recording the user's actions during the simulation, emotion analysis means for analyzing the user's emotions using emotion recognition technology, and information generation means for generating feedback based on the data obtained by the recording means and the emotion analysis means. This enables personalized disaster prevention learning in real time according to the user's emotional state.

[0186] "Information processing means for knowledge management" refers to devices and systems that process information to appropriately collect, organize, and provide disaster prevention-related information to users.

[0187] "Visual display means" refers to devices or systems for visually presenting simulations and learning content generated by information processing means to users.

[0188] "Recording means" refers to a device or system that has the function of saving the user's actions and responses during a simulation.

[0189] "Emotion analysis means" refers to technologies and devices that use information such as a user's facial expressions and voice to recognize and analyze their emotions in real time.

[0190] "Information generation means" refers to devices or systems for creating appropriate feedback and educational content based on data obtained from recording means and emotion analysis means.

[0191] The system for implementing this invention consists of a server and a terminal. The server is equipped with information processing means for knowledge management and stores disaster prevention-related information in a database. This data is delivered to the terminal in a simulation format via a visual display means described later. The server is also equipped with emotion analysis means that receives and analyzes emotion data transmitted by the user in real time. For emotion analysis, general emotion recognition software such as AWS® Rekognition or Google® Cloud Vision AI can be used.

[0192] The device is equipped with visual display and recording capabilities. The visual display allows users to access simulation and problem-solving content. The device also includes a camera and microphone, which capture and transmit the user's facial expressions and voice data to a server. Based on this information, an emotion analysis system analyzes the user's stress level and concentration to determine the optimal learning pace and content. The generated feedback is communicated to the user using natural language processing technology. Google Dialogflow, for example, can be used for natural language processing.

[0193] As a concrete example, when a user participates in a simulation using a device, their current emotional state is analyzed based on real-time data collected through the camera and microphone. Depending on the results, if the user is feeling fear or tension, for example, a simple response tailored to that situation can be generated. The generated feedback is presented to the user in a gentle and reassuring tone. Through this process, the user's learning experience is made natural and effective.

[0194] An example of a prompt message is, "What are the three first steps you should take in the event of an earthquake? Please explain the reasons why."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] When the device starts up, the user is presented with a login screen via a visual display. The user enters and submits their information, which is then received by the server. This login information includes an identification ID and past learning history.

[0198] Step 2:

[0199] The server uses the user's login information to search the database for relevant disaster prevention simulation content. The retrieved data is optimized based on the user's past behavioral history and emotional data. This optimization determines the simulation content appropriate to the user's level and sends it to the terminal.

[0200] Step 3:

[0201] The terminal presents the received content to the user through visual means. The user participates in the displayed simulations and quizzes and makes choices on the spot. The user's facial expressions and voice data are acquired in real time via the camera and microphone and transmitted to the server.

[0202] Step 4:

[0203] The server processes real-time emotional data transmitted from the terminal using emotion analysis tools. Emotion recognition technology analyzes the user's emotional state (e.g., tension, relief). The results of this analysis are used to generate feedback.

[0204] Step 5:

[0205] The server generates feedback using information generation tools based on user behavior data and sentiment analysis results. Natural language processing technology is used to generate feedback in a language easily understood by the user. This feedback, which includes a summary of the learning content and advice, is sent to the terminal.

[0206] Step 6:

[0207] The terminal presents the user with feedback received from the server, either visually or audibly. The user then proceeds to the next simulation step based on this feedback. The user's reactions and emotional changes are reflected in the optimization of the next session.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] The disaster prevention education system of this invention aims to enable users to effectively learn about disasters and acquire practical response skills. This system is centered around an information processing device for knowledge management and provides users with an interactive learning experience.

[0225] Server operation

[0226] The server maintains a database of extensive disaster prevention content and delivers the most relevant learning materials in response to user requests. The server tracks users' learning history and progress, generating personalized simulations and quizzes. Using natural language processing technology, it generates feedback tailored to each user, aiming to improve their understanding.

[0227] Terminal operation

[0228] The terminal provides an interface for the user to interact with the system. Based on the content selected by the user, it visually reproduces various disaster simulations, such as earthquakes and typhoons. User knowledge is evaluated through quiz-style questions, and actions and selections are recorded. The terminal also records user behavior, which serves as basic data for the server to generate feedback based on that data.

[0229] User interaction

[0230] Users can access disaster prevention content through their devices and engage in an interactive learning experience. They can check their understanding by answering quizzes and deepen their learning by receiving feedback from the server. Furthermore, they can develop practical response skills through simulations.

[0231] Specific example

[0232] For example, if an earthquake scenario is selected, the server provides information on earthquake mechanisms and evacuation methods. The terminal presents the user with a virtual space during the earthquake and prompts them to choose a response. The user answers a quiz based on their chosen action, and the results are evaluated by the server, which provides feedback to guide them toward appropriate action. Based on this feedback, the user can review their response strategy.

[0233] These specific embodiments enable the present invention to provide effective learning support for users to develop a high level of understanding of and response capabilities to disasters.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user logs into the system via their device. The device sends the user's authentication information to the server, and if authentication is successful, the server retrieves the user's past learning history.

[0237] Step 2:

[0238] The server analyzes the user's learning history and selects the most suitable learning content based on their current level of understanding. It then sends the selected content information to the user's device.

[0239] Step 3:

[0240] The device displays learning content in its user interface. The user reviews the displayed information and selects simulations or quizzes that interest them.

[0241] Step 4:

[0242] When the user selects a simulation, the terminal launches the simulation operation interface. The server sends the simulation data, and the terminal displays the virtual disaster scenario.

[0243] Step 5:

[0244] The user selects a specified action within the simulation and performs that action. The terminal records the user's selection and sends it to the server.

[0245] Step 6:

[0246] The server analyzes the user's behavior record and generates appropriate feedback. The generated feedback is sent to the device and displayed to the user.

[0247] Step 7:

[0248] The device provides feedback to the user, who then evaluates their own learning based on that feedback. If necessary, they can select additional learning content or simulations to continue learning.

[0249] Step 8:

[0250] When a user ends a learning session, the device sends the latest progress data to the server, which then stores it in a database. This allows the user to continue learning the next time they log in.

[0251] (Example 1)

[0252] 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."

[0253] In disaster prevention education, traditional methods of delivery are limited to passive learning, making it difficult for users to effectively acquire practical response skills. Furthermore, there is a need for a system that provides personalized learning experiences tailored to individual users, rather than simply providing one-way information.

[0254] 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.

[0255] In this invention, the server includes means for performing information processing for knowledge management, means for visually displaying a virtual environment provided by the device performing the information processing, and means for accumulating user selections in the virtual environment. This enables interactive and personalized disaster prevention education and learning for users.

[0256] A "device that performs information processing for knowledge management" is a device that organizes and stores information related to disaster prevention and has the function of selecting the most suitable content according to the user's learning status.

[0257] "Means of visually representing a virtual environment" refers to a means of simulating and visually representing natural disasters such as earthquakes and typhoons, so that users can safely learn about disaster response.

[0258] A "means for accumulating user choices" refers to a means that records user behavior and choices within a virtual environment and stores them for later analysis and feedback.

[0259] A "means for generating corrective information" refers to a means that analyzes accumulated user data and generates feedback, including suggestions for improvement and advice, regarding user behavior.

[0260] The system of this invention provides disaster prevention education aimed at improving disaster response skills. This system functions through the interaction of a server, terminals, and users.

[0261] The server manages a database of disaster prevention information using an information processing device. Web server software such as Apache or Nginx is used here. It also selects the most appropriate disaster prevention content based on the user's learning progress and generates personalized feedback and quizzes using natural language processing technologies such as TensorFlow.

[0262] The terminal provides an interface for users to interact with the system. This could be an iOS or Android mobile device, or a PC running Windows or macOS. The terminal receives content delivered from the server and visually recreates a virtual disaster environment. Here, the terminal records the user's choices and actions, and sends this data to the server for evaluation.

[0263] Users acquire practical disaster preparedness skills through simulations and quizzes provided via their devices. For example, they can simulate scenarios of how to act in the event of an earthquake and experience appropriate responses.

[0264] As a concrete example, if an earthquake is simulated, the server delivers educational materials on the mechanism of shaking and evacuation routes. The terminal provides a visual representation of the virtual space during the earthquake, and the user selects an evacuation action. The user's answers to quizzes presented after this action selection are recorded by the server, and feedback is generated regarding the appropriate action selection.

[0265] An example of a prompt might be, "Please explain what preparations are necessary in the event of a disaster." This prompt provides users with clues to gain deeper insights related to disaster response.

[0266] This invention provides a personalized learning experience, enabling users to efficiently acquire disaster preparedness skills.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The user sends a request to access disaster prevention content using their device. This input involves the user selecting a scenario, such as an earthquake or a typhoon, and choosing from different disaster simulations. The user's selection is transmitted to the server as string information.

[0270] Step 2:

[0271] The server receives a user request and searches the database for relevant disaster prevention content. The input includes the user's selection information and past learning history. The server uses this data to filter and select information, and utilizes a generative AI model to generate optimal learning content. As a result, a personalized set of learning materials is output to the user.

[0272] Step 3:

[0273] The server sends the selected learning content to the device. The output includes visual simulations and quiz-style questions. In this step, data is transferred via network communication, and the device prepares to visually present the received content to the user.

[0274] Step 4:

[0275] The terminal displays a simulation and provides a quiz to the user. It uses content received from the server as input and executes it under its control. The terminal also records user input, such as selections and quiz answers. During this process, animations of the virtual environment are executed, and a user-interactive interface is provided.

[0276] Step 5:

[0277] Users experience a simulation on their device and answer quizzes. Their input, including selections and answers, is recorded. This data is collected for subsequent evaluation and sent to a server.

[0278] Step 6:

[0279] The server receives the user's selection and response data and generates feedback using a generative AI model. It processes the user data as input and creates a specific feedback message as the corresponding output. This feedback is created based on the analysis results of the actions taken by the user and helps with learning and improvement.

[0280] Step 7:

[0281] The terminal receives the feedback from the server and presents it to the user. Here, it includes a specific process of receiving the output from the server, visualizing it, and displaying it to the user. The user can check this feedback and review their disaster prevention skills.

[0282] (Application Example 1)

[0283] 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".

[0284] For urban residents to respond quickly and accurately in case of disasters, it is important to improve disaster prevention awareness in peacetime and acquire specific knowledge and skills regarding the disaster risks specific to the region. However, conventional disaster prevention education has been formal and has limitations in acquiring practical response skills. To improve this, an interactive and personalized learning experience is necessary.

[0285] 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.

[0286] In this invention, the server includes an information processing device for knowledge management, a visualization means for displaying a simulation based on the urban environment, and a recording means for recording the actions of the user in the simulation displayed through the visualization means. Thereby, it becomes possible for urban residents to acquire practical disaster prevention skills according to the characteristics of the region.

[0287] A "knowledge management information processing device" is an electronic device that organizes and stores disaster prevention data and provides it to users in the most optimal format.

[0288] A "visualization means for displaying simulations based on the urban environment" is a device that uses real-world urban geographic information to virtually recreate disaster situations and presents them in a format that users can visually understand.

[0289] A "recording system for documenting user behavior" is a system that saves the choices and responses of users during the learning process as digital data, and uses this data for later analysis and feedback generation.

[0290] An "information generation means" is a processing mechanism that uses collected data to generate user-specific feedback and learning advice.

[0291] A "navigation system" is a device or system that uses urban geographical information to instruct users on appropriate evacuation routes or directions to their destinations.

[0292] This disaster preparedness education system is designed to help urban residents acquire practical response skills to local disasters. The system consists of multiple components, including a server and user terminals.

[0293] The server acts as an information processing device for knowledge management, managing content in various data formats related to disaster prevention education. The data is stored via a cloud service, and personalized simulations and quizzes are generated based on the user's learning history. Specific technologies used include an artificial intelligence-based natural language processing engine built in Python and TensorFlow.

[0294] The terminal is a device that enables interactive communication with the user. For example, an application on a smartphone uses Unity or ARCore to visually recreate a simulated urban environment based on real-world geographical data using augmented reality (AR). This allows users to participate in disaster prevention training in a virtual space in real time. In addition, by recording the user's actions, the server generates and provides feedback to the user based on that data.

[0295] Users access learning content through their devices and assess their abilities through quiz-based evaluations. Furthermore, they learn about real-world evacuation routes and countermeasures using simulations. For example, when heavy rain is expected, the app displays the optimal route to the nearest evacuation shelter and provides interactive instructions.

[0296] Specific examples of prompt statements are as follows:

[0297] "Please select a route from your current location to the nearest safe shelter. Pay attention to your surroundings and confirm the safe route using AR display."

[0298] This will enable urban residents to raise their awareness of disaster prevention and respond quickly and effectively to disasters.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server uses the user's current location and individual learning history as input when they access the system to search for relevant disaster prevention content from a database in the cloud. This data processing outputs optimal learning materials that take into account the user's geographical location and past learning content.

[0302] Step 2:

[0303] The server generates scenario data tailored to the city based on the selected disaster prevention simulation. At this time, a natural language processing engine built in Python is used to automatically construct quizzes and feedback content according to the user. The input is the content data selected in the previous step, and the output is individualized simulation scenarios and quiz questions.

[0304] Step 3:

[0305] The terminal receives the simulation data sent from the server and displays it as an AR environment using Unity and ARCore. The user's input (action selection in the simulation) is recorded, and the impact on the next action is shown. The input is the action selected by the user, and the output is the resulting visual change.

[0306] Step 4:

[0307] The user answers the quiz presented by the terminal, and the result is sent to the server. Here, the user's thought process and options are accumulated, and data enabling individual learning feedback is generated. The input is the user's quiz answer, and the output is the answer result and its analysis data.

[0308] Step 5:

[0309] The server aggregates the user's action data and quiz results and generates feedback using natural language processing technology. This feedback is sent as the user's email address or in-app message and provided as insights for continuous learning. The input is the past learning history and newly obtained data, and the output is a personalized feedback message.

[0310] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0311] This disaster prevention education system is designed to enable users to effectively learn the knowledge and skills necessary to prepare for disasters. The system incorporates an emotion engine that recognizes and responds to the user's emotional state.

[0312] Server operation

[0313] The server stores disaster prevention education-related content in a database and delivers appropriate learning materials in response to user requests. In addition, it uses an emotion engine to process user emotion data and personalize learning content and feedback. Emotional information is combined with the user's learning history to generate optimized feedback. Furthermore, natural language processing technology is used to provide feedback that is easy for users to understand.

[0314] Terminal operation

[0315] The terminal visually displays disaster prevention content through a user interface, providing an environment where users can interact with the system. It displays simulation and quiz-style content and records user selections in real time. In addition, it uses a camera and microphone to input the user's facial expressions and voice tone into an emotion engine to sense the user's emotional state.

[0316] User interaction

[0317] Users access the disaster prevention system and learn through presented simulations and quizzes. During learning, the emotion engine analyzes the user's facial expressions and voice to recognize changes in stress levels and concentration. Based on this information, the system adjusts the learning pace and content to suit the user and provides appropriate support. For example, if the user is feeling stressed, the system may simplify explanations or display encouraging messages to help them relax.

[0318] Specific example

[0319] For example, when a user participates in an earthquake evacuation simulation, the device displays the simulation environment and prompts the user to act. When the user is acting calmly, the system gradually increases the difficulty of the tasks and provides more positive feedback. On the other hand, if the emotion engine detects the user's anxiety, it emphasizes positive feedback more when actions are successful, working to boost the user's confidence.

[0320] This invention makes it possible to improve the efficiency and effectiveness of learning by accurately recognizing the user's emotional state and providing flexible educational content accordingly.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The user logs into the system using their device. The device sends the user's input information to the server, which verifies the user's authentication and then retrieves their past learning history.

[0324] Step 2:

[0325] The server utilizes an emotion engine to analyze user emotion data (e.g., facial expressions and tone of voice) transmitted in real time from the device. This allows it to determine the user's current emotional state.

[0326] Step 3:

[0327] The server generates optimal learning content based on the user's past learning history and current emotional state. For example, if the user is relaxed, it may suggest a more challenging simulation.

[0328] Step 4:

[0329] The device visually displays learning content sent from the server to the user. The user views the content (simulations and quizzes) and selects or performs specific actions.

[0330] Step 5:

[0331] When a user starts a simulation, the device records the user's actions and choices, and simultaneously sends data to the server in real time to track changes in the user's facial expressions and voice using an emotion engine.

[0332] Step 6:

[0333] The server generates appropriate feedback based on recorded user behavior and emotional data. It also adjusts the content of the feedback according to the user's state and offers suggestions for stress reduction as needed.

[0334] Step 7:

[0335] The device presents the generated feedback to the user. Based on the feedback, the user can reflect on their learning and choose what to learn next.

[0336] Step 8:

[0337] When a user finishes a learning session, the device sends data on their latest learning progress and emotional responses to the server. The server stores this data in a database and uses it to optimize the learning experience during their next login.

[0338] (Example 2)

[0339] 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".

[0340] Traditional disaster prevention education systems often provided uniform content without considering users' learning progress or emotional states. This made it difficult to provide personalized learning experiences tailored to individual users, resulting in decreased learning efficiency. Furthermore, they lacked features to provide appropriate support when users felt stressed or anxious.

[0341] 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.

[0342] In this invention, the server includes information processing means for processing knowledge information, display means for displaying a virtual experience, recording means for recording the user's activities, emotion analysis means for detecting and analyzing emotional states, and content adjustment means for adjusting learning content. This makes it possible to provide optimal learning materials and feedback tailored to each user's emotional state and learning history.

[0343] "Information processing means for processing knowledge information" refers to means that have the function of analyzing the user's learning data and past history to select the most suitable educational content.

[0344] A "display means for displaying virtual experiences" is a device that provides users with visual learning simulations and quizzes, thereby realizing an interactive learning environment.

[0345] "Means for recording user activity" refers to means that have the function of tracking the choices and actions taken by the user during a virtual experience and saving them as data.

[0346] An "emotional analysis means for detecting and analyzing emotional states" is a means that has the function of grasping the user's emotional state in real time through facial expressions and voice, and analyzing that information.

[0347] "Content adjustment means for adjusting learning content" refers to a means for flexibly changing the difficulty level and content of learning materials according to the user's emotional state and learning progress, thereby providing an individually optimized learning experience.

[0348] The disaster prevention education system of this invention is designed to enable users to effectively learn the knowledge and skills necessary in the event of a disaster. Embodiments of this system are described below.

[0349] First, the server uses software to process knowledge information and provides personalized educational content based on the user's learning history and emotional data. This software stores content using a database management system and selects appropriate learning materials in response to user requests. In addition, computer vision and speech analysis technologies are used as emotional analysis tools to analyze the user's facial expressions and tone of voice in real time. This allows for the acquisition of data to adjust the learning pace and content of the learning materials.

[0350] The terminal includes an interface device for visually providing a virtual experience. Specific examples include a device equipped with a display, a touchscreen, a camera, and a microphone. The terminal displays simulation and quiz-style content received from the server and interactively records the user's choices and responses. It also uses its built-in camera and microphone to continuously transmit the user's emotional state to an emotion analysis system.

[0351] Users participate in simulations and quizzes presented through their devices. For example, in an earthquake evacuation simulation, users select actions based on the displayed scenario. Through emotion analysis, it is determined whether the user is relaxed or tense, and the results are reflected in the learning process.

[0352] As a concrete example, when a user answers a quiz asking "What should you do first during an earthquake?", the server can consider the user's past accuracy rate and current emotional state to generate appropriate feedback. The generative AI model used generates feedback and encouragement based on a variety of prompts from the user.

[0353] An example of a prompt might be, "In a disaster simulation, please tell us what to do if the user is stressed. Also, please give an example of how to alleviate the user's stress." Through such prompts, the system implements techniques to provide appropriate support.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user logs into the system using a terminal. Login information is sent to the terminal as input, and the terminal forwards this information to the server. The server authenticates the user's login information and performs data processing by retrieving relevant data from the learning history database. The authentication result and the user's learning history are generated as output.

[0357] Step 2:

[0358] The server receives the user's learning history as input and selects appropriate disaster prevention education content based on past learning. The selected content is generated as output and sent to the user's terminal. This data processing determines personalized initial learning materials for the user.

[0359] Step 3:

[0360] The terminal receives educational content sent from the server and displays it through a user interface. Specifically, it presents the user with visual simulations and text-based quiz content. The input is content data from the server, and the output is interface information displayed on the screen.

[0361] Step 4:

[0362] The user progresses through the presented simulations and quizzes in sequence. The user's selected actions are recorded as input on the terminal. The terminal prepares to send this action data to the server, and the selected data is returned as output. For example, if the user selects "evacuate," that selection is recorded and transmitted to the server.

[0363] Step 5:

[0364] The device's camera and microphone capture the user's facial expressions and voice tone as input. The device sends this data to an emotion analysis system, which then performs specific actions to analyze the user's emotional state. The analysis results are generated as output and sent to the server.

[0365] Step 6:

[0366] The server receives the results of sentiment analysis and user selection data as input, and uses a generative AI model to generate feedback for the user. The feedback includes advice and encouraging messages to adjust the learning pace. The output feedback takes into account the user's emotional state.

[0367] Step 7:

[0368] The terminal receives feedback from the server and presents it to the user. The input to this process is feedback data sent from the server, and the output is messages and guidance displayed on the user's screen. Specifically, it displays evaluations based on the user's progress and advice on the next steps.

[0369] (Application Example 2)

[0370] 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".

[0371] In modern society, with the proliferation of autonomous vehicles, disaster preparedness education for drivers and passengers is crucial. However, conventional disaster preparedness education systems have struggled to provide personalized feedback tailored to the individual user's emotions and circumstances. Furthermore, these systems lacked the real-time interaction and emotional recognition necessary to maximize the effectiveness of education for users.

[0372] 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.

[0373] In this invention, the server includes information processing means for managing knowledge, visual display means for displaying simulations provided by the information processing means, recording means for recording the user's actions during the simulation, emotion analysis means for analyzing the user's emotions using emotion recognition technology, and information generation means for generating feedback based on the data obtained by the recording means and the emotion analysis means. This enables personalized disaster prevention learning in real time according to the user's emotional state.

[0374] "Information processing means for knowledge management" refers to devices and systems that process information to appropriately collect, organize, and provide disaster prevention-related information to users.

[0375] "Visual display means" refers to devices or systems for visually presenting simulations and learning content generated by information processing means to users.

[0376] "Recording means" refers to a device or system that has the function of saving the user's actions and responses during a simulation.

[0377] "Emotion analysis means" refers to technologies and devices that use information such as a user's facial expressions and voice to recognize and analyze their emotions in real time.

[0378] "Information generation means" refers to devices or systems for creating appropriate feedback and educational content based on data obtained from recording means and emotion analysis means.

[0379] The system for implementing this invention consists of a server and a terminal. The server is equipped with information processing means for knowledge management and stores disaster prevention-related information in a database. This data is delivered to the terminal in a simulation format via a visual display means described later. The server is also equipped with emotion analysis means that receives and analyzes emotion data transmitted by the user in real time. General emotion recognition software, such as AWS Rekognition or Google Cloud Vision AI, can be used for emotion analysis.

[0380] The device is equipped with visual display and recording capabilities. The visual display allows users to access simulation and problem-solving content. The device also includes a camera and microphone, which capture and transmit the user's facial expressions and voice data to a server. Based on this information, an emotion analysis system analyzes the user's stress level and concentration to determine the optimal learning pace and content. The generated feedback is communicated to the user using natural language processing technology. Google Dialogflow, for example, can be used for natural language processing.

[0381] As a concrete example, when a user participates in a simulation using a device, their current emotional state is analyzed based on real-time data collected through the camera and microphone. Depending on the results, if the user is feeling fear or tension, for example, a simple response tailored to that situation can be generated. The generated feedback is presented to the user in a gentle and reassuring tone. Through this process, the user's learning experience is made natural and effective.

[0382] An example of a prompt message is, "What are the three first steps you should take in the event of an earthquake? Please explain the reasons why."

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] When the device starts up, the user is presented with a login screen via a visual display. The user enters and submits their information, which is then received by the server. This login information includes an identification ID and past learning history.

[0386] Step 2:

[0387] The server uses the user's login information to search the database for relevant disaster prevention simulation content. The retrieved data is optimized based on the user's past behavioral history and emotional data. This optimization determines the simulation content appropriate to the user's level and sends it to the terminal.

[0388] Step 3:

[0389] The terminal presents the received content to the user through visual means. The user participates in the displayed simulations and quizzes and makes choices on the spot. The user's facial expressions and voice data are acquired in real time via the camera and microphone and transmitted to the server.

[0390] Step 4:

[0391] The server processes real-time emotional data transmitted from the terminal using emotion analysis tools. Emotion recognition technology analyzes the user's emotional state (e.g., tension, relief). The results of this analysis are used to generate feedback.

[0392] Step 5:

[0393] The server generates feedback using information generation tools based on user behavior data and sentiment analysis results. Natural language processing technology is used to generate feedback in a language easily understood by the user. This feedback, which includes a summary of the learning content and advice, is sent to the terminal.

[0394] Step 6:

[0395] The terminal presents the user with feedback received from the server, either visually or audibly. The user then proceeds to the next simulation step based on this feedback. The user's reactions and emotional changes are reflected in the optimization of the next session.

[0396] 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.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] 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.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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".

[0412] The disaster prevention education system of this invention aims to enable users to effectively learn about disasters and acquire practical response skills. This system is centered around an information processing device for knowledge management and provides users with an interactive learning experience.

[0413] Server operation

[0414] The server maintains a database of extensive disaster prevention content and delivers the most relevant learning materials in response to user requests. The server tracks users' learning history and progress, generating personalized simulations and quizzes. Using natural language processing technology, it generates feedback tailored to each user, aiming to improve their understanding.

[0415] Terminal operation

[0416] The terminal provides an interface for the user to interact with the system. Based on the content selected by the user, it visually reproduces various disaster simulations, such as earthquakes and typhoons. User knowledge is evaluated through quiz-style questions, and actions and selections are recorded. The terminal also records user behavior, which serves as basic data for the server to generate feedback based on that data.

[0417] User interaction

[0418] Users can access disaster prevention content through their devices and engage in an interactive learning experience. They can check their understanding by answering quizzes and deepen their learning by receiving feedback from the server. Furthermore, they can develop practical response skills through simulations.

[0419] Specific example

[0420] For example, if an earthquake scenario is selected, the server provides information on earthquake mechanisms and evacuation methods. The terminal presents the user with a virtual space during the earthquake and prompts them to choose a response. The user answers a quiz based on their chosen action, and the results are evaluated by the server, which provides feedback to guide them toward appropriate action. Based on this feedback, the user can review their response strategy.

[0421] These specific embodiments enable the present invention to provide effective learning support for users to develop a high level of understanding of and response capabilities to disasters.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user logs into the system via their device. The device sends the user's authentication information to the server, and if authentication is successful, the server retrieves the user's past learning history.

[0425] Step 2:

[0426] The server analyzes the user's learning history and selects the most suitable learning content based on their current level of understanding. It then sends the selected content information to the user's device.

[0427] Step 3:

[0428] The device displays learning content in its user interface. The user reviews the displayed information and selects simulations or quizzes that interest them.

[0429] Step 4:

[0430] When the user selects a simulation, the terminal launches the simulation operation interface. The server sends the simulation data, and the terminal displays the virtual disaster scenario.

[0431] Step 5:

[0432] The user selects a specified action within the simulation and performs that action. The terminal records the user's selection and sends it to the server.

[0433] Step 6:

[0434] The server analyzes the user's behavior record and generates appropriate feedback. The generated feedback is sent to the device and displayed to the user.

[0435] Step 7:

[0436] The device provides feedback to the user, who then evaluates their own learning based on that feedback. If necessary, they can select additional learning content or simulations to continue learning.

[0437] Step 8:

[0438] When a user ends a learning session, the device sends the latest progress data to the server, which then stores it in a database. This allows the user to continue learning the next time they log in.

[0439] (Example 1)

[0440] 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."

[0441] In disaster prevention education, traditional methods of delivery are limited to passive learning, making it difficult for users to effectively acquire practical response skills. Furthermore, there is a need for a system that provides personalized learning experiences tailored to individual users, rather than simply providing one-way information.

[0442] 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.

[0443] In this invention, the server includes means for performing information processing for knowledge management, means for visually displaying a virtual environment provided by the device performing the information processing, and means for accumulating user selections in the virtual environment. This enables interactive and personalized disaster prevention education and learning for users.

[0444] A "device that performs information processing for knowledge management" is a device that organizes and stores information related to disaster prevention and has the function of selecting the most suitable content according to the user's learning status.

[0445] "Means of visually representing a virtual environment" refers to a means of simulating and visually representing natural disasters such as earthquakes and typhoons, so that users can safely learn about disaster response.

[0446] A "means for accumulating user choices" refers to a means that records user behavior and choices within a virtual environment and stores them for later analysis and feedback.

[0447] A "means for generating corrective information" refers to a means that analyzes accumulated user data and generates feedback, including suggestions for improvement and advice, regarding user behavior.

[0448] The system of this invention provides disaster prevention education aimed at improving disaster response skills. This system functions through the interaction of a server, terminals, and users.

[0449] The server manages a database of disaster prevention information using an information processing device. Web server software such as Apache or Nginx is used here. It also selects the most appropriate disaster prevention content based on the user's learning progress and generates personalized feedback and quizzes using natural language processing technologies such as TensorFlow.

[0450] The terminal provides an interface for users to interact with the system. This could be an iOS or Android mobile device, or a PC running Windows or macOS. The terminal receives content delivered from the server and visually recreates a virtual disaster environment. Here, the terminal records the user's choices and actions, and sends this data to the server for evaluation.

[0451] Users acquire practical disaster preparedness skills through simulations and quizzes provided via their devices. For example, they can simulate scenarios of how to act in the event of an earthquake and experience appropriate responses.

[0452] As a concrete example, if an earthquake is simulated, the server delivers educational materials on the mechanism of shaking and evacuation routes. The terminal provides a visual representation of the virtual space during the earthquake, and the user selects an evacuation action. The user's answers to quizzes presented after this action selection are recorded by the server, and feedback is generated regarding the appropriate action selection.

[0453] An example of a prompt might be, "Please explain what preparations are necessary in the event of a disaster." This prompt provides users with clues to gain deeper insights related to disaster response.

[0454] This invention provides a personalized learning experience, enabling users to efficiently acquire disaster preparedness skills.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The user sends a request to access disaster prevention content using their device. This input involves the user selecting a scenario, such as an earthquake or a typhoon, and choosing from different disaster simulations. The user's selection is transmitted to the server as string information.

[0458] Step 2:

[0459] The server receives a user request and searches the database for relevant disaster prevention content. The input includes the user's selection information and past learning history. The server uses this data to filter and select information, and utilizes a generative AI model to generate optimal learning content. As a result, a personalized set of learning materials is output to the user.

[0460] Step 3:

[0461] The server sends the selected learning content to the device. The output includes visual simulations and quiz-style questions. In this step, data is transferred via network communication, and the device prepares to visually present the received content to the user.

[0462] Step 4:

[0463] The terminal displays a simulation and provides a quiz to the user. It uses content received from the server as input and executes it under its control. The terminal also records user input, such as selections and quiz answers. During this process, animations of the virtual environment are executed, and a user-interactive interface is provided.

[0464] Step 5:

[0465] Users experience a simulation on their device and answer quizzes. Their input, including selections and answers, is recorded. This data is collected for subsequent evaluation and sent to a server.

[0466] Step 6:

[0467] The server receives user selection and response data and generates feedback using a generative AI model. It processes user data as input and creates specific feedback messages as corresponding output. This feedback is based on an analysis of the user's actions and supports learning improvement.

[0468] Step 7:

[0469] The terminal receives feedback from the server and presents it to the user. This includes a specific process of receiving output from the server, visualizing it, and displaying it to the user. The user can review this feedback and reassess their disaster preparedness skills.

[0470] (Application Example 1)

[0471] 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."

[0472] For urban residents to respond quickly and effectively during disasters, it is crucial to raise disaster preparedness awareness during peacetime and acquire specific knowledge and skills regarding region-specific disaster risks. However, traditional disaster prevention education has been formal and has limitations in acquiring practical response skills. To improve this, interactive and personalized learning experiences are necessary.

[0473] 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.

[0474] In this invention, the server includes an information processing device for knowledge management, a visualization means for displaying a simulation based on the urban environment, and a recording means for recording the user's actions during the simulation displayed through the visualization means. This makes it possible for urban residents to acquire practical disaster prevention skills tailored to the characteristics of their region.

[0475] A "knowledge management information processing device" is an electronic device that organizes and stores disaster prevention data and provides it to users in the most optimal format.

[0476] A "visualization means for displaying simulations based on the urban environment" is a device that uses real-world urban geographic information to virtually recreate disaster situations and presents them in a format that users can visually understand.

[0477] A "recording system for documenting user behavior" is a system that saves the choices and responses of users during the learning process as digital data, and uses this data for later analysis and feedback generation.

[0478] An "information generation means" is a processing mechanism that uses collected data to generate user-specific feedback and learning advice.

[0479] A "navigation system" is a device or system that uses urban geographical information to instruct users on appropriate evacuation routes or directions to their destinations.

[0480] This disaster preparedness education system is designed to help urban residents acquire practical response skills to local disasters. The system consists of multiple components, including a server and user terminals.

[0481] The server acts as an information processing device for knowledge management, managing content in various data formats related to disaster prevention education. The data is stored via a cloud service, and personalized simulations and quizzes are generated based on the user's learning history. Specific technologies used include an artificial intelligence-based natural language processing engine built in Python and TensorFlow.

[0482] The terminal is a device that enables interactive communication with the user. For example, an application on a smartphone uses Unity or ARCore to visually recreate a simulated urban environment based on real-world geographical data using augmented reality (AR). This allows users to participate in disaster prevention training in a virtual space in real time. In addition, by recording the user's actions, the server generates and provides feedback to the user based on that data.

[0483] Users access learning content through their devices and assess their abilities through quiz-based evaluations. Furthermore, they learn about real-world evacuation routes and countermeasures using simulations. For example, when heavy rain is expected, the app displays the optimal route to the nearest evacuation shelter and provides interactive instructions.

[0484] Specific examples of prompt statements are as follows:

[0485] "Please select a route from your current location to the nearest safe shelter. Pay attention to your surroundings and confirm the safe route using AR display."

[0486] This will enable urban residents to raise their awareness of disaster prevention and respond quickly and effectively to disasters.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] The server uses the user's current location and individual learning history as input when they access the system to search for relevant disaster prevention content from a database in the cloud. This data processing outputs optimal learning materials that take into account the user's geographical location and past learning content.

[0490] Step 2:

[0491] The server generates scenario data tailored to the city based on the selected disaster prevention simulation. It utilizes a natural language processing engine built in Python to automatically construct user-specific quizzes and feedback. The input is the content data selected in the previous step, and the output is a personalized simulation scenario and quiz questions.

[0492] Step 3:

[0493] The device receives simulation data sent from the server and displays it as an AR environment using Unity and ARCore. User input (action selections in the simulation) is recorded, and its impact on subsequent actions is shown. The input is the user's selected action, and the output is the resulting visual change.

[0494] Step 4:

[0495] The user answers a quiz presented on their device, and the results are sent to the server. Here, the user's thought process and choices are accumulated, generating data that enables personalized learning feedback. The input is the user's quiz answers, and the output is the answer results and their analysis data.

[0496] Step 5:

[0497] The server aggregates user behavior data and quiz results, and generates feedback using natural language processing technology. This feedback is sent to the user's email address or as an in-app message, providing insights for continuous learning. The input consists of past learning history and newly acquired data, and the output is a personalized feedback message.

[0498] 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.

[0499] This disaster prevention education system is designed to enable users to effectively learn the knowledge and skills necessary to prepare for disasters. The system incorporates an emotion engine that recognizes and responds to the user's emotional state.

[0500] Server operation

[0501] The server stores disaster prevention education-related content in a database and delivers appropriate learning materials in response to user requests. In addition, it uses an emotion engine to process user emotion data and personalize learning content and feedback. Emotional information is combined with the user's learning history to generate optimized feedback. Furthermore, natural language processing technology is used to provide feedback that is easy for users to understand.

[0502] Terminal operation

[0503] The terminal visually displays disaster prevention content through a user interface, providing an environment where users can interact with the system. It displays simulation and quiz-style content and records user selections in real time. In addition, it uses a camera and microphone to input the user's facial expressions and voice tone into an emotion engine to sense the user's emotional state.

[0504] User interaction

[0505] Users access the disaster prevention system and learn through presented simulations and quizzes. During learning, the emotion engine analyzes the user's facial expressions and voice to recognize changes in stress levels and concentration. Based on this information, the system adjusts the learning pace and content to suit the user and provides appropriate support. For example, if the user is feeling stressed, the system may simplify explanations or display encouraging messages to help them relax.

[0506] Specific example

[0507] For example, when a user participates in an earthquake evacuation simulation, the device displays the simulation environment and prompts the user to act. When the user is acting calmly, the system gradually increases the difficulty of the tasks and provides more positive feedback. On the other hand, if the emotion engine detects the user's anxiety, it emphasizes positive feedback more when actions are successful, working to boost the user's confidence.

[0508] This invention makes it possible to improve the efficiency and effectiveness of learning by accurately recognizing the user's emotional state and providing flexible educational content accordingly.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user logs into the system using their device. The device sends the user's input information to the server, which verifies the user's authentication and then retrieves their past learning history.

[0512] Step 2:

[0513] The server utilizes an emotion engine to analyze user emotion data (e.g., facial expressions and tone of voice) transmitted in real time from the device. This allows it to determine the user's current emotional state.

[0514] Step 3:

[0515] The server generates optimal learning content based on the user's past learning history and current emotional state. For example, if the user is relaxed, it may suggest a more challenging simulation.

[0516] Step 4:

[0517] The device visually displays learning content sent from the server to the user. The user views the content (simulations and quizzes) and selects or performs specific actions.

[0518] Step 5:

[0519] When a user starts a simulation, the device records the user's actions and choices, and simultaneously sends data to the server in real time to track changes in the user's facial expressions and voice using an emotion engine.

[0520] Step 6:

[0521] The server generates appropriate feedback based on recorded user behavior and emotional data. It also adjusts the content of the feedback according to the user's state and offers suggestions for stress reduction as needed.

[0522] Step 7:

[0523] The device presents the generated feedback to the user. Based on the feedback, the user can reflect on their learning and choose what to learn next.

[0524] Step 8:

[0525] When a user finishes a learning session, the device sends data on their latest learning progress and emotional responses to the server. The server stores this data in a database and uses it to optimize the learning experience during their next login.

[0526] (Example 2)

[0527] 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."

[0528] Traditional disaster prevention education systems often provided uniform content without considering users' learning progress or emotional states. This made it difficult to provide personalized learning experiences tailored to individual users, resulting in decreased learning efficiency. Furthermore, they lacked features to provide appropriate support when users felt stressed or anxious.

[0529] 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.

[0530] In this invention, the server includes information processing means for processing knowledge information, display means for displaying a virtual experience, recording means for recording the user's activities, emotion analysis means for detecting and analyzing emotional states, and content adjustment means for adjusting learning content. This makes it possible to provide optimal learning materials and feedback tailored to each user's emotional state and learning history.

[0531] "Information processing means for processing knowledge information" refers to means that have the function of analyzing the user's learning data and past history to select the most suitable educational content.

[0532] A "display means for displaying virtual experiences" is a device that provides users with visual learning simulations and quizzes, thereby realizing an interactive learning environment.

[0533] "Means for recording user activity" refers to means that have the function of tracking the choices and actions taken by the user during a virtual experience and saving them as data.

[0534] An "emotional analysis means for detecting and analyzing emotional states" is a means that has the function of grasping the user's emotional state in real time through facial expressions and voice, and analyzing that information.

[0535] "Content adjustment means for adjusting learning content" refers to a means for flexibly changing the difficulty level and content of learning materials according to the user's emotional state and learning progress, thereby providing an individually optimized learning experience.

[0536] The disaster prevention education system of this invention is designed to enable users to effectively learn the knowledge and skills necessary in the event of a disaster. Embodiments of this system are described below.

[0537] First, the server uses software to process knowledge information and provides personalized educational content based on the user's learning history and emotional data. This software stores content using a database management system and selects appropriate learning materials in response to user requests. In addition, computer vision and speech analysis technologies are used as emotional analysis tools to analyze the user's facial expressions and tone of voice in real time. This allows for the acquisition of data to adjust the learning pace and content of the learning materials.

[0538] The terminal includes an interface device for visually providing a virtual experience. Specific examples include a device equipped with a display, a touchscreen, a camera, and a microphone. The terminal displays simulation and quiz-style content received from the server and interactively records the user's choices and responses. It also uses its built-in camera and microphone to continuously transmit the user's emotional state to an emotion analysis system.

[0539] Users participate in simulations and quizzes presented through their devices. For example, in an earthquake evacuation simulation, users select actions based on the displayed scenario. Through emotion analysis, it is determined whether the user is relaxed or tense, and the results are reflected in the learning process.

[0540] As a concrete example, when a user answers a quiz asking "What should you do first during an earthquake?", the server can consider the user's past accuracy rate and current emotional state to generate appropriate feedback. The generative AI model used generates feedback and encouragement based on a variety of prompts from the user.

[0541] An example of a prompt might be, "In a disaster simulation, please tell us what to do if the user is stressed. Also, please give an example of how to alleviate the user's stress." Through such prompts, the system implements techniques to provide appropriate support.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The user logs into the system using a terminal. Login information is sent to the terminal as input, and the terminal forwards this information to the server. The server authenticates the user's login information and performs data processing by retrieving relevant data from the learning history database. The authentication result and the user's learning history are generated as output.

[0545] Step 2:

[0546] The server receives the user's learning history as input and selects appropriate disaster prevention education content based on past learning. The selected content is generated as output and sent to the user's terminal. This data processing determines personalized initial learning materials for the user.

[0547] Step 3:

[0548] The terminal receives educational content sent from the server and displays it through a user interface. Specifically, it presents the user with visual simulations and text-based quiz content. The input is content data from the server, and the output is interface information displayed on the screen.

[0549] Step 4:

[0550] The user progresses through the presented simulations and quizzes in sequence. The user's selected actions are recorded as input on the terminal. The terminal prepares to send this action data to the server, and the selected data is returned as output. For example, if the user selects "evacuate," that selection is recorded and transmitted to the server.

[0551] Step 5:

[0552] The device's camera and microphone capture the user's facial expressions and voice tone as input. The device sends this data to an emotion analysis system, which then performs specific actions to analyze the user's emotional state. The analysis results are generated as output and sent to the server.

[0553] Step 6:

[0554] The server receives the results of sentiment analysis and user selection data as input, and uses a generative AI model to generate feedback for the user. The feedback includes advice and encouraging messages to adjust the learning pace. The output feedback takes into account the user's emotional state.

[0555] Step 7:

[0556] The terminal receives feedback from the server and presents it to the user. The input to this process is feedback data sent from the server, and the output is messages and guidance displayed on the user's screen. Specifically, it displays evaluations based on the user's progress and advice on the next steps.

[0557] (Application Example 2)

[0558] 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."

[0559] In modern society, with the proliferation of autonomous vehicles, disaster preparedness education for drivers and passengers is crucial. However, conventional disaster preparedness education systems have struggled to provide personalized feedback tailored to the individual user's emotions and circumstances. Furthermore, these systems lacked the real-time interaction and emotional recognition necessary to maximize the effectiveness of education for users.

[0560] 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.

[0561] In this invention, the server includes information processing means for managing knowledge, visual display means for displaying simulations provided by the information processing means, recording means for recording the user's actions during the simulation, emotion analysis means for analyzing the user's emotions using emotion recognition technology, and information generation means for generating feedback based on the data obtained by the recording means and the emotion analysis means. This enables personalized disaster prevention learning in real time according to the user's emotional state.

[0562] "Information processing means for knowledge management" refers to devices and systems that process information to appropriately collect, organize, and provide disaster prevention-related information to users.

[0563] "Visual display means" refers to devices or systems for visually presenting simulations and learning content generated by information processing means to users.

[0564] "Recording means" refers to a device or system that has the function of saving the user's actions and responses during a simulation.

[0565] "Emotion analysis means" refers to technologies and devices that use information such as a user's facial expressions and voice to recognize and analyze their emotions in real time.

[0566] "Information generation means" refers to devices or systems for creating appropriate feedback and educational content based on data obtained from recording means and emotion analysis means.

[0567] The system for implementing this invention consists of a server and a terminal. The server is equipped with information processing means for knowledge management and stores disaster prevention-related information in a database. This data is delivered to the terminal in a simulation format via a visual display means described later. The server is also equipped with emotion analysis means that receives and analyzes emotion data transmitted by the user in real time. General emotion recognition software, such as AWS Rekognition or Google Cloud Vision AI, can be used for emotion analysis.

[0568] The device is equipped with visual display and recording capabilities. The visual display allows users to access simulation and problem-solving content. The device also includes a camera and microphone, which capture and transmit the user's facial expressions and voice data to a server. Based on this information, an emotion analysis system analyzes the user's stress level and concentration to determine the optimal learning pace and content. The generated feedback is communicated to the user using natural language processing technology. Google Dialogflow, for example, can be used for natural language processing.

[0569] As a concrete example, when a user participates in a simulation using a device, their current emotional state is analyzed based on real-time data collected through the camera and microphone. Depending on the results, if the user is feeling fear or tension, for example, a simple response tailored to that situation can be generated. The generated feedback is presented to the user in a gentle and reassuring tone. Through this process, the user's learning experience is made natural and effective.

[0570] An example of a prompt message is, "What are the three first steps you should take in the event of an earthquake? Please explain the reasons why."

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] When the device starts up, the user is presented with a login screen via a visual display. The user enters and submits their information, which is then received by the server. This login information includes an identification ID and past learning history.

[0574] Step 2:

[0575] The server uses the user's login information to search the database for relevant disaster prevention simulation content. The retrieved data is optimized based on the user's past behavioral history and emotional data. This optimization determines the simulation content appropriate to the user's level and sends it to the terminal.

[0576] Step 3:

[0577] The terminal presents the received content to the user through visual means. The user participates in the displayed simulations and quizzes and makes choices on the spot. The user's facial expressions and voice data are acquired in real time via the camera and microphone and transmitted to the server.

[0578] Step 4:

[0579] The server processes real-time emotional data transmitted from the terminal using emotion analysis tools. Emotion recognition technology analyzes the user's emotional state (e.g., tension, relief). The results of this analysis are used to generate feedback.

[0580] Step 5:

[0581] The server generates feedback using information generation tools based on user behavior data and sentiment analysis results. Natural language processing technology is used to generate feedback in a language easily understood by the user. This feedback, which includes a summary of the learning content and advice, is sent to the terminal.

[0582] Step 6:

[0583] The terminal presents the user with feedback received from the server, either visually or audibly. The user then proceeds to the next simulation step based on this feedback. The user's reactions and emotional changes are reflected in the optimization of the next session.

[0584] 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.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] 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.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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".

[0601] The disaster prevention education system of this invention aims to enable users to effectively learn about disasters and acquire practical response skills. This system is centered around an information processing device for knowledge management and provides users with an interactive learning experience.

[0602] Server operation

[0603] The server maintains a database of extensive disaster prevention content and delivers the most relevant learning materials in response to user requests. The server tracks users' learning history and progress, generating personalized simulations and quizzes. Using natural language processing technology, it generates feedback tailored to each user, aiming to improve their understanding.

[0604] Terminal operation

[0605] The terminal provides an interface for the user to interact with the system. Based on the content selected by the user, it visually reproduces various disaster simulations, such as earthquakes and typhoons. User knowledge is evaluated through quiz-style questions, and actions and selections are recorded. The terminal also records user behavior, which serves as basic data for the server to generate feedback based on that data.

[0606] User interaction

[0607] Users can access disaster prevention content through their devices and engage in an interactive learning experience. They can check their understanding by answering quizzes and deepen their learning by receiving feedback from the server. Furthermore, they can develop practical response skills through simulations.

[0608] Specific example

[0609] For example, if an earthquake scenario is selected, the server provides information on earthquake mechanisms and evacuation methods. The terminal presents the user with a virtual space during the earthquake and prompts them to choose a response. The user answers a quiz based on their chosen action, and the results are evaluated by the server, which provides feedback to guide them toward appropriate action. Based on this feedback, the user can review their response strategy.

[0610] These specific embodiments enable the present invention to provide effective learning support for users to develop a high level of understanding of and response capabilities to disasters.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The user logs into the system via their device. The device sends the user's authentication information to the server, and if authentication is successful, the server retrieves the user's past learning history.

[0614] Step 2:

[0615] The server analyzes the user's learning history and selects the most suitable learning content based on their current level of understanding. It then sends the selected content information to the user's device.

[0616] Step 3:

[0617] The device displays learning content in its user interface. The user reviews the displayed information and selects simulations or quizzes that interest them.

[0618] Step 4:

[0619] When the user selects a simulation, the terminal launches the simulation operation interface. The server sends the simulation data, and the terminal displays the virtual disaster scenario.

[0620] Step 5:

[0621] The user selects a specified action within the simulation and performs that action. The terminal records the user's selection and sends it to the server.

[0622] Step 6:

[0623] The server analyzes the user's behavior record and generates appropriate feedback. The generated feedback is sent to the device and displayed to the user.

[0624] Step 7:

[0625] The device provides feedback to the user, who then evaluates their own learning based on that feedback. If necessary, they can select additional learning content or simulations to continue learning.

[0626] Step 8:

[0627] When a user ends a learning session, the device sends the latest progress data to the server, which then stores it in a database. This allows the user to continue learning the next time they log in.

[0628] (Example 1)

[0629] 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".

[0630] In disaster prevention education, traditional methods of delivery are limited to passive learning, making it difficult for users to effectively acquire practical response skills. Furthermore, there is a need for a system that provides personalized learning experiences tailored to individual users, rather than simply providing one-way information.

[0631] 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.

[0632] In this invention, the server includes means for performing information processing for knowledge management, means for visually displaying a virtual environment provided by the device performing the information processing, and means for accumulating user selections in the virtual environment. This enables interactive and personalized disaster prevention education and learning for users.

[0633] A "device that performs information processing for knowledge management" is a device that organizes and stores information related to disaster prevention and has the function of selecting the most suitable content according to the user's learning status.

[0634] "Means of visually representing a virtual environment" refers to a means of simulating and visually representing natural disasters such as earthquakes and typhoons, so that users can safely learn about disaster response.

[0635] A "means for accumulating user choices" refers to a means that records user behavior and choices within a virtual environment and stores them for later analysis and feedback.

[0636] A "means for generating corrective information" refers to a means that analyzes accumulated user data and generates feedback, including suggestions for improvement and advice, regarding user behavior.

[0637] The system of this invention provides disaster prevention education aimed at improving disaster response skills. This system functions through the interaction of a server, terminals, and users.

[0638] The server manages a database of disaster prevention information using an information processing device. Web server software such as Apache or Nginx is used here. It also selects the most appropriate disaster prevention content based on the user's learning progress and generates personalized feedback and quizzes using natural language processing technologies such as TensorFlow.

[0639] The terminal provides an interface for users to interact with the system. This could be an iOS or Android mobile device, or a PC running Windows or macOS. The terminal receives content delivered from the server and visually recreates a virtual disaster environment. Here, the terminal records the user's choices and actions, and sends this data to the server for evaluation.

[0640] Users acquire practical disaster preparedness skills through simulations and quizzes provided via their devices. For example, they can simulate scenarios of how to act in the event of an earthquake and experience appropriate responses.

[0641] As a concrete example, if an earthquake is simulated, the server delivers educational materials on the mechanism of shaking and evacuation routes. The terminal provides a visual representation of the virtual space during the earthquake, and the user selects an evacuation action. The user's answers to quizzes presented after this action selection are recorded by the server, and feedback is generated regarding the appropriate action selection.

[0642] An example of a prompt might be, "Please explain what preparations are necessary in the event of a disaster." This prompt provides users with clues to gain deeper insights related to disaster response.

[0643] This invention provides a personalized learning experience, enabling users to efficiently acquire disaster preparedness skills.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The user sends a request to access disaster prevention content using their device. This input involves the user selecting a scenario, such as an earthquake or a typhoon, and choosing from different disaster simulations. The user's selection is transmitted to the server as string information.

[0647] Step 2:

[0648] The server receives a user request and searches the database for relevant disaster prevention content. The input includes the user's selection information and past learning history. The server uses this data to filter and select information, and utilizes a generative AI model to generate optimal learning content. As a result, a personalized set of learning materials is output to the user.

[0649] Step 3:

[0650] The server sends the selected learning content to the device. The output includes visual simulations and quiz-style questions. In this step, data is transferred via network communication, and the device prepares to visually present the received content to the user.

[0651] Step 4:

[0652] The terminal displays a simulation and provides a quiz to the user. It uses content received from the server as input and executes it under its control. The terminal also records user input, such as selections and quiz answers. During this process, animations of the virtual environment are executed, and a user-interactive interface is provided.

[0653] Step 5:

[0654] Users experience a simulation on their device and answer quizzes. Their input, including selections and answers, is recorded. This data is collected for subsequent evaluation and sent to a server.

[0655] Step 6:

[0656] The server receives user selection and response data and generates feedback using a generative AI model. It processes user data as input and creates specific feedback messages as corresponding output. This feedback is based on an analysis of the user's actions and supports learning improvement.

[0657] Step 7:

[0658] The terminal receives feedback from the server and presents it to the user. This includes a specific process of receiving output from the server, visualizing it, and displaying it to the user. The user can review this feedback and reassess their disaster preparedness skills.

[0659] (Application Example 1)

[0660] 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".

[0661] For urban residents to respond quickly and effectively during disasters, it is crucial to raise disaster preparedness awareness during peacetime and acquire specific knowledge and skills regarding region-specific disaster risks. However, traditional disaster prevention education has been formal and has limitations in acquiring practical response skills. To improve this, interactive and personalized learning experiences are necessary.

[0662] 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.

[0663] In this invention, the server includes an information processing device for knowledge management, a visualization means for displaying a simulation based on the urban environment, and a recording means for recording the user's actions during the simulation displayed through the visualization means. This makes it possible for urban residents to acquire practical disaster prevention skills tailored to the characteristics of their region.

[0664] A "knowledge management information processing device" is an electronic device that organizes and stores disaster prevention data and provides it to users in the most optimal format.

[0665] A "visualization means for displaying simulations based on the urban environment" is a device that uses real-world urban geographic information to virtually recreate disaster situations and presents them in a format that users can visually understand.

[0666] A "recording system for documenting user behavior" is a system that saves the choices and responses of users during the learning process as digital data, and uses this data for later analysis and feedback generation.

[0667] An "information generation means" is a processing mechanism that uses collected data to generate user-specific feedback and learning advice.

[0668] A "navigation system" is a device or system that uses urban geographical information to instruct users on appropriate evacuation routes or directions to their destinations.

[0669] This disaster preparedness education system is designed to help urban residents acquire practical response skills to local disasters. The system consists of multiple components, including a server and user terminals.

[0670] The server acts as an information processing device for knowledge management, managing content in various data formats related to disaster prevention education. The data is stored via a cloud service, and personalized simulations and quizzes are generated based on the user's learning history. Specific technologies used include an artificial intelligence-based natural language processing engine built in Python and TensorFlow.

[0671] The terminal is a device that enables interactive communication with the user. For example, an application on a smartphone uses Unity or ARCore to visually recreate a simulated urban environment based on real-world geographical data using augmented reality (AR). This allows users to participate in disaster prevention training in a virtual space in real time. In addition, by recording the user's actions, the server generates and provides feedback to the user based on that data.

[0672] Users access learning content through their devices and assess their abilities through quiz-based evaluations. Furthermore, they learn about real-world evacuation routes and countermeasures using simulations. For example, when heavy rain is expected, the app displays the optimal route to the nearest evacuation shelter and provides interactive instructions.

[0673] Specific examples of prompt statements are as follows:

[0674] "Please select a route from your current location to the nearest safe shelter. Pay attention to your surroundings and confirm the safe route using AR display."

[0675] This will enable urban residents to raise their awareness of disaster prevention and respond quickly and effectively to disasters.

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The server uses the user's current location and individual learning history as input when they access the system to search for relevant disaster prevention content from a database in the cloud. This data processing outputs optimal learning materials that take into account the user's geographical location and past learning content.

[0679] Step 2:

[0680] The server generates scenario data tailored to the city based on the selected disaster prevention simulation. It utilizes a natural language processing engine built in Python to automatically construct user-specific quizzes and feedback. The input is the content data selected in the previous step, and the output is a personalized simulation scenario and quiz questions.

[0681] Step 3:

[0682] The device receives simulation data sent from the server and displays it as an AR environment using Unity and ARCore. User input (action selections in the simulation) is recorded, and its impact on subsequent actions is shown. The input is the user's selected action, and the output is the resulting visual change.

[0683] Step 4:

[0684] The user answers a quiz presented on their device, and the results are sent to the server. Here, the user's thought process and choices are accumulated, generating data that enables personalized learning feedback. The input is the user's quiz answers, and the output is the answer results and their analysis data.

[0685] Step 5:

[0686] The server aggregates user behavior data and quiz results, and generates feedback using natural language processing technology. This feedback is sent to the user's email address or as an in-app message, providing insights for continuous learning. The input consists of past learning history and newly acquired data, and the output is a personalized feedback message.

[0687] 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.

[0688] This disaster prevention education system is designed to enable users to effectively learn the knowledge and skills necessary to prepare for disasters. The system incorporates an emotion engine that recognizes and responds to the user's emotional state.

[0689] Server operation

[0690] The server stores disaster prevention education-related content in a database and delivers appropriate learning materials in response to user requests. In addition, it uses an emotion engine to process user emotion data and personalize learning content and feedback. Emotional information is combined with the user's learning history to generate optimized feedback. Furthermore, natural language processing technology is used to provide feedback that is easy for users to understand.

[0691] Terminal operation

[0692] The terminal visually displays disaster prevention content through a user interface, providing an environment where users can interact with the system. It displays simulation and quiz-style content and records user selections in real time. In addition, it uses a camera and microphone to input the user's facial expressions and voice tone into an emotion engine to sense the user's emotional state.

[0693] User interaction

[0694] Users access the disaster prevention system and learn through presented simulations and quizzes. During learning, the emotion engine analyzes the user's facial expressions and voice to recognize changes in stress levels and concentration. Based on this information, the system adjusts the learning pace and content to suit the user and provides appropriate support. For example, if the user is feeling stressed, the system may simplify explanations or display encouraging messages to help them relax.

[0695] Specific example

[0696] For example, when a user participates in an earthquake evacuation simulation, the device displays the simulation environment and prompts the user to act. When the user is acting calmly, the system gradually increases the difficulty of the tasks and provides more positive feedback. On the other hand, if the emotion engine detects the user's anxiety, it emphasizes positive feedback more when actions are successful, working to boost the user's confidence.

[0697] This invention makes it possible to improve the efficiency and effectiveness of learning by accurately recognizing the user's emotional state and providing flexible educational content accordingly.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The user logs into the system using their device. The device sends the user's input information to the server, which verifies the user's authentication and then retrieves their past learning history.

[0701] Step 2:

[0702] The server utilizes an emotion engine to analyze user emotion data (e.g., facial expressions and tone of voice) transmitted in real time from the device. This allows it to determine the user's current emotional state.

[0703] Step 3:

[0704] The server generates optimal learning content based on the user's past learning history and current emotional state. For example, if the user is relaxed, it may suggest a more challenging simulation.

[0705] Step 4:

[0706] The device visually displays learning content sent from the server to the user. The user views the content (simulations and quizzes) and selects or performs specific actions.

[0707] Step 5:

[0708] When a user starts a simulation, the device records the user's actions and choices, and simultaneously sends data to the server in real time to track changes in the user's facial expressions and voice using an emotion engine.

[0709] Step 6:

[0710] The server generates appropriate feedback based on recorded user behavior and emotional data. It also adjusts the content of the feedback according to the user's state and offers suggestions for stress reduction as needed.

[0711] Step 7:

[0712] The device presents the generated feedback to the user. Based on the feedback, the user can reflect on their learning and choose what to learn next.

[0713] Step 8:

[0714] When a user finishes a learning session, the device sends data on their latest learning progress and emotional responses to the server. The server stores this data in a database and uses it to optimize the learning experience during their next login.

[0715] (Example 2)

[0716] 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".

[0717] Traditional disaster prevention education systems often provided uniform content without considering users' learning progress or emotional states. This made it difficult to provide personalized learning experiences tailored to individual users, resulting in decreased learning efficiency. Furthermore, they lacked features to provide appropriate support when users felt stressed or anxious.

[0718] 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.

[0719] In this invention, the server includes information processing means for processing knowledge information, display means for displaying a virtual experience, recording means for recording the user's activities, emotion analysis means for detecting and analyzing emotional states, and content adjustment means for adjusting learning content. This makes it possible to provide optimal learning materials and feedback tailored to each user's emotional state and learning history.

[0720] "Information processing means for processing knowledge information" refers to means that have the function of analyzing the user's learning data and past history to select the most suitable educational content.

[0721] A "display means for displaying virtual experiences" is a device that provides users with visual learning simulations and quizzes, thereby realizing an interactive learning environment.

[0722] "Means for recording user activity" refers to means that have the function of tracking the choices and actions taken by the user during a virtual experience and saving them as data.

[0723] An "emotional analysis means for detecting and analyzing emotional states" is a means that has the function of grasping the user's emotional state in real time through facial expressions and voice, and analyzing that information.

[0724] "Content adjustment means for adjusting learning content" refers to a means for flexibly changing the difficulty level and content of learning materials according to the user's emotional state and learning progress, thereby providing an individually optimized learning experience.

[0725] The disaster prevention education system of this invention is designed to enable users to effectively learn the knowledge and skills necessary in the event of a disaster. Embodiments of this system are described below.

[0726] First, the server uses software to process knowledge information and provides personalized educational content based on the user's learning history and emotional data. This software stores content using a database management system and selects appropriate learning materials in response to user requests. In addition, computer vision and speech analysis technologies are used as emotional analysis tools to analyze the user's facial expressions and tone of voice in real time. This allows for the acquisition of data to adjust the learning pace and content of the learning materials.

[0727] The terminal includes an interface device for visually providing a virtual experience. Specific examples include a device equipped with a display, a touchscreen, a camera, and a microphone. The terminal displays simulation and quiz-style content received from the server and interactively records the user's choices and responses. It also uses its built-in camera and microphone to continuously transmit the user's emotional state to an emotion analysis system.

[0728] Users participate in simulations and quizzes presented through their devices. For example, in an earthquake evacuation simulation, users select actions based on the displayed scenario. Through emotion analysis, it is determined whether the user is relaxed or tense, and the results are reflected in the learning process.

[0729] As a concrete example, when a user answers a quiz asking "What should you do first during an earthquake?", the server can consider the user's past accuracy rate and current emotional state to generate appropriate feedback. The generative AI model used generates feedback and encouragement based on a variety of prompts from the user.

[0730] An example of a prompt might be, "In a disaster simulation, please tell us what to do if the user is stressed. Also, please give an example of how to alleviate the user's stress." Through such prompts, the system implements techniques to provide appropriate support.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] The user logs into the system using a terminal. Login information is sent to the terminal as input, and the terminal forwards this information to the server. The server authenticates the user's login information and performs data processing by retrieving relevant data from the learning history database. The authentication result and the user's learning history are generated as output.

[0734] Step 2:

[0735] The server receives the user's learning history as input and selects appropriate disaster prevention education content based on past learning. The selected content is generated as output and sent to the user's terminal. This data processing determines personalized initial learning materials for the user.

[0736] Step 3:

[0737] The terminal receives educational content sent from the server and displays it through a user interface. Specifically, it presents the user with visual simulations and text-based quiz content. The input is content data from the server, and the output is interface information displayed on the screen.

[0738] Step 4:

[0739] The user progresses through the presented simulations and quizzes in sequence. The user's selected actions are recorded as input on the terminal. The terminal prepares to send this action data to the server, and the selected data is returned as output. For example, if the user selects "evacuate," that selection is recorded and transmitted to the server.

[0740] Step 5:

[0741] The device's camera and microphone capture the user's facial expressions and voice tone as input. The device sends this data to an emotion analysis system, which then performs specific actions to analyze the user's emotional state. The analysis results are generated as output and sent to the server.

[0742] Step 6:

[0743] The server receives the results of sentiment analysis and user selection data as input, and uses a generative AI model to generate feedback for the user. The feedback includes advice and encouraging messages to adjust the learning pace. The output feedback takes into account the user's emotional state.

[0744] Step 7:

[0745] The terminal receives feedback from the server and presents it to the user. The input to this process is feedback data sent from the server, and the output is messages and guidance displayed on the user's screen. Specifically, it displays evaluations based on the user's progress and advice on the next steps.

[0746] (Application Example 2)

[0747] 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".

[0748] In modern society, with the proliferation of autonomous vehicles, disaster preparedness education for drivers and passengers is crucial. However, conventional disaster preparedness education systems have struggled to provide personalized feedback tailored to the individual user's emotions and circumstances. Furthermore, these systems lacked the real-time interaction and emotional recognition necessary to maximize the effectiveness of education for users.

[0749] 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.

[0750] In this invention, the server includes information processing means for managing knowledge, visual display means for displaying simulations provided by the information processing means, recording means for recording the user's actions during the simulation, emotion analysis means for analyzing the user's emotions using emotion recognition technology, and information generation means for generating feedback based on the data obtained by the recording means and the emotion analysis means. This enables personalized disaster prevention learning in real time according to the user's emotional state.

[0751] "Information processing means for knowledge management" refers to devices and systems that process information to appropriately collect, organize, and provide disaster prevention-related information to users.

[0752] "Visual display means" refers to devices or systems for visually presenting simulations and learning content generated by information processing means to users.

[0753] "Recording means" refers to a device or system that has the function of saving the user's actions and responses during a simulation.

[0754] "Emotion analysis means" refers to technologies and devices that use information such as a user's facial expressions and voice to recognize and analyze their emotions in real time.

[0755] "Information generation means" refers to devices or systems for creating appropriate feedback and educational content based on data obtained from recording means and emotion analysis means.

[0756] The system for implementing this invention consists of a server and a terminal. The server is equipped with information processing means for knowledge management and stores disaster prevention-related information in a database. This data is delivered to the terminal in a simulation format via a visual display means described later. The server is also equipped with emotion analysis means that receives and analyzes emotion data transmitted by the user in real time. General emotion recognition software, such as AWS Rekognition or Google Cloud Vision AI, can be used for emotion analysis.

[0757] The device is equipped with visual display and recording capabilities. The visual display allows users to access simulation and problem-solving content. The device also includes a camera and microphone, which capture and transmit the user's facial expressions and voice data to a server. Based on this information, an emotion analysis system analyzes the user's stress level and concentration to determine the optimal learning pace and content. The generated feedback is communicated to the user using natural language processing technology. Google Dialogflow, for example, can be used for natural language processing.

[0758] As a concrete example, when a user participates in a simulation using a device, their current emotional state is analyzed based on real-time data collected through the camera and microphone. Depending on the results, if the user is feeling fear or tension, for example, a simple response tailored to that situation can be generated. The generated feedback is presented to the user in a gentle and reassuring tone. Through this process, the user's learning experience is made natural and effective.

[0759] An example of a prompt message is, "What are the three first steps you should take in the event of an earthquake? Please explain the reasons why."

[0760] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0761] Step 1:

[0762] When the device starts up, the user is presented with a login screen via a visual display. The user enters and submits their information, which is then received by the server. This login information includes an identification ID and past learning history.

[0763] Step 2:

[0764] The server uses the user's login information to search the database for relevant disaster prevention simulation content. The retrieved data is optimized based on the user's past behavioral history and emotional data. This optimization determines the simulation content appropriate to the user's level and sends it to the terminal.

[0765] Step 3:

[0766] The terminal presents the received content to the user through visual means. The user participates in the displayed simulations and quizzes and makes choices on the spot. The user's facial expressions and voice data are acquired in real time via the camera and microphone and transmitted to the server.

[0767] Step 4:

[0768] The server processes real-time emotional data transmitted from the terminal using emotion analysis tools. Emotion recognition technology analyzes the user's emotional state (e.g., tension, relief). The results of this analysis are used to generate feedback.

[0769] Step 5:

[0770] The server generates feedback using information generation tools based on user behavior data and sentiment analysis results. Natural language processing technology is used to generate feedback in a language easily understood by the user. This feedback, which includes a summary of the learning content and advice, is sent to the terminal.

[0771] Step 6:

[0772] The terminal presents the user with feedback received from the server, either visually or audibly. The user then proceeds to the next simulation step based on this feedback. The user's reactions and emotional changes are reflected in the optimization of the next session.

[0773] 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.

[0774] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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."

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] The following is further disclosed regarding the embodiments described above.

[0795] (Claim 1)

[0796] An information processing device for knowledge management,

[0797] A display means for displaying the simulation provided by the information processing device,

[0798] A recording means for recording the user's actions during the aforementioned simulation,

[0799] Information generation means that generates feedback based on the data obtained by the recording means,

[0800] A disaster prevention education system that includes this.

[0801] (Claim 2)

[0802] The disaster prevention education system according to claim 1, further comprising an evaluation means for providing a user with a quiz-style evaluation via a user interface.

[0803] (Claim 3)

[0804] The disaster prevention education system according to claim 1, further comprising an information generation means for providing feedback to the user using natural language processing technology.

[0805] "Example 1"

[0806] (Claim 1)

[0807] A device that performs information processing for knowledge management,

[0808] Means for visually displaying the virtual environment provided by the device that performs the aforementioned information processing,

[0809] A means for accumulating user selections in the aforementioned virtual environment,

[0810] A means for generating corrective information based on the information acquired by the storage means,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising means for providing a user with an evaluation in the form of a problem through a user connection surface.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for providing corrective information to users using natural language processing technology.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] An information processing device for knowledge management,

[0819] A visualization means for displaying a simulation based on the urban environment provided by the aforementioned information processing device,

[0820] A recording means for recording the user's actions during the simulation displayed through the visualization means,

[0821] Information generation means that generates personalized feedback using data obtained by the recording means,

[0822] A navigation system that uses urban geographical information to show evacuation routes,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, further comprising an evaluation means for providing users with a quiz-style evaluation via a user interface to raise disaster prevention awareness in cities.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising an information generation means that uses artificial intelligence technology to provide feedback to the user in natural language.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] Information processing means for processing knowledge information,

[0831] A display means for displaying the virtual experience provided by the information processing means,

[0832] A recording means for recording the user's activities during the virtual experience,

[0833] Information generation means that generates a response based on the data obtained by the recording means,

[0834] An emotion analysis method that detects and analyzes the user's emotional state,

[0835] Content adjustment means that adjusts the learning content based on the output of the emotion analysis means,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, further comprising an evaluation means that provides a user with a problem-format test through a user interface.

[0839] (Claim 3)

[0840] The system according to claim 1, further comprising information generation means for providing a response to a user using structured language processing technology.

[0841] "Application example 2 when combining with an emotional engine"

[0842] (Claim 1)

[0843] Information processing means for knowledge management,

[0844] A visual display means for displaying the simulation provided by the information processing means,

[0845] A recording means for recording the user's actions during the aforementioned simulation,

[0846] An emotion analysis method that analyzes the user's emotions using emotion recognition technology,

[0847] Information generation means for generating feedback based on data obtained by the recording means and emotion analysis means,

[0848] An educational system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising an evaluation means that provides a user with an evaluation in the form of a problem via a user interface, thereby supporting disaster prevention learning within a vehicle.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising information generation means for providing feedback to the user using natural language processing technology and adjusting the content of the feedback according to the user's emotional state. [Explanation of symbols]

[0853] 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. An information processing device for knowledge management, A display means for displaying the simulation provided by the information processing device, A recording means for recording the user's actions during the aforementioned simulation, Information generation means that generates feedback based on the data obtained by the recording means, A disaster prevention education system that includes this.

2. The disaster prevention education system according to claim 1, further comprising an evaluation means for providing a quiz-style evaluation to a user via a user interface.

3. The disaster prevention education system according to claim 1, further comprising an information generation means for providing feedback to the user using natural language processing technology.

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